Continuous ambulatory monitoring of peripheral vascular perfusion could enable earlier detection of vascular dysfunction in individuals with diabetes mellitus and more timely management of cardiovascular disease. Clinical imaging modalities provide high-fidelity vascular information but are impractical for ambulatory use, whereas most wearable devices are limited to single-modality sensing and do not provide imaging. Electrical bioimpedance has the potential to bridge this gap by enabling rapid spatial and temporal imaging while remaining sensitive to hemodynamic changes. Here, we introduce a wearable ring with 8 electrodes and 32-channel bioimpedance sensing for finger blood flow imaging. In 96 healthy participants measured at rest and during autonomic maneuvers, we resolve conductivity images in the digital arteries associated with pulsatile blood flow and train neural network models for continuous cuffless blood pressure waveform estimation. We demonstrate the feasibility of bioimpedance imaging in a ring form factor, supporting its potential for ambulatory cuffless hemodynamic monitoring.

BACKGROUND:Hypoglossal neuropathy is the most common lower cranial neuropathy detected as a delayed sequelae of Human Papillomavirus (HPV) -driven oropharyngeal cancer (OPC). Needle electromyography (EMG) is the gold standard for electrodiagnostic testing, but it is invasive and relies on subjective interpretation of the EMG signal. This study explores the potential of non-invasive high-density surface electromyography (HDSEMG) to detect and quantify hypoglossal neuropathy in OPC survivors. OBJECTIVE:In an exploratory study, examine the feasibility of HDSEMG for rapid, non-invasive screening of hypoglossal nerve (CN XII) function and estimate the prevalence of hypoglossal neuropathy before and after oropharyngeal radiotherapy, and associate with patient-reported and clinician-graded functional outcomes. Machine learning performance will be measured through sensitivity, specificity, and F1 score, with a target area under the curve > 0.7 based on literature-reported EMG sensitivity and specificity. METHODS:This protocol will recruit patients aged ≥ 18 years who receive radiation therapy for OPC at MD Anderson Cancer Center (MDACC) between 2024-2025 and consent to experimental HDSEMG testing. Sanchez Research Lab (The University of Utah, Salt Lake City, UT) will perform data analysis. Clinical data-including electrical impedance measurement (EIM), patient-reported outcomes, dysphagia grading, tongue functions, fibrosis grading, and needle EMG-will be collected from n = 36 patients. Features extracted from HDSEMG will be correlated with other clinical outcomes and used to train a machine learning classifier to quantify the severity of hypoglossal neuropathy.

Wearable technologies have the potential to transform ambulatory and at-home hemodynamic monitoring by providing continuous assessments of cardiovascular health metrics and guiding clinical management. However, existing cuffless wearable devices for blood pressure (BP) monitoring often rely on methods lacking theoretical foundations, such as pulse wave analysis or pulse arrival time, making them vulnerable to physiological and experimental confounders that undermine their accuracy and clinical utility. Here, we developed a smartwatch device with real-time electrical bioimpedance (BioZ) sensing for cuffless hemodynamic monitoring. We elucidate the biophysical relationship between BioZ and BP via a multiscale analytical and computational modeling framework, and identify physiological, anatomical, and experimental parameters that influence the pulsatile BioZ signal at the wrist. A signal-tagged physics-informed neural network incorporating fluid dynamics principles enables estimation of BP and radial and axial blood velocity. We successfully tested our approach with healthy individuals at rest and after physical activity including physical and autonomic challenges, and with patients with hypertension and cardiovascular disease in outpatient and intensive care settings. Our findings demonstrate the feasibility of BioZ technology for cuffless BP and blood velocity monitoring, addressing critical limitations of existing cuffless technologies.

Bacteriophage attacks represent a major threat in the dairy industry. Here, an unstructured mechanistic model predicting the dynamics of milk acidification in case of phage attack was developed and experimentally validated. Multiple acidification experiments were run with different combinations of initial phage titers and bacterial concentrations and the resulting pH dynamics were recorded. The model could successfully predict the success or failure of milk acidification. Using the model, important biological parameters were deduced from simple, low-cost acidification measurements. These parameters included bacteria’s maximum growth and lysis rates, phages’ burst size, etc. Sensitivity analysis helped identify biologically relevant aspects of phage-host interactions. Growth and lysis kinetics were shown to have the most important impacts. This knowledge can be used to develop easy routine strategies to fight phage attack in the dairy industry. The model can be used to raise awareness amongst cheese makers on the importance of cleaning to avoid food and material waste.

Bioimpedance (BioZ) sensing serve as the foundation for numerous healthcare applications, enabling characterization and monitoring across a variety of medical use cases and contexts. At high frequencies, measured BioZ data often contain experimental artifacts caused by current leakage from stray load losses, resulting in reduced accuracy and reliability of the measurements. Here, we present a generalized nonlinear modeling framework for calibrating BioZ including stray load disturbances.

Peripheral nerve injuries (PNIs) present a significant clinical challenge, with current diagnostic tools often falling short in guiding optimal surgical management. Traditional intraoperative techniques, such as recording nerve action potentials, are rarely utilized due to technical limitations including poor signal quality, artifact interference, and lengthy procedure times. To address this gap, we propose electrical impedance neurography (ING), a technology for real-time, intraoperative assessment of nerve health. In this pilot simulation study, we aim to evaluate the capability of ING to detect the location and quantify nerve injury severity using PNI. ING technology could assist intraoperative nerve assessment by providing insights into nerve integrity, improving surgical outcomes, and ultimately enhancing recovery for patients with traumatic nerve injuries.

Wearable technologies have the potential to transform ambulatory and at-home hemodynamic monitoring by providing continuous assessments of cardiovascular health metrics and guiding clinical management. However, existing cuffless wearable devices for blood pressure (BP) monitoring often rely on methods lacking theoretical foundations, such as pulse wave analysis or pulse arrival time, making them vulnerable to physiological and experimental confounders that undermine their accuracy and clinical utility. Here, we developed a smartwatch device with real-time electrical bioimpedance (BioZ) sensing for cuffless hemodynamic monitoring. We elucidate the biophysical relationship between BioZ and BP via a multiscale analytical and computational modeling framework, and identify physiological, anatomical, and experimental parameters that influence the pulsatile BioZ signal at the wrist. A signal-tagged physics-informed neural network incorporating fluid dynamics principles enables calibration-free estimation of BP and radial and axial blood velocity. We successfully tested our approach with healthy individuals at rest and after physical activity including physical and autonomic challenges, and with patients with hypertension and cardiovascular disease in outpatient and intensive care settings. Our findings demonstrate the feasibility of BioZ technology for cuffless BP and blood velocity monitoring, addressing critical limitations of existing cuffless technologies.

BACKGROUND:This proof-of-concept case-control study examined the feasibility of tongue high-density surface electromyography (HDS-EMG) to detect hypoglossal neuropathy. METHODS:We analyzed tongue HDS-EMG for N = 2 participants. Subjects were graded through clinical tongue functional measures and gold-standard needle EMG. We collected HDS-EMG in three tongue tasks: relaxation, protrusion, and isometric maximum effort. The HDS-EMG was decomposed into motor unit spike train estimates. RESULTS:Muscle relaxation HDS-EMG contained spikes consistent with fasciculation potential morphology. During tongue protrusion, a case of confirmed CN XII neuropathy exhibited an elevated motor unit (MU) discharge rate and fewer MUs (45.71 peaks/s, 3 estimated MUs) compared to the control (11.63 peaks/s, 11 estimated MUs). During isometric contraction, the case exhibited an average discharge rate of 3.96 peaks/s on 3 MU estimates, whereas the control had a discharge rate of 2.58 peaks/s on 5 MU estimates. CONCLUSIONS:Non-invasive tongue HDS-EMG appears to show potential sensitivity for detecting reduced MU recruitment in hypoglossal neuropathy.

Electrical impedance imaging (EII) is a functional imaging modality with high temporal resolution. Image reconstruction in EII seeks to estimate the electrical conductivity distribution inside of an object from external voltage measurements. It is a non-linear and ill-posed inverse problem and requires iterative techniques. Here, we describe the implementation and validation of the Sanchez Research Lab-EII (SRL-EII) solver, an efficient algorithm using finite element model to solve the linearized reconstruction problem. The SRL-EII solver is benchmarked against reference EIDORS algorithm using experimental data, achieving a reconstruction error of 1.38.10(-10) and 6.76.10(-10) S/m for phantom tank and lung test experiments. The results demonstrate the numerical accuracy and scalability of the SRL-EII algorithm and its potential for standalone hardware implementations.

Electrical impedance dermography (EID), based on electrical impedance spectroscopy, is a specific technique for the evaluation of skin disorders that relies upon the application and measurement of painless, alternating electrical current. EID assesses pathological changes to the normal composition and architecture of the skin that influence the flow of electrical current, including changes associated with inflammation, keratinocyte and melanocyte carcinogenesis, and scarring. Assessing the electrical properties of the skin across a range of frequencies and in multiple directions of current flow can provide diagnostic information to aid in the identification of pathologic skin conditions. EID holds the promise of serving as a diagnostic biomarker and potential to be used in skin cancer detection and staging. EID may also be useful as a biomarker in monitoring effectiveness of treatment in individual patients and in therapeutic research. This review highlights ongoing efforts to improve mechanistic understanding of skin electrical changes, study of EID in a variety of clinical contexts, and further refine the technology to find greater clinical use in dermatology and dermatologic research.

Bioimpedance (BioZ) is a non-invasive and low-cost technology capable of monitoring conductivity rapidly. BioZ holds promise for integration into wearable devices for outpatient monitoring. Here, we propose the integration of BioZ into a smart ring form factor using electrodes coated with poly(3,4-ethylenedioxythiophene) (PEDOT) conductive polymer. PEDOT reduces contact electrode impedance by increasing the effective contact area at the skin-electrode interface and having high ionic conductivity. In this feasibility study, we developed a ring sensor, deposited PEDOT on the electrodes, and compared its capability to record pulsatile BioZ data at the finger against untreated electrodes. We performed electrochemical impedance spectroscopy (EIS) and BioZ measurements. EIS results showed that electrode contact impedance decreased by a factor of 1.2 compared to the untreated electrodes at the relevant frequency. Our results show the capability of our smart ring sensor to detect pulsatile BioZ at the digital arteries.

The proliferation of wearable health monitors has prompted the investigation of bioimpedance (BioZ) to deliver meaningful health data. The Texas Instruments Analog Front End 4500 (AFE4500) is equipped with BioZ functionality but has not been characterized. This study provides an initial characterization of the AFE4500’s BioZ function in RC loads. We evaluate and report the performance of the AFE4500 for wearable body composition monitoring. The AFE4500 exhibits the greatest precision with higher excitation currents and low BioZ sampling frequency. The lowest standard deviation (0.20 Ω) was achieved at 6.67 samples per second. AFE4500 measurements showed the lowest root mean square error of 4.37 Ω compared to ground truth at 50 kHz excitation. The AFE4500 measurement precision and small form factor prepare it for physiological monitoring in wearable body composition assessment using smartwatches and rings.

Genome-scale metabolic models (GEMs) can facilitate metabolism-focused multi-omics integrative analysis. Since Yeast8, the yeast-GEM of Saccharomyces cerevisiae, published in 2019, has been continuously updated by the community. This has increased the quality and scope of the model, culminating now in Yeast9. To evaluate its predictive performance, we generated 163 condition-specific GEMs constrained by single-cell transcriptomics from osmotic pressure or reference conditions. Comparative flux analysis showed that yeast adapting to high osmotic pressure benefits from upregulating fluxes through central carbon metabolism. Furthermore, combining Yeast9 with proteomics revealed metabolic rewiring underlying its preference for nitrogen sources. Lastly, we created strain-specific GEMs (ssGEMs) constrained by transcriptomics for 1229 mutant strains. Well able to predict the strains' growth rates, fluxomics from those large-scale ssGEMs outperformed transcriptomics in predicting functional categories for all studied genes in machine learning models. Based on those findings we anticipate that Yeast9 will continue to empower systems biology studies of yeast metabolism.

Peripheral neuroregenerative research and therapeutic options are expanding exponentially. With this expansion comes an increasing need to reliably evaluate and quantify nerve health. Valid and responsive measures of the nerve status are essential for both clinical and research purposes for diagnosis, longitudinal follow-up, and monitoring the impact of any intervention. Furthermore, novel biomarkers can elucidate regenerative mechanisms and open new avenues for research. Without such measures, clinical decision-making is impaired, and research becomes more costly, time-consuming, and sometimes infeasible. Part 1 of this two-part scoping review focused on neurophysiology. In part 2, we identify and critically examine many current and emerging non-invasive imaging techniques that have the potential to evaluate peripheral nerve health, particularly from the perspective of regenerative therapies and research.

Objective: Modern lifestyles are triggering stress at a disproportionate rate for longer periods of time. Chronic or long-lasting stress can pose a risk to our health. Despite advances in physiological recording methods, mental stress remains challenging to quantify and monitor. Methods: We describe an Internet of Medical Things (IoMT) device with electrocardiogram (ECG) recording features. The recorded ECG signal is processed on-the-fly to calculate, in real time, heart rate (HR), HR variability, energy expenditure, and mental stress. Data are sent to an online platform using a standard Internet of Things (IoT) publish-subscribe messaging transport protocol for continuous monitoring. Results: The system functionality is first validated by performing hardware-in-the-loop measurements connected to a patient simulator. We, then, monitored induced stress by recording ECG in subjects using liquid metal electrodes performing a plank walking task in a virtual reality (VR) environment with high heights exposure. The results demonstrate our IoMT system’s ability to provide accurate ECG metrics using novel liquid metal electrodes by detecting continuously increased stress values in a VR setting and at home. Conclusion: The IoMT measurement device presented provides a novel strategy for monitoring stress in real time. Significance: Our work provides the opportunity for future research on psychological stress and emotion regulation within daily life and the physiological mechanisms through which it influences the health of both children and adults.

Abstract Biological functions are orchestrated by intricate networks of interacting genetic elements. Predicting the interaction landscape remains a challenge for systems biology and new research tools allowing simple and rapid mapping of sequence to function are desirable. Here, we describe CRI-SPA, a method allowing the transfer of chromosomal genetic features from a CRI-SPA Donor strain to arrayed strains in large libraries of Saccharomyces cerevisiae. CRI-SPA is based on mating, CRISPR-Cas9-induced gene conversion, and Selective Ploidy Ablation. CRI-SPA can be massively parallelized with automation and can be executed within a week. We demonstrate the power of CRI-SPA by transferring four genes that enable betaxanthin production into each strain of the yeast knockout collection (≈4800 strains). Using this setup, we show that CRI-SPA is highly efficient and reproducible, and even allows marker-free transfer of genetic features. Moreover, we validate a set of CRI-SPA hits by showing that their phenotypes correlate strongly with the phenotypes of the corresponding mutant strains recreated by reverse genetic engineering. Hence, our results provide a genome-wide overview of the genetic requirements for betaxanthin production. We envision that the simplicity, speed, and reliability offered by CRI-SPA will make it a versatile tool to forward systems-level understanding of biological processes.

ABSTRACT Smart consumer devices with bioimpedance sensing technology apply an electrical current to the body for health and wellness. However, whether these smart devices interfere with cardiac implantable electronic devices (CIEDs) remains unknown. We report electrical interference with benchtop testing from the Galaxy Watch5 Pro and smart scale Body+ to cardiac resynchronization therapy device generators from different manufacturers. These results highlight the need of establishing standard testing procedures to assess the safety of smart devices with bioimpedance sensing in patients with CIEDs.

Peripheral neuroregeneration research and therapeutic options are expanding exponentially. With this expansion comes an increasing need to reliably evaluate and quantify nerve health. Valid and responsive measures that can serve as biomarkers of the nerve status are essential for both clinical and research purposes for diagnosis, longitudinal follow-up, and monitoring the impact of any intervention. Furthermore, such biomarkers can elucidate regeneration mechanisms and open new avenues for research. Without these measures, clinical decision-making falls short, and research becomes more costly, time-consuming, and sometimes infeasible. As a companion to Part 2, which is focused on non-invasive imaging, Part 1 of this two-part scoping review systematically identifies and critically examines many current and emerging neurophysiological techniques that have the potential to evaluate peripheral nerve health, particularly from the perspective of regenerative therapies and research.

Objective. To date, measurement of the conductivity and relative permittivity properties of anisotropic biological tissues using electrical impedance myography (EIM) has only been possible through an invasive ex vivo biopsy procedure. Here, we present a novel forward and inverse theoretical modeling framework to estimate these properties combining surface and needle EIM measurements. Methods. The framework here presented models the electrical potential distribution within a monodomain, homogeneous, and three-dimensional anisotropic tissue. Finite-element method (FEM) simulations and tongue experimental results verify the validity of our method to reverse-engineer three-dimensional conductivity and relative permittivity properties from EIM measurements. Results. FEM-based simulations confirm the validity of our analytical framework, with relative errors between analytical predictions and simulations smaller than 0.12% and 2.6% in a cuboid and tongue model, respectively. Experimental results confirm qualitative differences in the conductivity and the relative permittivity properties in the x, y, and z directions. Conclusion. Our methodology enables EIM technology to reverse-engineer the anisotropic tongue tissue conductivity and relative permittivity properties, thus unfolding full forward and inverse EIM predictability capabilities. Significance. This new method of evaluating anisotropic tongue tissue will lead to a deeper understanding of the role of biology necessary for the development of new EIM tools and approaches for tongue health measurement and monitoring.