Blood vessels are like big-city highways; full of curves, branches, merges, and congestion. Yet for years, lab models replicated vessels like straight, simple roads. To better capture the complex architecture of real human blood vessels, researchers in the Department of Biomedical Engineering at Texas A&amp;M University have developed a customizable vessel-chip method, enabling more accurate vascular disease research and a drug discovery platform.Vessel-chips are engineered microfluidic devices that mimic human vasculature on a microscopic scale. These chips can be patient-specific and provide a non-animal method for pharmaceutical testing and studying blood flow. Jennifer Lee, a biomedical engineering master’s student, joined Dr. Abhishek Jain’s lab and designed an advanced vessel-chip that could replicate real variations in vascular structure.“There are branched vessels, or aneurysms that have sudden expansion, and then stenosis that restricts the vessel. All these different types of vessels cause the blood flow pattern to be significantly changed, and the inside of the blood vessel is affected by the level of shear stress caused by these flow patterns,” Lee said. “That's what we wanted to model.”<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1748609845947-News-BMEN-Collage-14May2025.jpg" style="width:100%px" class="fr-fic fr-dib" />The varying blood vessel shapes Dr. Abhishek Jain and Jennifer Lee have replicated on the vessel-chip. | Image: Courtesy of Dr. Abhishek Jain.Lee’s research was published in <a href="https://pubs.rsc.org/en/content/articlelanding/2025/lc/d4lc00968a" rel="noopener" target="_blank">Lab on a Chip</a> and came only a few years after her mentor and former biomedical engineering graduate student Dr. Tanmay Mathur designed the straight vessel-chip. Lee and Mathur conducted their research in the <a href="https://jain.engr.tamu.edu/" rel="noopener" target="_blank">Bioinspired Translational Microsystems Laboratory</a> under Jain, an associate professor and Barbara and Ralph Cox ’53 faculty fellow in the biomedical engineering department. The research will also feature on the cover of the May 2025 issue of the journal."We can now start learning about vascular disease in ways we’ve never been able to before,” Jain said. “Not only can you make these structures complex, you can put actual cellular and tissue material inside them and make them living. These are the sites where vascular diseases tend to develop, so understanding them is critical."Lee entered Jain’s lab as an undergraduate honors student seeking research experience. Lee said she didn’t know much about the organs-on-a-chip platform but became interested in its potential to shape the future of medicine. As she transitioned into graduate studies, Lee developed an interest in vessel-chips and joined the Master of Science fast-track program to continue her newfound passion for research.“Jennifer demonstrated perseverance, curiosity, and creativity and started taking up research projects very quickly. Our fast-track program enables students like Jennifer to take on sort of high-impact, high-risk research and not just do a science project, but take it all the way to its outcome and get it published,” Jain said.<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1748609871571-News-BMEN-Gloves-14May2025.jpg" style="width:100%px" class="fr-fic fr-dib" />The vessel-chip | Image: Texas A&amp;M EngineeringWhile this iteration of the vessel-chip improves physiological relevance, Jain and Lee hope to expand their research by including various cell types. Lee’s research currently only uses endothelial cells — or cells that make up the lining of the blood vessel — but they hope to include other cells to see the effects of their interactions with each other and blood flow.“We are progressing and creating what we call the fourth dimensionality of organs-on-a-chip, where we not only focus on the cells and the flow, but this interaction of cells and flow in more complex architectural states, which is a new direction in the field,” Jain said.In addition to research experience, Lee has gained a multitude of soft skills and the ability to apply concepts learned in class to a real-world experience.“It’s such a good environment to interact with not only peers but also graduate students and postdoctoral researchers,” she said. “You’re able to learn teamwork and communication, work ethic, and just trying different things out. I think it’s such a valuable experience that students have available. We have such good faculty research labs.”The research was supported by the U.S. Army Medical Research Program, NASA, Biomedical Advanced Research and Development Authority, National Institutes of Health, U.S. Food and Drug Administration, National Science Foundation, and Texas A&amp;M University Office of Innovation Translational Investment Funds.

Dr. Abhishek Jain, Jennifer Lee and the vessel-chip. | Image: Texas A&M Engineering

A new platform mimics various blood vessel structures, enabling more accurate models of vascular disease and patient-specific research.

Replicating Real-Life Blood Vessels to Cure Vascular Disease

This article presents rigid-flex circuit board design guidelines for the development of a core body temperature (CBT) sensor device.

Rigid-Flex Circuit Design Considerations for Wearable Temperature Sensor Applications

Modern medical instrumentation demands signal integrity at extremely low levels. Certain medical devices, such as electroencephalography (EEG) and electrocardiography (ECG), rely on circuitry operating as low as the microvolt range. But the performance of medical devices can often be a matter of life or death. Performance compromises are not an option, even at such low voltages. Amplifying these low-level signals presents a fundamental design challenge: mitigating sources of degradation such as noise, distortion, and input loading. Traditional bipolar and even CMOS solutions frequently introduce limitations in one or more of these areas. <a href="https://www.interfet.com/">Junction Field-Effect Transistors</a> (JFETs), however, are an optimal device for many front-end analog stages in medical applications.With a unique combination of ultra-low gate leakage, low input capacitance, and excellent voltage noise performance, JFETs are uniquely positioned to maximize the performance of modern medical instrumentation.<h1>Sensor Characteristics in Medical Applications</h1>Many sensors used in medical instrumentation exhibit high output impedance, which places stringent demands on the design of the signal acquisition path. Piezoelectric accelerometers and hydrophones, for example, generate charge in response to mechanical deformation, while photodiodes used in pulse oximetry and optical heart rate monitoring convert light intensity into small current signals. Similarly, ECG and EEG electrodes detect biopotentials through skin-electrode interfaces that often exceed 1 MΩ in impedance.<img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNzQ4MzQ1MjkxNzA5LWltYWdlIDEgbWVkIGpmZXQuanBnIiwiZWRpdHMiOnsicmVzaXplIjp7IndpZHRoIjo5NTAsImZpdCI6ImNvdmVyIn19fQ==" style="width:100%px" class="fr-fic fr-dib" />An electrical model of a piezoelectric sensor.These high-impedance sources pose multiple challenges. First, they are highly sensitive to input bias current. Any appreciable current drawn by the amplifier's input can develop significant voltage drops across the source and cause signal distortion or attenuation. Additionally, parasitic capacitance at the amplifier input can form unintended low-pass filters with the source impedance, limiting bandwidth and introducing phase distortion in time-critical measurements.  To interface effectively with such sources, amplifiers must meet three requirements. The first is high input impedance, typically in the GΩ range, to avoid loading the signal source and degrading signal amplitude. Second, ultra-low input noise current is needed to prevent noise from coupling through the high source impedance, which can dominate the overall system noise figure. Finally, low input capacitance is a must for preserving bandwidth and maintaining signal integrity across the entire frequency spectrum of interest.<h1>Why JFETs Excel in Medical Analog Front Ends </h1>JFETs offer a unique set of electrical characteristics that make them a great choice for the front-end amplification of weak, high-impedance signals in medical systems.<h2>Gate Leakage and Noise Current Performance</h2>A major performance advantage lies in their exceptionally low gate leakage current, typically less than 1 picoampere, which allows the use of high-value feedback resistors without introducing significant offset or noise. This is valuable in transimpedance amplifier configurations, such as those used with photodiodes and piezoelectric sensors. In these situations, low gate leakage allows for the circuit to maintain DC stability and low input bias current to preserve measurement integrity.JFETs also produce extraordinarily low input noise current. Noise current is proportional to the square root of twice the product of the electron charge and gate current, as defined by the following equation.in=2qIWherein  is the shot noise current (A/√Hz)q  is the elementary charge (~1.602x10^-19 C)I  is the DC current through the junction. In this case, it’s the gate leakage current That means that JFET’s sub-nanoampere gate currents translate to noise contributions in the femtoampere-per-root-Hertz range, which is practically negligible even with megohm-range sensor impedances. This behavior enables JFET-based circuits to maintain low total input-referred noise.<h2>Voltage Noise and Capacitance Advantages</h2>In addition to current noise performance, JFETs exhibit excellent voltage noise characteristics across the audio and low-kilohertz spectrum. Many biomedical signals, including ECG, EMG, and EEG, reside in this frequency range. With voltage noise densities below 2 nV/√Hz, well-designed JFETs can outperform many low-noise bipolar and CMOS alternatives, especially when high source impedance would otherwise translate current noise into voltage noise.Low input capacitance (i.e., often just a few picofarads) is another advantage. This reduces bandwidth-limiting interactions with sensor impedance and minimizes capacitive loading, preserves fast transient response, and reduces the impact of noise gain in wideband or feedback-sensitive configurations.<h2>Thermal Stability and Matching</h2>JFETs also exhibit strong thermal stability, which is important in medical environments to minimize drift. In precision applications, matched JFET pairs can be used to construct differential stages with minimal offset voltage and excellent common-mode rejection. These characteristics make JFETs especially effective in diagnostic-grade instrumentation.<h1>Circuit Architectures Using JFETs </h1>While JFETs are a great choice for medical applications, their circuit architecture will ultimately define the performance of the system as a whole.<h2>Charge Amplifiers for Piezo Sensors</h2><img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNzQ4MzQ3MTg2ODAxLTE3NDgzNDcxODY4MDEucG5nIiwiZWRpdHMiOnsicmVzaXplIjp7IndpZHRoIjo5NTAsImZpdCI6ImNvdmVyIn19fQ==" class="fr-fic fr-dib" /><img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNzQ4MzQ2MDgxNzc2LWNoIGdlbmVyYXRvci5qcGciLCJlZGl0cyI6eyJyZXNpemUiOnsid2lkdGgiOjk1MCwiZml0IjoiY292ZXIifX19" style="width:100%px" class="fr-fic fr-dib" />A low-noise charge signal amplifier.JFETs configured as source followers, such as InterFET’s IF170, provide a high-impedance buffer between the sensor and amplifier that minimizes loading and maintains fidelity. To ensure amplifier stability, resistor pairs like R5 and R6 are inserted in the feedback path to control phase margin. This mitigates the phase shift caused by capacitive loading and prevents oscillation in high-gain or unity-gain configurations.<h2>Photodiode Transimpedance Amplifiers</h2>Photodiodes are used in pulse oximetry and optical sensors and require low-noise transimpedance amplifiers (TIAs) to convert current into voltage. High-impedance photodiodes are sensitive to input bias current and capacitance. Placing a JFET source follower, like <a href="https://www.interfet.com/jfet-master-table/">InterFET’s IF170 or IF389</a>, in front of the op amp dramatically lowers effective input capacitance and noise current seen by the photodiode. This improves both frequency response and signal-to-noise ratio.  <img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNzQ4MzQ2MDQ2NTkzLW1heCBwZXJmLmpwZyIsImVkaXRzIjp7InJlc2l6ZSI6eyJ3aWR0aCI6OTUwLCJmaXQiOiJjb3ZlciJ9fX0=" style="width:100%px" class="fr-fic fr-dib" />A transimpedance amplifier with source follower JFET.<h2 id="isPasted">Instrumentation Amplifiers with JFET Front-Ends</h2>Electrophysiological measurements such as ECG and EEG benefit from JFET-input differential buffer stages due to their high input impedance, low noise, and balanced signal handling. The IF389 dual JFET is well-suited for this purpose, offering tight VGS matching and low gate leakage. When configured as a differential pair, it serves as a front-end buffer to a traditional op-amp-based instrumentation amplifier to enhance input common-mode rejection and minimize offset drift. Symmetrical layout and thermal coupling of the two JFETs further improve matching and performance, enabling robust detection of µV-level bioelectric signals.<h1>Practical Design Considerations </h1>Designing with JFETs in medical instrumentation requires attention to several practical aspects to maintain signal integrity and ensure long-term stability.  <h2>Stability and Frequency Response</h2>JFET stages, particularly when used as high-impedance front ends in transimpedance or charge amplifier circuits, are susceptible to phase margin degradation. The combination of high feedback resistance and input capacitance can create significant phase shift at higher frequencies, risking oscillation. To counter this, resistive dividers or source degeneration resistors can be used to control gain and bandwidth.In addition, capacitive equalization networks are often inserted in feedback paths to cancel capacitive roll-off and extend frequency response. These networks are designed to balance the pole formed by the input and feedback capacitance to maximize bandwidth without compromising stability.<h2>PCB Layout and Leakage Control</h2>Due to the ultra-high input impedance of JFET gates, circuit layouts must minimize leakage currents and parasitic capacitance. Board contamination, such as flux residue or moisture, can lead to picoamp-level leakage that distorts low-level signals. Guard rings tied to the same potential as the gate, placed around sensitive traces, can intercept leakage currents and provide electrostatic shielding. Using solder mask openings to isolate high-impedance nodes and employing low-dielectric materials can further reduce parasitic effects.<h2>Power Supply and Biasing Techniques</h2>JFETs require careful biasing to maintain optimal transconductance and noise performance. This is often accomplished using current source loads or resistive voltage dividers, filtered to eliminate power supply ripple that could couple into sensitive signal paths. Because JFETs are compatible with a wide range of supply voltages, they can operate from dual rails in analog systems or from 48V phantom power, as found in medical-grade condenser microphones. Ensuring quiet and stable biasing is central to unlocking the full precision of JFET-based analog front ends.<h1>Conclusion</h1>In medical instrumentation, accuracy at the microvolt level can influence life-critical decisions. JFETs offer a rare combination of traits that align directly with the demands of high-impedance, low-amplitude sensor interfaces and elevate the performance of front-end circuitry beyond what is possible with bipolar or CMOS alternatives. With proper circuit architecture and layout, JFETs can unlock systems that deliver reliable data so that doctors can make life-saving decisions.  <h1>References</h1><ol><li><a href="https://www.interfet.com/jfet-datasheets/siliconix-an101.pdf">Siliconix AN101 – An Introduction to FETs</a></li><li><a href="https://www.interfet.com/jfet-datasheets/jfet-an-015-interfet.pdf">InterFET AN-015 – JFET Fundamentals</a></li><li><a href="https://www.interfet.com/jfet-application-notes/jfet-transistor-vs-mosfet/">MOSFETs vs. JFETs: Understanding Structures, Differences &amp; Applications | InterFET</a></li></ol>

JFETs are uniquely positioned to maximize the performance of modern medical instrumentation.

Maximizing Performance with JFETs in Medical Instrumentation

EPFL researchers have developed a flexible auditory brainstem implant (ABI) that closely conforms to the curved surface of the brainstem. The technology has been successfully demonstrated high-resolution “prosthetic hearing” in macaques.Over the last couple of decades, many people have regained hearing functionality with the most successful neurotech device to date: the cochlear implant. But for those whose cochlear nerve is too damaged for a standard cochlear implant, a promising alternative is an auditory brainstem implant (ABI). Unfortunately, current ABIs are rigid implants that do not allow for good tissue contact. As a result, doctors commonly switch off a majority of the electrodes due to unwanted side effects such as dizziness or facial twitching—leading most ABI users to perceive only vague sounds, with little speech intelligibility.Designing a soft implant that truly conforms to the brainstem environment is a critical milestone in restoring hearing for patients who can’t use cochlear implants.Stéphanie P. LacourNow, a team at EPFL’s Laboratory for Soft Bioelectronic Interfaces has developed a soft, thin-film ABI. The device uses micrometer-scale platinum electrodes embedded in silicone, forming a pliable array just a fraction of a millimeter thick. This novel approach, published in Nature Biomedical Engineering, enables better tissue contact, potentially preventing off-target nerve activation and reducing side effects.“Designing a soft implant that truly conforms to the brainstem environment is a critical milestone in restoring hearing for patients who can’t use cochlear implants. Our success in macaques shows real promise for translating this technology to the clinic and delivering richer, more precise hearing,” says Stéphanie P. Lacour, head of the <a href="https://www.epfl.ch/labs/lsbi/">Laboratory for Soft Bioelectronic</a><a href="http://www.epfl.ch/labs/lsbi/"> Interfaces</a> (LSBI) at EPFL.<h2>Probing “prosthetic hearing” with a complex behavioral task</h2>Rather than simply relying on surgical tests, the researchers ran extensive behavioral experiments in macaques with normal hearing. This allowed them to measure how well the animals could distinguish electrical stimulation patterns as they would with natural acoustic hearing.“Half the challenge is coming up with a viable implant, the other half is teaching an animal to show us, behaviorally, what it actually hears,” says Emilie Revol, co-first author on the project and a former PhD student at EPFL. She meticulously trained the animals to perform an auditory discrimination task: the monkeys learned to press and release a lever to indicate whether consecutive tones were the “same” or “different.”“We then introduced stimulation from the soft ABI step by step, blending it with normal tones at first so the monkey could bridge the gap between acoustic and prosthetic hearing,” says Revol. “Ultimately, the goal was then to see if the animal could detect small shifts from one electrode pair to another when only stimulating the soft ABI. Our results suggest that the animal treated these pulses almost the same way it treated real sounds.”<h2>Why a soft array?</h2>“Our main idea was to leverage soft, bioelectronic interfaces to improve electrode-tissue match,” explains Alix Trouillet, a former postdoctoral researcher at EPFL and co-first author of the study. “If the array naturally follows the brainstem’s curved anatomy, we can lower stimulation thresholds and maintain more active electrodes for high-resolution hearing.”Conventional ABIs rest on the dorsal surface of the cochlear nucleus, which has a 3 mm radius and a complex shape. Rigid electrodes leave air gaps, leading to excessive current spread and undesired nerve stimulation. By contrast, the EPFL team’s ultra-thin silicone design easily bends around the tissue.Beyond conformability, the soft array’s flexible microfabrication means it can be reconfigured for different anatomies. “The design freedom of microlithography is enormous,” says Trouillet. “We can envision higher electrode counts or new layouts that further refine frequency-specific tuning. Our current version houses 11 electrodes—future iterations may substantially increase this number.”<h2>Improved comfort and fewer side effects</h2>A crucial outcome of the macaque study was the absence of noticeable off-target effects. The researchers report that, within the tested range of electrical currents, the animal showed no signs of discomfort or muscle twitches around the face—common complaints from human ABI users. “The monkey pressed the lever to trigger stimulation itself, time and again,” explains Revol. “If the prosthetic input had been unpleasant, it probably would have stopped.”<h2>Path to clinical translation</h2>Although these findings are promising, the path to a commercially available soft ABI will require additional research and regulatory steps. “One immediate possibility is to test the device intraoperatively in human ABI surgeries,” says Lacour, noting that the team’s clinical partners in Boston regularly perform ABI procedures for patients with severe cochlear nerve damage. “They could briefly insert our soft array before the standard implant to measure if we truly reduce stray nerve activation.”In addition, every material in an implant destined for human use must be fully medical grade and show robust, long-term reliability. Yet the researchers are confident, thanks to the demanding tests the device has already withstood: “Our implant remained in place in the animal for several months, with no measurable electrode migration,” notes Trouillet. “That’s a critical step forward given how standard ABIs often migrate over time.”<hr />Research with animals at EPFL The scientific process that led to the publication of this article involved laboratory animals. Even today it may still be necessary to conduct in vivo experiments for biologists and other scientists to understand and model complex biological processes.EPFL is committed to high-quality research while fully respecting current ethical and legal standards governing research involving animal models. Sensitive to the welfare of animals and the responsibility it has towards them, EPFL implements an internal policy with strict rules based on the 3R principles: Replace, Reduce, Refine. Whenever possible, we use the latest and most relevant methods that do not require the use of animals (computer modeling, cell culture, organoids, etc.). We only use the necessary number of animals while ensuring that any distress they might experience is kept to an absolute minimum. The process is supervised and monitored in Switzerland by the cantonal and federal veterinary authorities.<hr />ReferencesTrouillet, A., Revol, E., Coen, F.-V., Fallegger, F., Chanthany, A., Delacombaz, M., Kolly, L., Furfaro, I., Lanz, F., Kanumuri, V., Adenis, V., Garcia-Chavez, A., Brown, M. C., Anschuetz, L., Bloch, J., Lee, D. J., Lacour, S. P. “High-resolution prosthetic hearing with a soft auditory brainstem implant in macaques.” (2025) Nature Biomedical Engineering. <a href="https://www.nature.com/articles/s41551-025-01378-9" target="_blank" rel="noopener">DOI: 10.1038/s41551-025-01378-9</a>

EPFL researchers have developed a flexible auditory brainstem implant ABI that closely conforms to the curved surface of the brainstem.

Soft brainstem implant delivers high-resolution hearing

Biocompatibility is vital to medical device safety. It ensures implants and other devices can be used in and on the body with minimal adverse reactions. Medical devices must be highly biocompatible, as they often contact the body's internal tissues and fluids.<a href="https://hzo.com/coatings/parylene-coating" rel="noopener" target="_blank">Parylene's biocompatibility</a> is crucial for its use in medical devices. Additionally, Parylene is physiologically non-reactive and isn't toxic.<h2>Parylene's Biocompatibility</h2>Medical professionals and device makers rely on strict biocompatibility testing for safety. They follow established standards, especially<a href="https://en.wikipedia.org/wiki/ISO_10993" rel="noopener" target="_blank"> ISO 10993 </a>and the USP VI Standard.A complete analysis of Parylene shows it is biocompatible. It meets the strict standards for medical device use. Third-party sources have tested the material, and it causes no harm to tissues or body fluids. Parylene's chemical inertness makes it compatible. It provides a safe interface between the device and human tissue.<h3>Why is Parylene Biocompatible?</h3>Parylene's chemistry and structure make it biocompatible. It meets the USP's highest, Class VI, biocompatibility standards. Parylene has passed the USP's strict tests, <a href="https://www.nes-ips.com/usp-class-vi-testing-what-is-it/" rel="noopener" target="_blank">described here</a>. Also, it has a proven track record of reliable performance in the medical device industry.<h3>Biocompatible Parylene Coatings Last</h3>Decades of use show Parylene coatings maintain their performance and integrity. Bodily fluids, varying pH levels, and enzymes do not reduce their effectiveness. <a href="https://hzo.com/blog/what-is-parylene-used-for-is-it-right-for-my-project" rel="noopener" target="_blank">Parylene-coated medical devices are vital</a>. They provide reliable, long-lasting protection and function.Parylene's uniform, pinhole-free coating does more than protect. It provides a reliable interface between the device and the body. So, encapsulated medical components can serve patients for as long as they are used. The difference is not just in Parylene's inertness. Parylene can also resist the harsh conditions of the human body.Below, you can see a Parylene-coated Raspberry Pi computer running underwater in HZO’s lobby. As of the date of publication, this computer has been running fully submerged in tap water for over two years now:<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1745392853577-HZO-Coating-Raspberry-Pi-1.webp" style="width:100%px" class="fr-fic fr-dib" /><h2 id="isPasted">Parylene Versus Other Biocompatible Materials</h2>Parylene often stands out for its unique properties. So, it is a good biocompatible material for medical uses. But, a range of alternatives exists. They include silicone, Teflon, and various medical-grade metals and polymers. Each material has a unique set of traits best in certain conditions.Parylene is known for its great chemical resistance. It can also form pinhole-free coatings at a micron-level thickness. This lets devices work reliably in the harsh human body. Parylene coatings are ultrathin. They keep the feel and function of the underlying material. This is a big advantage over bulkier options like silicone rubber or <a href="https://hzo.com/blog/polyurethane-conformal-coating-services-for-electronics" rel="noopener" target="_blank">polyurethane</a>.<h3>Parylene vs. Stainless Steel</h3>Consider Parylene versus stainless steel, which is a common choice for implants. Stainless steel is strong and fatigue-resistant. But it can't match Parylene's ability to conform to complex shapes. The difference shows in applications needing a seamless coat over complex parts. Here, Parylene's conformal nature gives a uniform layer. It fully isolates the parts from the body's fluids and tissues.<h3>Parylene vs. Teflon</h3>Unlike Teflon, a popular biocompatible material, Parylene is PFAS-free. It is REACH and RoHS compliant, and its ability to maintain physical integrity under sterilization methods boosts its comparability.<h3>The Limitations of Biocompatible Parylene</h3>However, Parylene is not without its limitations. Parylene has <a href="https://hzo.com/coatings/parylene-coating/properties/" rel="noopener" target="_blank">great barrier properties</a> and electrical insulation. But, its mechanical strength is not as good as that of metals or ceramics. This property requires careful thought in load-bearing implant uses where strength is vital. Also, the Parylene application process uses <a href="https://hzo.com/coatings/parylene-coating/process" rel="noopener" target="_blank">chemical vapor deposition</a>. It's very effective but may complicate the manufacturing workflow. Simpler methods, like dip or spray coating, are used for silicone and latex.Other materials may have specific advantages, like higher tensile strength or elasticity. But, Parylene's combination of properties often justifies its selection. It depends on the intended use, performance, and regulatory standards. Manufacturers must balance performance and <a href="https://hzo.com/blog/parylene-masking-general-concepts-explained" rel="noopener" target="_blank">material costs</a>. They also consider the <a href="https://hzo.com/blog/how-do-you-remove-parylene-coating" rel="noopener" target="_blank">complexity of application methods</a> for producing and using medical devices.<h2>Why is Parylene Used in Medical Applications?</h2>Parylene is a very popular protective coating in the medical industry. In <a href="https://www.verifiedmarketresearch.com/product/parylene-market/" rel="noopener" target="_blank">2023, the parylene market was valued at $98.38 million; by 2030, it is expected to grow to $118.52 million.</a> Why is Parylene so widely used? The answer lies in its powerful protective properties.Parylene is highly resistant to moisture, chemicals, and other environmental factors. The coatings protect medical devices in and on the body at <a href="https://hzo.com/blog/parylene-coating-thickness-how-much-is-enough" rel="noopener" target="_blank">50% of the thickness of conventional conformal coatings</a>, reducing product bulk and weight. Also, Parylene has very high dielectric strength, up to 7,000 V/mil. This ensures that electrical components can function uninterrupted by external stimuli.Parylene's conformal coating attributes ensure a uniform, pinhole-free application. It adheres to every contour of the complex geometries in medical devices. This ensures complete encapsulation with no areas left vulnerable. Parylene's coating uniformity greatly improves device protection.<ul><li><a href="https://hzo.com/blog/parylene-chemical-resistance-properties-overview" rel="noopener" target="_blank">Chemical Resistance</a>: Parylene surpasses many materials in its resistance to corrosive bodily fluids.</li><li>Dielectric Strength: Parylene N has an extremely high dielectric strength of 7,000 V/mil</li><li>Biocompatibility: Parylene causes minimal tissue reaction. It compares well to common <a href="https://hzo.com/blog/biocompatible-coatings" rel="noopener" target="_blank">biocompatible materials</a>.</li><li>Barrier Protection: Parylene's seamless coatings provide unmatched <a href="https://hzo.com/blog/waterresistant-waterrepellent-waterproof-whats-difference" rel="noopener" target="_blank">barrier protection.</a> Porous materials, like silicone foams, cannot match it.</li></ul><h2>Common Uses for Parylene in Medical Settings</h2>Parylene coatings have established their utility in a wide range of medical devices. They improve protection and longevity. Here are some use cases for Parylene:<ul><li>Cardiac assist devices use Parylene. Its thin, uniform coating <a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10318457/" rel="noopener" target="_blank">reduces thrombogenicity without harming the device's inherent functionality.</a></li><li>Parylene-coated needles and cannulas have less friction. This makes them easier to insert, which is more comfortable for patients.</li><li>Parylene coatings improve stents. They prevent corrosion and reduce friction, aiding in their insertion and long-term performance in the body.</li></ul>Diverse biomedical applications capitalize on Parylene's biocompatibility. It is present in both in vivo and in vitro environments. These include surgical instruments, cochlear implants, and diagnostic equipment sensors.<h3>Partner with HZO for Your Next Medical Project</h3>Do you have any questions about Parylene or what it can do for your next project? Please <a href="https://hzo.com/contact-us" rel="noopener" target="_blank">reach out to our team</a>. We’re standing by to answer your questions and help you determine your next step.

Biocompatibility is vital to medical device safety. It ensures implants and other devices can be used in and on the body with minimal adverse reactions. Medical devices must be highly biocompatible, as they often contact the body's internal tissues and fluids.

Parylene Biocompatibility - Parylene Compared, Analyzed For Medical

In the urgent field of digital health, the combination of artificial intelligence and ultra-low-power computing is transforming possibilities for personal wellness and medical monitoring. In a recent Wevolver webinar, Carlos Morales, Vice President of AI at <a href="https://ambiq.com/">Ambiq</a>, engaged in a presentation with Samir Jaber on how the company's technology is paving the way for a new generation of intelligent, energy-efficient health monitoring devices that operate at the edge.<h2>Why Health Monitoring Needs Edge AI</h2>The growing demand for continuous health tracking—spanning heart rate, sleep quality, respiratory function, and beyond—requires devices that are always on, always aware, yet discreet and energy-efficient. Traditional cloud-based processing can’t meet the latency, privacy, or power constraints of wearables or other on-body sensors. That’s where Ambiq comes in.Carlos Morales explained how Ambiq’s ultra-low-power solutions are enabling real-time, on-device AI for wearable health monitors that can run sophisticated algorithms while consuming minuscule amounts of energy. This technology makes it possible to track vital biometrics without frequent recharging, a crucial advantage for devices like fitness trackers, medical patches, and smartwatches.<h3>Always-On Intelligence for Personalized Health</h3>One of the key insights from Morales was that Ambiq’s platform supports “always-on” capabilities—devices that continuously monitor signals such as heart rate variability or blood oxygen levels and flag anomalies without relying on cloud connectivity. This opens up possibilities for early warning systems, personalized fitness tracking, and even remote patient care.He emphasized that “health monitoring is one of the clearest use cases for edge AI,” citing how Ambiq’s solutions allow developers to run complex machine learning models directly on-device. Whether it’s detecting abnormal breathing patterns or monitoring cardiovascular strain, these models can now run efficiently on wearable hardware that lasts days—or even weeks—on a single charge.<h2>Use Cases: From Fitness to Clinical-Grade Monitoring</h2>Throughout the webinar, Morales highlighted several real-world applications made possible with Ambiq's technology:<ul><li>Fitness wearables that can adapt workouts based on real-time physiological data. </li><li>Sleep trackers that use AI to analyze sleep stages and recommend behavioral changes. </li><li>Medical devices that monitor ECG or blood pressure trends and alert users or healthcare professionals about irregularities. </li></ul>In one notable case, Ambiq-enabled wearables were used to monitor post-operative patients remotely, enabling hospitals to discharge patients earlier while still maintaining visibility over recovery—reducing costs and improving patient satisfaction.<h3>Energy Efficiency as a Health Imperative</h3>Energy efficiency isn’t just a nice-to-have in health devices—it’s a core requirement. Morales pointed out that minimizing energy consumption extends battery life and reduces device size, which enhances user comfort and long-term adoption. Ambiq’s SPOT® platform (Subthreshold Power Optimized Technology) lies at the heart of this value, allowing for ultra-efficient AI inference at the edge.This enables not just better battery life, but also devices that can be thinner, lighter, and more discreet—all essential for wearable health monitors and implantable tech.<h2>Closing Thoughts</h2>Carlos Morales closed the session with a call to action for engineers and developers to consider how they can leverage edge AI for the next wave of health innovations. He encouraged the audience to think not just about what AI can do, but how and where it operates—and to prioritize power efficiency and user comfort alongside performance.With edge AI platforms like Ambiq’s, the future of health monitoring is not just smarter—it’s more accessible, reliable, and personal. Watch the full webinar above. <h2>Have you missed our 2025 Edge AI Report? Download Now</h2> <img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1741340294712-5+Rising+Trends+for+AI+Adoption+in+Manufacturing+%281920+x+500+px%29+%281%29.png" class="fr-fic fr-dii" /><iframe id="JotFormIFrame-250412152873451" src="https://form.jotform.com/250412152873451" frameborder="0" style="min-width:100%;max-width:100%;height:599px;border:none" scrolling="yes" class="fr-draggable"> </iframe>

Ambiq’s ultra-low-power edge AI technology is pushing health monitoring by enabling always-on, intelligent wearable devices that track vital signs in real time without relying on cloud connectivity. 

Health Monitoring with Edge AI: Insights from Ambiq's Carlos Morales

Healthcare is facing a growing workforce crisis. The World Health Organization estimates a global shortage of 10 million skilled healthcare workers by 2030 [1]. To maintain quality patient care, healthcare systems must leverage technology to augment clinicians, streamline workflows, and optimize resource allocation without replacing human expertise.Artificial Intelligence (AI) is emerging as a key enabler in this space. In 2024, AI-powered digital health companies received 42% of total global funding, up from just 7% less than a decade ago. However, AI adoption remains fragmented: while over a quarter of clinicians worldwide now use AI in their work, only 11% of healthcare organizations have multiple AI solutions in production [2]. This signals a clear demand for AI-driven efficiency but also reveals barriers to adoption, including integration challenges and resource-intensive AI models.Axelera AI addresses this challenge by empowering healthcare professionals with real-time, AI-driven insights at the edge. Its MetisTM AI Processing Unit (AIPU) enhances medical imaging diagnostics, treatment planning, and operational efficiency, helping clinicians reduce workloads, minimize diagnostic errors, and improve patient outcomes without replacing human decision-making. By delivering high-performance AI with low power consumption, Axelera AI is helping bridge the workforce gap and shape the next era of digital healthcare.<h2>Medical Imaging and Diagnostics</h2>Diagnostic errors pose a serious challenge in healthcare. Studies estimate that diagnostic errors contribute to 10% of patient deaths and up to 17% of adverse events in hospitals [3]. Many of these errors result from the sheer complexity of interpreting medical data, particularly in imaging, where radiologists analyze thousands of scans under time constraints.AI is helping to reduce these errors by identifying patterns in medical images that might not be immediately apparent to the human eye. Advanced machine learning algorithms help analyze large datasets, identify intricate patterns, and make remarkably accurate predictions about the course of diseases. AI-powered medical imaging tools use deep learning algorithms, convolutional neural networks, and computer vision techniques to analyze medical images with unprecedented precision.Axelera AI provides real-time AI inference processing for medical imaging, helping clinicians improve diagnostic accuracy and efficiency. Its Metis AIPU is designed to process large volumes of imaging data quickly while maintaining low power consumption, which is an important factor in resource-intensive hospital environments. By bringing this performance capability to the hospitals, doctors can also protect the patient’s identity and information by not having to share with a data center located offsite.<h3>Enhanced Imaging Analysis</h3>AI systems in medical imaging rely on deep learning models to process data from MRI, CT, X-ray, and ultrasound scans. These models can detect abnormalities such as tumors, fractures, or early-stage disease indicators. By enabling real-time image analysis, Axelera AI helps radiologists make informed decisions faster. In a radiology department, for example, Axelera AI can process MRI scans in real time, highlighting potential areas of concern, such as tumors or abnormal tissue growths. This allows radiologists to review flagged areas more efficiently and improve diagnostic confidence.<h3>Early Disease Detection</h3>Early detection is critical for conditions such as cancer, cardiovascular disease, and neurological disorders. AI models trained on vast medical datasets can identify subtle indicators of disease that may not yet be visible to the clinician’s eye. Axelera AI’s architecture supports these AI-driven detection models, allowing for high-speed analysis without requiring cloud processing. For instance, a healthcare provider can use Axelera AI to analyze mammograms, identifying early signs of breast cancer with higher precision. This supports timely intervention and better patient outcomes.<h2>Personalized Treatment and Precision Medicine</h2>AI is reshaping precision medicine by enabling data-driven, patient-specific treatment strategies. By analyzing vast datasets, AI algorithms identify patterns, predict individual treatment responses, and support clinicians in tailoring therapies for better patient outcomes. These models examine patient-specific data, including medical history, genetic information, lifestyle factors, and treatment outcomes of comparable patients.One of AI’s key strengths lies in integrating anonymized, diverse health data sources. AI-powered systems aggregate information from wearable technology, electronic health records (EHRs), imaging reports, and patient-reported outcomes. By leveraging computational modeling and real-time simulations, these systems can track treatment effectiveness dynamically, allowing healthcare providers to make adjustments as needed. This capability is particularly valuable in fields like oncology, where genetic variations and molecular signatures influence treatment success.Axelera AI brings high-speed, real-time AI processing to precision medicine applications. Its Metis AIPU is optimized for analyzing large-scale medical datasets on-device, reducing latency and eliminating reliance on cloud processing. By accelerating AI-driven genomic analysis and treatment simulations, Axelera AI enables clinicians to make faster, data-informed decisions for personalized care.<h3>AI-Driven Treatment Recommendations</h3>Precision medicine relies on identifying biomarkers and genetic markers that influence how a patient responds to specific treatments. AI models trained on large genomic datasets help physicians predict which therapies are most likely to succeed while minimizing adverse effects.Edge AI can enhance this process by rapidly analyzing patient-specific data, including genetic profiles and treatment history, to recommend optimal therapies. By running complex AI models directly at the edge, it delivers real-time insights without the processing delays associated with cloud-based systems.One use case is in oncology clinics. AI can analyze a patient’s genetic profile and medical history to suggest a personalized chemotherapy plan. It can also identify potential clinical trial options based on the patient’s specific biomarkers, expanding treatment opportunities beyond standard protocols.<h3>Predicting Drug Interactions and Side Effects</h3>AI can play a critical role in preventing adverse drug reactions by analyzing a patient’s medical history, existing medications, and genetic predispositions. AI-driven drug interaction models assess potential conflicts between prescribed drugs and provide recommendations for alternative medications that may be safer or more effective.Axelera AI’s high-throughput processing can enable real-time cross-analysis of patient medication data, minimizing the risk of harmful interactions. By leveraging AI inference at the edge, it can flag potential drug conflicts before prescriptions are finalized, ensuring proactive decision-making.A hospital pharmacy, for example, can integrate AI into its medication review system. When a physician prescribes a new medication, AI instantly scans the patient’s history, identifying potential interactions with existing treatments. If a risk is detected, it suggests alternative dosages or substitute medications, reducing the likelihood of adverse effects.<h2>Remote Patient Monitoring and Telemedicine</h2>The convergence of AI and telemedicine is driven by the need to overcome traditional healthcare challenges. With geographic barriers, clinician shortages, and rising healthcare costs, there is a growing need for solutions that reduce in-person visits while maintaining quality care. AI-powered remote patient monitoring (RPM) is an integral part of telemedicine, allowing healthcare providers to track patient health data in real time, detect early warning signs, and support virtual consultations more effectively.Axelera AI enhances RPM by providing real-time, AI-driven health monitoring at the edge. Unlike cloud-dependent solutions, its low-latency processing enables immediate detection of abnormalities, ensuring faster clinical responses. This is particularly beneficial for managing chronic conditions where continuous monitoring can prevent complications.Wearable devices and remote monitoring systems are now integral to chronic disease management, post-surgical recovery, and elderly care. AI-powered analysis of vital signs, heart rhythms, and other health metrics allows for timely alerts and early intervention.A patient with a chronic heart condition can wear a device that continuously monitors heart rate and rhythm. If an irregular heartbeat is detected, the system immediately alerts the patient’s cardiologist, prompting medical intervention before complications arise.Beyond monitoring, AI-driven telemedicine platforms enhance remote consultations by analyzing patient history, symptoms, and real-time data. This allows clinicians to make informed decisions without requiring an in-person visit.For instance, during a virtual consultation, Axelera AI would analyze a patient’s medical history and symptoms in real time, providing the physician with potential diagnoses and treatment recommendations. This enhances diagnostic accuracy while improving the efficiency of remote care.<h2>Operational Efficiency in Healthcare Facilities</h2>Hospitals and healthcare facilities operate under constant pressure to balance patient care, resource allocation, and administrative efficiency. Emergency room congestion, staff shortages, and unpredictable patient influxes create operational challenges that impact both costs and patient outcomes. AI-driven predictive analytics and workflow automation are helping healthcare providers manage resources more effectively, reduce administrative burdens, and optimize patient flow.Axelera AI enhances hospital efficiency by processing real-time operational data at the edge, enabling immediate decision-making without cloud dependency or the risky transmission of personalized data. Its high-speed AI inference capabilities allow hospitals to anticipate patient admission trends, optimize staffing, and automate resource allocation by training AI models on historical and real-time hospital data. This allows hospitals to proactively adjust staffing levels, manage bed availability, and allocate medical resources before shortages occur, which, in turn, enable them toA hospital using Axelera AI could, for example, predict an increase in emergency room admissions during flu season by analyzing trends in local infection rates, patient records, and historical data. This allows it to allocate additional staff and resources in advance, ensuring that patient care remains efficient and timely.Beyond predictive analytics, AI streamlines administrative and clinical workflows, reducing the burden of manual processes. Axelera AI enables automated scheduling, patient record management, and real-time prioritization of critical cases.For instance, in a busy clinic, Axelera AI can automate appointment scheduling, billing, and patient record updates. Instead of relying on manual coordination, the system dynamically adjusts schedules based on patient no-shows, appointment urgency, and physician availability. This allows staff to focus more on patient care rather than administrative tasks, improving overall efficiency.By integrating AI into hospital operations, healthcare providers can reduce inefficiencies, minimize administrative overhead, and improve resource utilization, leading to better patient experiences and lower operational costs.<h2>AI in Medical Research and Drug Discovery</h2>Developing new drugs is a slow, expensive, and resource-intensive process. Traditional methods rely on manual screening, lengthy clinical trials, and iterative testing, often taking years and requiring hundreds of millions of dollars in investment. Despite these efforts, many drug candidates fail in late-stage trials, delaying the arrival of effective treatments.AI is helping pharmaceutical companies overcome these bottlenecks by accelerating drug discovery, molecular analysis, and clinical research. Recent estimates suggest that AI-driven approaches can cut costs and reduce preclinical development timelines by as much as 25 to 50% [4]. With these efficiency gains, AI is expected to play a role in nearly one-third of new drug discoveries in 2025 [5]. This includes drug design, chemical synthesis, drug screening, and drug repurposing.<h3>Accelerating Drug Discovery</h3>AI-powered models can analyze biological and chemical datasets to identify promising drug candidates faster than conventional research methods. Machine learning algorithms help predict molecular interactions, toxicity risks, and drug efficacy, reducing the need for costly trial-and-error screening.Axelera AI enhances this process with real-time, AI-driven analysis at the edge. It accelerates large-scale computations, enabling researchers to simulate drug interactions efficiently without cloud-based delays.For example, a pharmaceutical company can identify new drug candidates for a rare disease by processing genetic and biochemical data using AI. AI-driven simulations can help the company cut development time and lower research costs, bringing new treatments to market faster.<h3>Genomic Data Analysis for Precision Medicine</h3>AI is also transforming genomic research, allowing scientists to identify genetic patterns linked to diseases and develop targeted therapies. By analyzing large genomic datasets, AI detects biomarkers and genetic variations, helping researchers predict how patients may respond to different treatments.Axelera AI processes genomic data at high speed, helping researchers analyze mutations, identify disease-linked genes, and refine precision medicine strategies. Its high-performance AI inference allows for real-time genomic analysis, reducing the computational workload of conventional sequencing methods.By integrating AI into drug discovery and genomics, AI helps researchers streamline drug development, reduce costs, and advance precision medicine.<h2>Enhancing Patient Experience and Care</h2>Patients today expect faster access to healthcare services, accurate information, and seamless communication with providers. However, long wait times, inefficient appointment scheduling, and fragmented patient records continue to create frustration. AI is helping healthcare providers bridge these gaps by enabling real-time patient interactions and improving data accuracy [6].<h3>AI-Powered Chatbots and Virtual Assistants</h3>By using Natural Language Processing (NLP) and machine learning, AI-driven chatbots and virtual assistants can interpret patient queries, automate scheduling, and provide instant access to health-related information. Beyond reducing response times and administrative workload, they enhance patient engagement by offering 24/7 accessibility. Axelera AI powers chatbots and virtual assistants with real-time AI inference at the edge. Unlike cloud-dependent solutions, it delivers low-latency processing, allowing AI-driven assistants to respond instantly, process multiple queries simultaneously, and provide context-aware answers.<h2>Conclusion</h2>Improving access to healthcare is a global concern. AI can significantly support this effort by supporting clinicians by making diagnostics more accurate, patient care more accessible, and hospital operations more efficient. Axelera AI plays a key role in this shift by enabling real-time AI processing for medical imaging, remote monitoring, and workflow automation. Its low-latency, edge-based AI capabilities help healthcare providers reduce diagnostic errors, improve care coordination, and accelerate medical research.As the industry continues to adopt AI-driven healthcare solutions, technologies like Axelera AI will be instrumental in scaling precision medicine, enhancing hospital efficiency, and supporting clinicians with real-time insights. By integrating AI into everyday healthcare workflows, providers can deliver faster, more personalized, and cost-effective care.<h2>References</h2>[1] A. Rivlin and T. Lumley, “Why is the world now facing a medical recruitment crisis?,” World Economic Forum. Jan. 9, 2023. [Online]. Available: <a href="https://www.weforum.org/stories/2023/01/medical-recruitment-crisis-davos23/">https://www.weforum.org/stories/2023/01/medical-recruitment-crisis-davos23/</a> [2] C. Stewart, “AI in healthcare,” Statista, Feb. 19, 2025. [Online]. Available: <a href="https://www.statista.com/topics/10011/ai-in-healthcare/#topicOverview">https://www.statista.com/topics/10011/ai-in-healthcare/#topicOverview</a>[3] RCSEd Communications Team, “The Potential of AI to Help Reduce Diagnostic Errors,” RCSEd. Sep. 3, 2023. [Online]. Available: <a href="https://www.rcsed.ac.uk/news-resources/rcsed-blog/2024/september/the-potential-of-ai-to-help-reduce-diagnostic-errors">https://www.rcsed.ac.uk/news-resources/rcsed-blog/2024/september/the-potential-of-ai-to-help-reduce-diagnostic-errors</a> [4] N. Sheynin, “How Artificial Intelligence is Transforming Drug Development,” AlphaSense. [Online]. Available: <a href="https://www.alpha-sense.com/blog/trends/expert-insights-artificial-intelligence-drug-development/">https://www.alpha-sense.com/blog/trends/expert-insights-artificial-intelligence-drug-development/</a> [5] P. Hudson, “How 2025 can be a pivotal year of progress for Biopharma,” World Economic Forum. Jan. 16, 2025. [Online]. Available: <a href="https://www.weforum.org/stories/2025/01/2025-can-be-a-pivotal-year-of-progress-for-pharma/">https://www.weforum.org/stories/2025/01/2025-can-be-a-pivotal-year-of-progress-for-pharma/</a> [6] S. Chowdhary, “Reimagining healthcare industry service operations in the age of AI,” McKinsey &amp; Company. Sep. 19, 2024. [Online]. Available: <a href="https://www.mckinsey.com/industries/healthcare/our-insights/reimagining-healthcare-industry-service-operations-in-the-age-of-ai">https://www.mckinsey.com/industries/healthcare/our-insights/reimagining-healthcare-industry-service-operations-in-the-age-of-ai</a>

Healthcare is facing a critical workforce shortage, with AI emerging as a key tool to support clinicians by improving diagnostics, treatment planning, and efficiency without replacing human expertise. 

Revolutionizing Digital Healthcare with Axelera AI: Enhancing Patient Care and Operational Efficiency

Chronic organ failure is a major cause of death – around 500,000 people are diagnosed with heart failure every year in the U.S. And yet, only a few thousand heart transplants are performed annually. Most people just aren't good candidates because of their age or condition and the scarcity of donor organs.At Stanford, I’ve spent the last five years pursuing a science fiction dream: What if we could 3D print new hearts and other organs on demand, using cells from a patient’s own body? How do we take a few cells in a petri dish and turn them into a kilogram of tissue that you can watch with your own eyes beating?There’s a lot of very hard challenges associated with scaling-up in biology. How do you make billions of cells affordably, reliably, and reproducibly? How do you print them quickly, mature them, let them thrive? We have to vascularize and feed the cells, put blood vessels into the tissues, so we can provide them the oxygen and nutrients they need to survive.In three and a half years, my lab plans to have a 3D-printed human organ inside a pig model. We hope that large animal study becomes a springboard to then lay out the milestones needed for a first in-human trial. That will likely be more than a decade away. There’s a lot of work to be done, but there are real forward steps that we're taking.The dream outcome is that we get a bioprinted heart in a human. With xenotransplants from pigs, you still need to be on a very high dose of immunosuppressants, and even then, it usually gets rejected after a month or two. Our goal would be to make your own heart, made of your own cells, on demand, with no need for immunosuppression. It really is the holy grail of what I think curative medicine ought to move towards: You’ve got a failing organ? Here’s a new one.I present this work to venture capitalists sometimes, and I talk to business people, and the response I get is that we’re being too ambitious. And to me, that means we’re doing academia right.I present this work to venture capitalists sometimes, and I talk to business people, and the response I get is that we’re being too ambitious. And to me, that means we’re doing academia right.”The private sector will fund what it’s willing to, which is five or 10 years in the future. When you’re really trying to do something different, train up a new workforce with a new set of skills, create new paradigms, that’s the role of academia. That’s our job. And it’s a privilege and an extremely exciting place to work. I just love my job, and I’m very grateful for the opportunity to take a problem that is impossible and make it not quite impossible.

Stanford bioengineer Mark Skylar-Scott on his “science fiction” quest to 3D print human hearts and other organs on demand, using cells from a patient’s own body.

'It really is the holy grail of curative medicine'

In this episode, we explore how a generative AI tool is marking a major milestone in biology and accelerating advancements in healthcare, genetics, and drug development.<iframe height="200px" width="100%" frameborder="no" scrolling="no" src="https://player.simplecast.com/8b738738-5a69-4f97-ac33-8d48a50ec427?dark=false"></iframe> <hr /><h3>Episode Notes</h3>In this episode, we explore how a generative AI tool is marking a major milestone in biology and accelerating advancements in healthcare, genetics, and drug development.Become a founding reader of our newsletter: <a href="http://read.thenextbyte.com/">http://read.thenextbyte.com/</a><hr /><h2 id="isPasted">Transcript</h2>What's up folks, welcome back to the Next Byte podcast. And in this one, we're talking about DNA sequencing. Specifically, we're talking about a team over at Stanford that has created a ChatGPT-like AI model that can predict what comes next if you give it any DNA sequence, including yours.  that's got you a little bit spooked or excited or just curious. Well, then let's get into it.What's up friends, this is The Next Byte Podcast where one gentleman and one scholar explore the secret sauce behind cool tech and make it easy to understand.Farbod: This is another one of those biology ones. So, you already know I'm gonna struggle, but that's okay. We learn when we struggle. I was…Daniel: You just need to test your limits, man!Farbod: And that's what I'm doing. I'm trying here, but it was so interesting that I knew we needed to talk about it. So, for the audience, please bear with me. This is not my area of expertise, but God am I trying. So, for this episode, we're going to Stanford. You know the West Coast is the best coast, but these folks at Stanford, they're doing some crazy things with DNA sequencing.  And to be completely honest, they've kind of done this before, but now they're doing it better. So, what am I talking about? They've developed a chatGPT-like model, right? It's structured like a large language model, but instead of processing the meaning of the word that you're typing or the paragraph that you're writing or whatever and then giving you an answer or a follow-up or completing your train of thought, it takes as an input. A sequence of nucleotides, a sequence of DNA, and it completes it for you or it makes sense of it and it tells you what genes might be associated with each other. Now, before we get into that, I think we got to take it a couple steps back and by we, I mean me because I know I need to take it, a couple steps back. So, they did an amazing job in this article, by the way, explaining things like you're a five-year-old, which again is wonderful for me, and they have a three-minute video that I highly recommend you check out. It's going to be attached to the show notes. But they explained the issue at hand like this. They said, imagine you're reading the book, and this book has three billion letters in it, right? And every one of those letters is a nucleotide. These nucleotides make up the DNA that everything we know of in this world is pretty much composed of. And it's actually kind of straightforward. We represent them as four letters, A, C, G, and T. And standalone, they don't really mean much, but the sequence of them in different patterns together make up specific genes. Now, as we know, if you've been watching the news, this gene causes this, and poor, broken nucleotide sequences can, for most people, be harmless. For some people, it can cause cancer. So, there's a lot of value in understanding what those sequences mean, how they're related to each other, et cetera, et. Now, as I mentioned, this team had already worked on a chatGPT-like model before called the EVO-1 that was trained using 300 billion nucleotides, mostly from bacteria and stuff like that, to help them proof of concept this idea of, if we had a model that could predict what comes next. Could we better understand what the next evolutionary step would be for this plant or these bacteria or this human? Or could we better understand how the sequence that is at the one millionth element is related to the one at the 100,000th element? And if so, what does that tell us about the chances that you might have cancer? Or could we edit this out? The possibilities and the downsides of what already exists, basically.Daniel: Well, and when we talked about the technological development of having sequenced the human genome several decades ago, that didn't necessarily crack the code for us on what everything in human DNA means. That was just us looking at the DNA and writing a list, writing that three billion letter long book of just A’s, C’s, G’s, and T’s. It's awesome that like there's only four letters, but essentially, we didn't understand how those letters comprise genes and genes, they liken it in the article to like words. So, like basically, the major development in sequencing the human genome was like figuring out the alphabet and the order of the letters in the alphabet. But we did not yet understand the vocabulary. We didn't understand the words. And beyond that, we don't understand the grammar about how you put several words together to create phrases. And that's how physical characteristics are expressed or through a combination of several different genes. So, this is essentially helping us bridge the gap. And like in my mind, truly cracked the code on the meaning of DNA. If we can start to predict protein form and function by looking at the order of the A's, C's, G's and T's, predict harmful slash beneficial genes, potentially create new genetic sequences for medicines or treatments like this. This is bridging the gap between the letters in the physical realm, whereas before it was awesome that we understood what word of the letters were in.Farbod: You know, just like EVO2 is making sense of these nucleotide sequences, I'm happy you're here to make sense of my train of thought about biology.Daniel: EVO2 wouldn't exist without EVO1, buddy.Farbod: You're right. And like, I guess that frame of thinking is even better when comparing it to something like ChatGPT or really any other large language model, because the key breakthrough there, I think we mentioned it five episodes ago. The key breakthrough there was the transformer architecture, which said in relation to this wider body of text, what does this letter mean and this letter mean, instead of trying to make sense of a sequence of words back-to-back, which then lost meaning once you reach the 50th word. So again, they've taken the same underlying engine here, but now applying it to what does this sequence of genes mean, these nucleotides mean. And I think, I mentioned that EVO1 had 300 billion nucleotides, which already sounds quite impressive, but it was mostly bacteria and whatnot. Well, they upped it for EVO2, and now they include animals, plants. Overall, there's nine trillion nucleotides that are represented in this model. The only thing that they left out by design was viruses. The team said they didn't want this tool to be something that bad actors could use to develop and refine existing viruses, which I personally thank them. I think no one wants another round of the coronavirus. So, kudos for thinking ahead on that.Daniel: And this is like, it's pretty monumental here. So, in addition to like excluding viruses, right? Basically, except for viruses, the training data now is 27 times bigger than before. It includes all known living species that we have a DNA sequence for, plus some that are extinct. So, it's like, it's pretty much everything that we've ever sequenced ever, except for viruses so that we don't have people going, creating designer viruses to go try and get people sick. But every living being that we have the DNA sequence for has been included in the training data for this next generation. That's a big deal to increase your training data size, your training data set by a factor of 27, right? It's not like they've trained on 10 % more sequences than before. This is a huge deal.Farbod: Absolutely. And not only have they increased the number of data points that they're training on, but the input is also, what does ChatGPT call them, the number of tokens that you can input for a query? Yeah.Daniel: Yeah.Farbod: So, they've upped it to a one million letter sequence of nucleotides. So, you can have one million letters of combination of ACGT and it can then do whatever you want with it, analyze it, predict the next set of sequences, cetera, et. And I was wondering why one million? Apparently in the world of biology, the one million mark is, it's critical because it's long enough that it takes into account historically significantly far enough genes to make sense of rather than just taking into account the recent developments or deficiencies.Daniel: That makes sense. I appreciate the significance around there. And this makes me feel more so.  ChatGPT is an awesome analogy to give people, especially because I feel like a lot of people have had exposure to ChatGPT. But I'm hoping within our audience, maybe there's folks who've had a greater exposure to things like a co-pilot in coding or things like cursor composer. This actually feels a lot like cursor composer to me where in the cursor editor, you can go in the composer bar and you can have your code base living there, which is like a sequence of DNA. And you can tell it, hey, I want to generate a change to do this to the code  and composer will go and make those changes based off of its understanding of the code. This feels a lot like that, right? So, some of the technical features that it has, similar actually again to cursor composer is like autocomplete. They make a big deal about it in cursor. They call them tabs, but basically like you're writing and it understands what you're trying to code and you can just hit tab and it completes it, right? Autocomplete for code. They have autocomplete for DNA here based off of patterns in DNA sequences. They also have prediction models that can understand real world function. So instead of saying like change all the A's to C's or you know, control F my ACGTs and change them to TCGAs, you can say, I actually just want to change how this physical characteristic appears in the real world. And then it'll go change the DNA to reflect that. And then the interesting part, which is really awesome in my mind, it tends to be somewhat controversial. But we've seen a lot of work with CRISPR gene editing, which basically allows us to like sequence DNA, fabricate it, and then inject it into a DNA sequence. And then in a living being and basically edit a living being's DNA. They're able to essentially predict what the outcome of CRISPR gene editing will look like in the real world to propose edits essentially propose future experiments and tests.Farbod: Yes. That's what I gonna bring up I think, you know, the audience might have mixed feelings about that but what stood out to me? They were like look within this model with an EVO2 we can whenever you're predicting a new gene, we can tell you if it already exists if it doesn't if you implement it like what kind of behavior it could have, and then obviously you can do lab testing, put it into a living cell and see how it pans out. One thing that I thought was interesting in regards to CRISPR and gene editing, they were like, what if we could use this tool to modify bacteria to consume microplastics and to clean up oil spills, right? So again, I know it's a mixed feeling type of topic, especially as it relates to human beings, but the benefits of this tool are actually pretty significant. And even on the human-Daniel: This doesn't necessarily dictate how the findings are applied, right? This is an engine for understanding DNA, right? That's what we should be excited about, is it's an engine for us to understand what's going on. Even if you decide not to go edit your genes, to get rid of a disease, you can understand what diseases you might be predisposed to based off of your DNA.Farbod: Yeah, and you know, most of the stuff that they talked about are all the positives that we can all agree on, like modifying the bacteria. They said this can be used to significantly cut down on the discovery phase of new drug development because you can find which proteins work best for addressing whatever symptom. We talked about these two episodes ago, I think, on the snake venom episode, which has been a hit, by the way. They did mention that EVO2 could help with human evolution because we could predict what genes could be beneficial to us, but that's a topic for another day. I think what we should focus on here is that this is an incredible platform that they have open source. It's available to all researchers that they want others to use in their workflow and build on top of so that we can make better drugs and help our environment.Daniel: Well, and I think two really important things to mention. And you mentioned one of them and I kind of want to double down on it. One of them is direct quote from the researchers. They said it's really, really good at drug discovery, which is awesome. So obviously, helping identify treatments for potential diseases is awesome. But one of the other things that they can mention is like they, this model can distinguish between harmless DNA mutations to disease, disease causing ones. So, where we've got a lot of concern around free radicals and/or mutations in your DNA, potentially cancer causing, as you mentioned before, is like, we're trying to limit exposure to anything that could cause any type of mutation in your DNA. And that's where we get a lot of conflicting advice from people like, oh, like don't put on sunscreen, but also don't get sunburned and don't eat this and don't eat that. And don't do that. Like it feels like you're, you're screwed either way. I feel like this will potentially help us get more clarity around which types of causes for DNA mutations are most likely to cause disease and then help us hone our focus on like, what's the 80-20? What's the Pareto principle? Like what's the 20% of effort that I can do to protect myself from 80% of bad outcomes? I feel like that's one of the most important things that you and I will experience in our day-to-day life is a little bit more clarity around a lot of the conflicting guidance that we get. I think the most specific one that I think of is like around sun exposure. Like they're like, you need sun exposure for vitamin D, but don't get so much sun exposure that it causes mutations in your skin can cause melanoma. So, if you're outside, make sure you put on sunscreen, but also sunscreen can like cross through the blood brain barrier and cause disruptions in your hormones. Try and give us some clarity around a lot of the conflicting advice like that, where it feels like you can't win either way. Like, hopefully this will help us understand more what changes to make in a day-to-day life that actually have an impact on the presence of disease in our DNA.Farbod: Dude. Yeah, I totally agree that was a good hot take that I completely missed. And the sunscreen one, I'm totally with you. I think about it every morning when I put on my sunscreen. With that said, I'm going to wrap things up. So, folks, if you've heard about DNA sequencing and what a miracle it was that we cracked it a couple decades ago, you might be shocked to hear that it didn't crack the code all the way. Look, we understand what the nucleotides are that make up our DNA. You know, the four different ones, the A, C, G and T. But these folks over at Stanford are now developing a model or have already developed a model that can make sense of the different genes that are part of this DNA sequence and how they interact with each other. But more importantly, almost like you're using ChatGPT, it can predict what comes next. Now, why is this valuable? Well, by understanding the relation between the different genes, they can understand what is harmful, what can be used to develop new tools, how we can modify an existing sequence to give us beneficial properties. For example, one they called out was modifying bacteria to eat microplastics or clean up oil spills. And the next bit, it could even help us understand what good way we can go for evolving the human genome so that we get all the best properties possible.Daniel: That's Pretty fireman.Farbod: Sweet. That's the pod.Daniel: All right. See you.<hr />As always, you can find these and other interesting &amp; impactful engineering articles on Wevolver.com.To learn more about this show, please visit our<a href="https://www.wevolver.com/profile/the.next.byte"> shows page</a>. By following the page, you will get automatic updates by email when a new show is published. Be sure to give us a follow and review on<a href="https://podcasts.apple.com/nl/podcast/the-next-byte/id1548255861?l=en" target="_blank" rel="nofollow"> Apple</a> podcasts,<a href="https://open.spotify.com/show/6lPPsRtegnivsRUEsQ09R7?si=IPMiah7xT_apJtPjqvXMlA" target="_blank" rel="nofollow"> Spotify</a>, and most of your favorite podcast platforms!--The Next Byte: We're two engineers on a mission to simplify complex science &amp; technology, making it easy to understand. In each episode of our show, we dive into world-changing tech (such as AI, robotics, 3D printing, IoT, &amp; much more), all while keeping it entertaining &amp; engaging along the way.

In this episode, we explore how a generative AI tool is marking a major milestone in biology and accelerating advancements in healthcare, genetics, and drug development.

Podcast: Stanford Predicts The Next Human Evolution

Across the UK and Europe – and indeed globally – there is broad agreement that a focus on STEM - Science, Technology, Engineering, and Math – is essential. One year ago, &nbsp;the <a href="https://www.stemcoalition.eu/publications/conference-future-stem-europe">EU STEM Coalition</a> hosted a gathering with the objective of highlighting the importance of STEM education in Europe to ensure Europe's future competitiveness and ability to cope with transition. In the UK, previous governments have announced a range of measures, including <a href="https://www.computerweekly.com/news/252528203/Government-announces-490m-education-investment#:~:text=The%20government%20has%20announced%20almost,development%20in%20several%20different%20disciplines.">funding worth almost £490M&nbsp;</a> for universities and colleges to help deliver better STEM education. The fund will contribute to better facilities, equipment and course development in several different disciplines. Immediately, upon its election victory in the UK, the new Labour administration was urged by leading industry body, EngineeringUK, ‘to take immediate action to tackle the growing skills shortage in STEM fields’. Even before being elected, policies were being put in place, such as a commitment to publish a cross-government action plan to break down barriers in STEM, &nbsp;setting ‘a clear direction for government policy around diversity in STEM’ to improve ‘equality, diversity and inclusion (EDI) in STEM central to work across government’.<a href="http://www.mikroe.com">MikroElektronika</a> (MIKROE) is a proud advocate of STEM activities. MIKROE is an embedded solutions company with a mission to reduce development time and costs by introducing innovative hardware and software products based on proven standards. In 2011, the company invented the <a href="https://www.mikroe.com/mikrobus">mikroBUS</a>™ development socket standard and the compact add-on <a href="https://www.mikroe.com/click/">Click™</a> boards for peripherals that use the standard. Click boards enable designers to get to work immediately without having to develop their own hardware. This can cut months from the development cycle and save considerable expense. Currently, over 1700 boards are available that address wireless connectivity, sensors, power management, interfacing, displays, motor drives, HMI and more. Many of the world’s top microcontroller makers, including Microchip, NXP, Renesas, Silicon Labs and Infineon include the mikroBUS socket on development boards.Recently, MIKROE unveiled&nbsp;<a href="https://embeddedwiki.com/">EmbeddedWiki</a> – the world's largest embedded projects platform that already details over 1,000,000 projects, based around the huge and diverse Click board product range.&nbsp;<h2 dir="ltr">The idea behind EmbeddedWiki?</h2>The internet is a place where everything is scattered, and it can take a lot of time to find required information. Even when an answer is found, after hours and hours of searching, there is no guarantee that the solution provided will work or be accurate.EmbeddedWiki uses pre-designed and standardized hardware and software solutions, and serves as a starting point for developing customized products or applications. The platform covers 12 topics and 92 applications. Each entry contains a full description of the project, plus a list of parts that is required, schematics, 100% validated, working code and a step-by-step assembly guide. Therefore, an EmbeddedWiki project presents all the essential information for a successful result – all available from one source.In this issue we are going to focus on four projects concerning health. But visitors to the site will quickly see that there are very many examples of similar projects using different MCUs, so they can delve deep, and drill down to find a project based on devices of their choice.<h2 dir="ltr">Cardiovascular clarity: ECG excellence for a healthier you</h2>For this <a href="https://embeddedwiki.com/articles/template38-with-ecg-4-click/ecg-4-click-arduino-uno-rev3-necto-atmega328p">project</a> you will need: the <a href="https://www.mikroe.com/ecg-4-click">ECG 4 Click</a> board, an <a href="https://store.arduino.cc/en-dk/products/arduino-uno-rev3">Arduino UNO Rev3</a> development board; and the <a href="https://www.mikroe.com/necto">NECTO Studio</a> compiler. MCU: ATmega328PECG 4 Click is based on the BMD101, a highly integrated specialized bio-signal sensing SoC from NeuroSky, which produces heart-monitoring-related ICs and applications. This IC features complete HR and ECG functionality: the analogue front-end (AFE) section contains a very precise and low-noise instrumentation amplifier (LNA), which allows very low bio-signals generated by the heart to be amplified, enabling the 16-bit ADC to be able to sample them. These voltage impulses are naturally weak - in the range of a few millivolts, even microvolts. Therefore, any external interferences can obscure them. Interference might be produced in the heart itself or as the result of other muscular activity. Therefore, the input signal from the electrodes is processed by several filtering sections, both in the analogue (HP filter at the input) and digital domains (LP filter at 100 Hz and BP filter for removing the 50/60 Hz hum from the mains).&nbsp;<h2 dir="ltr"><a href="https://embeddedwiki.com/articles/template43-with-eeg-click/eeg-click-nucleo-64-with-stm32l476rg-mcu-necto-stm32l476rg">Your brain's story, captured in every wave</a></h2>You will need:&nbsp;<a href="https://www.mikroe.com/eeg-click">EEG Click</a> board;&nbsp;<a href="https://www.st.com/en/evaluation-tools/nucleo-l476rg.html">Nucleo-64 with STM32L476RG MCU</a>; and the&nbsp;<a href="https://www.mikroe.com/necto">NECTO Studio</a> compiler. MCU: STM32L476RG.<a href="https://embeddedwiki.com/articles/template43-with-eeg-click/eeg-click-nucleo-64-with-stm32l476rg-mcu-necto-stm32l476rg">EEG Click</a> allows monitoring of brain activity. Although not suitable for clinical examination, it is quite sufficient to allow some insight into brain activity. EEG click is equipped with a high-sensitivity circuit which amplifies faint electrical signals from the brain, allowing them to be sampled by a host MCU. To allow sufficiently high gain with no interference, EEG click uses the Texas Instruments’ INA114, a precision instrumentation amplifier, LASER trimmed for very low offset voltage, featuring very good common mode rejection ratio. Since ‘brain waves’ can be both positive and negative, EEG click uses a virtual GND at the potential of 2.048V. This also helps to reduce the noise from the common GND, improving the quality of the readings. The amplified brain activity signal is available at the AN pin of the mikroBUS™, allowing sampling by the host MCU.<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1740047666932-eeg-click-banner.jpg" style="width: 100%px;" class="fr-fic fr-dib"><h2 dir="ltr">Have you got the fever?</h2>You will need:&nbsp;<a href="https://www.mikroe.com/fever-click">Fever click</a> board;&nbsp;<a href="https://www.microchip.com/en-us/development-tool/dm164150">Curiosity Nano with PIC18F57Q43</a> eval kit; and the&nbsp;<a href="https://www.mikroe.com/necto">NECTO Studio</a> compiler. MCU:&nbsp;PIC18F57Q43<a href="https://embeddedwiki.com/articles/template36-with-fever-click/fever-click-curiosity-nano-with-pic18f57q43-necto-pic18f57q43">Fever Click</a> is used for monitoring the body temperature, or it can be set to alert about ‘fever/no fever’ states (A fever is considered to be when the human body temperature exceeds 37.5℃). This Click board is based on the MAX30205, a human body temperature sensor from Analog Devices. The sensor converts temperature measurement to a digital form using a high-resolution sigma-delta ADC. The sensor can work in one-shot mode and shutdown mode, which helps reduce power usage. In addition, it features the selectable timeout, which prevents bus lockup and separate open-drain overtemperature shutdown (OS) output that operates as interrupt or as comparator/thermostat output.&nbsp;<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1740047634173-fever-click-square.jpg" style="width: 100%px;" class="fr-fic fr-dib"><h2 dir="ltr">Real-time heart rate and blood oxygen saturation data</h2>You will need:&nbsp;<a href="https://www.mikroe.com/oximeter-2-click">Oximeter 2 Click</a> board;&nbsp;<a href="https://www.mikroe.com/clicker-2-kinetis">Clicker 2 for Kinetis</a> dev board; and the&nbsp;<a href="https://www.mikroe.com/necto">NECTO Studio</a> compiler. MCU:&nbsp;MK64FN1M0VDC12.<a href="https://bwwcomms.sharepoint.com/sites/BWWFileServer/Shared%20Documents/Clients/MikroE/Track%20your%20blood%20oxygen%20levels%20with%20ADPD144RI%20and%20MK64FN1M0VDC12%20%7C%20EmbeddedWiki">Oximeter 2 Click</a> is a compact add-on board suitable for measuring blood oxygen saturation. This board features the ADPD144RI, a PPG optical sensor for photoplethysmography detection of blood oxygenation from Analog Devices. It combines LED emitters and sensitive 4-channel photodiodes with a custom ASIC that provides optical isolation between the integrated LED emitters and the detection photodiodes to improve the signal-to-noise ratio (SNR). PPG detection of blood oxygenation is achieved by synchronous detection in red and infrared wavelengths. Synchronous measurement allows rejection of both DC and AC ambient light interference with low power consumption.&nbsp;<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1740047648328-oximeter-2-click-banner.jpg" style="width: 100%px;" class="fr-fic fr-dib"><h2 dir="ltr">Everything in one place</h2>With these, and all other EmbeddedWiki projects, the user will find everything they need at a single url. Starting with a description of the project, entries include a description of the required hardware (Click board, development board and MCU), step-by-step assembly instructions and schematic. Pins are mapped, working code and library descriptions are provided, and links to all other resources given. NECTO is free for non-commercial uses, and the Click boards mentioned here range in price from just $13 to $55. Click <a href="https://embeddedwiki.com/">here</a> to get started.

EmbeddedWiki is the world's largest embedded projects platform that already details over 1,000,000 projects. 

STEM, health and EmbeddedWiki

This article was discussed in our <a href="https://www.wevolver.com/profile/the.next.byte" rel="noopener noreferrer">Next Byte podcast</a>. <iframe height="200px" width="100%" frameborder="no" scrolling="no" src="https://player.simplecast.com/c9fa5746-f9fe-40aa-8c37-c9069bdb9cb9?dark=false" class="fr-draggable"></iframe> The full article will continue below.<hr />When you think about cancer research, thoughts go quickly to biological tests, drug testing, and patient studies. But what if we could use engineering to discover how cancer cells manage to leave the tumor to wander through our bodies? If we know how they do that, we could stop them right then and there. The relatively new organ-on-a-chip technology allows researchers like Mohammad Jouybar to build a biomechanical model of specific cases out of the human body to closely study how cancer behaves.TU/e researcher <a href="https://www.tue.nl/en/research/researchers/mohammad-jouy-bar" target="_blank">Mohammad Jouybar</a> is an engineer who has been very motivated to make a difference in health care from the start of his career.<img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNzM4ODg5MzgxMzc1LWNzbV9Cdk9GXzIwMjVfMDEyN18wNDM1IE1vaGFtbWFkIEpvdXkgQmFyX2JmNjI2MTg1ZGEuanBnIiwiZWRpdHMiOnsicmVzaXplIjp7IndpZHRoIjo5NTAsImZpdCI6ImNvdmVyIn19fQ==" style="width:100%px" class="fr-fic fr-dib" />Mohammad Jouybar at work in the Microfab laboratory. Photo: Bart van OverbeekeJouybar says: “When I was looking for a PhD, the offer from Jaap den Toonder to build an organ-on-a-chip research model was exactly what I was looking for. Even though I never expected to find myself working in Mechanical Engineering as a bioengineer, it couldn’t have been a more perfect fit.”<h3>Organ-on-a-chip technology</h3>“The organ-on-a-chip technology is still relatively new in the field of disease modeling and therapeutic testing. I think it’s been around for about fifteen years,” says Jouybar. “It is mainly gaining importance as an alternative or supplementary option for animal models. It’s a more ethical and less expensive choice to gain the same insights.”Organ-on-a-chip offers a smarter, ethical, and cost-effective alternative to animal testing.Mohammad Jouybar<img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNzM4ODg5NDMwNDUzLWNzbV9Cdk9GXzIwMjVfMDEyN18wMjM5IE1vaGFtbWFkIEpvdXkgQmFyXzE0MWM4ZWJiZTguanBnIiwiZWRpdHMiOnsicmVzaXplIjp7IndpZHRoIjo5NTAsImZpdCI6ImNvdmVyIn19fQ==" style="width:100%px" class="fr-fic fr-dib" />Close-up of the chip used in the breast cancer study. Material containing the cancer cells was placed in the left boxes. In the long cannula in the middle, the blood vessels were grown. The fluid streams were connected to them. It was then possible to measure how many cancer cells migrated through the vessels in the tissue in the right boxes. Photo: Bart van Overbeeke“That’s what intrigued me when I learned to work with the technology during my MSc studies in Milan, and why I went actively looking for a PhD to work with this technology.”In an organ-on-a-chip, researchers typically grow human organ cells in a hydrogel and/or in tiny microchannels. Because they grow the cells, they can determine exactly what type of cells they grow next to each other and what specific biomechanical cues to apply, such as blood flow within channels or mechanical stretch affecting cell-cell interaction.Effectively zooming in on a single function of an organ or single mechanism they want to study.<iframe width="640" height="360" src="https://www.youtube.com/embed/LK7L6vgGNcI?&amp;wmode=opaque&amp;enablejsapi=1&amp;origin=https://www.wevolver.com" frameborder="0" class="fr-draggable"> </iframe>“There are many startups who have jumped at the opportunity to make organ-on-a-chip, or even cancer-on-a-chip models for regular testing in clinical settings. However the industry does not have a standard organ-on-a-chip model among the available models yet. Which makes it difficult to assess and compare results.”“Also, in academia, we want to study new settings, which will always call for making innovative models. And that’s what I did during my PhD.”<h3>First model</h3>There are several models Jouybar designed, built, and grew for his PhD. Jouybar: “My PhD was not very defined or detailed at first. The team wanted to study the metastatic phase of cancers. And that’s not a single event that happens, but rather a cascade of consecutive processes.”The metastatic phase of cancer isn't a single event - it's a cascading process.Mohammad Jouybar<img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNzM4ODg5NDczMDg4LWNzbV9Cdk9GXzIwMjVfMDEyN18wMzM3IE1vaGFtbWFkIEpvdXkgQmFyXzlkMmZjZjc4M2EuanBnIiwiZWRpdHMiOnsicmVzaXplIjp7IndpZHRoIjo5NTAsImZpdCI6ImNvdmVyIn19fQ==" style="width:100%px" class="fr-fic fr-dib" />Mohammad Jouybar places a sample in the incubator for measurements in the Mircrofab lab. Photo: Bart van OverbeekeIn metastasis, cancer cells break away from where they first formed (primary cancer), travel through the blood or lymph system, and form new tumors (metastatic tumors) in other parts of the body. The metastatic tumor is the same type of cancer as the primary tumor.“<a href="https://www.tue.nl/en/research/researchers/jaap-den-toonder" target="_blank">Jaap den Toonder</a> asked me to design and build a model to study how breast cancer cells leave their tumor, invade the tissue, and enter the bloodstream. We wanted to study how these cells invade the bloodstream from the breast duct.”The team chose to study breast cancer - <a href="https://www.mayoclinic.org/diseases-conditions/dcis/symptoms-causes/syc-20371889" target="_blank" rel="noreferrer">ductal carcinoma in situ</a> -because it is very prevalent and is a good case for studying the metastatic process.“We know that the cancer cells invade the blood or lymphatic vessels, but how some of these steps occur, especially the initial invasion steps, are not well known. And once the cells enter the blood vessel, they can travel anywhere,” says Jouybar.“We could not mimic the entire process on our chip, of course. So, we focused on the initial step of metastasis because little was known about it.”<img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNzM4ODg5NDk2NzgzLWNzbV9VbnRpdGxlZF8wYzQ2NjZkNWM1LmpwZyIsImVkaXRzIjp7InJlc2l6ZSI6eyJ3aWR0aCI6OTUwLCJmaXQiOiJjb3ZlciJ9fX0=" style="width:100%px" class="fr-fic fr-dib" />The tiny size of the chips allows for higher throughput models including several chips. The multi-Lumina-Chip includes 9 single chips on a platform, compatible with multi-channel pipetting. Photo: Mohammad Jouybar<h3 id="isPasted">Building the cancer-on-a-chip</h3>The first step is to build, or rather grow, the elements of your cancer-on-a-chip. In this case, the cancer cells are in a microchannel within the chip, emulating the breast duct, the surrounding hydrogel mimicking the structural (stromal, ed.) tissue, and the neighboring microchannels mimicking the tiny blood vessels.Jouybar fabricated the circular cross-section channels on a chip, a similar geometry found in human blood vessels. Next, he could vary the flow through these channels to study the effect of various types of flows on blood- or lymphatic vessel cells.“In <a href="https://pubmed.ncbi.nlm.nih.gov/37914546/" target="_blank" rel="noreferrer">one of our studies</a>, I have shown that the flow rate has an effect on the orientation of cells lining the blood or lymphatic vessels we grow on the chip. The chip is 2-3 centimeters squared, but the vessels are only around 10-100’s micrometers in diameter. Just like the tiny vessels in the tissue we want to study.”Growing the model to mimic living tissue is a challenge all of its own. “We had different components in the model: the breast ducts, the surrounding tissue, and the blood vessels. The trick to building the model with all those components adjacent to each other was to compartmentalize building those elements,” says Jouybar.“We used the facilities in our labs, like a femtosecond laser and 3D sugar printing, to build the circular geometries of the blood vessels. Additionally, we had to make sure that fabricated reservoirs were fully working to connect the models to the pumps for the blood flow or just simply for exchanging culture media.”<img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNzM4ODg5NTM3NDkyLWNzbV9Cdk9GXzIwMjVfMDEyN18wMjIwIE1vaGFtbWFkIEpvdXkgQmFyXzMwOWY4Zjc0N2EuanBnIiwiZWRpdHMiOnsicmVzaXplIjp7IndpZHRoIjo5NTAsImZpdCI6ImNvdmVyIn19fQ==" style="width:100%px" class="fr-fic fr-dib" />Mohammad Jouybar with the chip for the femtolaser used to make the molds for the chips. The molds are made of glass, the chips themselves of plastic. Photo: Bart van Overbeeke<h3>Including lymphatic vessels</h3>In a collaboration with Amsterdam UMC, Jouybar included not only blood vessels but also built lymphatic vessels in the cancer-on-a-chip models.Jouybar: “This was a simpler model from a fabrication point of view because we fabricated a microchannel cast in hydrogel utilizing a thin acupuncture needle. We added lymphatic cancer cells (B cell lymphoma) and fibroblasts to a hydrogel matrix.”“And then, we grew the lymphatic microvessel, lined by lymphatic endothelial cells, inside the hydrogel. With this model, we studied how donor’s specific fibroblasts and lymphatic vessel cells influence the movements of cancer cells in and around lymphatic vessels.”Both models focus on the step where the cancer cells start to move from their original location, where they might still be very treatable, throughout the body. The models help to understand what is happening on a fundamental level and can also help to study which types of medicine might affect the key mechanisms of the cancer. This opens up whole new areas of drug development and treatments.<h3>The value of cancer-on-a-chip</h3>“Depending on the type of cancer you are studying, due to the broad area of oncology, you can build a model to answer questions for the specific type of cancer and tissue, or even specific patient you are studying, so-called personalized medicine,” says Jouybar. “The models help us to fill in the blanks of how cancer originates, develops, and moves.”The cancer-on-a-chip models help us to fill in the blanks of how cancer originates, develops, and moves.Mohammad Jouybar“I really enjoy this type of collaborative research, where medical research, mechanical engineering, and biomedical engineering are colliding. We cannot do one without the other, and I expect much from how engineering can assist and boost medical research,” concludes Jouybar.

Mohammad Jouybar shows the chip he used to do his research on breast cancer. Photo: Bart van Overbeeke

During his PhD research, biomedical engineer Mohammad Jouybar explored how to build specific organ-on-a-chip or even cancer-on-a-chip devices.

Cancer-on-a-chip technology advances our understanding of how cancer operates

In this episode, we explore how tiny wireless antennas use light to monitor cellular communication with groundbreaking precision.

Podcast: Tiny Antennas for Big Cellular Insights

Experiments from the Caltech lab of Chiara Daraio, G. Bradford Jones Professor of Mechanical Engineering and Applied Physics and Heritage Medical Research Institute Investigator, have yielded a fascinating new type of matter, neither granular nor crystalline, that responds to some stresses as a fluid would and to others like a solid. The new material, known as PAM (for polycatenated architected materials) could have uses in areas ranging from helmets and other protective gear to biomedical devices and robotics.<iframe width="640" height="360" src="https://www.youtube.com/embed/Z-P7lfW-p-8?&amp;wmode=opaque&amp;rel=0&amp;enablejsapi=1&amp;origin=https://www.wevolver.com" frameborder="0" allowfullscreen="" class="fr-draggable" data-lf-form-tracking-inspected-kn9eq4roawkarlvp="true" data-lf-yt-playback-inspected-kn9eq4roawkarlvp="true" data-lf-vimeo-playback-inspected-kn9eq4roawkarlvp="true"></iframe>PAMs in Action.&nbsp;Credit: Wenjie Zhou and Peter Holderness/CaltechPAMs are not found in nature, though their basic form is known to us through the millennia-old manufacture of chain mail: small metal rings linked together to form a mesh, most often used as a flexible form of armor. PAMs, however, are like chain mail on steroids. Following the basic principle of interlocking shapes, like those found in a chain, PAMs are made up of a variety of shapes linked together to form three-dimensional patterns whose configurations are almost unimaginably variable. The resulting materials, which Daraio and her colleagues have rendered using 3D printers, exhibit behaviors not found in other types of materials.Wenjie Zhou, postdoctoral scholar research associate in mechanical and civil engineering, has been working on these types of materials for two years in Daraio's lab. "I was a chemist, and I wanted to make these structures at a molecular scale, but that proved too challenging. In order to get answers to the questions I had about how these structures behave, I decided to join Chiara's group and study PAMs at a larger scale."The PAMs that Daraio's group created and studied were first modeled on a computer and were designed to replicate lattice structures found in crystalline substances but with the crystal's fixed particles replaced by entangled rings or cages with multiple sides.These lattices were then printed out three-dimensionally using a variety of materials, including acrylic polymers, nylon, and metals. Once the PAMs could be held in the palm of one's hand—most of the prototypes are 5-centimeter (2-inch) cubes or spheres with a 5-centimeter diameter—they were exposed to various types of physical stress. "We started with compression," Zhou explains, "compressing the objects a bit harder each time. Then we tried a simple shear, a lateral force, like what you would apply if you were trying to tear the material apart. Finally, we did rheology tests, seeing how the materials responded to twisting, first slowly and then more quickly and strongly."In some scenarios, these PAMs behaved like liquids. "Imagine applying a shear stress to water," Zhou says. "There would be zero resistance. Because PAMs have all these coordinated degrees of freedom, with the rings and cages they are composed of sliding against one another as the links of a chain would, many have very little shear resistance." But when these structures are compressed, they may become fully rigid, behaving like solids.This dynamism makes PAMs unique. "PAMs are really a new type of matter," Daraio says. "We all have a clear distinction in mind when we think of solid materials and granular matter. Solid materials are often described as crystalline lattices. This is what you see in the classic ball-and-stick models of atomic, chemical, or larger crystalline structures. It is these materials that have formed our conventional understanding of solid matter. The other class of materials is granular, as we see in substances like rice, flour, or ground coffee. These materials are made up of discrete particles, free to move and slide relative to one another."PAMs defy this binary classification. "With PAMs, the individual particles are linked as they are in crystalline structures, and yet, because these particles are free to move relative to one another, they flow, they slide on top of each other, and they change their relative positions, more like grains of sand," Daraio explains. "PAMs can be very different from one another. You can print them in squishy materials or hard ones. You can change the shape of each particle, and you can change the lattice that you use to connect these particles. Each of these parameters affects the behavior of the resulting material. But all of them show a characteristic transition between fluid and solid-like behavior. This transition may happen under different circumstances, but it always happens.""Architected materials have been a significant subfield in material science and engineering for the past 20 to 30 years," Daraio says. "But as hybrids between granular materials and elastic deformable materials, PAMs are exciting and new. We have theories to describe granular matter and theories to describe elastic deformable matter, but nothing that captures these in-between materials. It's a fascinating frontier that promises to redefine what materials are and how they behave."At this point, potential uses for PAMs are largely speculative but nevertheless intriguing, Daraio says: "These materials have unique energy-absorption properties. Because each element can slide and rotate and reorganize relative to each other, they can dissipate energy very efficiently," making them better candidates for use in helmets or other forms of protective gear than the currently used foams. This property makes them similarly attractive for use in packaging or in any environment where cushioning or stabilization is required.Experiments with microscale PAMs have shown that they will expand or contract in response to applied electrical charges as well as physical forces, suggesting possible uses in biomedical devices or soft robotics.Co-author Liuchi Li (PhD '20), now assistant professor of civil and environmental engineering at Princeton University, is enthusiastic about the future of PAMs: "We can envision incorporating advanced artificial intelligence techniques to accelerate the exploration of this vast design space. We are only scratching the surface of what is possible."This research is published in&nbsp;Science under the title&nbsp;<a href="https://www.science.org/doi/10.1126/science.adr9713">"3D polycatenated architected materials."</a> Co-authors include Zhou, Daraio, Sujeeka Nadarajah, Hujie Yan (MS '24), Aashutosh K. Prachet, and Payal Patel of Caltech; Li of Princeton University; and Anna Guell Izard and Xiaoxing Xia (PhD '19) of Lawrence Livermore National Laboratory (LLNL). Computational resources were provided by the High-Performance Computing Center at Caltech, and the research was funded by the Army Research Office, the Gary Clinard Innovation Fund, LLNL, and the US Department of Energy.

The new material, known as PAM (for polycatenated architected materials) could have uses in areas ranging from helmets and other protective gear to biomedical devices and robotics.

Reimagining Chain Mail: 3D Architected Materials That Adapt and Protect

This article was discussed in our <a href="https://www.wevolver.com/profile/the.next.byte" rel="noopener noreferrer">Next Byte podcast</a>.&nbsp;<iframe height="200px" width="100%" frameborder="no" scrolling="no" seamless="" src="https://player.simplecast.com/f1468a9e-3b5e-4bb2-a089-53a90f0a433c?dark=false" class="fr-draggable" data-lf-form-tracking-inspected-kn9eq4roawkarlvp="true" data-lf-yt-playback-inspected-kn9eq4roawkarlvp="true" data-lf-vimeo-playback-inspected-kn9eq4roawkarlvp="true"></iframe> The full article will continue below.<hr>Monitoring electrical signals in biological systems helps scientists understand how cells communicate, which can aid in the diagnosis and treatment of conditions like arrhythmia and Alzheimer’s.But devices that record electrical signals in cell cultures and other liquid environments often use wires to connect each electrode on the device to its respective amplifier. Because only so many wires can be connected to the device, this restricts the number of recording sites, limiting the information that can be collected from cells.MIT researchers have now developed a biosensing technique that eliminates the need for wires. Instead, tiny, wireless antennas use light to detect minute electrical signals.Small electrical changes in the surrounding liquid environment alter how the antennas scatter the light. Using an array of tiny antennas, each of which is one-hundredth the width of a human hair, the researchers could measure electrical signals exchanged between cells, with extreme spatial resolution.The devices, which are durable enough to continuously record signals for more than 10 hours, could help biologists understand how cells communicate in response to changes in their environment. In the long run, such scientific insights could pave the way for advancements in diagnosis, spur the development of targeted treatments, and enable more precision in the evaluation of new therapies.“Being able to record the electrical activity of cells with high throughput and high resolution remains a real problem. We need to try some innovative ideas and alternate approaches,” says Benoît Desbiolles, a former postdoc in the MIT Media Lab and lead author of <a href="https://doi.org/10.1126/sciadv.adr8380" target="_blank">a paper on the devices</a>.He is joined on the paper by Jad Hanna, a visiting student in the Media Lab; former visiting student Raphael Ausilio; former postdoc Marta J. I. Airaghi Leccardi; Yang Yu, a scientist at Raith America, Inc.; and senior author Deblina Sarkar, the AT&amp;T Career Development Assistant Professor in the Media Lab and MIT Center for Neurobiological Engineering and head of the Nano-Cybernetic Biotrek Lab. The research appears today in Science Advances.“Bioelectricity is fundamental to the functioning of cells and different life processes. However, recording such electrical signals precisely has been challenging,” says Sarkar. “The organic electro-scattering antennas (OCEANs) we developed enable recording of electrical signals wirelessly with micrometer spatial resolution from thousands of recording sites simultaneously. This can create unprecedented opportunities for understanding fundamental biology and altered signaling in diseased states as well as for screening the effect of different therapeutics to enable novel treatments.”<h2>Biosensing with light</h2>The researchers set out to design a biosensing device that didn’t need wires or amplifiers. Such a device would be easier to use for biologists who may not be familiar with electronic instruments.“We wondered if we could make a device that converts the electrical signals to light and then use an optical microscope, the kind that is available in every biology lab, to probe these signals,” Desbiolles says.Initially, they used a special polymer called PEDOT:PSS to design nanoscale transducers that incorporated tiny pieces of gold filament. Gold nanoparticles were supposed to scatter the light — a process that would be induced and modulated by the polymer. But the results weren’t matching up with their theoretical model.The researchers tried removing the gold and, surprisingly, the results matched the model much more closely.“It turns out we weren’t measuring signals from the gold, but from the polymer itself. This was a very surprising but exciting result. We built on that finding to develop organic electro-scattering antennas,” he says.The&nbsp;organic electro-scattering antennas,&nbsp;or OCEANs, are composed of&nbsp;PEDOT:PSS. This polymer attracts or repulses positive ions from the surrounding liquid environment when there is electrical activity nearby. This modifies its chemical configuration and electronic structure, altering an optical property known as its refractive index, which changes how it scatters light.When researchers shine light onto the antenna, the intensity of the light changes in proportion to the electrical signal present in the liquid.<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1735259967015-ezgif-4-326702faf8.gif" style="width: 100%px;" class="fr-fic fr-dib">The brightness of light scattered by the tiny antennas the researchers developed, called OCEANs, changes in response to changing electrical signals in the liquid environment that surrounds them, as shown here. By capturing and measuring the light with an optical microscope, researchers could decode the intricate signals used for cellular communication. Credit: Courtesy of the researchersWith thousands or even millions of tiny antennas in an array, each only 1 micrometer wide, the researchers can capture the scattered light with an optical microscope and measure electrical signals from cells with high resolution. Because each antenna is an independent sensor, the researchers do not need to pool the contribution of multiple antennas to monitor electrical signals, which is why OCEANs can detect signals with micrometer resolution.Intended for in vitro&nbsp;studies, OCEAN arrays are designed to have cells cultured directly on top of them and put under an optical microscope for analysis.<h2>“Growing” antennas on a chip</h2>Key to the devices is the precision with which the researchers can fabricate arrays in the MIT.nano facilities.They start with a glass substrate and deposit layers of conductive then insulating material on top, each of which is optically transparent. Then they use a focused ion beam to cut hundreds of nanoscale holes into the top layers of the device. This special type of focused ion beam enables high-throughput nanofabrication.“This instrument is basically like a pen where you can etch anything with a 10-nanometer resolution,” he says.They submerge the chip in a solution that contains the precursor building blocks for the polymer. By applying an electric current to the solution, that precursor material is attracted into the tiny holes on the chip, and mushroom-shaped antennas “grow” from the bottom up.The entire fabrication process is relatively fast, and the researchers could use this technique to make a chip with millions of antennas.“This technique could be easily adapted so it is fully scalable. The limiting factor is how many antennas we can image at the same time,” he says.The researchers optimized the dimensions of the antennas and adjusted parameters, which enabled them to achieve high enough sensitivity to monitor signals with voltages as low as 2.5 millivolts in simulated experiments. Signals sent by neurons for communication are usually around 100 millivolts.“Because we took the time to really dig in and understand the theoretical model behind this process, we can maximize the sensitivity of the antennas,” he says.OCEANs also responded to changing signals in only a few milliseconds, enabling them to record electrical signals with fast kinetics. Moving forward, the researchers want to test the devices with real cell cultures. They also want to reshape the antennas so they can penetrate cell membranes, enabling more precise signal detection.In addition, they want to study how OCEANs could be integrated into nanophotonic devices, which manipulate light at the nanoscale for next-generation sensors and optical devices.This research is funded, in part, by the U.S. National Institutes of Health and the Swiss National Science Foundation. Research reported in this press release was supported by the National Heart, Lung, and Blood Institute (NHLBI) of the National Institutes of Health and does not necessarily represent the official views of the NIH.

To improve biosensing techniques that can aid in diagnosis and treatment, MIT researchers developed tiny, wireless antennas that use light to detect minute electrical signals in liquid environments, which are shown in this rendering. Credit: Marta Airaghi and Benoit Desbiolles

As part of a high-resolution biosensing device without wires, the antennas could help researchers decode intricate electrical signals sent by cells.

Tiny, wireless antennas use light to monitor cellular communication

The healthcare sector is undergoing a transformation driven by artificial intelligence (AI). This transformation brings with it new standards of patient care, diagnosis, and treatment efficacy. Worldwide, healthcare systems face challenges ranging from improving patient outcomes to providing efficient patient care. These challenges require innovative solutions and engender technologies that will help achieve new medical practices.AI has incredible analytical capabilities and offers new opportunities to the healthcare industry, such as enhanced diagnostic precision by leveraging vast datasets and identifying patterns and anomalies. AI does not diminish the doctors’ roles but enhances their ability to make decisions with swift and accurate diagnoses. With AI-driven insights, doctors can validate findings with their expertise and understanding of patient care.But AI's impact goes beyond diagnostics into predictive analysis in patient care to personalized treatment plans. It could optimize healthcare delivery. These advancements could lead to a reduction in wait times, targeted therapies, and better health outcomes.Acknowledging AI's capacity requires a balanced conversation, considering the ethical, privacy, and data security concerns associated with integrating such technology into healthcare.The goal is to create a working relationship between AI and medical professionals, where the technology heightens human expertise instead of replacing it. Collaboration paves the way for a future where healthcare is more accessible, efficient, and effective.<h2>Transitioning to the Digital Age</h2>Historically, medical records were kept as handwritten or transcribed hardcopy notes for every patient visit, treatment, and progress. When computers became commonplace, medical data became stored electronically; but even then, it took many years for doctors to adjust to this new system. Early on, health professionals used non-networked, closed computer systems to store and retrieve patient information locally as needed.Internet connectivity allowed software companies to serve the medical community with data capture, report generation, relational database management, and, more importantly, medical billing and accounting. Insurance companies changed the operational aspects of medical practice, requiring medical practitioners to code records in very specific ways. Computerized data systems could help file insurance claims electronically via modems and faxes. There was hope that this would allow doctors to spend more quality time with their patients. While it indeed saved time, it encountered initial challenges.In 1996, the US Congress introduced the <a href="https://www.hhs.gov/hipaa/for-professionals/privacy/laws-regulations/index.html" target="_blank" data-internal="false" rel="noopener">Health Insurance Portability and Accountability Act (HIPAA)</a> to centralize and protect patient health information. However, the act inadvertently imposed significant challenges on small medical practices. Transitioning years of hardcopy patient records into a HIPAA-compliant, searchable database proved costly and labor-intensive without the option to use simple scanning methods. Even filing insurance claims required some expertise and posed additional challenges.HIPAA tried to integrate all health information with the goal of preserving privacy and security, as well as reporting breaches. Using an electronic protected health information (ePHI) approach, all protected health information produced, saved, transferred, or received electronically needed to conform to HIPAA’s rules. Only authorized users could see the data.Centralizing these data streamlined how health professionals, agencies, insurers, and statistical data-gathering arms of the government (such as the US Centers for Disease Control and Prevention (CDC) and its Environmental Public Health Tracking (EPHT) Network) shared information. This aimed to improve efficiency and the quality of care through digital innovation.<h2>Ambient Clinical Intelligence</h2>One such innovation is ambient clinical intelligence (ACI), which uses speech recognition and voice recording to capture interactions between patients and medical staff. Using auto-dictation for record-keeping reduces the number of practitioners who need to attend to a patient. For example, instead of requiring an assistant to take notes during an examination, a computer with ACI listens, records, and transcribes all interactions between the physician and the patient. The assistant can then perform other tasks as needed.When AI is combined with this data exchange, it can help eliminate or reduce transcription errors, and the physician can continue with the examination. While it is possible today to use standard hardware to implement some of these functions, pervasive AI technology is expected to play a more dominant role, especially with context and medical pattern recognition for diagnosis and prognosis assistance.For example, medical monitoring services could combine AI with sensors and video feeds to detect a patient's fall. Phones and medical alert pendants can do that now, but patients may remove their pendants or may not be near their phones. However, an AI-enabled video system can detect if the pendant is removed while still detecting a fall.As AI has evolved, staff can now create documents automatically and format data to comply with electronic health records standards. This simplifies clinical review and helps to ensure timely filing of data and claims. It can also reduce—possibly eliminate—burnout among medical professionals who have been overworked and not given as much time with patients as they would like.<h2>How ACI Works</h2>Initially, ACI will function like a digital butler designed for transcription, documentation, coding, formatting, storage, and transmission. But AI medical technology will continue to evolve, with increasingly larger data sets as health sensors are placed or implanted in patients. With continuous monitoring of patients' stats, AI that has access can be trained on anonymized, ultra-large, HIPAA-compliant datasets. AI will be not only making diagnoses but also discoveries that can further advance medical knowledge and treatment modalities.By accessing this (anonymized) information, the AI could discover new potentially hazardous drug interactions. For example, an AI may discover that a statistically significant number of patients taking medication A for a specific affliction and medication B for another affliction develop condition C.Implanted and worn sensors are not the only tools AI health and wellness monitoring technology can use. Computerized video processing can determine heart rate and blood pressure by monitoring blood vessels humans may not notice or even be able to see.The combined knowledge and experience of humans and AI can be used to diagnose patients more effectively than ever before. AI trained with large data sets and experience has already demonstrated its ability to read biopsy data and even catch cancers that experienced doctors have missed.<a href="https://resources.mouser.com/medical/creating-better-medical-practices-with-ai#_edn1" name="_ednref1" data-internal="self">[1]</a>What does this mean for medical facilities? In the short term, it will help unburden staff from mundane paperwork and filing. It will allow doctors to spend more time with their patients, possibly leading to more reliable diagnoses. It may also help reduce the number of practitioners and staff needed to operate a medical facility under normal conditions.<h2>Future Considerations</h2>As medical facilities continue to navigate regulations like HIPAA, insurance companies' requirements, and evolving patient-doctor relationships, the healthcare sector is looking for new solutions to enhance efficiency and reduce error. Medical malpractice is real, and the industry is prone to human error.Integrating AI and robotics into healthcare offers exciting opportunities to complement human expertise with the precision and recall of AI technologies. Of course, questions arise with new technologies, including cost, government support, data oversight, security, and the logistics of system updates. Cloud-based and corporate entities are emerging to assist medical practitioners with their filing and coding, signifying a trend toward digital transformation in the healthcare industry.With consistent advancements in dexterity, vision, and data processing, robotics will eventually emerge as an even more valuable tool in medical procedures (Figure 1). When we look to the future, the key will be a balanced approach and a collaboration between technology and humans to advance healthcare and maintain trust.<img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNzM0OTQ5ODIzNDIzLXJvYm90IHN1cmdlcnkuanBnIiwiZWRpdHMiOnsicmVzaXplIjp7IndpZHRoIjo5NTAsImZpdCI6ImNvdmVyIn19fQ==" style="width: 832px;" class="fr-fic fr-dib">Figure 1: As medical robotic technology improves, robotic surgeons may increasingly perform tasks that humans previously performed. They have better vision, dexterity, and a deeper knowledge base. Image credit: Futek This AI revolution in healthcare means that medical training will have to change. Doctors may need to focus on more personalized medicine that leverages large datasets to predict, prevent, and cure diseases effectively and efficiently. Medical curricula may include data science, machine learning, and ethical considerations in AI technology. Transitioning into this AI-driven healthcare era means doctors must understand privacy concerns in digital healthcare.Centralizing personal and medical data raises security concerns, as data is always susceptible to breach. While data may be encrypted, it is not guaranteed to be safe; even the most prominent companies with the best IT departments have been breached. With continuous advancements in cybersecurity and data protection protocols, the healthcare industry is becoming increasingly adept at safeguarding this sensitive information, ensuring that the benefits of data centralization can be realized while minimizing risks.<h2>Conclusion</h2>As the medical field evolves to integrate AI, it must balance technological advancements and data security. While there will always be risks associated with digitizing aspects of our lives, the potential of AI to revolutionize healthcare by improving efficiency and patient outcomes is inspiring. As healthcare industries embrace AI while staying abreast of cybersecurity measures, a brighter future that changes how we treat patients and save lives awaits.<h2 id="isPasted">Sources</h2><a href="https://resources.mouser.com/medical/creating-better-medical-practices-with-ai#_ednref1" name="_edn1" data-internal="self"></a><a href="https://resources.mouser.com/medical/creating-better-medical-practices-with-ai#_ednref1">[1]</a> "More Breast Cancers Detected in First Evaluation of Breast Screening AI,” University of Aberdeen, March 21, 2024, https://www.abdn.ac.uk/news/22964/.<h3> </h3>

AI is transforming healthcare by enhancing diagnostic accuracy, optimizing patient care, and improving treatment outcomes, while also necessitating careful consideration of ethics, privacy, and data security.

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