Orthogonalized human protease control of secreted signals
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A novel wearable device integrating ECG and PCG for cardiac health monitoring
The alarming prevalence and mortality rates associated with cardiovascular diseases have emphasized the urgency for innovative detection solutions. Traditional methods, often costly, bulky, and prone to subjectivity, fall short of meeting the need for daily monitoring. Digital and portable wearable monitoring devices have emerged as a promising research frontier. This study introduces a wearable system that integrates electrocardiogram (ECG) and phonocardiogram (PCG) detection. By ingeniously pairing a contact-type PZT heart sound sensing structure with ECG electrodes, the system achieves the acquisition of high-quality ECG and PCG signals. Notably, the signal-to-noise ratios (SNR) for ECG and PCG signals were measured at 44.13 dB and 30.04 dB, respectively, demonstrating the system’s remarkable stability across varying conditions. These collected signals were subsequently utilized to derive crucial feature values, including electromechanical delay (EMD), left ventricular ejection time (LVET), and pre-ejection period (PEP). Furthermore, we collected a dataset comprising 40 cases of ECG and PCG signals, enabling a comparative analysis of these three feature parameters between healthy individuals and coronary heart disease patients. This research endeavor presents a significant step forward in the realm of early, non-invasive, and intelligent monitoring of cardiovascular diseases, offering hope for earlier detection and more effective management of these life-threatening conditions.
Subcellular proteomics and iPSC modeling uncover reversible mechanisms of axonal pathology in Alzheimer’s disease
Dystrophic neurites (also termed axonal spheroids) are found around amyloid deposits in Alzheimer’s disease (AD), where they impair axonal electrical conduction, disrupt neural circuits and correlate with AD severity. Despite their importance, the mechanisms underlying spheroid formation remain incompletely understood. To address this, we developed a proximity labeling approach to uncover the proteome of spheroids in human postmortem and mouse brains. Additionally, we established a human induced pluripotent stem cell (iPSC)-derived AD model enabling mechanistic investigation and optical electrophysiology. These complementary approaches revealed the subcellular molecular architecture of spheroids and identified abnormalities in key biological processes, including protein turnover, cytoskeleton dynamics and lipid transport. Notably, the PI3K/AKT/mTOR pathway, which regulates these processes, was activated in spheroids. Furthermore, phosphorylated mTOR levels in spheroids correlated with AD severity in humans. Notably, mTOR inhibition in iPSC-derived neurons and mice ameliorated spheroid pathology. Altogether, our study provides a multidisciplinary toolkit for investigating mechanisms and therapeutic targets for axonal pathology in neurodegeneration.
Structure and function relationships of mucociliary clearance in human and rat airways
Mucociliary clearance is a vital defense mechanism of the human airways, protecting against harmful particles and infections. When this process fails, it contributes to respiratory diseases like chronic obstructive pulmonary disease (COPD) and asthma. While advances in single-cell transcriptomics have revealed the complexity of airway composition, much of what we know about how airway structure impacts clearance relies on animal studies. This limits our ability to create accurate human-based models of airway diseases. Here we show that the airways in female rats and in humans exhibit species-specific differences in the distribution of ciliated and secretory cells as well as in ciliary beat, resulting in significantly higher clearance effectiveness in humans. We further reveal that standard lab-grown cultures exhibit lower clearance effectiveness compared to human airways, and we identify the underlying structural differences. By combining diverse experiments and physics-based modeling, we establish universal benchmarks to assess human airway function, interpret preclinical models, and better understand disease-specific impairments in mucociliary clearance.
Implantation of engineered adipocytes suppresses tumor progression in cancer models
Tumors exhibit an increased ability to obtain and metabolize nutrients. Here, we implant engineered adipocytes that outcompete tumors for nutrients and show that they can substantially reduce cancer progression, a technology termed adipose manipulation transplantation (AMT). Adipocytes engineered to use increased amounts of glucose and fatty acids by upregulating UCP1 were placed alongside cancer cells or xenografts, leading to significant cancer suppression. Transplanting modulated adipose organoids in pancreatic or breast cancer genetic mouse models suppressed their growth and decreased angiogenesis and hypoxia. Co-culturing patient-derived engineered adipocytes with tumor organoids from dissected human breast cancers significantly suppressed cancer progression and proliferation. In addition, cancer growth was impaired by inducing engineered adipose organoids to outcompete tumors using tetracycline or placing them in an integrated cell-scaffold delivery platform and implanting them next to the tumor. Finally, we show that upregulating UPP1 in adipose organoids can outcompete a uridine-dependent pancreatic ductal adenocarcinoma for uridine and suppress its growth, demonstrating the potential customization of AMT.
Low-power Spiking Neural Network audio source localisation using a Hilbert Transform audio event encoding scheme
Sound source localisation is used in many consumer devices, to isolate audio from individual speakers and reject noise. Localization is frequently accomplished by “beamforming”, which combines phase-shifted audio streams to increase power from chosen source directions, under a known microphone array geometry. Dense band-pass filters are often needed to obtain narrowband signal components from wideband audio. These approaches achieve high accuracy, but narrowband beamforming is computationally demanding, and not ideal for low-power IoT devices. We introduce a method for sound source localisation on arbitrary microphone arrays, designed for efficient implementation in ultra-low-power spiking neural networks (SNNs). We use a Hilbert transform to avoid dense band-pass filters, and introduce an event-based encoding method that captures the phase of the complex analytic signal. Our approach achieves high accuracy for SNN methods, comparable with traditional non-SNN super-resolution beamforming. We deploy our method to low-power SNN inference hardware, with much lower power consumption than super-resolution methods. We demonstrate that signal processing approaches co-designed with spiking neural network implementations can achieve much improved power efficiency. Our Hilbert-transform-based method for beamforming can also improve the efficiency of traditional digital signal processing.
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