Hasil untuk "Physiology"

Purnomo Yudi, Wahyuningsih Doti, Tilaqza Andri

Urena lobata is medicinal plant used to cure Diabetes mellitus traditionally. However, the phytoconstituent regulating blood glucose levels and their mechanism remain unexlpored. The study aim to evaluate antidiabetic potency of U.lobata leaf fractions. Ethanol solvent is used to extract U. lobata leaf by kinetic maceration methods therefore the concentrated extract was fractionated using n-hexane (Fraction A), n-butanol (Fraction B) and aqueous solvent (Fraction C) respectively. The anti-diabetic potency U.lobata was examined by oral glucose tolerance test. Blood glucose level of rat were measured after loading of glucose at the certain time and Area Under Curve (AUC) integrated were calculated using trape zoidal methods. Data was analyzed using one way ANOVA and continued with LSD test (p<0.05). Molecular docking was conducted by the PyRx 0.8 program after phytoconstituent identification of U.lobata extract using LC-HRMS. Ethanolic extract, fraction A, B and C decreased glucose AUC about 16 %, 8 %, 11% and 23% respectively compared to control group (p<0.05). Meanwhile, glibenclamid as reference drugs decreased glucosa AUC about 30 % and stronger than extract of U. lobata and its fractions. (+)-ar-Turmerone is predicted as potential compound in U.lobata leaf extract as anti-diabetic by binding on SUR1 based on in silico approach.

G. V. Pavan Kumar

C.V. Raman (1888-1970) was one of the pioneering scientists to have emerged from India during the colonial era. His scientific explorations were driven by his curiosity to understand wave phenomena. Naturally, optics and related physical effects were at the heart of such an exploration. Apart from his Nobel prize-winning discovery of the Raman effect, his research included topics such as oblique diffraction, light scattering from liquids and amorphous solids, classical and quantum nature of light, acousto-optics, haloes and coronae (speckles), crystal dynamics and soft modes, optics of minerals, floral colors, physiology of vision and many other aspects related to light in natural settings. In this article, I give a historical overview of some of the work by C.V. Raman and his group that had a direct connection to optics and optical spectroscopy.

Paraskevi Valergaki, Vassilis C. Nicodemou, Iason Oikonomidis et al.

Reliable stress recognition is critical in applications such as medical monitoring and safety-critical systems, including real-world driving. While stress is commonly detected using physiological signals such as perinasal perspiration and heart rate, facial activity provides complementary cues that can be captured unobtrusively from video. We propose a multimodal stress estimation framework that combines facial videos and physiological signals, remaining effective even when biosignal acquisition is challenging. Facial behavior is represented using a dense 3D Morphable Model, yielding a 56-dimensional descriptor that captures subtle expression and head-pose dynamics over time. To study how stress modulates facial motion, we perform extensive experiments alongside established physiological markers. Paired hypothesis tests between baseline and stressor phases show that 38 of 56 facial components exhibit consistent, phase-specific stress responses comparable to physiological markers. Building on these findings, we introduce a Transformer-based temporal modeling framework and evaluate unimodal, early-fusion, and cross-modal attention strategies. Cross-modal attention fusion of 3D-derived facial features with physiological signals substantially improves performance over physiological signals alone, increasing AUROC from 52.7% and accuracy from 51.0% to 92.0% and 86.7%, respectively. Although evaluated on driving data, the proposed framework and protocol may generalize to other stress estimation settings.

Daiana de Souza Machado, Rogério Ribeiro Vicentini, Luana da Silva Gonçalves et al.

Despite the growing preference for cats as companion animals, beliefs and misperceptions about them are still common. Cultural and sociodemographic aspects can influence society’s attitudes towards cats, affecting the way they are kept and cared for and, consequently, their welfare. This narrative review aimed to identify and categorize beliefs and misconceptions about domestic cats commonly described in the scientific literature, particularly those related to behavior, sociality, health, and management, in order to foster critical thinking about these perceptions. Our bibliographic search included three electronic databases (Web of Science, PubMed and Scopus). This review highlights a set of recurrent beliefs of general society reported by the scientific literature, such as the perception of cats as independent and non-social animals, bringers of bad luck, incapable of forming emotional bonds with their guardians, or in need of outdoor access to thrive. Although widespread, many of these beliefs lack empirical support. Identifying and fostering critical reflection on such beliefs can contribute to deconstructing them, improving the guardian–cat relationship and enhancing the welfare of both cats and humans.

Zhuo Zeng, Chengyu Zhou, Wenhui Yin et al.

Introduction This study investigated whether stepwise load reduction training (SLRT) yields comparable or superior effects to medium load resistance training (MLRT) on one-repetition maximum (1RM) barbell back squat, thigh circumference (TC), muscle endurance (ME), counter movement jump (CMJ) performance, and acute blood lactate (BL) levels.Methods Thirty healthy, physically active males completed both the SLRT and MLRT protocols in a crossover design to assess acute blood lactate responses firstly. Then they were randomly assigned to SLRT, MLRT, or control (CON) groups using a sealed envelope method for an 8 weeks intervention. Anthropometric data were collected at baseline. Performance metrics (1RM, TC, ME, and CMJ) were measured at baseline, week 4, and post intervention. Blinding was not feasible due to the visible nature of interventions. To minimize bias, testing was conducted by staff not involved in training, with standardized warm-ups and protocols applied across groups. Training volume, frequency and assessment timing were matched between SLRT and MLRT. Participants were instructed to avoid other structured training, and adherence was monitored weekly. Results The results showed that both SLRT and MLRT significantly improved 1RM and ME, but SLRT produced greater gains. No significant differences were observed in TC. Additionally, SLRT led to significantly better CMJ performance and higher BL levels at immediate, 4th, 7th, and 9th minutes post exercise. The CON group performed significantly worse on all long-term outcomes compared to both SLRT and MLRT. While both SLRT and MLRT effectively enhance muscle strength, SLRT yields superior improvements in 1RM, ME, CMJ performance, and acute BL accumulation under equivalent training volumes.Discussion These results suggest that SLRT may offer enhanced anaerobic conditioning benefits and superior adaptation potential. However, the findings should be interpreted with consideration of certain limitations, including the homogeneity of the sample and the relatively short intervention duration.

Jens Behrmann, Maria R. Cervera, Antoine Wehenkel et al.

Smart wearables enable continuous tracking of established biomarkers such as heart rate, heart rate variability, and blood oxygen saturation via photoplethysmography (PPG). Beyond these metrics, PPG waveforms contain richer physiological information, as recent deep learning (DL) studies demonstrate. However, DL models often rely on features with unclear physiological meaning, creating a tension between predictive power, clinical interpretability, and sensor design. We address this gap by introducing PPGen, a biophysical model that relates PPG signals to interpretable physiological and optical parameters. Building on PPGen, we propose hybrid amortized inference (HAI), enabling fast, robust, and scalable estimation of relevant physiological parameters from PPG signals while correcting for model misspecification. In extensive in-silico experiments, we show that HAI can accurately infer physiological parameters under diverse noise and sensor conditions. Our results illustrate a path toward PPG models that retain the fidelity needed for DL-based features while supporting clinical interpretation and informed hardware design.

Chenqi Li, Yu Liu, Timothy Denison et al.

Biosignals offer valuable insights into the physiological states of the human body. Although biosignal modalities differ in functionality, signal fidelity, sensor comfort, and cost, they are often intercorrelated, reflecting the holistic and interconnected nature of human physiology. This opens up the possibility of performing the same tasks using alternative biosignal modalities, thereby improving the accessibility, usability, and adaptability of health monitoring systems. However, the limited availability of large labeled datasets presents challenges for training models tailored to specific tasks and modalities of interest. Unsupervised cross-modal knowledge transfer offers a promising solution by leveraging knowledge from an existing modality to support model training for a new modality. Existing methods are typically based on knowledge distillation, which requires running a teacher model alongside student model training, resulting in high computational and memory overhead. This challenge is further exacerbated by the recent development of foundation models that demonstrate superior performance and generalization across tasks at the cost of large model sizes. To this end, we explore a new framework for unsupervised cross-modal knowledge transfer of biosignals by training a lightweight bridge network to align the intermediate representations and enable information flow between foundation models and across modalities. Specifically, we introduce an efficient strategy for selecting alignment positions where the bridge should be constructed, along with a flexible prototype network as the bridge architecture. Extensive experiments across multiple biosignal modalities, tasks, and datasets show that BioX-Bridge reduces the number of trainable parameters by 88--99\% while maintaining or even improving transfer performance compared to state-of-the-art methods.

Thomas Saigre, Vincent Chabannes, Christophe Prud'Homme et al.

We present a comprehensive computational model to simulate the coupled dynamics of aqueous humor flow and heat transfer in the human eye. To manage the complexity of the model, we make significant efforts in meshing and efficient solution of the discrete problem using high-performance resources. The model accurately describes the dynamics of the aqueous humor in the anterior and posterior chambers and accounts for convective effects due to temperature variations. Results for fluid velocity, pressure, and temperature distribution are in good agreement with existing numerical results in the literature. Furthermore, the effects of postural changes and wall shear stress behavior are analyzed, providing new insights into the mechanical forces acting on ocular tissues. Overall, the present contribution provides a detailed three-dimensional simulation that enhances the understanding of ocular physiology and may contribute to further progress in clinical research and treatment optimization in ophthalmology.

Nurilmala Mala, Ramadhan Wahyu, Putri Andini Nabila

Surimi is a minced fish commonly used as a raw material for fish jelly production. A strong gel formation in surimi can support fish jelly products to be compact, elastic, and durable. Gelatin is known to function as a gelling agent in food products. This study aimed to determine the characteristics of tilapia scale gelatin and the effect of adding tilapia scale gelatin to threadfin bream surimi on the characteristics of kamaboko. The gelatin analysis included yield, moisture content, ash content, pH, viscosity, setting point, and molecular weight. Surimi analysis included pH, texture profile (hardness, springiness, cohesiveness, and chewiness), water holding capacity, and whiteness. The yield and temperature points of tilapia scale gelatin gel were 13.01±0.59% and 18.17±0.28 oC. The gelatin characteristics of tilapia scales in this study met SNI 8622:2018 and GMIA 2019. The treatment of variations in the gelatin concentration of tilapia scales on surimi had a significant effect on the pH characteristics of surimi, whiteness, hardness, and chewiness of kamaboko.

Jacob Moran, Lucas C. Graham, Mikhail Tikhonov

Microbial ecosystems exhibit a surprising amount of functionally relevant diversity at all levels of taxonomic resolution, presenting a significant challenge for most modeling frameworks. A long-standing hope of theoretical ecology is that some patterns might persist despite community complexity -- or perhaps even emerge because of it. A deeper understanding of such "emergent simplicity" could enable new approaches for predicting the behaviors of the complex ecosystems in nature. However, the concept remains partly intuitive with no consistent definition, and most empirical examples described so far afford limited predictive power. Here, we propose an information-theoretic framework for defining and quantifying emergent simplicity in empirical data based on the ability of coarsened descriptions to predict community-level functional properties. Applying this framework to two published datasets, we demonstrate that all five properties measured across both experiments exhibit robust evidence of what we define as "emergent predictability": surprisingly, as community richness increases, simple compositional descriptions become more predictive. We show that standard theoretical models of high-diversity ecosystems fail to recapitulate this behavior. This is in contrast to simple self-averaging, which is well-understood and generic across models. We propose that, counterintuitively, emergent predictability arises when physiological or environmental feedbacks _oppose_ statistical self-averaging along some axes of community variation. As a result, these axes of variation become increasingly predictive of community function at high richness. We demonstrate this mechanism in a minimal model, and argue that explaining and leveraging emergent predictability will require integrating large-N theoretical models with a minimal notion of physiology, which the dominant modeling frameworks currently omit.

Kenneth B. Bader, Katia Flores Basterrechea, Samuel A. Hendley

Introduction: For venous thrombosis patients, catheter-directed thrombolytic therapy is the standard-of-care to recanalize the occluded vessel. Limitations with thrombolytic drugs make the development of adjuvant treatments an active area of research. One potential adjuvant is histotripsy, a focused ultrasound therapy that lyses red blood cells within thrombus via the spontaneous generation of bubbles. Histotripsy has also been shown to improve the efficacy of thrombolytic drugs, though the precise mechanism of enhancement has not been elucidated. In this study, in silico calculations were performed to determine the contribution of histotripsy-induced changes in thrombus diffusivity to alter catheter-directed therapy.Methods: An established and validated Monte Carlo calculation was used to predict the extent of histotripsy bubble activity. The distribution of thrombolytic drug was computed with a finite-difference time domain (FDTD) solution of the perfusion-diffusion equation. The FDTD calculation included changes in thrombus diffusivity based on outcomes of the Monte Carlo calculation. Fibrin degradation was determined using the known reaction rate of thrombolytic drug.Results: In the absence of histotripsy, thrombolytic delivery was restricted in close proximity to the catheter. Thrombolytic perfused throughout the focal region for calculations that included the effects of histotripsy, resulting in an increased degree of fibrinolysis.Discussion: These results were consistent with the outcomes of in vitro studies, suggesting histotripsy-induced changes in the thrombus diffusivity are a primary mechanism for enhancement of thrombolytic drugs.

L. K. Dzeranova, S. Y. Vorotnikova, A. S. Shutova et al.

One of the causes of non-tumor related hyperprolactinemia is taking a medications. Physicians of various specialties, such as cardiologists, gastroenterologists, endocrinologists, and neurologists, encounter hyperprolactinemia as a side effect of drug therapy in their practice, but it is most often observed in the practice of a psychiatrist when treating patients with psychotropic medications. Over the past few years, there has been an increase in the frequency of prescriptions of antidepressants and neuroleptics due to post-COVID-19 syndrome, anxiety and stress caused by the pandemic of a new coronavirus infection. There is also a predisposition to the development of hyperprolactinemia on the background of taking neuroleptics due to genetic features of patients. Currently, there is no established common algorithm for diagnosis and treatment of drug-induced hyperprolactinemia in the world. Based on a review of foreign and domestic literature, the article discusses in detail the mechanisms of development and various approaches to the correction of iatrogenic (drug-induced) hyperprolactinemia, assesses the prolactogenic activity of neuroleptics, and proposes algorithms for prolactin monitoring and correction of hyperprolactinemia using dopamine agonists. Often the tactics of management of such patients need to be discussed by a team of specialized physicians.

Judit Aizpuru, Maxim Borisyak, Peter Neubauer et al.

Mechanistic growth models play a major role in bioprocess engineering, design, and control. Their reasonable predictive power and their high level of interpretability make them an essential tool for computer aided engineering methods. Additionally, since they contain knowledge about cell physiology, the parameter estimates provide meaningful insights into the metabolism of the microorganism under study. However, the assumption of time invariance of the model parameters is often violated in real experiments, limiting their capacity to fully explain the observed dynamics. In this work, we propose a framework for identifying such violations and producing insights into misspecified mechanisms. The framework achieves this by allowing kinetic and process parameters to vary in time. We demonstrate the framework's capabilities by fitting a hybrid model based on a simple mechanistic growth model for E. coli with data generated in-silico by a much more complex one and identifying missing kinetics.

Dimitri Marques Abramov, Constantino Tsallis, Henrique Santos Lima

The brain is a complex system whose understanding enables potentially deeper approaches to mental phenomena. Dynamics of wide classes of complex systems have been satisfactorily described within $q$-statistics, a current generalization of Boltzmann-Gibbs (BG) statistics. Here, we study human electroencephalograms of typical human adults (EEG), very specifically their inter-occurrence times across an arbitrarily chosen threshold of the signal (observed, for instance, at the midparietal location in scalp). The distributions of these inter-occurrence times differ from those usually emerging within BG statistical mechanics. They are instead well approached within the $q$-statistical theory, based on non-additive entropies characterized by the index $q$. The present method points towards a suitable tool for quantitatively accessing brain complexity, thus potentially opening useful studies of the properties of both typical and altered brain physiology.

K. Wasserman, A. Van Kessel, G. Burton

Finn Petersen, Johannes Demann, Dina Restemeyer et al.

Duckweeds can be potentially used in human and animal nutrition, biotechnology or wastewater treatment. To cultivate large quantities of a defined product quality, a standardized production process is needed. A small-scale, re-circulating indoor vertical farm (IVF) with artificial lighting and a nutrient control and dosing system was used for this purpose. The influence of different light intensities (50, 100 and 150 µmol m⁻² s⁻¹) and spectral distributions (red/blue ratios: 70/30, 50/50 and 30/70%) on relative growth rate (RGR), crude protein content (CPC), relative protein yield (RPY) and chlorophyll a of the duckweed species <i>Lemna minor</i> and <i>Wolffiella hyalina</i> were investigated. Increasing light intensity increased RGR (by 67% and 76%) and RPY (by 50% and 89%) and decreased chlorophyll a (by 27% and 32%) for <i>L. minor</i> and <i>W. hyalina</i>, respectively. The spectral distributions had no significant impact on any investigated parameter. <i>Wolffiella hyalina</i> achieved higher values in all investigated parameters compared to <i>L. minor</i>. This investigation proved the successful cultivation of duckweed in a small-scale, re-circulating IVF with artificial lighting.

Gabriel M. Aldridge, Tyler A. Zarin, Adam J. Brandner et al.

Hypocretin/orexin (HCRT) is a neuropeptide that is associated with both stress and reward systems in humans and rodents. The different contributions of signaling at hypocretin-receptor 1 (HCRT-R1) and hypocretin-receptor 2 (HCRT-R2) to compulsive alcohol drinking are not yet fully understood. Thus, the current studies used pharmacological and viral-mediated targeting of HCRT to determine participation in compulsive alcohol drinking and measured HCRT-receptor mRNA expression in the extended amygdala of both alcohol-dependent and non-dependent male rats. Rats were made dependent through chronic intermittent exposure to alcohol vapor and were tested for the acute effect of HCRT-R1-selective (SB-408124; SB-R1), HCRT-R2-selective (NBI-80713; NB-R2), or dual HCRT-R1/2 (NBI-87571; NB-R1/2) antagonism on alcohol intake. NB-R2 and NB-R1/2 antagonists each dose-dependently decreased overall alcohol drinking in alcohol-dependent rats, whereas, SB-R1 decreased alcohol drinking in both alcohol-dependent and non-dependent rats at the highest dose (30 mg/kg). SB-R1, NB-R2, and NB-R1/2 treatment did not significantly affect water drinking in either alcohol-dependent or non-dependent rats. Additional PCR analyses revealed a significant decrease in Hcrtr1 mRNA expression within the central amygdala (CeA) of dependent rats under acute withdrawal conditions compared to non-dependent rats. Lastly, a shRNA-encoding adeno-associated viral vector with retrograde function was used to knockdown HCRT in CeA-projecting neurons from the lateral hypothalamus (LH). LH-CeA HCRT knockdown significantly attenuated alcohol self-administration in alcohol-dependent rats. These observations suggest that HCRT signaling in the CeA is necessary for alcohol-seeking behavior during dependence. Together, these data highlight a role for both HCRT-R1 and -R2 in dependent alcohol-seeking behavior.

Abhiram Kumar, Abhiram Kumar, Kalyani Sakhare et al.

Conveyance of pathogens between organisms causes communicable diseases. On the other hand, a non-communicable disease (NCD) was always thought to have no causative transmissible infective agents. Today, this clear distinction is increasingly getting blurred and NCDs are found to be associated with some transmissible components. The human microbiota carries a congregation of microbes, the majority and the most widely studied being bacteria in the gut. The adult human gut harbors ginormous inhabitant microbes, and the microbiome accommodates 150-fold more genes than the host genome. Microbial communities share a mutually beneficial relationship with the host, especially with respect to host physiology including digestion, immune responses, and metabolism. This review delineates the connection between environmental factors such as infections leading to gut dysbiosis and NCDs and explores the evidence regarding possible causal link between them. We also discuss the evidence regarding the value of appropriate therapeutic immunomodulatory nutritional interventions to reduce the development of such diseases. We behold such immunomodulatory effects have the potential to influence in various NCDs and restore homeostasis. We believe that the beginning of the era of microbiota-oriented personalized treatment modalities is not far away.

R. J. King, J. Clements

Karen D. Rudolph, Megan M. Davis, Haley V. Skymba et al.

The adaptive calibration model suggests exposure to highly stressful or highly supportive early environments sensitizes the brain to later environmental input. We examined whether family and peer experiences predict neural sensitivity to social cues in 85 adolescent girls who completed a social feedback task during a functional brain scan and an interview assessing adversity. Whole-brain functional connectivity (FC) analyses revealed curvilinear associations between social experiences and FC between the ventral striatum and regions involved in emotion valuation, social cognition, and salience detection (e.g., insula, MPFC, dACC, dlPFC) during social reward processing, such that stronger FC was found at both very high and very low levels of adversity. Moreover, exposure to adversity predicted stronger FC between the amygdala and regions involved in salience detection, social cognition, and emotional memory (e.g., sgACC, precuneus, lingual gyrus, parahippocampal gyrus) during social threat processing. Analyses also revealed some evidence for blunted FC (VS-PCC for reward; amygdala-parahippocampal gyrus for threat) at very high and low levels of adversity. Overall, results suggest social experiences may play a critical role in shaping neural sensitivity to social feedback during adolescence. Future work will need to elucidate the implications of these patterns of neural function for the development of psychopathology.