Hasil untuk "Physiology"

Magnus Ross, Nel Swanepoel, Akish Luintel et al.

Timely transition from intravenous (IV) to oral antibiotic therapy shortens hospital stays, reduces catheter-related infections, and lowers healthcare costs, yet one in five patients in England remain on IV antibiotics despite meeting switching criteria. Clinical decision support systems can improve switching rates, but approaches that learn from historical decisions reproduce the delays and inconsistencies of routine practice. We propose using neural processes to model vital sign trajectories probabilistically, predicting switch-readiness by comparing forecasts against clinical guidelines rather than learning from past actions, and ranking patients to prioritise clinical review. The design yields interpretable outputs, adapts to updated guidelines without retraining, and preserves clinical judgement. Validated on MIMIC-IV (US intensive care, 6,333 encounters) and UCLH (a large urban academic UK hospital group, 10,584 encounters), the system selects 2.2-3.2$\times$ more relevant patients than random. Our results demonstrate that forecasting patient physiology offers a principled foundation for decision support in antibiotic stewardship.

Barasa Larsen, Cahyadi Tri, Simanjuntak Marihot et al.

This investigation explores maritime professionals' perspectives on blockchain-enabled agricultural supply chain integration for island community sustainability. Through qualitative analysis of ten experienced maritime graduates with decade-long industry expertise, the research examines professional readiness for technological transformation within traditional shipping operations. Using phenomenological methodology, the study reveals sophisticated professional understanding of blockchain potential for transparency verification, carbon credit integration, and multi- stakeholder coordination, while identifying critical capacity-building needs for successful implementation. Findings show strong recognition of blockchain benefits for supply chain transparency (80% high recognition) and environmental stewardship (80% high integration potential), but reveal significant development needs in blockchain technology literacy (80% high priority) and agricultural supply chain understanding (70% high priority). The research contributes frameworks for maritime education transformation and industry collaboration strategies supporting comprehensive sustainability initiatives.

Seyed Mohammad Mohammadi, Mohammad Lotfi, Naser Kamyari et al.

Background: Obesity and osteoporosis are prevalent global health problems. This study aims to investigate the relationship between bone density and body mass index (BMI) in patients with osteoporosis and osteopenia. Methods: Demographic data, BMI, bone mineral density (BMD), and T-scores of the lumbar spine (L1-L4) and neck of the left femur were collected using the files of individuals who were referred to the Bone Density Measurement Center, Nuclear Medicine Center, Abadan, Iran, from February 2022 to September 2023. The relationship between BMD of the lumbar spine and neck of the left femur and BMI in individuals with osteoporosis, osteopenia, and normal BMD, with varying weight categories ranging from underweight to obese or overweight, was investigated. Results: In this study, 475 people were included in three groups. The mean BMI was higher than normal. In the group with osteoporosis, the BMD of the lumbar spine of the overweight and obese group was higher than the underweight and normal weight groups (P < 0.001). There was a direct significant correlation between BMD of the spine and BMI in the group with osteoporosis (r = 0.389, P < 0.001). A direct and significant correlation was observed between BMI and BMD of the femur (r = 0.296) and between BMI and BMD of the lumbar spine (r = 0.233). Conclusion: BMI and BMD of the neck of the femur and lumbar spine were directly correlated.

E. Bergström, K. Pola, B. Kjellström et al.

Abstract Precapillary pulmonary hypertension (PH) is characterized by increased pulmonary vascular resistance (PVR), with progressively altered right (RV) and left ventricular (LV) hemodynamics and function. Kinetic energy (KE) from 4D flow cardiovascular magnetic resonance (CMR) is a measure of intracardiac hemodynamics. In this observational case–control study, we investigate physiological mechanisms influencing RV‐KE and LV‐KE in PH. Twenty PH patients and 12 healthy controls underwent CMR including cine images and 4D flow. LV contractility was derived from noninvasive pressure‐volume loops, and PVR from right heart catheterization. RV‐KE and LV‐KE were computed for systole, early and late diastolic filling, and indexed to stroke volume (SV). Systolic RV‐KE did not differ between patients and controls. In patients, systolic RV‐KE was associated with RV‐SV but not with PVR, suggesting that the RV may still be able to compensate for the increased afterload. Systolic LV‐KE indexed to LV‐SV, LV contractility, and heart rate were all higher in patients than controls, suggesting sympathetic upregulation as a possible driving mechanism behind increased systolic LV‐KE. LV contractility was negatively associated with systolic LV‐KE and LV‐SV. Late filling KE was increased in both ventricles in patients, suggesting an enhanced importance of the atrial kick to the filling of both ventricles.

Francesco Febbraio, Simona Collina, Christina Lepida et al.

Colour is a fundamental determinant of affective experience in immersive virtual reality (VR), yet the emotional and physiological impact of individual hues remains poorly characterised. This study investigated how fifteen calibrated Munsell hues influence subjective and autonomic responses when presented in immersive VR. Thirty-six adults (18-45 years) viewed each hue in a within-subject design while pupil diameter and skin conductance were recorded continuously, and self-reported emotions were assessed using the Self-Assessment Manikin across pleasure, arousal, and dominance. Repeated-measures ANOVAs revealed robust hue effects on all three self-report dimensions and on pupil dilation, with medium to large effect sizes. Reds and red-purple hues elicited the highest arousal and dominance, whereas blue-green hues were rated most pleasurable. Pupil dilation closely tracked arousal ratings, while skin conductance showed no reliable hue differentiation, likely due to the brief (30 s) exposures. Individual differences in cognitive style and personality modulated overall reactivity but did not alter the relative ranking of hues. Taken together, these findings provide the first systematic hue-by-hue mapping of affective and physiological responses in immersive VR. They demonstrate that calibrated colour shapes both experience and ocular physiology, while also offering practical guidance for educational, clinical, and interface design in virtual environments.

Valeriya Strizhkova, Hadi Kachmar, Hava Chaptoukaev et al.

Human emotions entail a complex set of behavioral, physiological and cognitive changes. Current state-of-the-art models fuse the behavioral and physiological components using classic machine learning, rather than recent deep learning techniques. We propose to fill this gap, designing the Multimodal for Video and Physio (MVP) architecture, streamlined to fuse video and physiological signals. Differently then others approaches, MVP exploits the benefits of attention to enable the use of long input sequences (1-2 minutes). We have studied video and physiological backbones for inputting long sequences and evaluated our method with respect to the state-of-the-art. Our results show that MVP outperforms former methods for emotion recognition based on facial videos, EDA, and ECG/PPG.

Xiao Yang, Jiyao Wang, Yuxuan Fan et al.

Remote physiological measurement (RPM) has emerged as a promising non-invasive method for monitoring physiological signals using the non-contact device. Although various domain adaptation and generalization methods were proposed to promote the adaptability of deep-based RPM models in unseen deployment environments, considerations in aspects such as privacy concerns and real-time adaptation restrict their application in real-world deployment. Thus, we aim to propose a novel fully Test-Time Adaptation (TTA) strategy tailored for RPM tasks in this work. Specifically, based on prior knowledge in physiology and our observations, we noticed not only there is spatio-temporal consistency in the frequency domain of BVP signals, but also that inconsistency in the time domain was significant. Given this, by leveraging both consistency and inconsistency priors, we introduce an innovative expert knowledge-based self-supervised \textbf{C}onsistency-\textbf{i}n\textbf{C}onsistency-\textbf{i}ntegration (\textbf{CiCi}) framework to enhances model adaptation during inference. Besides, our approach further incorporates a gradient dynamic control mechanism to mitigate potential conflicts between priors, ensuring stable adaptation across instances. Through extensive experiments on five diverse datasets under the TTA protocol, our method consistently outperforms existing techniques, presenting state-of-the-art performance in real-time self-supervised adaptation without accessing source data. The code will be released later.

Kieran J. Smith, Tristan C. Endsley, Torin K. Clark

Situation awareness (SA)--comprising the ability to 1) perceive critical elements in the environment, 2) comprehend their meanings, and 3) project their future states--is critical for human operator performance. Due to the disruptive nature of gold-standard SA measures, researchers have sought physiological indicators to provide real-time information about SA. We extend prior work by using a multimodal suite of neurophysiological, psychophysiological, and behavioral signals, predicting all three levels of SA along a continuum, and predicting a comprehensive measure of SA in a complex multi-tasking simulation. We present a lab study in which 31 participants controlled an aircraft simulator task battery while wearing physiological sensors and responding to SA 'freeze-probe' assessments. We demonstrate the validity of task and assessment for measuring SA. Multimodal physiological models predict SA with greater predictive performance ($Q^2$ for levels 1-3 and total, respectively: 0.14, 0.00, 0.26, and 0.36) than models built with shuffled labels, demonstrating that multimodal physiological signals provide useful information in predicting all SA levels. Level 3 SA (projection) was best predicted, and level 2 SA comprehension) was the most challenging to predict. Ablation analysis and single sensor models found EEG and eye-tracking signals to be particularly useful to predictions of level 3 and total SA. A reduced sensor fusion model showed that predictive performance can be maintained with a subset of sensors. This first rigorous cross-validation assessment of predictive performance demonstrates the utility of multimodal physiological signals for inferring complex, holistic, objective measures of SA at all levels, non-disruptively, and along a continuum.

Na Kyung Lee, Woong Bi Jang, Dong Sik Seo et al.

Cardiovascular disease remains a global health concern. Stem cell therapy utilizing human cardiac progenitor cells (hCPCs) shows promise in treating cardiac vascular disease. However, limited availability and senescence of hCPCs hinder their widespread use. To address these challenges, researchers are exploring innovative approaches. In this study, a bioengineered cell culture plate was developed to mimic the natural cardiac tissue microenvironment. It was coated with a combination of extracellular matrix (ECM) peptide motifs and mussel adhesive protein (MAP). The selected ECM peptide motifs, derived from fibronectin and vitronectin, play crucial roles in hCPCs. Results revealed that the Fibro-P and Vitro-P coated plates significantly improved hCPC adhesion, proliferation, migration, and differentiation compared to uncoated plates. Additionally, long-term culture on the coated plates delayed cellular senescence and maintained hCPC stemness. These enhancements were attributed to the activation of integrin downstream signaling pathways. The findings suggest that the engineered ECM peptide motif-MAP-coated plates hold potential for enhancing the therapeutic efficacy of stem cell-based therapies in cardiac tissue engineering and regenerative medicine.

Ek Martin, Godiksen Anita, Arnarson Logi et al.

Luigi Santacroce, Skender Topi, Ioannis Alexandros Charitos et al.

Superantigens, i.e., staphylococcal enterotoxins and toxic shock syndrome toxin-1, interact with T cells in a different manner in comparison to conventional antigens. In fact, they activate a larger contingent of T lymphocytes, binding outside the peptide-binding groove of the major histocompatibility complex class II. Involvement of many T cells by superantigens leads to a massive release of pro-inflammatory cytokines, such as interleukin (IL)-1, IL-2, IL-6, tumor necrosis factor-alpha and interferon-gamma. Such a storm of mediators has been shown to account for tissue damage, multiorgan failure and shock. Besides conventional drugs and biotherapeutics, experiments with natural and biological products have been undertaken to attenuate the toxic effects exerted by superantigens. In this review, emphasis will be placed on polyphenols, probiotics, beta-glucans and antimicrobial peptides. In fact, these substances share a common functional denominator, since they skew the immune response toward an anti-inflammatory profile, thus mitigating the cytokine wave evoked by superantigens. However, clinical applications of these products are still scarce, and more trials are needed to validate their usefulness in humans.

Zdeněk Kejík, Jan Hajduch, Nikita Abramenko et al.

Abstract Mitochondrial dysregulation plays a significant role in the carcinogenesis. On the other hand, its destabilization strongly represses the viability and metastatic potential of cancer cells. Photodynamic and photothermal therapies (PDT and PTT) target mitochondria effectively, providing innovative and non-invasive anticancer therapeutic modalities. Cyanine dyes, with strong mitochondrial selectivity, show significant potential in enhancing PDT and PTT. The potential and limitations of cyanine dyes for mitochondrial PDT and PTT are discussed, along with their applications in combination therapies, theranostic techniques, and optimal delivery systems. Additionally, novel approaches for sonodynamic therapy using photoactive cyanine dyes are presented, highlighting advances in cancer treatment.

Aranyak Chakravarty, Debjit Kundu, Mahesh V. Panchagnula et al.

The need to understand how infection spreads to the deep lung was acutely realized during the Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) pandemic. The challenge of modeling virus laden aerosol transport and deposition in the airways, coupled with mucus clearance, and infection kinetics, became evident. This perspective provides a consolidated view of coupled one-dimensional physics-based mathematical models to probe multifaceted aspects of lung physiology. Successes of 1D trumpet models in providing mechanistic insights into lung function and optimalities are reviewed while identifying limitations and future directions. Key non-dimensional numbers defining lung function are reported. The need to quantitatively map various pathologies on a physics-based parameter space of non-dimensional numbers (a virtual disease landscape) is noted with an eye on translating modeling to clinical practice. This could aid in disease diagnosis, get mechanistic insights into pathologies, and determine patient specific treatment plan. 1D modeling could be an important tool in developing novel measurement and analysis platforms that could be deployed at point-of-care.

Eleonora Lopez, Aurelio Uncini, Danilo Comminiello

Emotion recognition is essential across numerous fields, including medical applications and brain-computer interface (BCI). Emotional responses include behavioral reactions, such as tone of voice and body movement, and changes in physiological signals, such as the electroencephalogram (EEG). The latter are involuntary, thus they provide a reliable input for identifying emotions, in contrast to the former which individuals can consciously control. These signals reveal true emotional states without intentional alteration, thus increasing the accuracy of emotion recognition models. However, multimodal deep learning methods from physiological signals have not been significantly investigated. In this paper, we introduce PHemoNet, a fully hypercomplex network for multimodal emotion recognition from physiological signals. In detail, the architecture comprises modality-specific encoders and a fusion module. Both encoders and fusion modules are defined in the hypercomplex domain through parameterized hypercomplex multiplications (PHMs) that can capture latent relations between the different dimensions of each modality and between the modalities themselves. The proposed method outperforms current state-of-the-art models on the MAHNOB-HCI dataset in classifying valence and arousal using electroencephalograms (EEGs) and peripheral physiological signals. The code for this work is available at https://github.com/ispamm/MHyEEG.

Soundariya Ananthan, Nan Gao, Flora D. Salim

Student engagement plays a vital role in academic success with high engagement often linked to positive educational outcomes. Traditionally, student engagement is measured through self-reports, which are both labour-intensive and not real-time. An emerging alternative is monitoring physiological signals such as Electrodermal Activity (EDA) and Inter-Beat Interval (IBI), which reflect students' emotional and cognitive states. In this research, we analyzed these signals from 23 students wearing Empatica E4 devices in real-world scenarios. Diverging from previous studies focused on lab settings or specific subjects, we examined physiological synchrony at the intra-student level across various courses. We also assessed how different courses influence physiological responses and identified consistent temporal patterns. Our findings show unique physiological response patterns among students, enhancing our understanding of student engagement dynamics. This opens up possibilities for tailoring educational strategies based on unobtrusive sensing data to optimize learning outcomes.

Shamas Ul Ebad Khan, Rejin John Varghese, Panagiotis Kassanos et al.

Space poses significant challenges for humans, leading to physiological adaptations in response to an environment vastly different from Earth. A comprehensive understanding of these physiological adaptations is needed to devise effective countermeasures to support human life in space. This narrative review first focuses on the impact of the environment in space on the musculoskeletal system. It highlights the complex interplay between bone and muscle adaptations and their implications on astronaut health. Despite advances in current countermeasures, such as resistive exercise and pharmacological interventions, they remain partially effective, bulky, and resource-intensive, posing challenges for future missions aboard compact spacecraft. This review proposes wearable sensing and robotic technology as a promising alternative to overcome these limitations. Wearable systems, such as sensor-integrated suits and (soft) exoskeletons, can provide real-time monitoring, dynamic loading, and exercise protocols tailored to individual needs. These systems are lightweight, modular, and capable of operating in confined environments, making them ideal for long-duration missions. In addition to space applications, wearable technologies hold significant promise for terrestrial uses, supporting rehabilitation and assistance for the ageing population, individuals with musculoskeletal disorders, and enhance physical performance in healthy users. By integrating advanced materials, sensors and actuators, and intelligent and energy-efficient control, these technologies can bridge gaps in current countermeasures while offering broader applications on Earth.

Dzhamilya Saralinova, Fursov Victor, Dokhkilgova Diba

At the present stage, one of the scientific problems of research is the study of processes and factors influencing sustainable development. The increasing relevance of methods for achieving sustainable development of the world economy is due to several factors. The fuel and energy complex is not only the backbone of the Russian economy, but also one of the most important elements in ensuring (taking into account climatic conditions) the country’s energy security. The geopolitical factor today forms the energy reality, and this trend implies the growing importance of developing an optimal mechanism for managing all processes in this area. Particular attention in public administration should be given to updating the priority tasks of the state energy policy and energy strategies of the Russian Federation, taking into account the goals of sustainable development. The strategic task today is to protect Russia’s interests in the dynamic system of regulation of world energy markets.

Anatolii V. Pomortsev, Nikolay V. Dorofeev, Svetlana Yu. Zorina et al.

Winter rye has a high adaptive capacity to abiotic and biotic stressors compared to other winter crops (wheat, triticale, barley, and oats). High resistance of winter rye to adverse environmental factors and a wide range of its uses increase interest in this crop. The purpose of this research was to evaluate the adaptive capacity of population and hybrid varieties of winter rye and to identify varieties suitable for the soil and climate conditions of Eastern Siberia. A number of winter rye varieties of various geographical origins were tested during three field seasons. In all the field seasons, the population varieties (Tagna, Mininskaya, and Chulpan) were the most productive and most resistant to adverse environmental factors compared to the hybrid wheat (KWS Aviator, KWS Prommo, and KWS Ravo). Statistically significant (<i>p</i> < 0.001 in 2019/2020 and <i>p</i> < 0.001 in 2021/2022) differences in field survival and yield between the population and hybrid varieties were noted.

A. V. Alieva, A. A. Djalilov, F. A. Khaydarova et al.

BACKGROUND: Since the very first outbreak, scientists have been trying to determine the most critical pathogenetic mechanisms for the development of COVID-19 and related complications, analyze individual subpopulations of patients with chronic diseases and develop optimal tactics to combat not only the infection itself but also its acute and chronic complications.AIM: to assess the COVID-19 course among patients with Type 1 and Type 2 DM.MATERIALS AND METHODS: A retrospective cohort study of Tashkent inhabitants, who had COVID-19 from April to D ecember 2020, was performed. The data were obtained from the single electronic database of registered cases of COVID-19. All data were analyzed using a logistic regression in STATA 17.0 software. Further, the matched case-control study was performed for patients with type 2 DM and no DM based on age, gender, and BMI.RESULTS: Of the 5023 analyzed subjects, 72.63% had no diabetes mellitus (DM), 4.24% had type 1 DM, 15.19% had type 2 DM, and 7.94% was diagnosed with DM during the COVID-19 infection. DM, overweight, and obesity were associated with severe COVID-19; the most significant risk of a severe course was found in persons with type 2 DM. The risk of a lethal outcome and the need for prescription of glucocorticoids did not show a significant association with diabetes in Tashkent. The clinical features of COVID-19 were more common in patients with type 2 DM, especially for shortness of breath, chest pain, and arrhythmia. The persons receiving SU have complained of dyspnea significantly more often than matched patients without DM. Metformin and DPP4i were the groups of drugs that were not associated with significantly increased risk of hospitalization of patients because of COVID-19. The matched case-control study did not reveal statistically significant differences in the disease course severity, need for hospitalization and glucocorticoids, and death depending on the glucose-lowering therapy preceding the onset of COVID-19.CONCLUSION: Diabetes, age and overweight/obesity were associated with severe course of COVID-19 in Tashkent. There was no statistical difference in COVID-19 severity depending on initial glucose-lowering therapy.

Francesco Chiossi, Ekaterina R. Stepanova, Benjamin Tag et al.

Recently, we saw a trend toward using physiological signals in interactive systems. These signals, offering deep insights into users' internal states and health, herald a new era for HCI. However, as this is an interdisciplinary approach, many challenges arise for HCI researchers, such as merging diverse disciplines, from understanding physiological functions to design expertise. Also, isolated research endeavors limit the scope and reach of findings. This workshop aims to bridge these gaps, fostering cross-disciplinary discussions on usability, open science, and ethics tied to physiological data in HCI. In this workshop, we will discuss best practices for embedding physiological signals in interactive systems. Through collective efforts, we seek to craft a guiding document for best practices in physiological HCI research, ensuring that it remains grounded in shared principles and methodologies as the field advances.