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Effects of excess bodyweight and flexible Flatfoot on female stance-phase lower-limb biomechanics assessed by statistical non-parametric mapping
WACSAW: An adaptive, statistical method to classify movement into sleep and wakefulness states
Wearable actimeters can improve our understanding of sleep in the natural environments. Current algorithms may produce inaccuracies in specific individuals and circumstances, such as quiet wakefulness. New hardware allows data collection at higher frequencies enabling sophisticated analytical methods. We have developed a novel statistical algorithm, the Wasserstein Algorithm for Classifying Sleep and Wakefulness (WACSAW), to identify behavioral states from recordings of everyday movement. WACSAW employs optimal transport techniques to identify segments with differing activity variability. Functions characterizing the segments’ movement distributions were clustered into two groups using a k-nearest neighbors and labeled as sleep or wake based on their proximity to an idealized sleep distribution. It returned >95% overall accuracy validated against participant logs in the test data and performed ~10% better than a clinically validated actimetry system. We present the methodology describing how WACSAW results in a novel, individually-tuned, statistical approach to actimetry that improves sleep/wakefulness classification and provides auxiliary information as part of the calculations that can be further related to sleep-relevant outcomes.
Comprehensive genome-wide identification and analysis of MYB transcription factors related to abiotic and biotic stress regulation in rice
Intensive Care Unit admission and long-term survival in older patients after elective major noncardiac surgery: A secondary analysis
Background Intensive care unit (ICU) admission after surgery is an important part of perioperative care but was found not effective in reducing perioperative mortality. This study was designed to test our hypothesis that ICU admission in older patients after elective major noncardiac surgery was not associated with improved long-term survival. Methods This was a secondary analysis of database from a previous trial and long-term follow-up. Patients were analyzed according to whether they were admitted to ICU after surgery or not. We used propensity score-matching to balance baseline, perioperative, and long-term variables and multivariable Cox proportional hazard regression to adjust confounding factors. Our primary endpoint was overall survival. Secondary endpoints included recurrence-free survival and event-free survival. Results A total of 1712 patients (mean age 69.5 years, 65.3% male, 91.9% cancer surgery) completed long-term follow-up (median 74 months) and were included in this secondary analysis. After propensity score matching, 464 patients remained in the matched cohort, with 232 patients in each group. At the end of follow-up, there were 112 deaths of 232 patients (48.3%) who were admitted to the ICU, compared with 111 deaths of 232 patients (47.8%) who were not: adjusted HR 1.08, 95% CI 0.83–1.41, P = 0.573. Secondary endpoints, including recurrence-free survival and event-free survival, also did not differ significantly between groups. Conclusions For older patients after elective major noncardiac surgery mainly for cancer, ICU admission was not associated with improved long-term survival. Studies are required to identify patients who would benefit from postoperative ICU admission regarding long-term outcomes.
Toward the universality of the Caldeira-Leggett oscillator bath as a model for quantum environments I
Is structural equation modeling applicable in a population health determinants assessment? An experience from the European Union
The purpose of this study is to examine the relations between health determinants and overall health within population of the European Union by using the structural equation model, particularly given that not all health-determining factors have been theoretically identified. The significance of this issue stems, among other things, from the fact that the factors determining health are essential for policymakers for implementing an effective and accurate intervention in society, particularly considering that not all such factors have yet been theoretically identified. We hypothesized that (1) There is a statistically significant positive relationship between socioeconomic status and the health of the European Union population, (2) The structural equation model (SEM) is applicable for examining the complex interrelationships between socioeconomic status and health outcomes in the European Union population at the regional level. We used a dataset from EUROSTAT covering finally 258 regions at NUTS 2 in European regions for the year 2022 (or the nearest). We employed the SEM in this study due to its ability to simultaneously analyze multiple variables and latent constructs, thereby minimizing measurement error and enhancing the validity of the findings. So, our research has shown that a better social status of communities in European regions is associated with a higher level of health. Furthermore, a better economic situation of a region significantly improves the health of its inhabitants. However, in general economic factors have a stronger impact on health than social status. Thus, these findings have both theoretical and practical significance, as they identified key modifiable socioeconomic determinants of health and provided valuable insights for shaping effective public health policies and targeted interventions aimed at reducing health inequalities across European regions. Moreover, this study demonstrated the utility of SEM as a robust approach for examining complex relationships among health determinants including direct and indirect effects. By applying SEM, the research aligns methodologically with the growing body of literature in health sciences and contributes to a broader understanding of how socioeconomic factors influence health under varying regional conditions.
Prevalence and impact of long COVID on health-related quality of life in previously hospitalized COVID-19 patients: a 2-year follow-up study
Feasibility study of a novel technique for treating refractory ascites using a peritoneobladder shunt in a swine model
Ascites is often treated through paracentesis, which requires repeated application and poses risks. In this study, we developed a peritoneobladder shunt that allows natural ascites drainage from the peritoneal cavity to the bladder. We conducted an experiment to determine the functional effect of the peritoneobladder shunt in a swine model. Peritoneobladder shunts were developed and placed in 4 swine models to test their effectiveness in draining ascitic fluid from the peritoneal cavity to the bladder. The peritoneobladder shunts were inserted laparoscopically; some models received the shunts with one-way check valves to prevent fluid reflux, while one received peritoneobladder shunts without the valves. After a short (7 days) survival period, experiments were conducted to verify that the peritoneobladder shunts were properly fixed in the bladder. During the survival period, two peritoneobladder shunts could be placed in the bladder wall without rupture or tearing. When the peritoneal cavity was filled with an ascitic fluid substitute, the fluid naturally drained into the bladder; when the peritoneobladder shunts with one-way check valves were used, no reflux occurred from the bladder into the peritoneal cavity. In experiments using swine models, the peritoneobladder shunts effectively drained fluid, and one-way check valves successfully prevented reflux. These findings suggest that the peritoneobladder shunt could be an alternative option to alleviate the burden on patients who require paracentesis, allowing home-based treatment. Further studies are needed to assess the long-term stability and safety of this procedure in humans.
How to get science back into policymaking
Enhanced operational efficacy of direct absorption solar collectors using multi-walled carbon nanotubes/MoS2 nanofluids
Effect of depression and serotonin reuptake inhibitors antidepressant treatment during pregnancy on protein expression in the human placenta: A quantitative proteomics analysis
Depression is one of the most prevalent mental health disorders affecting pregnant individuals, and serotonin reuptake inhibitors (SRIs) are the most prescribed medications to treat depressive symptoms during pregnancy. Both depression and SRI exposure may have developmental impacts, and as randomizing exposures and non-treatment is not feasible in humans, distinguishing the effect of each factor often remains challenging. To date, much of what guides clinical practice stems from reports of pregnant individuals and fetal/infant outcomes, overlooking the significant role of the placenta in maintaining a healthy pregnancy and mother-fetal health. In this study, we explored the effect of depression and SRI antidepressant treatment during pregnancy on the placental proteome. A shotgun proteomics method with liquid chromatography and tandem mass spectrometry (LC-MS/MS) was performed. A cohort of pregnant individuals (n = 82) was recruited in their 2 nd trimester, and clinician-rated mood symptoms measured using the Hamilton Depression Rating Scale (HAM-D) were obtained from them during pregnancy to identify three exposure groups: non-depressed; depressed/non-SRI-treated; depressed/SRI-treated. Differential protein expression and over-representation analyses on the placentas of the group with depression/non-SRI compared with placentas from healthy individuals showed an increase in antioxidant enzymes and various senescence-associated secretory phenotypes (SASP) as well as a decrease in histone proteins. Such protein expression patterns are potentially indicative of placental senescence. Our findings reveal that depression not treated with SRIs is associated with the upregulation of proteins involved in platelet activation, degranulation, coagulation cascade, and amyloid fiber formation in the placenta. In contrast, when comparing placentas from the depressed group treated with SRIs to those with depressive symptoms without SRI treatment, we observed a downregulation of proteins related to senescence, amyloid fiber formation, and platelet activation in the SRI-treated group. This study suggests that treatment with SRIs may prevent the placental alterations observed in antenatal depression, such as placental senescence, platelet activation, and amyloid fiber formation, and, ultimately, pregnancy and fetal outcomes. Further studies are needed to confirm these findings.
Graph-augmented transformer ensemble framework for robust and scalable fake news detection in social media ecosystems
Abstract The recent boom in the spread of false information on social media and web platforms has emerged as a worldwide threat to public opinion, social coherence, and democratic establishments. Traditional fact checking strategies are not sufficient to address the scale and speed of disinformation spreading. So, scalable, automatic, and intelligent fake news detection systems are now in high demand. In this paper, we present a new hybrid model named Graph-Augmented Transformer Ensemble (GETE) for efficient and scalable fake news detection. The primary objective of GETE is to leverage both linguistic and relational features of news spreading by integrating transformer-based language models with graph neural networks (GNNs) with a meta-learned ensemble strategy. The proposed architecture combines the semantic strength of transformer-based models such as BERT (Bidirectional Encoder Representations from Transformers) and RoBERTa (Robustly Optimized BERT Pretraining Approach) with the structure understanding provided by GNNs constructed from user-news interactions and source credibility graphs. The fusion module based on meta-learning is used to train the fusion of these heterogeneous modalities to allow dynamic weighting based on the characteristics of the input data. The combination of deep contextual language understanding and graph-based relational modeling produces synergistic advantages in detection accuracy and generalization. Experimental evaluations on benchmarking datasets FakeNewsNet and LIAR demonstrate GETE’s better performance than existing state-of-the-art methods. Specifically, GETE achieves 96.5% accuracy, 96.5% F1-score, and ROC-AUC of 97.3%, boosting F1-score by 4.2% and AUC by 5.6% over high-performing baseline methods. Additionally, proposed model demonstrates enhanced scalability, explainable predictions, and robustness across diversified domains and source distributions. The integration of the meta-ensemble module facilitates adaptive decision-making, hence enabling enhanced detection performance in real-world noisy situations. “With its high performance, explainability, and scalability, the GETE framework presents a solid foundation for the next generation of reliable and adaptive fake news detection systems.
Thermal insulation of poly(methyl methacrylate) bone cement and hydroxyapatite coatings under induction heating of metal implants
Prosthetic joint infection after joint arthroplasty has serious consequences for patients. Finding new treatments against prosthesis infection is increasingly urgent due to the growing number of prosthetic joints and increasing prevalence of antimicrobial resistance. This study investigates the feasibility of non-contact induction heating to supplement debridement, antibiotics and implant retention using an implant model. Upper temperature limits for induction heating treatment are established, based on the presence or absence of biomaterials commonly associated with joint replacement implants. Titanium grade 5 coupons were heated using an induction device to 50, 70 and 90°C. Heat transfer through poly(methyl methacrylate) bone cement and hydroxyapatite coatings was studied, with poly(acrylic acid) gel phantoms serving as tissue mimic. Thermal doses delivered at the biomaterial-gel interface were quantified. Thermographic images supported the findings. Thermal doses (CEM43) were calculated to estimate damage to human bone tissue. Safe induction temperatures vary by implant configuration. Cemented implants can be heated up to 70–80°C without risk of mechanical failure or patient harm, depending on the thickness of the cement mantle. For uncemented and hydroxyapatite-coated implants, the temperature limit is 50°C. Since temperatures were measured at the metal-biomaterial interface, higher temperatures could be safe for implant sections farther away from the bone. Thus, non-contact induction heating is a safe treatment strategy for prosthetic joint infection in both modalities.
Plasma-based digital PCR assay for early detection of gastric cancer using multiple methylation biomarkers
High BMI-attributed diabetic kidney disease in China versus globally, 1990–2021, with projections to 2045: Divergent trends and an accelerating burden
Objective To analyze the accelerating burden and unique trends of diabetic kidney disease (DKD) attributable to high body mass index (BMI) in China from 1990 to 2021, contextualized against global patterns. Methods Relevant data were extracted from the Global Burden of Disease 2021 study. Trends in disease burden were analyzed using a joinpoint regression model. The disease burdens during the period of 2022–2045 were forecasted using the NORDPRED model. Results From 1990 to 2021, the absolute burden of high BMI-attributable DKD increased dramatically in China, with the number of deaths surging by 355%. While global trends also rose, China exhibited unique characteristics: a disproportionately high burden was observed in men aged 85 and above, a pattern not seen globally. Spatiotemporal analysis revealed China progressed to a higher global burden stratum over the three decades. Projections indicate the number of deaths and DALYs in China will continue to rise steeply through 2045. Conclusion The burden of DKD attributable to high BMI has increased at an alarming rate in China, with distinct demographic vulnerabilities. These findings underscore the urgent need for targeted national public health interventions to mitigate this formidable challenge.
Physical activity attenuates PD-1/PD-L1 expression and enhances CD4⁺ T-cell function in COPD patients
The carbon footprint of tourism businesses in Pavlodar region (Kazakhstan): Baseline assessment and decarbonization hot spots
The study presents the first detailed carbon footprint assessment of tourism businesses in Kazakhstan’s Pavlodar region, aiming to establish a baseline and identify decarbonization pathways that align with the country’s 2060 carbon neutrality target. Using the GHG Protocol methodology, we calculated the footprint for two tourism properties in the Bayanaul district: a year-round holiday home and a seasonal summer hotel. Two pilot sites were selected to be representative of the region’s tourism businesses, exhibiting distinct operational and energy consumption patterns. The analysis revealed distinct emission profiles and significant contributions from indirect sources. The year-round facility, reliant on coal for heating, generated a total of 529 tCO 2 -eq. in З, whereas the seasonal hotel produced 185 tCO 2 -eq. A critical finding for both businesses was the dominance of Scope 3 emissions, which accounted for 57% to 62% of their total footprints, primarily driven by tourist transportation, waste generation, and food procurement. Scope 1 emissions from on-site fuel combustion were significant only for the year-round property, while Scope 2 emissions from purchased electricity were a key factor for both. Based on these findings and guided by the Science-Based Targets initiative (SBTi) and Kazakhstan’s national strategies, such as the Concept for Transition to Green Economy and the Strategy for Achieving Carbon Neutrality by 2060, we propose a decarbonization roadmap. The targets include an absolute emissions reduction of 15% by 2030, 25% by 2040, and 50% by 2050. Key recommendations include phasing out coal by 2050, transitioning to low-carbon electricity by 2035, and implementing strategies to mitigate value chain emissions, including those associated with transportation, nutrition, and waste. This research provides a replicable framework for tourism operators in Kazakhstan and similar regions to measure and manage their environmental impact, contributing to a more sustainable tourism sector.
Development and validation of a motivation scale for deep vein thrombosis prophylaxis in postdischarge hip and knee arthroplasty patients
Targeted virome deep sequencing reveals frequent herpesvirus detection in intestinal biopsies of inflammatory bowel disease patients
Background The intestinal virome is increasingly recognized for its impact on intestinal health and disease. Inflammatory bowel disease (IBD) has been linked to microbial dysbiosis, yet most studies rely on fecal samples. Here, we characterized the mucosa-associated virome directly from intestinal biopsies, providing a more localized view of viral activity at the site of pathology. Methods We conducted a retrospective metagenomic study of 56 residual intestinal biopsy samples from IBD patients including ulcerative colitis (n = 37; 66.1%), IBD-Unclassified (n = 9; 16.1%), ulcerative proctitis (n = 7; 12.5%), and Crohn’s disease (n = 3; 5.4%), applying high-throughput sequencing after viral nucleic acid enrichment using a probe-based capture approach. Metagenomic data were processed using the Chan Zuckerberg ID (CZ ID) platform. Results Viruses were detected in 58.9% (33/56) of the biopsies, primarily members of the Herpesviridae family. EBV was the most frequently detected virus (33.9%), followed by HHV-7 (21.4%), and both CMV and HHV-6 (12.5% each), after decomposing coinfections. Other viruses such as Norovirus and human papillomavirus (HPV) were detected at lower frequencies. Coinfections were also identified. No statistically significant associations were found between viral presence and IBD (ulcerative colitis, Crohn’s disease, ulcerative proctitis, and IBD-Unclassified). Conclusions Herpesviruses are rarely detected in healthy intestinal viromes and are generally considered absent, whereas their frequent presence in IBD biopsies suggests possible pathological relevance. Our findings highlight the value of metagenomic sequencing in characterizing the intestinal virome to assess the diagnostic or prognostic value of viral biomarkers in IBD.