Hasil untuk "Physiology"

Mashun Onishi, Koji Yamano, Miyuki Sato et al.

Degradation of mitochondria via a selective form of autophagy, named mitophagy, is a fundamental mechanism conserved from yeast to humans that regulates mitochondrial quality and quantity control. Mitophagy is promoted via specific mitochondrial outer membrane receptors, or ubiquitin molecules conjugated to proteins on the mitochondrial surface leading to the formation of autophagosomes surrounding mitochondria. Mitophagy‐mediated elimination of mitochondria plays an important role in many processes including early embryonic development, cell differentiation, inflammation, and apoptosis. Recent advances in analyzing mitophagy in vivo also reveal high rates of steady‐state mitochondrial turnover in diverse cell types, highlighting the intracellular housekeeping role of mitophagy. Defects in mitophagy are associated with various pathological conditions such as neurodegeneration, heart failure, cancer, and aging, further underscoring the biological relevance. Here, we review our current molecular understanding of mitophagy, and its physiological implications, and discuss how multiple mitophagy pathways coordinately modulate mitochondrial fitness and populations.

H. Sies, Dean P. Jones

G. Niel, G. D’angelo, G. Raposo

R. Mittler

Fergal Connolly, D. Byrne, S. Lydon et al.

Wei Gao, S. Emaminejad, H. Nyein et al.

Gennie Nguyen, Lei Wang, Yangxueqing Jiang et al.

Understanding how individuals physiologically respond to false information is crucial for advancing misinformation detection systems. This study explores the potential of using physiological signals, specifically electrodermal activity (EDA) and photoplethysmography (PPG), to classify both the veracity of information and its interaction with user belief. In a controlled laboratory experiment, we collected EDA and PPG signals while participants evaluated the truthfulness of climate-related claims. Each trial was labeled based on the objective truth of the claim and the participant's belief, enabling two classification tasks: binary veracity detection and a novel four-class joint belief-veracity classification. We extracted handcrafted features from the raw signals and trained several machine learning models to benchmark the dataset. Our results show that EDA outperforms PPG, indicating its greater sensitivity to physiological responses related to truth perception. However, performance significantly drops in the joint belief-veracity classification task, highlighting the complexity of modeling the interaction between belief and truth. These findings suggest that while physiological signals can reflect basic truth perception, accurately modeling the intricate relationships between belief and veracity remains a significant challenge. This study emphasizes the importance of multimodal approaches that incorporate psychological, physiological, and cognitive factors to improve fake news detection systems. Our work provides a foundation for future research aimed at enhancing misinformation detection via addressing the complexities of human belief and truth processing.

Haizhou Yang, Jiyang Zhang, Brahmajee K. Nallamothu et al.

Coronary microvascular dysfunction (CMD), characterized by impaired regulation of blood flow in the coronary microcirculation, plays a key role in the pathogenesis of ischemic heart disease and is increasingly recognized as a contributor to adverse cardiovascular outcomes. Despite its clinical importance, CMD remains underdiagnosed due to the reliance on invasive procedures such as pressure wire-based measurements of the index of microcirculatory resistance (IMR) and coronary flow reserve (CFR), which are costly, time-consuming, and carry procedural risks. To date, no study has sought to quantify CMD indices using data-driven approaches while leveraging the rich information contained in coronary angiograms. To address these limitations, this study proposes a novel data-driven framework for inference of CMD indices based on coronary angiography. A physiologically validated multi-physics model was used to generate synthetic datasets for data-driven model training, consisting of CMD indices and computational angiograms with corresponding contrast intensity profiles (CIPs). Two neural network architectures were developed: a single-input-channel encoder-MLP model for IMR prediction and a dual-input-channel encoder-MLP model for CFR prediction, both incorporating epistemic uncertainty estimation to quantify prediction confidence. Results demonstrate that the data-driven models achieve high predictive accuracy when evaluated against physics-based synthetic datasets, and that the uncertainty estimates are positively correlated with prediction errors. Furthermore, the utility of CIPs as informative surrogates for coronary physiology is demonstrated, underscoring the potential of the proposed framework to enable accurate, real-time, image-based CMD assessment using routine angiography without the need for more invasive approaches.

Vaibhav Gupta, Maria Maleshkova

Recent advances in wearable technology have enabled the continuous monitoring of vital physiological signals, essential for predictive modeling and early detection of extreme physiological events. Among these physiological signals, heart rate (HR) plays a central role, as it is widely used in monitoring and managing cardiovascular conditions and detecting extreme physiological events such as hypoglycemia. However, data from wearable devices often suffer from missing values. To address this issue, recent studies have employed various imputation techniques. Traditionally, the effectiveness of these methods has been evaluated using predictive accuracy metrics such as RMSE, MAPE, and MAE, which assess numerical proximity to the original data. While informative, these metrics fail to capture the complex statistical structure inherent in physiological signals. This study bridges this gap by presenting a comprehensive evaluation of four statistical imputation methods, linear interpolation, K Nearest Neighbors (KNN), Piecewise Cubic Hermite Interpolating Polynomial (PCHIP), and B splines, for short term HR data gaps. We assess their performance using both predictive accuracy metrics and statistical distance measures, including the Cohen Distance Test (CDT) and Jensen Shannon Distance (JS Distance), applied to HR data from the D1NAMO dataset and the BIG IDEAs Lab Glycemic Variability and Wearable Device dataset. The analysis reveals limitations in existing imputation approaches and the absence of a robust framework for evaluating imputation quality in physiological signals. Finally, this study proposes a foundational framework to develop a composite evaluation metric to assess imputation performance.

Richard R. Foster, Laura Ellwein Fix

The complexity of mathematical models describing respiratory mechanics has grown in recent years, however, parameter identifiability of such models has only been studied in the last decade in the context of observable data. This study investigates parameter identifiability of a nonlinear respiratory mechanics model tuned to the physiology of an extremely preterm infant, using global Morris screening, local deterministic sensitivity analysis, and singular value decomposition-based subset selection. The model predicts airflow and dynamic pulmonary volumes and pressures under varying levels of continuous positive airway pressure, and a range of parameters characterizing both surfactant-treated and surfactant-deficient lung. Sensitivity analyses indicated eleven parameters influence model outputs over the range of continuous positive airway pressure and lung health scenarios. The model was adapted to data from a spontaneously breathing 1 kg infant using gradient-based optimization to estimate the parameter subset characterizing the patient's state of health.

Enso Onill Torres Alegre

The gut microbiota has emerged as a fundamental regulator of sleep physiology, influencing neural, endocrine, and immune pathways through the gut-microbiota-brain axis (GMBA). This bidirectional communication system modulates neurotransmitter production, circadian rhythms, and metabolic homeostasis, while disruptions in microbial composition have been linked to sleep disorders, neuroinflammation, and systemic immune dysfunction. Recent findings suggest that gut dysbiosis contributes to sleep disturbances by altering serotonin, GABA, and short-chain fatty acid (SCFA) metabolism, with implications for neurodegenerative diseases, metabolic syndromes, and mood disorders. Additionally, the gut microbiota interacts with the endocrine and immune systems, shaping inflammatory responses and stress adaptation mechanisms. This review explores the intricate connections between sleep and the gut microbiota, integrating emerging research on microbiota-targeted therapies, such as probiotics, fecal microbiota transplantation (FMT), and chrononutrition, as potential interventions to restore sleep homeostasis and improve health outcomes

Sadiq Maifata, Abdullahi Adamu Ja'e, Aminat Anura et al.

Oxidative stress and inflammation drive diabetes. Conventional treatments inadequately target these. Neocarya macrophylla shows preliminary antioxidant promise. This study aims to investigate its antioxidant and anti-inflammatory effects in a diabetic rat model to address this research gap. Thirty-six male Wistar rats were randomised into four groups (n=6 per group): Normal Control, Diabetic Control, Neocarya macrophylla Ethyl Acetate Fraction (120 mg/kg), and Metformin (200 mg/kg). Type 2 diabetes was induced using a 60% HFD for four weeks, followed by a single intraperitoneal injection of STZ (40 mg/kg). Treatment was administered orally for four weeks. Post-treatment, oxidative stress biomarkers—Superoxide Dismutase (SOD), Catalase (CAT), Glutathione Peroxidase (GPx), and Malondialdehyde (MDA)—were quantified using spectrophotometric assays. Pro-inflammatory cytokines—Interleukin-6 (IL-6) and Tumour Necrosis Factor-alpha (TNF-α)—were evaluated using ELISA, while Catalase mRNA expression was assessed via RT-qPCR. Statistical analysis was performed using GraphPad Prism 9, applying One-way ANOVA followed by Tukey’s post hoc test, with p<0.05 considered significant.Treatment with the ethyl acetate fraction significantly increased SOD activity by 19% (p=0.007), GPx by 54% (p=0.04), and CAT by 136% (p<0.001) compared to diabetic controls, while reducing MDA levels by 41% (p<0.001), indicative of reduced lipid peroxidation. Pro-inflammatory cytokines IL-6 and TNF-α levels were reduced by 58% and 62%, respectively, in the treatment group compared to diabetic controls. Catalase mRNA expression showed a 1.4-fold increase (p < 0.001). Notably, the ethyl acetate fraction’s effects were comparable to Metformin in modulating oxidative stress and inflammatory markers.This study demonstrates that the ethyl acetate fraction of Neocarya macrophylla leaves exerts significant antioxidant and anti-inflammatory effects in HFD/STZ-induced diabetic rats, supporting its therapeutic potential as an adjunctive natural remedy in managing oxidative stress and inflammation associated with diabetes.

Olumide Samuel Ajani, Samuel Gbadebo Olukole, Matthew Olugbenga Oyeyemi et al.

Bisphenol A (BPA) is a widely used chemical in the plastic industry and a known endocrine disruptor which causes reproductive toxicity in animals. Also, melatonin is an antioxidant that can alleviate the toxicity caused by endocrine disruptors. Previous studies have demonstrated that melatonin protects the male reproductive functions. However, the protective mechanisms of melatonin are not well elucidated. This study investigated how melatonin protects against BPA-induced testicular dysfunction in rats. Forty male Wistar rats were grouped randomly into four. Animals in group A (control) received 0.2 mL of olive oil orally, B: melatonin (10 mg/kg) intraperitoneally, C: BPA (10 mg/kg) orally, and D: co-exposed with BPA and melatonin. All rats were treated daily for 45 days. Testicular samples were harvested and analysed on the 46th day. Results from this study showed that melatonin prevented the BPA-induced testicular necrosis and distortion of spermatozoa flagellar axoneme arrangement in the co-exposed rats. In addition, the induction of alpha-smooth muscle actin, vimentin, and S-100 proteins in the testes was significantly reduced in the BPA alone-treated rats. The melatonin upregulated the proteins in the co-treated group. Increased expression of alpha-smooth muscle actin, vimentin, and S-100 proteins in normal tissue have been associated with effective regulation of fibroblast contractile activity, cell migration and metastasis, and apoptosis, proliferation, differentiation, and inflammation in different cell types, respectively. Therefore, our findings provide insights into the protective mechanisms of melatonin against the bisphenol A-induced reproductive toxicity.

Dmitrij V. Zhidilyaev, Levan B. Berikashvili, Mikhail Ya. Yadgarov et al.

<b>Background/Objectives:</b> Accurate prognostication for patients with chronic critical illness (CCI) following brain injury remains challenging. Conventional scoring systems like the Acute Physiology and Chronic Health Evaluation II (APACHE II) and the Nutrition Risk in the Critically Ill (NUTRIC) score are validated as “left-side” models for risk stratification at intensive care unit (ICU) admission but may not capture the evolving trajectory of prolonged illness. This study aimed to evaluate the prognostic performance of APACHE II and NUTRIC as “right-side” models—assessed at intervals closer to the outcome—by testing the hypothesis that their predictive accuracy for in-hospital mortality improves when measured nearer to the time of death. <b>Methods:</b> In this real-world data analysis study, data were extracted from the electronic health records (Russian Intensive Care Dataset [RICD] v. 2.0) of 328 adult patients with CCI following brain injury. The discriminative ability of repeatedly assessed APACHE II and NUTRIC scores for predicting mortality was analyzed by calculating the area under the receiver operating characteristic curve (AUROC) for three predefined intervals before death: within ≤7 days, 8–14 days, and ≥15 days. <b>Results:</b> Among the 328 patients (median age 64 years; 18.3% in-hospital mortality), a total of 380 paired score assessments were analyzed. The predictive performance for both scores was highest within 7 days of death (APACHE II AUROC: 0.883; NUTRIC AUROC: 0.839). Discriminatory ability declined at 8–14 days (APACHE II AUROC: 0.807; NUTRIC AUROC: 0.778) and was poorest at ≥15 days before death (APACHE II AUROC: 0.671; NUTRIC AUROC: 0.681). The NUTRIC score consistently demonstrated higher AUROC values than APACHE II across all intervals, though the differences were not statistically significant. <b>Conclusions:</b> In patients with CCI following brain injury, the prognostic accuracy of APACHE II and NUTRIC scores is time-dependent, peaking immediately before death and offering poor long-term prediction from admission. These findings underscore the limitation of static, admission-based models and highlight the necessity for developing dynamic, personalized and time-sensitive prognostic tools tailored to the evolving course of chronic critical illness.

An Huynh Bao, Tam Tran Thai Thanh, Tho Pham Kieu Anh et al.

Po-Yu Hsieh, June-Hao Hou

Biomimicry has played a pivotal role in robotics. In contrast to rigid robots, bio-inspired robots exhibit an inherent compliance, facilitating versatile movements and operations in constrained spaces. The robot implementation in fabrication, however, has posed technical challenges and mechanical complexity, thereby underscoring a noticeable gap between research and practice. To address the limitation, the research draws inspiration from the unique musculoskeletal feature of vertebrate physiology, which displays significant capabilities for sophisticated locomotion. The research converts the biological paradigm into a tensegrity-based robotic system, which is formed by the design of rigid-flex coupling and a compliant mechanism. This integrated technique enables the robot to achieve a wide range of motions with variable stiffness and adaptability, holding great potential for advanced performance in ill-defined environments. In summation, the research aims to provide a robust foundation for tensegrity-based biomimetic robots in practice, enhancing the feasibility of undertaking intricate robotic constructions.

Revaz D. Chachanidze, Othmane Aouane, Jens Harting et al.

Margination, a fundamental process in which leukocytes migrate from the flowing blood to the vessel wall, is well-documented in physiology. However, it is still an open question on how the differences in cell size and stiffness of white and red cells contribute to this phenomenon. To investigate the specific influence of cell stiffness, we conduct experimental and numerical studies on the segregation of a binary mixture of artificially stiffened red blood cells within a suspension of healthy cells. The resulting distribution of stiffened cells within the channel is found to depend on the channel geometry, as demonstrated with slit, rectangular, and cylindrical cross-sections. Notably, an unexpected central peak in the distribution of stiffened RBCs, accompanied by fourfold peaks at the corners, emerges in agreement with simulations. Our results unveil a non-monotonic variation in segregation/margination concerning hematocrit and flow rate, challenging the prevailing belief that higher flow rates lead to enhanced margination.

Diane Soussan, Ali Tahrioui, R R de la Haba et al.

Antiterminators are essential components of bacterial transcriptional regulation, allowing the control of gene expression in response to fluctuating environmental conditions. RNA-binding antiterminators are particularly important regulatory proteins that play a significant role in preventing transcription termination by binding to specific RNA sequences. These RNA-binding antiterminators have been extensively studied for their roles in regulating various metabolic pathways. However, their role in modulating the physiology of pathogens requires further investigations. This review focuses on these RNA-binding proteins in both Gram-positive and Gram-negative bacteria, particularly on their structures, mechanism of action, and target genes. Additionally, the involvement of the antitermination mechanisms in bacterial pathogenicity will be discussed. This knowledge is crucial for understanding the regulatory mechanisms that govern bacterial pathogenicity, opening up exciting prospects for future research, and potentially new alternative strategies to fight against infectious diseases.

Anupam Sengupta

Planktonic active matter represents an emergent system spanning different scales: individual, population and community; and complexity arising from sub-cellular and cellular to collective and ecosystem scale dynamics. This cross-scale active matter system responds to a range of abiotic (temperature, fluid flow and light conditions) and biotic factors (nutrients, pH, secondary metabolites) characteristic to the relevant ecosystems they are part of. Active modulation of cell phenotypes, including morphology, motility, and intracellular organization enable planktonic microbes to dynamically interact with other individuals and species; and adapt - often rapidly - to the changes in their environment. In this chapter, I discuss both traditional and contemporary approaches to study the dynamics of this multi-scale active matter system from a mechanistic standpoint, with specific references to their local settings and their ability to actively tune the behaviour and physiology, and the emergent structures and functions they elicit under natural ecological constraints as well as due to the shifting climatic trends.

Xiao Qian

With the aging of the population in China in recent years, the number of stroke patients is increasing day by day. The sequelae of stroke-induced foot drop are expected to regain some mobility after timely rehabilitation, but if patients do not receive early intervention, they may miss the best rehabilitation period and lose the ability to walk and stand completely. The traditional means of rehabilitation treatment is manual rehabilitation by rehabilitation physicians, but with the increase in the number of patients with movement disorders, the number of rehabilitation physicians has become significantly insufficient. The use of rehabilitation robots to provide adjunctive therapy to patients has become a common and effective means of rehabilitation worldwide. The early rehabilitation robots could only drive the patient to do passive rehabilitation and could not reflect the patient’s true motor intentions. Since EMG signals on the surface of the human body can advance the onset of human movement, they can be used to identify the intention of human movement. This paper validates and explores the experiment based on the underlying data.