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1. Key developments that impacted the field of mechanobiology and mechanotransduction: MECHANOBIOLOGY AND SIGNAL TRANSDUCTION

   https://doi.org/10.1002/jor.23707  

   Wall, Michelle; Butler, David; Haj, Alicia El; Bodle, Josephine C.; Loboa, Elizabeth G.; Banes, Albert J. (February 2018, Journal of Orthopaedic Research)

   Advances in mechanobiology have evolved through insights from multiple disciplines including structural engineering, biomechanics, vascular biology, and orthopaedics. In this paper, we reviewed the impact of key reports related to the study of applied loads on tissues and cells and the resulting signal transduction pathways. We addressed how technology has helped advance the burgeoning field of mechanobiology (over 33,600 publications from 1970 to 2016). We analyzed the impact of critical ideas and then determined how these concepts influenced the mechanobiology field by looking at the citation frequency of these reports as well as tracking how the overall number of citations within the field changed over time. These data allowed us to understand how a key publication, idea, or technology guided or enabled the field. Initial observations of how forces acted on bone and soft tissues stimulated the development of computational solutions defining how forces affect tissue modeling and remodeling. Enabling technologies, such as cell and tissue stretching, compression, and shear stress devices, allowed more researchers to explore how deformation and fluid flow affect cells. Observation of the cell as a tensegrity structure and advanced methods to study genetic regulation in cells further advanced knowledge of specific mechanisms of mechanotransduction. The future of the field will involve developing gene and drug therapies to simulate or augment beneficial load regimens in patients and in mechanically conditioning organs for implantation. Here, we addressed a history of the field, but we limited our discussions to advances in musculoskeletal mechanobiology, primarily in bone, tendon, and ligament tissues. 

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2. Projection method as a probe for multiplexing/demultiplexing of magnetically enriched biological tissues

   https://doi.org/10.1039/D0RA01574A  

   Zamani Kouhpanji, Mohammad Reza; Stadler, Bethanie J. (April 2020, RSC Advances)

   null (Ed.)

   The unmet demand for cheap, accurate, and fast multiplexing of biomarkers has urged nanobiotechnology to prioritize the invention of new biomarkers that make feasible the remote detection, identification, and quantification of biological units, such as regenerative tissues. Here, we introduce a novel approach that highlights magnetic nanowires (MNWs) with such capabilities. This method employs the stable magnetization states of MNWs as a unique characteristic that can be realized by projecting the MNWs' switching field on the backward field ( P Hb ), also known as the irreversible switching field. Experimentally, several types of MNWs were directly synthesized inside polycarbonate tissues and their P Hb characteristics were measured and analyzed. Our results show that the P Hb gives an excellent identification and quantification characteristic for demultiplexing MNWs embedded in these tissues. Furthermore, this method significantly improves the characterization speed by a factor of 50×–100× that makes it superior to the current state of the art that ceased the progression of magnetic nanoparticles in multiplexing/demultiplexing applications. 

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3. Identifying Heterogeneous Micromechanical Properties of Biological Tissues via Physics‐Informed Neural Networks

   https://doi.org/10.1002/smtd.202400620  

   Wu, Wensi; Daneker, Mitchell; Turner, Kevin T; Jolley, Matthew A; Lu, Lu (January 2025, Small Methods)

   The heterogeneous micromechanical properties of biological tissues have profound implications across diverse medical and engineering domains. However, identifying full‐field heterogeneous elastic properties of soft materials using traditional engineering approaches is fundamentally challenging due to difficulties in estimating local stress fields. Recently, there has been a growing interest in data‐driven models for learning full‐field mechanical responses, such as displacement and strain, from experimental or synthetic data. However, research studies on inferring full‐field elastic properties of materials, a more challenging problem, are scarce, particularly for large deformation, hyperelastic materials. Here, a physics‐informed machine learning approach is proposed to identify the elasticity map in nonlinear, large deformation hyperelastic materials. This study reports the prediction accuracies and computational efficiency of physics‐informed neural networks (PINNs) in inferring the heterogeneous elasticity maps across materials with structural complexity that closely resemble real tissue microstructure, such as brain, tricuspid valve, and breast cancer tissues. Further, the improved architecture is applied to three hyperelastic constitutive models: Neo‐Hookean, Mooney Rivlin, and Gent. The improved network architecture consistently produces accurate estimations of heterogeneous elasticity maps, even when there is up to 10% noise present in the training data. 

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4. Wireless, Batteryless, and Secure Implantable System-on-a-Chip for 1.37mmHg Strain Sensing with Bandwidth Reconfigurability for Cross-Tissue Adaptation

   https://doi.org/10.1109/CICC53496.2022.9772815  

   Abdelhamid, M. R.; Ha, U.; Banerjee, U.; Adib, F.; Chandrakasan, A. (April 2022, 2022 IEEE Custom Integrated Circuits Conference (CICC))

   There is a growing interest in wireless and batteryless implants for long-term sensing of organ movements, core pressure, glucose levels, or other biometrics [1]. Most research on such implants has focused on ultrasonic [2] and nearfield inductive [3-4] methods for power and communication, which require direct contact or close proximity (<1-5cm) to the human body. Recently, RF backscatter has emerged as a promising alternative due to its ability to communicate with far-field (> 10cm) wireless devices at ultra-low-power [5]. While multiple proposals have demonstrated far-field RF backscatter in deep tissues, these proposals have been limited to tag identification and could neither perform biometric sensing nor secure the wireless communication links, which is critical for ensuring the confidentiality of the sensed biometrics and for responding to commands only from authorized users [6]. Moreover, such far-field RF implants are susceptible to tissue variations which impact their resonance and hence their efficiency in RF backscatter and energy harvesting. 

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5. Batteryless, Wireless, and Secure SoC for Implantable Strain Sensing

   https://doi.org/10.1109/OJSSCS.2022.3230000  

   Abdelhamid, Mohamed R.; Ha, Unsoo; Banerjee, Utsav; Adib, Fadel; Chandrakasan, Anantha P. (December 2022, IEEE Open Journal of the Solid-State Circuits Society)

   The past few years have witnessed a growing interest in wireless and batteryless implants, due to their potential in long-term biomedical monitoring of in-body conditions such as internal organ movements, bladder pressure, and gastrointestinal health. Early proposals for batteryless implants relied on inductive near-field coupling and ultrasound harvesting, which require direct contact between the external power source and the human body. To overcome this near-field challenge, recent research has investigated the use of RF backscatter in wireless micro-implants because of its ability to communicate with wireless receivers that are placed at a distance outside the body (∼0.5 m), allowing a more seamless user experience. Unfortunately, existing far-field backscatter designs remain limited in their functionality: they cannot perform biometric sensing or secure data transmission; they also suffer from degraded harvesting efficiency and backscatter range due to the impact of variations in the surrounding tissues. In this paper, we present the design of a batteryless, wireless and secure system-on-chip (SoC) implant for in-body strain sensing. The SoC relies on four features: 1) employing a reconfigurable in-body rectenna which can operate across tissues adapting its backscatter bandwidth and center frequency; 2) designing an energy efficient 1.37 mmHg strain sensing front-end with an efficiency of 5.9 mmHg·nJ/conversion; 3) incorporating an AES-GCM security engine to ensure the authenticity and confidentiality of sensed data while sharing the ADC with the sensor interface for an area efficient random number generation; 4) implementing an over-the-air closed-loop wireless programming scheme to reprogram the RF front-end to adapt for surrounding tissues and the sensor front-end to achieve faster settling times below 2 s. 

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