Millifluidic Nanogenerator Lab-on-a-Chip Device for Blood Electrical Conductivity Monitoring at Low Frequency.

The electrical conductivity of blood is a crucial physiological parameter with diverse applications in medical diagnostics. Here, a novel approach utilizing a portable millifluidic nanogenerator lab-on-a-chip device for measuring blood conductivity at low frequencies, is introduced. The proposed device employs blood as a conductive substance within its built-in triboelectric nanogenerator system. The voltage generated by this blood-based nanogenerator device is analyzed to determine the electrical conductivity of the blood sample. The self-powering functionality of the device eliminates the need for complex embedded electronics and external electrodes. Experimental results using simulated body fluid and human blood plasma demonstrate the device's efficacy in detecting variations in conductivity related to changes in electrolyte concentrations. Furthermore, artificial intelligence models are used to analyze the generated voltage patterns and to estimate the blood electrical conductivity. The models exhibit high accuracy in predicting conductivity based solely on the device-generated voltage. The 3D-printed, disposable design of the device enhances portability and usability, providing a point-of-care solution for rapid blood conductivity assessment. A comparative analysis with traditional conductivity measurement methods highlights the advantages of the proposed device in terms of simplicity, portability, and adaptability for various applications beyond blood analysis.

Identification of Novel Biomarkers for Alzheimer's Disease and Related Dementias Using Unbiased Plasma Proteomics.

Alzheimer's disease (AD) and related dementias (ADRD) is a complex disease with multiple pathophysiological drivers that determine clinical symptomology and disease progression. These diseases develop insidiously over time, through many pathways and disease mechanisms and continue to have a huge societal impact for affected individuals and their families. While emerging blood-based biomarkers, such as plasma p-tau181 and p-tau217, accurately detect Alzheimer neuropthology and are associated with faster cognitive decline, the full extension of plasma proteomic changes in ADRD remains unknown. Earlier detection and better classification of the different subtypes may provide opportunities for earlier, more targeted interventions, and perhaps a higher likelihood of successful therapeutic development. In this study, we aim to leverage unbiased mass spectrometry proteomics to identify novel, blood-based biomarkers associated with cognitive decline. 1,786 plasma samples from 1,005 patients were collected over 12 years from partcipants in the Massachusetts Alzheimer's Disease Research Center Longitudinal Cohort Study. Patient metadata includes demographics, final diagnoses, and clinical dementia rating (CDR) scores taken concurrently. The Proteograph™ Product Suite (Seer, Inc.) and liquid-chromatography mass-spectrometry (LC-MS) analysis were used to process the plasma samples in this cohort and generate unbiased proteomics data. Data-independent acquisition (DIA) mass spectrometry results yielded 36,259 peptides and 4,007 protein groups. Linear mixed effects models revealed 138 differentially abundant proteins between AD and healthy controls. Machine learning classification models for AD diagnosis identified potential candidate biomarkers including MBP, BGLAP, and APoD. Cox regression models were created to determine the association of proteins with disease progression and suggest CLNS1A, CRISPLD2, and GOLPH3 as targets of further investigation as potential biomarkers. The Proteograph workflow provided deep, unbiased coverage of the plasma proteome at a speed that enabled a cohort study of almost 1,800 samples, which is the largest, deep, unbiased proteomics study of ADRD conducted to date.

Modelling post-implantation human development to yolk sac blood emergence.

Implantation of the human embryo begins a critical developmental stage that comprises profound events including axis formation, gastrulation and the emergence of haematopoietic system1,2. Our mechanistic knowledge of this window of human life remains limited due to restricted access to in vivo samples for both technical and ethical reasons3-5. Stem cell models of human embryo have emerged to help unlock the mysteries of this stage6-16. Here we present a genetically inducible stem cell-derived embryoid model of early post-implantation human embryogenesis that captures the reciprocal codevelopment of embryonic tissue and the extra-embryonic endoderm and mesoderm niche with early haematopoiesis. This model is produced from induced pluripotent stem cells and shows unanticipated self-organizing cellular programmes similar to those that occur in embryogenesis, including the formation of amniotic cavity and bilaminar disc morphologies as well as the generation of an anterior hypoblast pole and posterior domain. The extra-embryonic layer in these embryoids lacks trophoblast and shows advanced multilineage yolk sac tissue-like morphogenesis that harbours a process similar to distinct waves of haematopoiesis, including the emergence of erythroid-, megakaryocyte-, myeloid- and lymphoid-like cells. This model presents an easy-to-use, high-throughput, reproducible and scalable platform to probe multifaceted aspects of human development and blood formation at the early post-implantation stage. It will provide a tractable human-based model for drug testing and disease modelling.

Maximizing Triboelectric Nanogenerators by Physics-Informed AI Inverse Design.

Triboelectric nanogenerators offer an environmentally friendly approach to harvesting energy from mechanical excitations. This capability has made them widely sought-after as an efficient, renewable, and sustainable energy source, with the potential to decrease reliance on traditional fossil fuels. However, developing triboelectric nanogenerators with specific output remains a challenge mainly due to the uncertainties associated with their complex designs for real-life applications. Artificial intelligence-enabled inverse design is a powerful tool to realize performance-oriented triboelectric nanogenerators. This is an emerging scientific direction that can address the concerns about the design and optimization of triboelectric nanogenerators leading to a next generation nanogenerator systems. This perspective paper aims at reviewing the principal analysis of triboelectricity, summarizing the current challenges of designing and optimizing triboelectric nanogenerators, and highlighting the physics-informed inverse design strategies to develop triboelectric nanogenerators. Strategic inverse design is particularly discussed in the contexts of expanding the four-mode analytical models by physics-informed artificial intelligence, discovering new conductive and dielectric materials, and optimizing contact interfaces. Various potential development levels of artificial intelligence-enhanced triboelectric nanogenerators are delineated. Finally, the potential of physics-informed artificial intelligence inverse design to propel triboelectric nanogenerators from prototypes to multifunctional intelligent systems for real-life applications is discussed.

Mechanical metamaterials and beyond.

Mechanical metamaterials enable the creation of structural materials with unprecedented mechanical properties. However, thus far, research on mechanical metamaterials has focused on passive mechanical metamaterials and the tunability of their mechanical properties. Deep integration of multifunctionality, sensing, electrical actuation, information processing, and advancing data-driven designs are grand challenges in the mechanical metamaterials community that could lead to truly intelligent mechanical metamaterials. In this perspective, we provide an overview of mechanical metamaterials within and beyond their classical mechanical functionalities. We discuss various aspects of data-driven approaches for inverse design and optimization of multifunctional mechanical metamaterials. Our aim is to provide new roadmaps for design and discovery of next-generation active and responsive mechanical metamaterials that can interact with the surrounding environment and adapt to various conditions while inheriting all outstanding mechanical features of classical mechanical metamaterials. Next, we deliberate the emerging mechanical metamaterials with specific functionalities to design informative and scientific intelligent devices. We highlight open challenges ahead of mechanical metamaterial systems at the component and integration levels and their transition into the domain of application beyond their mechanical capabilities.

Modelling Human Post-Implantation Development via Extra-Embryonic Niche Engineering.

Implantation of the human embryo commences a critical developmental stage that comprises profound morphogenetic alteration of embryonic and extra-embryonic tissues, axis formation, and gastrulation events. Our mechanistic knowledge of this window of human life remains limited due to restricted access to in vivo samples for both technical and ethical reasons. Additionally, human stem cell models of early post-implantation development with both embryonic and extra-embryonic tissue morphogenesis are lacking. Here, we present iDiscoid, produced from human induced pluripotent stem cells via an engineered a synthetic gene circuit. iDiscoids exhibit reciprocal co-development of human embryonic tissue and engineered extra-embryonic niche in a model of human post-implantation. They exhibit unanticipated self-organization and tissue boundary formation that recapitulates yolk sac-like tissue specification with extra-embryonic mesoderm and hematopoietic characteristics, the formation of bilaminar disc-like embryonic morphology, the development of an amniotic-like cavity, and acquisition of an anterior-like hypoblast pole and posterior-like axis. iDiscoids offer an easy-to-use, high-throughput, reproducible, and scalable platform to probe multifaceted aspects of human early post-implantation development. Thus, they have the potential to provide a tractable human model for drug testing, developmental toxicology, and disease modeling.

Deglycosylation of Excretory-Secretory Antigens of the Second-Stage Larvae of Toxocara cati Improves Its Efficacy in the Diagnosis of Human Toxocariasis.

Background: Toxocariasis is an important zoonotic infection, especially in tropical areas. One of the significant challenges in the serodiagnosis of human toxocariasis is the cross-reaction of Toxocara antigens with other parasites due to their relatively similar glycan structures. Removing the glycan structure from Toxocara excretory-secretory (TES) antigens may increase the efficacy of these antigens in the diagnosis of toxocariasis. The current study aimed to assess the efficacy of deglycosylated Toxocara cati excretory-secretory (dTES) antigens for the serodiagnosis of human toxocariasis. Methods: Toxocara ES antigens were prepared from T. cati second-stage larvae and deglycosylated using sodium hydroxide (NaOH). The TES antigens, along with the dTES antigens, were used in an ELISA as well as a western blotting system for the detection of anti-Toxocara antibodies. Sera samples collected from 30 confirmed cases of toxocariasis, 30 patients with other diseases, and 30 healthy subjects were evaluated by both systems. Results: The sensitivity of TES and dTES ELISA for the diagnosis of human toxocariasis was 96.67% (95% CI = 82.78-99.92) and 93.33% (95% CI = 77.93-99.18), respectively, while the specificity of dTES (88.33%; 95% CI = 77.43-95.18) increased significantly compared to the TES (80.00%; 95% CI = 67.67-89.22). The sensitivity of both antigens was 100% (95% CI = 88.43-100) by the western blotting system. Moreover, the specificity of TES and dTES antigens was 95% (95% CI = 86.08-98.96) and 98.33% (95% CI = 91.06-99.96), respectively, when using the western blotting system. Conclusion: Results of the current study indicate that the chemical removal of the glycan epitopes of T. cati ES antigens significantly reduces cross-reactivity rates with other parasitic infections. Considering the findings of the present study, the dTES antigens seem to be suitable antigens for the serodiagnosis of human toxocariasis.

Multifunctional Nanogenerator-Integrated Metamaterial Concrete Systems for Smart Civil Infrastructure.

Creating multifunctional concrete materials with advanced functionalities and mechanical tunability is a critical step toward reimagining the traditional civil infrastructure systems. Here, the concept of nanogenerator-integrated mechanical metamaterial concrete is presented to design lightweight and mechanically tunable concrete systems with energy harvesting and sensing functionalities. The proposed metamaterial concrete systems are created via integrating the mechanical metamaterial and nano-energy-harvesting paradigms. These advanced materials are composed of reinforcement auxetic polymer lattices with snap-through buckling behavior fully embedded inside a conductive cement matrix. We rationally design their composite structures to induce contact-electrification between the layers under mechanical excitations/triggering. The conductive cement enhanced with graphite powder serves as the electrode in the proposed systems, while providing the desired mechanical performance. Experimental studies are conducted to investigate the mechanical and electrical properties of the designed prototypes. The metamaterial concrete systems are tuned to achieve up to 15% compressibility under cycling loading. The power output of the nanogenerator-integrated metamaterial concrete prototypes reaches 330 µW. Furthermore, the self-powered sensing functionality of the nanogenerator concrete systems for distributed health monitoring of large-scale concrete structures is demonstrated. The metamaterial concrete paradigm can possibly enable the design of smart civil infrastructure systems with a broad range of advanced functionalities.

Multiset multicover methods for discriminative marker selection.

Markers are increasingly being used for several high-throughput data analysis and experimental design tasks. Examples include the use of markers for assigning cell types in scRNA-seq studies, for deconvolving bulk gene expression data, and for selecting marker proteins in single-cell spatial proteomics studies. Most marker selection methods focus on differential expression (DE) analysis. Although such methods work well for data with a few non-overlapping marker sets, they are not appropriate for large atlas-size datasets where several cell types and tissues are considered. To address this, we define the phenotype cover (PC) problem for marker selection and present algorithms that can improve the discriminative power of marker sets. Analysis of these sets on several marker-selection tasks suggests that these methods can lead to solutions that accurately distinguish different phenotypes in the data.

Very low frequency waves as selective probe for Cysticercus tenuicollis, Hydatid cyst and Coenurus cerebralis bio-analysis using single cell-signal recording.

Comparative electric behavior of Cysticercus tenuicollis, Hydatid cyst and Coenurus cerebralis at the Very Low Frequency (VLF) region has been studied in detail. This investigation could be significant, because of the economic and public health importance of these parasitic infections in domestic animals. In this report, a single cell signal recording technique has been adopted for comparison using a stainless steel (type: 316, diameter: ~ 300 µm, height: 2.00 cm) two identical electrode system, implanted on the surface of the tested cysts with inter electrode distance of 0.50 cm at a ~ 6.0 giga ohm (GΩ) sealed condition (based on the situation of the implanted electrode system). This process was achieved based on applying electrical interaction between the cysts and the VLF electrical signal. Relative to the measured time domain signal (Current-time diagram), the frequency domain (Current-frequency diagram) was estimated via applying a "Discrete Fast Fourier Transform" (DFFT) algorithm at a fixed time interval (5.0 min). Factors, having important influence on the sensitivity of the detection system including the type (waveform) of different alternating-current (AC) triggering stimulus signals (such as direct current, square wave, triangular, sin (t), etc.), the amplitude, as well as the frequency were optimized automatically through a written "Visual Basic 6" program by one-factor-at-a-time method. Direct applying this AC triggering VLF voltage to the cysts resulted in tracing an AC electrical current vs. time that considered as the time domain wave. However, this electrical current was decayed rapidly versus time during maximum 30.0 s time scale. Applying the DFFT algorithm to the measured time domain, resulted in accessing to the frequency domain at the selected frequency range between 2 and 5 kHz that was considered as the selected frequency for the selective differentiation of C. tenuicollis, Hydatid cyst and C. cerebralis. The related probable mechanism of this process may be attributed to the correlation between the triggering potential and the cyst's electrical surface charge (Zeta potential) as the current source under similar conditions. The results of this study may help to introduce a new detection system for in vivo recognition of the cysts in future.

De novo construction of T cell compartment in humanized mice engrafted with iPSC-derived thymus organoids.

Hematopoietic humanized (hu) mice are powerful tools for modeling the action of human immune system and are widely used for preclinical studies and drug discovery. However, generating a functional human T cell compartment in hu mice remains challenging, primarily due to the species-related differences between human and mouse thymus. While engrafting human fetal thymic tissues can support robust T cell development in hu mice, tissue scarcity and ethical concerns limit their wide use. Here, we describe the tissue engineering of human thymus organoids from inducible pluripotent stem cells (iPSC-thymus) that can support the de novo generation of a diverse population of functional human T cells. T cells of iPSC-thymus-engrafted hu mice could mediate both cellular and humoral immune responses, including mounting robust proinflammatory responses on T cell receptor engagement, inhibiting allogeneic tumor graft growth and facilitating efficient Ig class switching. Our findings indicate that hu mice engrafted with iPSC-thymus can serve as a new animal model to study human T cell-mediated immunity and accelerate the translation of findings from animal studies into the clinic.

Enhanced Competition at the Nano-Bio Interface Enables Comprehensive Characterization of Protein Corona Dynamics and Deep Coverage of Proteomes.

Introducing engineered nanoparticles (NPs) into a biofluid such as blood plasma leads to the formation of a selective and reproducible protein corona at the particle-protein interface, driven by the relationship between protein-NP affinity and protein abundance. This enables scalable systems that leverage protein-nano interactions to overcome current limitations of deep plasma proteomics in large cohorts. Here the importance of the protein to NP-surface ratio (P/NP) is demonstrated and protein corona formation dynamics are modeled, which determine the competition between proteins for binding. Tuning the P/NP ratio significantly modulates the protein corona composition, enhancing depth and precision of a fully automated NP-based deep proteomic workflow (Proteograph). By increasing the binding competition on engineered NPs, 1.2-1.7× more proteins with 1% false discovery rate are identified on the surface of each NP, and up to 3× more proteins compared to a standard plasma proteomics workflow. Moreover, the data suggest P/NP plays a significant role in determining the in vivo fate of nanomaterials in biomedical applications. Together, the study showcases the importance of P/NP as a key design element for biomaterials and nanomedicine in vivo and as a powerful tuning strategy for accurate, large-scale NP-based deep proteomic studies.

In Vivo Chemogenetic Biocompatibility of Mercury as a Specific Hypercalcemia Actuator in Snail's Spinal Cord Cell Manipulation: An Extracellular Field Potential Biosensor.

The in vivo chemogenetic property of mercuric ions (Hg2+) was investigated as a specific hypercalcemia actuator in snail's spinal cord cell manipulation by extracellular field potential biosensing analysis. For this purpose, a three-microelectrode system with working, counter, and pseudo reference electrodes was blindly implanted into the snail's spinal cord to electrically stimulate (triggering) the action potential with a staircase electrical voltage at a very low frequency level, along with measurement of the electrical current, as a detection system. Under optimum conditions, using the one-factor-at-a-time method, a wide linear range between 1.0 × 10-14 and 1.0 × 10-1 mol L-1 with correlation coefficients (R2) >0.98 and a response time (t90) of maximum 10.0 s were approximated. Percentages of relative standard deviation were estimated to be 3.08 (reproducibility, n = 50) and 7.31 (repeatability, n = 15). The detection limit was estimated to be sub 2.1 × 10-16 mol L-1 based on the Xb- + 3Sb definition. The reliability of this phenomenon was evidenced by the estimation of recovery percentages (between 95 and 107%) during spiking Hg2+ standard solutions. The probable mechanism behind this process could be attributed to the following: (i) the neuronal ephaptic coupling during electrical synchronization by a specific brain-triggered wave as a neuronal motor toolkit and (ii) chemical synchronization using a Hg2+ hypercalcemia actuator (biosensor). Linear correlation has been evidenced during interactions between Hg2+ and a calcium ionic channel's protein with a gram molecular weight of 66.2 ± 0.3 KCU. This process, therefore, caused an opening of the Ca2+ channel gates and majorly released the Ca2+ (hypercalcemia) that was detected as the main source of the measured electrical current. At this condition, ultratrace levels of Hg2+ ions not only were considered as nontoxic reagents but also had chemically regulating effects as ephaptic synchronizers to the neuron cells. This report may pave the way for using mercury ions at an ultratrace level for clinical controlling purposes during neuronal spinal cord cell manipulation.

Magnetic capsulate triboelectric nanogenerators.

Triboelectric nanogenerators have received significant research attention in recent years. Structural design plays a critical role in improving the energy harvesting performance of triboelectric nanogenerators. Here, we develop the magnetic capsulate triboelectric nanogenerators (MC-TENG) for energy harvesting under undesirable mechanical excitations. The capsulate TENG are designed to be driven by an oscillation-triggered magnetic force in a holding frame to generate electrical power due to the principle of the freestanding triboelectrification. Experimental and numerical studies are conducted to investigate the electrical performance of MC-TENG under cyclic loading in three energy harvesting modes. The results indicate that the energy harvesting performance of the MC-TENG is significantly affected by the structure of the capsulate TENG. The copper MC-TENG systems are found to be the most effective design that generates the maximum mode of the voltage range is 4 V in the closed-circuit with the resistance of 10 GΩ. The proposed MC-TENG concept provides an effective method to harvest electrical energy from low-frequency and low-amplitude oscillations such as ocean wave.

Real-time alerting system for COVID-19 and other stress events using wearable data.

Early detection of infectious diseases is crucial for reducing transmission and facilitating early intervention. In this study, we built a real-time smartwatch-based alerting system that detects aberrant physiological and activity signals (heart rates and steps) associated with the onset of early infection and implemented this system in a prospective study. In a cohort of 3,318 participants, of whom 84 were infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), this system generated alerts for pre-symptomatic and asymptomatic SARS-CoV-2 infection in 67 (80%) of the infected individuals. Pre-symptomatic signals were observed at a median of 3 days before symptom onset. Examination of detailed survey responses provided by the participants revealed that other respiratory infections as well as events not associated with infection, such as stress, alcohol consumption and travel, could also trigger alerts, albeit at a much lower mean frequency (1.15 alert days per person compared to 3.42 alert days per person for coronavirus disease 2019 cases). Thus, analysis of smartwatch signals by an online detection algorithm provides advance warning of SARS-CoV-2 infection in a high percentage of cases. This study shows that a real-time alerting system can be used for early detection of infection and other stressors and employed on an open-source platform that is scalable to millions of users.

Studying the Feasibility of Postoperative Monitoring of Spinal Fusion Progress Using a Self-Powered Fowler-Nordheim Sensor-Data-Logger.

OBJECTIVE: This study investigates the feasibility of using a new self-powered sensing and data logging system for postoperative monitoring of spinal fusion progress. The proposed diagnostic technology directly couples a piezoelectric transducer signal into a Fowler-Nordheim (FN) quantum tunneling-based synchronized dynamical system to record the mechanical usage of spinal fixation devices. The operation of the proposed implantable FN sensor-data-logger is completely self-powered by harvesting the energy from the micro-motion of the spine during the course of fusion. Bench-top testing is performed using corpectomy models to evaluate the performance of the proposed monitoring system. In order to simulate the spinal fusion process, different materials with gradually increasing elastic modulus are used to fill the intervertebral space gap. Besides, finite element models are developed to analyze the strains induced on the spinal rods during the applied cyclic loading. Data measured from the benchtop experiment is processed using an FN sensor-data-logger model to obtain time-evolution curves representing each spinal fusion state. This feasibility study shows that the obtained curves are viable tools to differentiate between conditions of osseous union and assess the effective fusion period.

Multifunctional Triboelectric Nanogenerator-enabled Structural Elements for Next Generation Civil Infrastructure Monitoring Systems.

There is a critical shortage in research needed to explore a new class of multifunctional structural components that respond to their environment, empower themselves and self-monitor their condition. Here, we propose the novel concept of triboelectric nanogenerator-enabled structural elements (TENG-SEs) to build the foundation for the next generation civil infrastructure systems with intrinsic sensing and energy harvesting functionalities. In order to validate the proposed concept, we develop proof-of-concept multifunctional composite rebars with built-in triboelectric nanogenerator mechanisms. The developed prototypes function as structural reinforcements, nanogenerators and distributed sensing mediums under external mechanical vibrations. Experiential and theoretical studies are performed to verify the electrical and mechanical performance of the developed self-powering and self-sensing composite structural components. We demonstrate the capability of the embedded structural elements to detect damage patterns in concrete beams at multiscale. Finally, we discuss how this new class of TENG-SEs could revolutionize the large-scale distributed monitoring practices in civil infrastructure and construction fields.

A scalable, secure, and interoperable platform for deep data-driven health management.

The large amount of biomedical data derived from wearable sensors, electronic health records, and molecular profiling (e.g., genomics data) is rapidly transforming our healthcare systems. The increasing scale and scope of biomedical data not only is generating enormous opportunities for improving health outcomes but also raises new challenges ranging from data acquisition and storage to data analysis and utilization. To meet these challenges, we developed the Personal Health Dashboard (PHD), which utilizes state-of-the-art security and scalability technologies to provide an end-to-end solution for big biomedical data analytics. The PHD platform is an open-source software framework that can be easily configured and deployed to any big data health project to store, organize, and process complex biomedical data sets, support real-time data analysis at both the individual level and the cohort level, and ensure participant privacy at every step. In addition to presenting the system, we illustrate the use of the PHD framework for large-scale applications in emerging multi-omics disease studies, such as collecting and visualization of diverse data types (wearable, clinical, omics) at a personal level, investigation of insulin resistance, and an infrastructure for the detection of presymptomatic COVID-19.

Multifunctional Meta-Tribomaterial Nanogenerators for Energy Harvesting and Active Sensing.

Discovering novel multifunctional metamaterials with energy harvesting and sensing functionalities is likely to be the next technological evolution of the metamaterial science. Here, we introduce a novel concept called self-aware composite mechanical metamaterial (SCMM) that can transform mechanical metamaterials into nanogenerators and active sensing mediums. In pursuit of this goal, we examine new paradigms where finely tailored and seamlessly integrated self-recovering snapping microstructures composed of topologically different triboelectric materials can form self-powering and self-sensing meta-tribomaterial systems. We explore various deformation mechanisms required to induce contact electrification between these snapping microstructures under periodic deformations. The multifunctional meta-tribomaterial systems created under the SCMM concept will act as triboelectric nanogenerators capable of generating electrical signals in response to the applied mechanical excitations. The generated electrical signal can be used for active sensing of the applied force and can be stored for empowering sensors and embedded electronics. We conduct theoretical and experimental studies to understand the mechanical and electrical behavior of the multifunctional SCMM systems. The broad application of the proposed SCMM concept for designing artificial materials with novel properties and functionalities is highlighted via prototyping self-powering and self-sensing blood vessel stents and shock absorbers.