Search Results (18,182)

Background: Parkinson’s disease (PD) is a progressive neurodegenerative disorder characterized by both motor and non-motor symptoms, including autonomic dysfunction. Structural alterations in the vagus nerve (VN) may contribute to PD pathophysiology, though existing data remain inconsistent. Objective: This study aimed to evaluate morphological changes in the VN using high-resolution ultrasound (USVN) and to investigate associations with autonomic symptoms, heart rate variability (HRV), and clinical characteristics in PD patients. Methods: A cross-sectional study was conducted involving 60 PD patients and 60 age- and sex-matched healthy controls. USVN was performed to assess VN cross-sectional area (CSA), echogenicity, and homogeneity bilaterally. Autonomic symptoms were measured using the Composite Autonomic Symptom Scale 31 (COMPASS-31). HRV parameters—SDNN, RMSSD, and pNN50—were obtained via 24 h Holter monitoring. Additional clinical data included Unified Parkinson’s Disease Rating Scale (UPDRS) scores, transcranial sonography findings, and third ventricle width. Results: PD patients showed significantly reduced VN CSA compared to controls (right: 1.90 ± 0.19 mm² vs. 2.07 ± 0.18 mm²; left: 1.74 ± 0.21 mm² vs. 1.87 ± 0.22 mm²; p < 0.001 and p < 0.02). Altered echogenicity and decreased homogeneity were also observed. Right VN CSA correlated with body weight, third ventricle size, and COMPASS-31 scores. Left VN CSA was associated with body size parameters and negatively correlated with RMSSD (p = 0.025, r = −0.21), indicating reduced vagal tone. Conclusions: USVN detects structural VN changes in PD, correlating with autonomic dysfunction. These findings support its potential as a non-invasive biomarker for early autonomic involvement in PD. Full article

Background/Objective: Sacubitril/Valsartan are a combination drug approved for heart failure treatment, known to enhance natriuretic peptide activity and inhibit the renin–angiotensin–aldosterone system (RAAS). While its clinical efficacy is well-established, its broader impact on human metabolism remains insufficiently characterized. This study aimed to explore the time-resolved metabolic changes induced by Sacubitril/Valsartan in healthy individuals using an untargeted metabolomics approach. Methods: Fourteen healthy male volunteers received a single oral dose of Sacubitril/Valsartan (200 mg; 97.2 mg Sacubitril and 102.8 mg Valsartan) across two phases separated by a two-week washout period. Plasma samples were collected at eight individualized time points based on pharmacokinetic profiles. Metabolites were extracted and analyzed using high-resolution liquid chromatography–mass spectrometry (LC-QToF HRMS). Data processing included peak alignment, annotation via HMDB and METLIN, and statistical modeling through multivariate (PLS-DA, OPLS-DA) and univariate (ANOVA with FDR correction) analyses. Results: Out of 20,472 detected features, 13,840 were retained after quality filtering. A total of 315 metabolites were significantly dysregulated (FDR p < 0.05), of which 31 were confidently annotated as endogenous human metabolites. Among these, key changes were observed in the pyrimidine metabolism pathway, particularly elevated levels of uridine triphosphate (UTP) associated with cellular proliferation and metabolic remodeling. OPLS-DA models demonstrated clear separation between pre-dose and Cmax samples (R²Y = 0.993, Q² = 0.768), supporting the robustness of the time-dependent effects. Conclusions: This is the first study to characterize the dynamic metabolomic signature of Sacubitril/Valsartan in healthy humans. The findings reveal a distinctive perturbation in pyrimidine metabolism, suggesting possible links to drug mechanisms relevant to cardiac cell cycle regulation. These results underscore the utility of untargeted pharmacometabolomics in uncovering systemic drug effects and highlight potential biomarkers for monitoring therapeutic response or guiding precision treatment strategies in heart failure. Full article

Background/Objectives: This randomized controlled trial aimed to evaluate the effects of casein-enriched milk (CEM) consumption and its timing (presleep vs. during the day) in the early postoperative period on body composition, muscle strength, physical function, and biochemical parameters in individuals undergoing laparoscopic sleeve gastrectomy (SG). Methods: Forty-five adults (60% female, 40% male; mean age 35.1 ± 9.7 years; mean BMI 41.4 ± 4.9 kg/m²) undergoing SG were randomly assigned to three groups: (1) 15 g protein CEM (12 g casein) presleep, (2) the same CEM during the day, or (3) standard-protein diet without supplementation. The primary endpoint was change in fat-free mass (FFM) at 12 weeks; secondary endpoints included handgrip strength, 30 s sit-to-stand test, and serum total protein, albumin, and prealbumin. Assessments were performed preoperatively and at weeks 4, 8, and 12. Results: No significant differences were found between the groups in terms of body composition, muscle strength, or physical performance measurements (p > 0.05). However, a significant increase in handgrip strength was observed over time in Groups 1 and 2 (p < 0.05), which was not observed in Group 3. Prealbumin levels at week 12 were 0.3 ± 0.0 mg/dL in Group 1 and 0.2 ± 0.0 mg/dL in Group 2, both higher than 0.2 ± 0.0 mg/dL in Group 3 (p < 0.05). No significant differences were found in albumin and total protein levels (p > 0.05). Conclusions: Early postoperative CEM consumption following SG did not significantly affect body composition or physical performance; however, the higher prealbumin levels indicate that this marker may be more sensitive in detecting early protein response, highlighting its potential clinical relevance in monitoring nutritional status after bariatric surgery. Full article

This study presents the development of a noise-monitoring system for the storage ring at the Siam Photon Source, designed to detect and classify noise patterns in real time using beam position monitor (BPM) data. Noise patterns were categorized into four classes: broad peak, multipeak, normal peak, and no beam. Two BPMs located at the multipole wiggler section, BPM-MPW1 and BPM-MPW2, were selected for detailed monitoring based on consistent noise trends observed across the ring. The dataset was organized in two complementary formats: two-dimensional (2D) images used for training and validating the models and one-dimensional (1D) CSV files containing the corresponding raw numerical signal data. Pre-trained deep learning and 1D convolutional neural network (CNN) models were employed to classify these patterns, achieving an overall classification accuracy of up to 99.83%. The system integrates with the EPICS control framework and archiver log data, enabling continuous data acquisition and long-term analyses. Visualization and monitoring features were developed using CS-Studio/Phoebus, providing both operators and beamline scientists with intuitive tools to track beam quality and investigate noise-related anomalies. This approach highlights the potential of combining beam diagnostics with machine learning to enhance operational stability and optimize the synchrotron radiation performance for user experiments. Full article

Background: The Space Omics and Medical Atlas (SOMA) is an extensive database containing gene expression information from samples collected during the short-duration Inspiration4 spaceflight mission in 2021. Given our prior understanding of the genetic basis for cardiovascular diseases in spaceflight, including orthostatic intolerance and cardiac deconditioning, we aimed to characterize changes in differential gene expression among astronauts using SOMA-derived data and curated cardiovascular pathways. Methods: Using the KEGG 2021 database, we curated a list of genes related to cardiovascular adaptations in spaceflight, focusing on pathways such as fluid shear stress and atherosclerosis, lipid metabolism, arrhythmogenic ventricular hypertrophy, and cardiac muscle contraction. Genes were cross-matched to spaceflight-relevant datasets from the Open Science Data Repository (OSDR). Differential expression analysis was performed using DESeq2 (v1.40.2, R) with normalization by median-of-ratios, paired pre-/post-flight covariates, and log2 fold change shrinkage using apeglm. Differentially expressed genes (DEGs) were defined as |log2FC| ≥ 1 and FDR < 0.05 (Benjamini–Hochberg correction). Module score analyses were conducted across SOMA cell types to confirm conserved cardiac adaptation genes. Results: A total of 185 spaceflight-relevant genes were analyzed. Statistically significant changes were observed in immune-related cardiovascular pathways, particularly within monocytes and T cells. Persistent upregulation of arrhythmogenic genes such as GJA1 was noted at post-flight day 82. WikiPathways enrichment revealed additional pathways, including focal adhesion, insulin signaling, and heart development. Conclusions: Short-duration spaceflight induces significant gene expression changes that are relevant to cardiovascular disease risk. These changes are mediated largely through immune signaling and transcriptional regulation in peripheral blood mononuclear cells. Findings highlight the need for tailored countermeasures and longitudinal monitoring in future long-duration missions. Full article

We aimed to explore SARS-CoV-2 evolution during in vitro neutralisation using next generation sequencing, and to determine whether sera from individuals immunised with two doses of the Pfizer-BioNTech vaccine (BNT162b2) were as effective at neutralising the variant of concern (VOC) Delta (B.1.617.2) compared to the earlier lineages Beta (B.1.351) and wild-type (A.2.2) virus. Using a live-virus SARS-CoV-2 neutralisation assay in Vero E6 cells, we determined neutralising antibody titres (nAbT) against three SARS-CoV-2 strains (wild type, Beta, and Delta) in 14 participants (vaccine-naïve (n = 2) and post-second dose of BNT162b2 vaccination (n = 12)), median age 45 years [IQR 29–65]; the median time after the second dose was 21 days [IQR 19–28]. The determination of nAbT was based on cytopathic effect (CPE) and in-house quantitative reverse transcriptase real-time quantitative polymerase chain reaction (RT-qPCR) to confirm SARS-CoV-2 replication. A total of 110 representative samples including inoculum, neutralisation breakpoints at 72 h, and negative and positive controls underwent genome sequencing. By integrating live-virus neutralisation assays with deep sequencing, we characterised both functional antibody responses and accompanying viral genetic changes. There was a reduction in nAbT observed against the Delta and Beta VOC compared with wild type, 4.4-fold (p ≤ 0.0006) and 2.3-fold (p = 0.0140), respectively. Neutralising antibodies were not detected in one vaccinated immunosuppressed participant and the vaccine-naïve participants (n = 2). The highest nAbT against the SARS-CoV-2 variants investigated was obtained from a participant who was vaccinated following SARS-CoV-2 infection 12 months prior. Limited consensus level mutations occurred in the various SARS-CoV-2 lineage genomes during in vitro neutralisation; however, consistent minority allele frequency variants (MFV) were detected in the SARS-CoV-2 polypeptide, spike (S), and membrane protein. Findings from countries with high COVID-19 incidence may not be applicable to low-incidence settings such as Australia; as seen in our cohort, nAbT may be significantly higher in vaccine recipients previously infected with SARS-CoV-2. Monitoring viral evolution is critical to evaluate the impact of novel SARS-CoV-2 variants on vaccine effectiveness, as mutational profiles in the sub-consensus genome could indicate increases in transmissibility and virulence or suggest the development of antiviral resistance. Full article

Background/Objectives: We identified the factors related to post-intensive care syndrome in a sample of patients from northern Colombia. Methods: This study employed a quantitative, observational, descriptive, and correlational approach. A sample of 277 adults was obtained through non-probabilistic convenience sampling, and a characterization form comprising sociodemographic and clinical variables was applied. The Healthy Aging Brain Care Monitor (HABC-M) instrument was also used, which is a clinical tool with a high capacity to detect post-intensive care syndrome (PICS) in surviving intensive care unit (ICU) patients. Results: The final sample consisted of 277 adults, 67.5% male, with university degrees, cohabiting in a marital union, working, from urban areas, and of the Catholic religion. Seventy percent of the sample presented both cardiovascular and neurological alterations and was admitted to the ICU, and 66% had a personal history of arterial hypertension (AHT) and type 2 diabetes mellitus (DM2). Patients had a mean ICU stay of 10.7 days, with a standard deviation of 4 days, and displayed a moderate risk of morbidity and mortality according to Acute Physiology and Chronic Health Evaluation II (APACHE II). A total of 38.6% of the sample received mechanical ventilation, with a mean duration of 8.3 days, and 7.5% underwent tracheostomy. As for sedation, 38.6% were administered fentanyl. In total, 83.4% of the sample presented the syndromes under study, with a predominance of the severe category. The global score of the scale was taken as the dependent variable, and statistical significance (p < 0.05) was found with sociodemographic variables, including origin and religion, and with clinical variables such as receiving pharmacological treatment. Conclusions: The sample presented PICS globally and showed how it affects the different dimensions, showing associations with the sociodemographic and clinical variables of interest. Full article

Background/Objectives: Fecal peritonitis following penetrating abdominal trauma is a serious condition that often results in sepsis and organ failure. The aim of our study was to develop a novel conscious porcine model of sepsis and organ dysfunction caused by multiple penetrating injuries to the small and large intestines. Methods: Twelve female Yorkshire pigs (average weight 50.6 ± 6.5 kg) were divided into two groups: Penetrating Abdominal Trauma (PAT) (n = 8) and Control (n = 4). All surgical procedures were performed under anesthesia with adequate analgesia. In the PAT group, the small and large intestines were punctured, and feces mixed with saline were introduced into the abdominal cavity to induce peritonitis. The Control group received sham surgery with only saline solution. The animals were observed in a conscious state over a period of 72 h, vital parameters were recorded, and blood samples were taken regularly. We adapted a pig-specific SOFA score and developed pig-specific SIRS criteria and NEWS2 score to assess organ function. The model was validated by independent investigators. Results: The survival rate in the PAT group was 75%, with an average survival time of 58.5 h, while all animals in the Control group survived to euthanasia. Monitoring showed pathophysiological changes, such as tachycardia, leucopenia, and thrombocytopenia, indicative of sepsis and organ dysfunction. Blinded investigators independently confirmed the model’s validity. Conclusions: A new swine model of penetrating abdominal trauma and sepsis has been successfully developed that demonstrates significant physiological and immunologic changes comparable to human sepsis. This new model provides a realistic platform for future research into sepsis, its diagnostics, and the evaluation of therapeutic strategies. Full article

The DHI data is crucial for monitoring the udder health of dairy cows during the breeding process. This study aimed to investigate the factors influencing milk production in dairy cows throughout this period. We analyzed DHI data from Holstein dairy cows in the Heilongjiang region, alongside the incidence of mastitis. The findings revealed that high-yielding cows demonstrated significantly higher peak milk yield days, peak milk yield, urea nitrogen levels, 305-day milk yield, and persistency (p < 0.0001) compared to their low-yielding counterparts. Conversely, high-yielding cows exhibited lower protein rates, fat-to-protein ratios, and milk fat rates (p < 0.0001). Additionally, the somatic cell count (SCC) in high-yielding cows was significantly lower than that in low-yielding cows (p < 0.0001). The multivariate linear regression analysis of the DHI data indicated that parity was the primary determinant affecting both milk yield and SCC. Statistical analysis of cows with clinical mastitis revealed that those experiencing a single episode of clinical mastitis during the lactation period were predominantly in their first and second parities, while recurrent cases were primarily observed in the second and third parities. These results suggest that as the number of lactations increases, the SCC also rises, reflecting the cumulative impact of parity on the udder health of dairy cows. Full article

Very little is known about goose-beaked whales (Ziphius cavirostris) and Sowerby’s beaked whales (Mesoplodon bidens), especially off the western coast of Ireland, due to their elusive behaviors. This study aimed to characterize the acoustics of these beaked whales and investigate whether temporal patterns may affect their occurrences. Using passive acoustic monitoring (PAM), beaked whale bioacoustic clicks were manually analyzed, revealing different click frequency ranges than expected. Double clicks and echoes produced by both beaked whale species were also present, which have previously been infrequently observed in these species. The occurrence of beaked whales and the presence of double clicks and echoes were further investigated, along with how the diel cycle may affect these click characteristics. Hourly presence of goose-beaked whale double clicks and echoes was found to have significance for both day and night. There was no significance found for Sowerby’s beaked whale double clicks and echoes for day and night, along with the hourly occurrences of both beaked whales and the occurrence of other beaked whales. These findings highlight the need for future research on PAM and beaked whale acoustics, which could aid in better monitoring of their presence to address the impacts of human activities. Full article

Since the emergence of SARS-CoV-2, the interplay of human behavior, environmental factors, viral evolution, and public health interventions has resulted in unexpected changes in the timing, intensity, and geography of respiratory virus outbreaks. For example, respiratory syncytial viruses (RSV) exhibited a surge during atypical summer months in several countries. Influenza, on the other hand, nearly vanished in the early years of the pandemic, but returned with unusual strength and altered seasonal patterns. Concurrently, new variants of concern in coronaviruses have demonstrated increased airborne transmissibility, greater resilience to environmental conditions, and the ability to evade both natural and vaccine-induced immunity. In this review article, we have synthesized the current understanding of the aerobiology of respiratory infectious viruses, with a particular emphasis on the paradoxical trends observed in recent years. We examined various aspects, including viral morphology and environmental survivability, shifts in seasonality, the drivers of mutation and resistance, and the impact of environmental and climatic factors. Key issues we explored include viral morphology adaptation in response to airborne selective pressures and climate variability influence on the ecology of airborne viruses. Lastly, we investigated future risks and proposed an interdisciplinary framework for monitoring and mitigating airborne viral threats in an ever-changing world. Full article

Industry 4.0 (I4.0) is reshaping manufacturing by integrating advanced digital technologies and is increasingly seen as an enabler of the circular economy (CE). However, most research treats digitalisation and circularity separately, with limited empirical insight regarding their combined implementation. This study investigates I4.0 adoption to support sustainability and CE across industries, focusing on how enterprise size influences adoption patterns. Based on survey data from 69 enterprises, the research examines which technologies are applied, at what stages of the product life cycle, and what barriers and drivers influence uptake. Findings reveal a modest but growing adoption led by the Internet of Things (IoT), big data, and integrated systems. While larger firms implement more advanced tools (e.g., robotics and simulation), smaller enterprises favour accessible solutions (e.g., IoT and cloud computing). A positive link is observed between digital adoption and CE practices, though barriers remain significant. Five main categories of perceived obstacles are identified: political/institutional, financial, social/market-related, technological/infrastructural, and legal/regulatory. Attitudinal resistance, particularly in micro and small enterprises, emerges as an additional challenge. Based on these insights, and to support the twin transition, the paper proposes targeted policies, including expanded funding, streamlined procedures, enhanced training, and tools for circular performance monitoring. Full article

Desertification poses critical threats to agricultural productivity and socio-economic stability, particularly in vulnerable regions like Thatta and Badin districts of Sindh, Pakistan. Traditional monitoring methods lack the accuracy and temporal resolution needed for effective early warning systems. This study presents a novel Spatio-Temporal Desertification Predictor (STDP) framework that integrates deep learning with next-generation satellite imaging for time-series desertification forecasting. The proposed encoder–decoder architecture combines Convolutional Neural Networks (CNNs) for spatial feature extraction from high-resolution satellite imagery with modified Long Short-Term Memory (LSTM) networks enhanced by multi-head attention to capture temporal dependencies. Environmental variables are fused through an adaptive data integration layer, and hyperparameter optimization is employed to enhance model performance for edge computing deployment. Experimental validation on a 15-year satellite dataset (2010–2024) demonstrates superior performance with MSE = 0.018, MAE = 0.079, and $R^2=0.94$, outperforming traditional CNN-only, LSTM-only, and hybrid baselines by 15–20% in prediction accuracy. The framework forecasts desertification trends through 2030, providing actionable signals for environmental management and policy-making. This work advances the integration of AI with satellite-based Earth observation, offering a scalable path for real-time environmental monitoring in IoT and edge computing infrastructures. Full article

Underwater biomimetic robotic fish are emerging as vital platforms for ocean exploration tasks such as environmental monitoring, biological observation, and seabed investigation, particularly in areas inaccessible to humans. Central to their effectiveness is high-precision fish pose estimation, which enables detailed analysis of swimming patterns and ecological behavior, while informing the design of agile, efficient bio-inspired robots. To address the widespread scarcity of high-quality motion datasets in this domain, this study presents a custom-built dual-camera experimental platform that captures multi-view sequences of carp exhibiting three representative swimming behaviors—straight swimming, backward swimming, and turning—resulting in a richly annotated dataset. To overcome key limitations in existing pose estimation methods, including heavy reliance on labeled data and inadequate modeling of temporal dependencies, a novel Semi-supervised Temporal Context-Aware Network (STC-Net) is proposed. STC-Net incorporates two innovative unsupervised loss functions—temporal continuity loss and pose plausibility loss—to leverage both annotated and unannotated video frames, and integrates a Bi-directional Convolutional Recurrent Neural Network to model spatio-temporal correlations across adjacent frames. These enhancements are architecturally compatible and computationally efficient, preserving end-to-end trainability. Experimental results on the proposed dataset demonstrate that STC-Net achieves a keypoint detection RMSE of 9.71, providing a robust and scalable solution for biological pose estimation under complex motion scenarios. Full article

The extensive distribution of quaternary sediments and the extraction of underground resources in the Yellow River Delta (YRD) have resulted in significant land subsidence, which accelerates relative sea level (RSL) rise and heightens the risk of coastal inundation. This study uses Sentinel-1A (S1A) imagery and the time-series synthetic aperture radar interferometry (TS-InSAR) method to obtain subsidence information for the YRD. By integrating data from groundwater level monitoring wells, hydrogeological conditions, extensometer monitoring, and drilling wells, we analyze the causes of subsidence and the deformation response to the groundwater level changes in the corresponding aquifers. For the first time in the YRD, this study introduces the high accuracy CoastalDEM v2.1 digital elevation model, combined with absolute sea level (ASL) data, to construct a coastal inundation simulation. This simulation maps the land inundation caused by RSL rise along the YRD in different scenarios. The results indicate significant subsidence bowls in coastal and inland regions, primarily attributed to shallow brine and deep groundwater extraction, respectively. The main subsidence layers in inland towns have been identified, and residual deformation has been observed. Currently, land subsidence has caused a maximum elevation loss of 141 mm/yr in coastal YRD areas, significantly contributing to RSL rise. Seawater inundation simulations suggest that if subsidence continues unabated, 12.84% of the YRD region will be inundated by 2100, with 8.74% of the built-up areas expected to be inundated. Compared to global warming-induced ASL rise, ongoing subsidence is the primary driver of inundation in the YRD coastal areas. Full article