Search Results (660)

Wearable pressure sensors have emerged as vital tools in personalized monitoring, promising transformative advances in patient care and diagnostics. Nevertheless, conventional devices frequently suffer from limited sensitivity, inadequate flexibility, and concerns regarding biocompatibility. Herein, we introduce silk fibroin, a naturally occurring protein extracted from silkworm cocoons, as a promising material platform for next-generation wearable sensors. Owing to its remarkable biocompatibility, mechanical robustness, and structural tunability, silk fibroin serves as an ideal substrate for constructing capacitive pressure sensors tailored to medical applications. We engineered silk-derived capacitive architecture and evaluated its performance in real-time human motion and physiological signal detection. The resulting sensor exhibits a high sensitivity of 18.68 kPa⁻¹ over a broad operational range of 0 to 2.4 kPa, enabling accurate tracking of subtle pressures associated with pulse, respiration, and joint articulation. Under extreme loading conditions, our silk fibroin sensor demonstrated superior stability and accuracy compared to a commercial resistive counterpart (FlexiForce™ A401). These findings establish silk fibroin as a versatile, practical candidate for wearable pressure sensing and pave the way for advanced biocompatible devices in healthcare monitoring. Full article

Prolonged inundations are altering coastal forest ecosystems of the southeastern US, causing extensive tree die-offs and the development of ghost forests. This hydrological stressor also alters carbon fluxes, threatening the stability of coastal carbon sinks. This study was conducted to investigate the interactions between hydrological drivers and ecosystem responses by analyzing daily eddy covariance flux data from a wetland forest in North Carolina, USA, spanning 2009–2019. We analyzed temporal patterns of net ecosystem exchange (NEE), gross primary productivity (GPP), and ecosystem respiration (RE) under both flooded and non-flooded conditions and evaluated their relationships with observed tree mortality. Generalized Additive Modeling (GAM) revealed that groundwater table depth (GWT), leaf area index (LAI), NEE, and net radiation (Rn) were key predictors of mortality transitions (R² = 0.98). Elevated GWT induces root anoxia; declining LAI reduces productivity; elevated NEE signals physiological breakdown; and higher Rn may amplify evapotranspiration stress. Receiver Operating Characteristic (ROC) analysis revealed critical early warning thresholds for tree mortality: GWT = 2.23 cm, LAI = 2.99, NEE = 1.27 g C m⁻² d⁻¹, and Rn = 167.54 W m⁻². These values offer a basis for forecasting forest mortality risk and guiding early warning systems. Our findings highlight the dominant role of hydrological variability in ecosystem degradation and offer a threshold-based framework for early detection of mortality risks. This approach provides insights into managing coastal forest resilience amid accelerating sea level rise. Full article

Background: There is growing interest in the potential role of manganese (Mn) in the development of Alzheimer’s Disease and related dementias (ADRD). Methods: In this nested pilot study of a ferroalloy worker cohort, we investigated the impact of chronic occupational Mn exposure on cognitive function through β-amyloid (Aβ) deposition and multi-omics profiling. We evaluated six male Mn-exposed workers (median age 63, exposure duration 31 years) and five historical controls (median age: 60 years), all of whom had undergone brain PET scans. Exposed individuals showed significantly higher Aβ deposition in exposed individuals (p < 0.05). The average annual cumulative respirable Mn was 329.23 ± 516.39 µg/m³ (geometric mean 118.59), and plasma Mn levels were significantly elevated in the exposed group (0.704 ± 0.2 ng/mL) compared to controls (0.397 ± 0.18 in controls). Results: LC-MS/MS-based pathway analyses revealed disruptions in olfactory signaling, mitochondrial fatty acid β-oxidation, biogenic amine synthesis, transmembrane transport, and choline metabolism. Simoa analysis showed notable alterations in ADRD-related plasma biomarkers. Protein microarray revealed significant differences (p < 0.05) in antibodies targeting neuronal and autoimmune proteins, including Aβ (25–35), GFAP, serotonin, NOVA1, and Siglec-1/CD169. Conclusion: These findings suggest Mn exposure is associated with neurodegenerative biomarker alterations and disrupted biological pathways relevant to cognitive decline. Full article

Continuous monitoring of respiratory patterns is essential for disease diagnosis and daily health care. Contact medical devices enable reliable respiratory monitoring, but can cause discomfort and are limited in some settings. Radar offers a noncontact respiration measurement method for continuous, real-time, high-precision monitoring. However, it is difficult for a single radar to characterize the coordination of chest and abdominal movements during measured breathing. Moreover, motion interference during prolonged measurements can seriously affect accuracy. This study proposes a dual radar system with customized narrow-beam antennas and signals to measure the chest and abdomen separately, and an adaptive dynamic time warping (DTW) algorithm is used to effectively suppress motion interference. The system is capable of reconstructing respiratory waveforms of the chest and abdomen, and robustly extracting various respiratory parameters via motion interference. Experiments on 35 healthy subjects, 2 patients with chronic obstructive pulmonary disease (COPD), and 1 patient with heart failure showed a high correlation between radar and respiratory belt signals, with correlation coefficients of 0.92 for both the chest and abdomen, a root mean square error of 0.80 bpm for the respiratory rate, and a mean absolute error of 3.4° for the thoracoabdominal phase angle. This system provides a noncontact method for prolonged respiratory monitoring, measurement of chest and abdominal asynchrony and apnea detection, showing promise for applications in respiratory disorder detection and home monitoring. Full article

Air pollution acts as a pervasive oxidative stressor, disrupting global crop production and ecosystem health through the overproduction of reactive oxygen species (ROS). Hazardous pollutants impair critical physiological processes—photosynthesis, respiration, and nutrient uptake—triggering oxidative damage and yield losses. This review synthesizes current knowledge on plant defense mechanisms, emphasizing the integration of enzymatic (SOD, POD, CAT, APX, GPX, GR) and non-enzymatic (polyphenols, glutathione, ascorbate, phytochelatins) antioxidant systems to scavenge ROS and maintain redox homeostasis. We highlight the pivotal roles of transcription factors (MYB, WRKY, NAC) in orchestrating stress-responsive gene networks, alongside MAPK and phytohormone signaling (salicylic acid, jasmonic acid, ethylene), in mitigating oxidative stress. Secondary metabolites (flavonoids, lignin, terpenoids) are examined as biochemical shields against ROS and pollutant toxicity, with evidence from transcriptomic and metabolomic studies revealing their biosynthetic regulation. Furthermore, we explore biotechnological strategies to enhance antioxidant capacity, including overexpression of ROS-scavenging genes (e.g., TaCAT3) and engineering of phenolic pathways. By addressing gaps in understanding combined stress responses, this review provides a roadmap for developing resilient crops through antioxidant-focused interventions, ensuring sustainability in polluted environments. Full article

Accurate sleep staging and sleep-disordered breathing (SDB) severity prediction are critical for the early diagnosis and management of sleep disorders. However, real-world polysomnography (PSG) data often suffer from modality heterogeneity, label scarcity, and non-independent and identically distributed (non-IID) characteristics across institutions, posing significant challenges for model generalization and clinical deployment. To address these issues, we propose a federated multi-task learning (FMTL) framework that simultaneously performs sleep staging and SDB severity classification from seven multimodal physiological signals, including EEG, ECG, respiration, etc. The proposed framework is built upon a hybrid deep neural architecture that integrates convolutional layers (CNN) for spatial representation, bidirectional GRUs for temporal modeling, and multi-head self-attention for long-range dependency learning. A shared feature extractor is combined with task-specific heads to enable joint diagnosis, while the FedAvg algorithm is employed to facilitate decentralized training across multiple institutions without sharing raw data, thereby preserving privacy and addressing non-IID challenges. We evaluate the proposed method across three public datasets (APPLES, SHHS, and HMC) treated as independent clients. For sleep staging, the model achieves accuracies of 85.3% (APPLES), 87.1% (SHHS_rest), and 79.3% (HMC), with Cohen’s Kappa scores exceeding 0.71. For SDB severity classification, it obtains macro-F1 scores of 77.6%, 76.4%, and 79.1% on APPLES, SHHS_rest, and HMC, respectively. These results demonstrate that our unified FMTL framework effectively leverages multimodal PSG signals and federated training to deliver accurate and scalable sleep disorder assessment, paving the way for the development of a privacy-preserving, generalizable, and clinically applicable digital sleep monitoring system. Full article

Background: Aerobic exercise induces a range of complex molecular adaptations in skeletal muscle. However, a complete understanding of the specific transcriptional changes following exercise warrants further research. Methods: This study aimed to identify gene expression patterns following acute aerobic exercise by analyzing Gene Expression Omnibus (GEO) datasets. We performed a comparative analysis of transcriptional profiles of related genes in two independent studies, focusing on both established and novel genes involved in muscle physiology. Results: Our analysis revealed ten consistently upregulated and eight downregulated genes across both datasets. The upregulated genes were predominantly associated with mitochondrial function and cellular respiration, including MDH1, ATP5MC1, ATP5IB, and ATP5F1A. Conversely, downregulated genes such as YTHDC1, CDK5RAP2, and PALS2 were implicated in vascular structure and cellular organization. Importantly, our findings also revealed novel exercise-responsive genes not previously characterized in this context. Among these, MRPL41 and VEGF were significantly upregulated and are associated with p53-mediated apoptotic signaling and fatty acid metabolism, respectively. Novel downregulated genes included LIMCH1, CMYA5, and FOXJ3, which are putatively involved in cytoskeletal dynamics and muscle fiber type specification. Conclusions: These findings enhance our understanding of the transcriptional landscape of skeletal muscle following acute aerobic exercise and identify novel molecular targets for further investigation in the fields of exercise physiology and metabolic health. Full article

Mechanotransduction plays an essential role in the fate determination of alveolar cells within the pulmonary system by translating mechanical forces into intricate biochemical signals. This process exclusively governs differentiation, phenotypic stability, and maintenance of alveolar epithelial cell subtypes, primarily the alveolar AT1/AT2 cells. Perturbed mechanical tension proportionally impacts alveolar cell phenotypic identity and their functional characteristics. The fundamental influence of respiratory mechanics on alveolar cell lineage commitment and sustenance is undeniable. AT1 cells are recognized as principal mechanosensors within the alveolus, directly perceiving and responding to mechanical forces imposed by respiration through cell–matrix interactions. These mechanical forces instigate a profound reorganization of the actin cytoskeleton within cells, indispensable for signal transduction and perpetuation of their differentiated phenotype, orchestrated by integrins and cell adhesion molecule-mediated signaling. The dysregulated mechanotransduction in the pulmonary system intrinsically contributes to the etiology and progression of various diseases, exemplified by pulmonary fibrosis. This review systematically elucidates the profound impact of mechanotransduction on alveolar cell differentiation and fate sustenance and underscores how its dysregulation contributes to the initiation and perpetuation of lung diseases. Full article

This study introduces an innovative dog emotion classification system that integrates four non-invasive physiological indicators—skin potential (SP), muscle potential (MP), respiration frequency (RF), and voice pattern (VP)—with the extreme gradient boosting (XGBoost) algorithm. A four-breed dataset was meticulously constructed by recording and labeling physiological signals from dogs exposed to four fundamental emotional states: happiness, sadness, fear, and anger. Comprehensive feature extraction (time-domain, frequency-domain, nonlinearity) was conducted for each signal modality, and inter-emotional variance was analyzed to establish discriminative patterns. Four machine learning algorithms—Neural Networks (NN), Support Vector Machines (SVM), Gradient Boosting Decision Trees (GBDT), and XGBoost—were trained and evaluated, with XGBoost achieving the highest classification accuracy of 90.54%. Notably, this is the first study to integrate a fusion of two complementary electrophysiological indicators—skin and muscle potentials—into a multi-modal dataset for canine emotion recognition. Further interpretability analysis using Shapley Additive exPlanations (SHAP) revealed skin potential and voice pattern features as the most contributive to model performance. The proposed system demonstrates high accuracy, efficiency, and portability, laying a robust groundwork for future advancements in cross-species affective computing and intelligent animal welfare technologies. Full article

State-of-the-art radar systems for the contactless monitoring of vital signs and respiratory diseases are typically based on single-channel continuous wave (CW) technology. This technique allows precise measurements of respiration patterns, periods of movement, and heart rate. Major practical problems arise as CW systems suffer from signal cancellation due to destructive interference, limited overall functionality, and a possibility of low signal quality over longer periods. This work introduces a sophisticated multiple-input multiple-output (MIMO) solution that captures a radar image to estimate the sleep pose and position of a person (first step) and determine key vital parameters (second step). The first step is enabled by processing radar data with a forked convolutional neural network, which is trained with reference data captured by a time-of-flight depth camera. Key vital parameters that can be measured in the second step are respiration rate, asynchronous respiratory movement of chest and abdomen and limb movements. The developed algorithms were tested through experiments. The achieved mean absolute error (MAE) for the locations of the xiphoid and navel was less than 5 cm and the categorical accuracy of pose classification and limb movement detection was better than 90% and 98.6%, respectively. The MAE of the breathing rate was measured between 0.06 and 0.8 cycles per minute. Full article

Alzheimer’s disease (AD), the most common form of dementia, is a progressive neurodegenerative disorder characterized by memory loss and cognitive decline. In addition to its two major pathological hallmarks, extracellular amyloid beta (Aβ) plaques and intracellular neurofibrillary tangles (NFTs), recent evidence highlights the critical roles of mitochondrial dysfunction and neuroinflammation in disease progression. Aβ impairs mitochondrial function, which, in part, can subsequently trigger inflammatory cascades, creating a vicious cycle of neuronal damage. Estrogen receptors (ERs) are widely expressed throughout the brain, and the sex hormone 17β-estradiol (E2) exerts neuroprotection through both anti-inflammatory and mitochondrial mechanisms. While E2 exhibits neuroprotective properties, its mechanisms against Aβ toxicity remain incompletely understood. In this study, we investigated the neuroprotective effects of E2 against Aβ-induced mitochondrial dysfunction and neuroinflammation in primary cortical neurons, with a particular focus on the role of AMP-activated protein kinase (AMPK). We found that E2 treatment significantly increased phosphorylated AMPK and upregulated the expression of mitochondrial biogenesis regulator peroxisome proliferator-activated receptor gamma coactivator-1 α (PGC-1α), leading to improved mitochondrial respiration. In contrast, Aβ suppressed AMPK and PGC-1α signaling, impaired mitochondrial function, activated the pro-inflammatory nuclear factor kappa-light-chain enhancer of activated B cells (NF-κB), and reduced neuronal viability. E2 pretreatment also rescued Aβ-induced mitochondrial dysfunction, suppressed NF-κB activation, and, importantly, prevented the decline in neuronal viability. However, the pharmacological inhibition of AMPK using Compound C (CC) abolished these protective effects, resulting in mitochondrial collapse, elevated inflammation, and cell death, highlighting AMPK’s critical role in mediating E2’s actions. Interestingly, while NF-κB inhibition using BAY 11-7082 partially restored mitochondrial respiration, it failed to prevent Aβ-induced cytotoxicity, suggesting that E2’s full neuroprotective effects rely on broader AMPK-dependent mechanisms beyond NF-κB suppression alone. Together, these findings establish AMPK as a key mediator of E2’s protective effects against Aβ-driven mitochondrial dysfunction and neuroinflammation, providing new insights into estrogen-based therapeutic strategies for AD. Full article

We explore the feasibility of classifying perceived pain intensity—despite the stimulus being identical—using multimodal physiological signals and self-reported emotional ratings. A total of 112 healthy participants watched the same anger-inducing video, yet reported varying pain intensities (5, 6, or 7 on a 7-point scale). We recorded electrocardiogram, skin conductance (SC), respiration, photoplethysmogram results, and finger temperature, extracting 12 physiological features. Participants also rated their valence and arousal. Using a random forest model, we classified pain versus baseline and distinguished intensity levels. Compared to baseline, the painful stimulus altered heart rate variability, SC, respiration, and pulse transit time (PTT). Higher perceived pain correlated with more negative valence, higher arousal, and elevated SC, suggesting stronger sympathetic activation. The classification of baseline versus pain using SC and respiratory features reached an F1 score of 0.83. For intensity levels 6 versus 7, including PTT and skin conductance response along with valence achieved an F1 score of 0.73. These findings highlight distinct psychophysiological patterns that reflect perceived intensity under the same stimulus. SC features emerged as key biomarkers, while valence and arousal offered complementary insights, supporting the development of personalized, psychologically informed pain assessment systems. Full article

Sleep position recognition plays a crucial role in diagnosing and managing various health conditions, such as sleep apnea, pressure ulcers, and musculoskeletal disorders. Accurate monitoring of body posture during sleep can provide valuable insights for clinicians and support the development of intelligent healthcare systems. This research presents a comparative analysis of sleep position recognition using two distinct approaches: image-based deep learning and accelerometer-based classification. There are five classes: prone, supine, right side, left side, and wake up. For the image-based method, the Visual Geometry Group 16 (VGG16) convolutional neural network was fine-tuned with data augmentation strategies including rotation, reflection, scaling, and translation to enhance model generalization. The image-based model achieved an overall accuracy of 93.49%, with perfect precision and recall for “right side” and “wakeup” positions, but slightly lower performance for “left side” and “supine” classes. In contrast, the accelerometer-based method employed a feedforward neural network trained on features extracted from segmented accelerometer data, such as signal sum, standard deviation, maximum, and spike count. This method yielded superior performance, reaching an accuracy exceeding 99.8% across most sleep positions. The “wake up” position was particularly easy to detect due to the absence of body movements such as heartbeat or respiration when the person is no longer in bed. The results demonstrate that while image-based models are effective, accelerometer-based classification offers higher precision and robustness, particularly in real-time and privacy-sensitive scenarios. Further comparisons of the system characteristics, data size, and training time are also carried out to offer crucial insights for selecting the appropriate technology in clinical, in-home, or embedded healthcare monitoring applications. Full article

Melanomas quickly acquire resistance to vemurafenib, an important therapeutic for BRAFV600 mutant melanomas. Although combating vemurafenib resistance (VemR) to counter mitochondrial metabolic shift using mitochondria-targeting therapies has promise, no studies have analyzed the relationship between vemurafenib tolerance levels and metabolic plasticity. To determine how vemurafenib endurance levels drive metabolic plasticity, we developed isogenic BRAFV600E VemR melanoma models with variant vemurafenib tolerances and performed an integrative analysis of metabolomic and transcriptome alterations using metabolome, Mitoplate-S1, Seahorse, and RNA-seq assays. Regardless of drug tolerance differences, both VemR models display resistance to MEK inhibitor and sensitivity to Wnt/β-catenin inhibitor, ICG-001. β-catenin, MITF, and ABCB5 levels are upregulated in both VemR models, and ICG-001 treatment restored vemurafenib sensitivity with reductions in MITF, ABCB5, phospho-ERK1/2, and mitochondrial respiration. Whereas β-catenin signaling induced TCA cycle and OXPHOS in highly drug tolerant A2058VemR cells, it activated pentose phosphate pathway in M14VemR cells with low vemurafenib tolerance, both of which are inhibited by ICG-001. These data implicate an important role for Wnt/β-catenin signaling in VemR-induced metabolic plasticity. Our data demonstrate that drug tolerance thresholds play a direct role in driving metabolic shifts towards specific routes, thus providing a new basis for delineating VemR melanomas for metabolism-targeting therapies. Full article