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High-strength wear-resistant materials have gained significant attention in current research. molybdenum disulfide (MoS2), as a solid self-lubricating material for reinforcing copper(Cu)-based composites, has become a focal point for researchers. However, the reaction between MoS2 and Cu during high temperature sintering leads to the formation of brittle phase, which compromises the interfacial bond. Therefore, this study attempts to improve the interfacial structure by introducing chromium (Cr) element through CuCr alloying with the expectation of mechanical and tribological properties improvement. Spark plasma sintering (SPS) is employed to prepare MoS2/CuCr composites and the effects of different MoS2 contents (0–10 wt%) under various normal loads on frictional wear will be evaluated. The results demonstrate that Cr element addition facilitates the formation of compact Cr3S4 compounds with MoS2 at the interfaces, enhancing interfacial bonding with Cu and providing a solid foundation for maintaining excellent wear resistance during friction processes. With increasing content of MoS2, both coefficient of friction (COF) and wear rate initially decrease and then show an upward trend. Under the normal load of 20 N, compared with the CuCr composite, the friction coefficient of the composite with 7.5 wt % MoS2 content is 0.281, which is significantly reduced by 37.7 %, and the wear rate is 0.1471( × 10−3 mm3/N.m), which is reduced by 50.9 %. This research content can provide a better theoretical basis for changing the problem of poor wettability between the lubricating phase and the matrix.

Abstract Background With the aging of society, cognitive impairment in elderly people is becoming increasingly common and has caused major public health problems. The screening of cognitive impairment in elderly people and its related influencing factors can aid in the development of relevant intervention and improvement strategies. Methods In this study, stratified random cluster sampling was used to conduct a cross-sectional survey of elderly individuals aged 65 years in Chengdu, Sichuan Province, through an electronic questionnaire from November 2022 to November 2023. Descriptive analysis and logistic regression analysis were used to investigate cognitive impairment and its relevant influencing factors. Results Among the 16,609 elderly people, 7524 (45.3%) were males and 9085 (54.7%) were females, with an average age of 73.6 ± 6.5 years (age range 65–101 years). The average years of education was 5.9 ± 6.2 years, and the proportion of individuals with cognitive impairment was 13.1%. With increasing age, the risk of cognitive impairment increased significantly. The risk factors for cognitive impairment in elderly individuals included advanced age, hypertension, heart disease, diabetes, cerebrovascular disease, depressive symptoms, and anxiety symptoms, while the protective factors included higher education level, married status, and greater life satisfaction. Conclusion Cognitive impairment in elderly individuals in Chengdu is serious. We can intervene in and improve cognitive impairment in elderly people by controlling blood pressure and blood sugar, treating depressive and anxiety symptoms and developing community colleges for elderly people and increasing satisfaction with life.

Abstract Background Advance care planning is an important part of palliative care. Public acceptance is a prerequisite for the widespread development and implementation of advance care planning. However, little is known about the level of public’s acceptance and influencing factors of advance care planning across different life cycles. Methods A cross-sectional study in mainland China was conducted from June 20 to August 31, 2022. We used multi-stage sampling strategy to recruit participators. A stepwise linear regression analysis was used to examine the influencing factors in different life cycles (nonage, mature age, middle age and old age). Results The final sample size was 18,002. The average acceptance score of advance care planning of the public throughout the entire life cycle was 64.03. The average score in nonage was 67.13, which is the highest. The average score in mature age was 63.87, in middle age was 63.51, and in old age was 63.54. Multiple linear stepwise regression results indicated that death education support level, well-being index, neighbor relations, health literacy, family social status, and siblings were influencing factors in nonage. Medical insurance, injury events, multiple properties, death education support level, health literacy, family social status, neighbor relation, social support, family health, media contact, and well-being index were influencing factors in mature age. In middle age, region, living alone, depression, debt, houses, death education support level, health literacy, social support, and family social status were influencing factors. In old age, injury event, death education support level, neighbor relation, well-being index, siblings and children were influencing factors. Conclusions This study is the first to compare the Chinese people with different life cycles. It found that the public’s acceptance and influencing factors of advance care planning varied across different life cycles. Governments and health care personnel should emphasize autonomy and initiate advance care planning based on different life cycles and individual approaches, then introduce appropriate public health policies into newer and broader fields.

Abstract Detecting distributed targets precisely in homogeneous environments has been a hot topic in radar signal processing. Generally, distributed targets are often modelled with subspace models of unknown coordinates, and clutter is modelled as the complex Gaussian distribution with zero mean and unknown covariance matrix, while covariance matrix is estimated with a set of training data without the target signal. However, in practice, the complexity of the external environment makes the training data that satisfy the condition of independent homogeneous distribution less available. Therefore, it is assumed that the covariance matrix of the clutter is persymmetric structure and the approach of dimensionality reduction using subspace transformations is introduced, two detectors based upon generalised likelihood ratio test criterion and Wald test criterion in homogeneous environments are proposed. Theoretical analyses indicate the constant false alarm rate characteristics of the two proposed detectors for unknown clutter covariance matrices. Simulation analyses indicate that the proposed detector works well even with fewer training data samples, and its detection performance outperforms that of existing contrast detectors.

Abstract Fatigue cracking of rib-to-deck conventional single-sided welded joints is a prevalent issue in orthotropic steel decks (OSDs), significantly impacting their structural integrity and durability. Rib-to-deck innovative double-sided welded joints have the potential to enhance the fatigue resistance of OSD. However, Welding Residual Stresses (WRS) significantly influence the fatigue life of these joints, mandating its consideration in fatigue assessments. This study introduces a novel approach for assessing the fatigue reliability of rib-to-deck double-sided welded joints in OSDs, accounting for the effects of traffic vehicle loading and WRS. Initially, a comprehensive fatigue damage equation for welded joints of OSDs was formulated, integrating WRS and dynamic vehicle loads, utilizing fracture mechanics theory. Subsequently, a measurement-based random traffic model was utilized to derive the vehicle-induced stress spectra at the welded joints. The effects of deck thickness, fatigue crack depth and fatigue crack aspect ratio on the stress intensity factor (SIF) of the crack at the weld toe were analyzed. These three variables were considered as feature vectors for the construction of a polynomial model of the shape functions, which was utilized to calculate the SIF. Finally, the fatigue reliability of the rib-to-deck double-sided welded joints in OSDs subject to traffic vehicle loading considering the WRS was estimated using Monte Carlo simulation. The influence of traffic volume growth on fatigue reliability was discussed. This research underscores the critical role of WRS in the fatigue performance of welded joints in OSDs and offers an innovative framework for assessing the fatigue reliability of steel bridge welds.

Abstract Patient smart access and self-triage systems have been in development for decades. As of now, no LLM for processing self-reported patient data has been published by health systems. Many expert systems and computational models have been released to millions. This review is the first to summarize progress in the field including an analysis of the exact self-triage solutions available on the websites of 647 health systems in the USA.

Abstract Objective The objectives of this study were to explore emotional behavioral problems (EBP) associated with learning burnout (LB) among returned migrant middle school students. In addition, the mediating mechanism of internet addiction (IA) and social support (SS) in this relationship was investigated. We also verified the chain-mediating effect of IA and SS on the EBP of returned migrant middle school students and LB. Methods This study used the sample data collected from the “Health Status Survey of Middle School Students in Guizhou Province, China” conducted by the research group. The baseline survey of 36 secondary schools from December 2021 to January 2022 in Guizhou Provine, China. Based on this sample data, middle school students who met the following conditions were selected as study subjects. 2020 returned migrant secondary school students were finally obtained as the subjects of this study. The Strength and Difficulty Questionnaire (SDQ), Adolescent Student Burnout Scale (ASBI), Internet Addiction Test (IAT), and Social Support Scale (SSS) were used to assess EBP, LB, IA, and SS among returned migrant secondary school students. Serial multiple mediation analysis and the bootstrap method were used to investigate the mediation effects of IA and SS (PROCESS model 6). Results Prevalence of EBP was classified in the abnormal (11.9%) or borderline (17.1%) categories based on the total difficulties scores among returned migrant middle school students. A serial multiple mediation model was generated. The mediation effect of IA and SS on the pathway from LB to EBP was 31.25% (direct effect = 0.373, total indirect effect = 0.170). The association between LB and SS was partially mediated by IA (direct effect=-0.356, indirect effect=-0.065). The proposed model fits the data well. Conclusion This study found that lower IA and higher SS can effectively reduce EBP caused by LB among returned migrant middle school students. It is suggested that parents, schools, and government departments pay attention to IA and SS problems caused by LB in returned migrant middle school students, and prevent and avoid EBP. Returned migrant middle school students should take the initiative to identify the factors that harm their physical and mental health for self-regulation, and establish reliable SS, rather than meet psychological needs through IA, to reduce the occurrence of EBP.

Evolutionary Agroecology theory predicts that the relationship between population yield and individual fitness among genotypes of a crop species is unimodal, and experimental evidence supports this. We test the theory further by investigating the role of resource availability on this relationship by comparing growth and reproductive output of three old and three modern cultivars of wheat (Triticum aestivum) in mixture and monocultures grown at three resource levels. The relationship between population grain yield and individual fitness (mean individual grain yield in mixture) of genotypes was resource dependent in a way that is consistent with the theory: when resource levels are low and limit individual growth directly, individual and population yield are positively correlated. When resource levels are high and the growth of individual plants is limited by competition for these resources, the relationship between individual fitness and population yield becomes negative. There was evidence for the unimodal relationship at the intermediate resource level. Old cultivars had higher fitness than newer cultivars at all three resource levels. Old cultivars had higher yields at low resource levels, but the newer cultivars yielded more when resource levels were high. Evaluating individual fitness and population yield in different environments may help wheat breeders to develop locally adapted, cooperative cultivars to increase production across large wheat-producing areas.

Abstract Background Pancreatitis is a digestive system disease that imposes a significant burden on society. However, there is a lack of comprehensive research on the incidence, prevalence, mortality, years of life lost (YLLs), years lived with disability (YLDs), and disability-adjusted life years (DALYs) of pancreatitis, as well as on health inequalities and future trends. Methods Pancreatitis burden data, including the number and age-standardized rates (ASR) of incidence, prevalence, deaths, years of life lost (YLLs), years lived with disability (YLDs), and disability-adjusted life years (DALYs), were collected from the Global Burden of Diseases 2019 (GBD 2019). SDI and HDI were used to analyze the influence of societal development on the burden of pancreatitis in the population. Additionally, the Gini coefficient and the Concentration index were used to assess health inequalities in the burden of pancreatitis. Global population data from 1990 to 2034 was obtained from WHO. Based on the population data and pancreatitis burden data, a prediction model of the burden was constructed to calculate the number and ASR of incidence, prevalence, deaths, YLLs, YLDs, and DALYs from 2019 to 2034 using the BAPC package and the Nordpred package. Results From 1990 to 2019, there has been a decreasing trend in the ASR of incidence, prevalence, deaths, YLLs, YLDs, and DALYs in pancreatitis. However, despite this decline, the number of cases has been on the rise. Furthermore, pancreatitis imposes a higher burden on males in comparison to females, and there exists a negative correlation between pancreatitis burden and both the Social Development Index (SDI) and the Human Development Index (HDI). Additionally, health inequalities have progressively worsened globally between 1990 and 2019, particularly concerning the burden of pancreatitis in countries with low Social Development Index (SDI). Looking to the future, it is projected that the number of deaths and new cases will continue to increase from 2020 to 2034. Conclusions Pancreatitis remains a mounting worldwide burden. In order to alleviate this challenge, preventive strategies should focus on males and middle-aged or older individuals, specifically in countries with a low SDI. Pancreatitis is expected to predominantly impact Eastern Europe, characterized by a high ASR of incidence, and Asia, boasting a substantial population.

Approaches to regenerating bone often rely on integrating biomaterials and biological signals in the form of cells or cytokines. However, from a translational point of view, these approaches are challenging due to the sourcing and quality of the biologic, unpredictable immune responses, complex regulatory paths, and high costs. We describe a simple manufacturing process and a material-centric 3D-printed composite scaffold system (CSS) that offers distinct advantages for clinical translation. The CSS comprises a 3D-printed porous polydiolcitrate-hydroxyapatite composite elastomer infused with a polydiolcitrate-graphene oxide hydrogel composite. Using a micro-continuous liquid interface production 3D printer, we fabricate a precise porous ceramic scaffold with 60 wt% hydroxyapatite resembling natural bone. The resulting scaffold integrates with a thermoresponsive hydrogel composite in situ to fit the defect, which is expected to enhance surface contact with surrounding tissue and facilitate biointegration. The antioxidative properties of citrate polymers prevent long-term inflammatory responses. The CSS stimulates osteogenesis in vitro and in vivo. Within 4 weeks in a calvarial critical-sized bone defect model, the CSS accelerated ECM deposition (8-fold) and mineralized osteoid (69-fold) compared to the untreated. Through spatial transcriptomics, we demonstrated the comprehensive biological processes of CSS for prompt osseointegration. Our material-centric approach delivers impressive osteogenic properties and streamlined manufacturing advantages, potentially expediting clinical application for bone reconstruction surgeries.

The electrochemical reaction mechanism of galvanized high-strength steel wires (GHSSWs) has not been fully elucidated. Scholars have observed a significant discrepancy between the theoretical mass loss rate calculated using the Faraday equation for electrochemical corrosion and the experimental values. To address this issue, this study conducted electrochemical corrosion tests on 195 sets of GHSSWs. Scanning electron microscopy (SEM), energy dispersive X-ray spectroscopy (EDS), X-ray photoelectron spectroscopy (XPS) and non-contact 3D scanning systems were used to characterize the surface morphology and elemental composition of specimens before and after rust removal, revealing the reaction mechanism during electrochemical corrosion in GHSSWs. The correlation between corrosion length, current intensity, and electrolysis time with the corrosion rate was investigated, and a correction function for mass loss in GHSSWs under constant current electrochemical corrosion was established. The results indicate that during the initial stage of electrochemical corrosion in GHSSWs, internal stress between the corrosion products and the GHSSWs substrate leads to the formation of apparent cracks in the corroded wire, increasing the contact area between the specimen and the electrolyte and accelerating the corrosion process. In later stages, the formation of needle-like α-FeOOH corrosion products provides protective effects, leading to a decrease in the corrosion rate.

Abstract Synaptojanin 2 (SYNJ2) has crucial role in various tumors, but its role in papillary thyroid carcinoma (PTC) remains unexplored. This study first detected SYNJ2 protein expression in PTC using immunohistochemistry method and further assessed SYNJ2 mRNA expression through mRNA chip and RNA sequencing data and its association with clinical characteristics. Additionally, KEGG, GSVA, and GSEA analyses were conducted to investigate potential biological functions, while single‐cell RNA sequencing data were used to explore SYNJ2's underlying mechanisms in PTC. Meanwhile, immune infiltration status in different SYNJ2 expression groups were analyzed. Besides, we investigated the immune checkpoint gene expression and implemented drug sensitivity analysis. Results indicated that SYNJ2 is highly expressed in PTC (SMD = 0.66 [95% CI: 0.17–1.15]) and could distinguish between PTC and non‐PTC tissues (AUC = 0.74 [0.70–0.78]). Furthermore, the study identified 134 intersecting genes of DEGs and CEGs, mainly enriched in the angiogenesis and epithelial‐mesenchymal transition (EMT) pathways. Subsequent analysis showed the above pathways were activated in PTC epithelial cells. PTC patients with high SYNJ2 expression showed higher sensitivity to the six common drugs. Summarily, SYNJ2 may promote PTC progression through angiogenesis and EMT pathways. High SYNJ2 expression is associated with better response to immunotherapy and chemotherapy.

Abstract Brain damage caused by acute hypoxia is associated with the physiological activities of mitochondria. Although mitochondria being dynamically regulated, our comprehensive understanding of the response of specific brain cell types to acute hypoxia remains ambiguous. Tumor necrosis factor receptor-associated protein 1 (TRAP1), a mitochondrial-based molecular chaperone, plays a role in controlling mitochondrial movements. Herein, we demonstrated that acute hypoxia significantly alters mitochondria morphology and functionality in both in vivo and in vitro brain injury experiments. Summary-data-based Mendelian Randomization (SMR) analyses revealed possible causative links between mitochondria-related genes and hypoxia injury. Advancing the protein-protein interaction network and molecular docking further elucidated the associations between TRAP1 and mitochondrial dynamics. Furthermore, it was shown that TRAP1 knockdown levels variably affected the expression of key mitochondrial dynamics proteins (DRP1, FIS1, and MFN1/2) in primary hippocampal neurons, astrocytes, and BV-2 cell, leading to changes in mitochondrial structure and function. Understanding the function of TRAP1 in altering mitochondrial physiological activity during hypoxia-induced acute brain injury could help serve as a potential therapeutic target to mitigate neurological damage.

Abstract The trends of diversified diets in China have generated a growing number of nutritional health problems, and healthcare investment is bearing a large burden from the diets, hindering the economic progress of the country and its residents toward affluence. This study linked the evolution of the Chinese dietary structure to changes in health expenditures and predicts future dietary patterns and their impact on health costs over the next 30 years. We found that in the past 30 years, the Chinese dietary structure has shifted towards a nutritional surplus type, and the structure of health expenditures has also shown a trend of two increases and one decrease. The consumption of plant-based foods is significant correlated with lower health expenditures, and animal-based foods show a significant impact on increase of health expenditures. Among nutrients, fat is significant correlated with increased health expenditures, whereas calorie intake is significant correlated with lower health expenditures. By 2030, the Chinese dietary structure will still evolve to a high-protein and high-fatty type. This shift will result in a decrease in per capita healthcare expenditure by 41.66 yuan and increases in household, state, personal, and total healthcare expenditures by 76.83 yuan, 18.76 billion yuan, 95.28 billion yuan, and 17.67 billion yuan, respectively. These findings demonstrate that adjusting dietary structures will bring the dual benefits of improved national health status and a favorable cycle of health expenditures.

Abstract Exploring efficient and easily implementable prenatal screening strategies aims at birth defect prevention and control. However, there have been limited economic evaluations of non-invasive prenatal screening (NIPS) strategies in China. Furthermore, these studies were predominantly confined to local or geographically proximate provinces and lacked universality and representativeness. This study assesses the health economics of current prenatal screening strategies and NIPS as first-line screening programs, analyzing their efficacy to determine an optimal strategy. From the perspective of health economics, cost-effectiveness, cost-benefit, and single-factor sensitivity were conducted for five different screening strategies using a decision tree model. Among pregnant women aged

Abstract Personal and family history of suicidal thoughts and behaviors (PSH and FSH, respectively) are significant risk factors associated with suicides. Research is limited in automatic identification of such data from clinical notes in Electronic Health Records. This study developed deep learning (DL) tools utilizing transformer models (Bio_ClinicalBERT and GatorTron) to detect PSH and FSH in clinical notes derived from three academic medical centers, and compared their performance with a rule-based natural language processing tool. For detecting PSH, the rule-based approach obtained an F1-score of 0.75 ± 0.07, while the Bio_ClinicalBERT and GatorTron DL tools scored 0.83 ± 0.09 and 0.84 ± 0.07, respectively. For detecting FSH, the rule-based approach achieved an F1-score of 0.69 ± 0.11, compared to 0.89 ± 0.10 for Bio_ClinicalBERT and 0.92 ± 0.07 for GatorTron. Across sites, the DL tools identified more than 80% of patients at elevated risk for suicide who remain undiagnosed and untreated.

ObjectiveAnimal models of nerve injury are important for studying nerve injury and repair, particularly for interventions that cannot be studied in humans. However, the vast majority of gait analysis in animals has been limited to univariate analysis even though gait data is highly multi-dimensional. As a result, little is known about how various spatiotemporal components of the gait relate to each other in the context of peripheral nerve injury and trauma. We hypothesize that a multivariate characterization of gait will reveal relationships among spatiotemporal components of gait with biological relevance to peripheral nerve injury and trauma. We further hypothesize that legitimate relationships among said components will allow for more accurate classification among distinct gait phenotypes than if attempted with univariate analysis alone.MethodsDigiGait data was collected of mice across groups representing increasing degrees of damage to the neuromusculoskeletal sequence of gait; that is (a) healthy controls, (b) nerve damage only via total nerve transection + reconnection of the femoral and sciatic nerves, and (c) nerve, muscle, and bone damage via total hind-limb transplantation. Multivariate relationships among the 30+ spatiotemporal measures were evaluated using exploratory factor analysis and forward feature selection to identify the features and latent factors that best described gait phenotypes. The identified features were then used to train classifier models and compared to a model trained with features identified using only univariate analysis.Results10-15 features relevant to describing gait in the context of increasing degrees of traumatic peripheral nerve injury were identified. Factor analysis uncovered relationships among the identified features and enabled the extrapolation of a set of latent factors that further described the distinct gait phenotypes. The latent factors tied to biological differences among the groups (e.g. alterations to the anatomical configuration of the limb due to transplantation or aberrant fine motor function due to peripheral nerve injury). Models trained using the identified features generated values that could be used to distinguish among pathophysiological states with high statistical significance (p < .001) and accuracy (>80%) as compared to univariate analysis alone.ConclusionThis is the first performance evaluation of a multivariate approach to gait analysis and the first demonstration of superior performance as compared to univariate gait analysis in animals. It is also the first study to use multivariate statistics to characterize and distinguish among different gradations of gait deficit in animals. This study contributes a comprehensive, multivariate characterization pipeline for application in the study of any pathologies in which gait is a quantitative translational outcome metric.

IntroductionThough COVID-19 as a public health emergency of international concern (PHEIC) was declared to be ended by the WHO, it continues to pose a significant threat to human society. Vaccination remains one of the most effective methods for preventing COVID-19. While most of the antigenic regions are found in the receptor binding domain (RBD), the N-terminal domain (NTD) of the S protein is another crucial region for inducing neutralizing antibodies (nAbs) against COVID-19.MethodsIn the two-dose immunization experiment, female BALB/c mice were intramuscularly immunized with different ratios of RBD-Fc and NTD-Fc proteins, with a total protein dose of 8 μg per mouse. Mice were immunized on day 0 and boosted on day 7. In the sequential immunization experiment, groups of female BALB/c mice were immunized with two doses of the inactivated SARS-CoV-2 vaccine (prototype strain) on day 0 and 7. On day 28, mice were boosted with RBD-Fc, NTD-Fc, RBD-Fc/NTD-Fc (9:1), RBD-Fc/NTD-Fc (3:1), inactivated SARS-CoV-2 vaccine (protoype strain), inactivated SARS-CoV-2 vaccine (omicron strain), individually. The IgG antibodies were detected using ELISA, while the neutralizing antibodies were measured through a microneutralization assay utilizing both the prototype and omicron strains. The ELISPOT assays were performed to measure the secretion of IL-4 and IFN-γ, and the concentrations of secreted IL-2 and IL-10 in the supernatants were measured by ELISA.ResultsWe have first developed a two-component recombinant vaccine for COVID-19 based on RBD-Fc and NTD-Fc proteins, with an optimal RBD-Fc/NTD-Fc ratio of 3:1. This novel two-component vaccine demonstrated the ability to induce durable and potent IgG antibodies, as well as the neutralizing antibodies in both the two-dose homologous and sequential vaccinations. Heterologous booster with this two-component vaccine could induce higher neutralizing antibody titers than the homologous group. Additionally, the vaccine elicited relatively balanced Th1- and Th2-cell immune responses.ConclusionThis novel two-component recombinant vaccine exhibits high immunogenicity and offers a potential booster strategy for COVID-19 vaccine development.

The conventional water filtration approach for collecting environmental DNA (eDNA) has critical limitations. The collection of eDNA via passive eDNA samplers (PEDS) has been proposed as an alternative to the water filtration method. Here, we developed a novel and rapid eDNA sampling approach and evaluated the extent to which this method enhances eDNA sampling efficiency. We drove boats along transects across nine large natural lakes and deployed PEDS either by briefly submerging them at each sampling location (“angling”) or towing them in the water (“trolling”). One liter of water was also collected at each location and processed via the filtration method. Fish biodiversity was determined by metabarcoding analysis of eDNA extracts. Despite a short total submersion time (42–66 min of “angling”) and substantially fewer samples (PEDS: 3–6 samples; filtration: 21–33 samples per lake), PEDS generally detected more fish species in each lake as well as per sample compared with filtration. Detection probabilities for fish species were significantly higher for PEDS compared with the filtration method. PEDS are also superior to the filtration method since sampling requires less equipment, labor, time, and costs. Our innovative sampling strategy is thus effective and could be used for the eDNA biomonitoring of large water systems.

Abstract BackgroundHealth-related rumors and misconceptions are spreading at an alarming rate, fueled by the rapid development of the internet and the exponential growth of social media platforms. This phenomenon has become a pressing global concern, as the dissemination of false information can have severe consequences, including widespread panic, social instability, and even public health crises. ObjectiveThe aim of the study is to compare the accuracy of rumor identification and the effectiveness of health science popularization between 2 generated large language models in Chinese (GPT-4 by OpenAI and Enhanced Representation through Knowledge Integration Bot [ERNIE Bot] 4.0 by Baidu). MethodsIn total, 20 health rumors and misconceptions, along with 10 health truths, were randomly inputted into GPT-4 and ERNIE Bot 4.0. We prompted them to determine whether the statements were rumors or misconceptions and provide explanations for their judgment. Further, we asked them to generate a health science popularization essay. We evaluated the outcomes in terms of accuracy, effectiveness, readability, and applicability. Accuracy was assessed by the rate of correctly identifying health-related rumors, misconceptions, and truths. Effectiveness was determined by the accuracy of the generated explanation, which was assessed collaboratively by 2 research team members with a PhD in nursing. Readability was calculated by the readability formula of Chinese health education materials. Applicability was evaluated by the Chinese Suitability Assessment of Materials. ResultsGPT-4 and ERNIE Bot 4.0 correctly identified all health rumors and misconceptions (100% accuracy rate). For truths, the accuracy rate was 70% (7/10) and 100% (10/10), respectively. Both mostly provided widely recognized viewpoints without obvious errors. The average readability score for the health essays was 2.92 (SD 0.85) for GPT-4 and 3.02 (SD 0.84) for ERNIE Bot 4.0 (PP ConclusionsERNIE Bot 4.0 demonstrated similar accuracy to GPT-4 in identifying Chinese rumors. Both provided widely accepted views, despite some inaccuracies. These insights enhance understanding and correct misunderstandings. For health essays, educators can learn from readable language styles of GLLMs. Finally, ERNIE Bot 4.0 aligns with Chinese expression habits, making it a good choice for a better Chinese reading experience.

Background/aim: Although some heavy metals and alcohol consumption are known to have adverse effects on neurobehavioral symptoms, studies on the relationship between exposure to multiple metals and interaction between these factors are limited. In this study, we aimed to explore how multiple exposure to heavy metals with drinking habit in affecting depression using the National Health and Nutrition Examination Survey (NHANES) data. Methods: Data from the U.S. NHANES between 2007 and 2014 were used to examine the cross-sectional relationships between heavy metal exposure and depression in adult over 20 years. After applying the exclusion criteria, 6021 subjects were included in the final analysis. We used four urinary metals, including mercury (Hg), cadmium (Cd), lead (Pb), and arsenic (As), as exposure variables. The Patient Health Questionnaire (PHQ-9) was used to assess the depression symptoms of the participants. Multivariate linear regression (MLR) and quantile g-computation models were applied to investigate the effects of individual and multiple heavy metal exposures on depression, respectively. We also performed stratified analysis according to the alcohol habit of the participants. Results: The MLR models revealed that urinary Cd was positively associated with a continuous depression score (β = 0.39, 95 % confidence interval (CI): 0.24–0.53). Meanwhile, other urinary metals showed an insignificant positive relationship with depression. In quantile g-computation model, statistically significant positive relationship was observed between urinary heavy metal mixture and depression score (difference in PHQ-9 score increase = 0.32, 95 % CI: 0.14–0.50). When the model was stratified by drinking habit, a stronger relationship was observed in the heavy drinker group. Conclusions: Comparing the results from different models, both individual urinary Cd and all the heavy metal mixtures were positively associated with depression. This association was stronger among those with heavy drinking habits. Future cohort studies are needed to confirm these associations and to clarify the causal relationship.

Objective: To generate data-driven phenogroups of cardiac structure and function based on echocardiographic measures assessed in asymptomatic middle-aged adults free of CVD, and examine associations between these newly defined phenogroups and incident premature cardiovascular disease (CVD). Methods: Data were analyzed from participants in the Coronary Artery Risk Development in Young Adults (CARDIA) cohort study free of CVD who underwent an echocardiogram at the Year 25 (2010-2011) in-person examination. Continuous echocardiographic measures of left heart structure, left ventricular systolic function (including strain) and diastolic function, right ventricular systolic function, and hemodynamic measures were included in latent class analysis to generate novel phenogroups. Associations between data-driven phenogroups and risk of premature CVD (coronary artery disease, stroke, or heart failure) were estimated using Cox proportional hazards regression adjusted for traditional CVD risk factors. Results: Among 3361 participants, mean (standard deviation) age was 50.1 (3.6) years, 57% were female, and 46% were non-Hispanic Black. Three overall phenogroups were identified and labeled as: (1) optimal cardiac mechanics (36.2%); (2) suboptimal systolic function (38.2%); and (3) suboptimal diastolic function (25.6%). Over a median 8.9 years of follow-up, 121 premature CVD events occurred. Risk of CVD was higher in the suboptimal diastolic function group (unadjusted hazard ratio [HR] 4.08 [95% CI: 2.48, 6.71] and adjusted HR 1.95 [1.12, 3.40]) compared with the optimal group. The suboptimal systolic function group had a higher unadjusted risk of CVD (1.86 [1.10, 3.15]), which was attenuated after adjustment for CVD risk factors (1.36 [0.79, 2.36]). Conclusions and relevance: Unbiased, data-driven clustering of echocardiographic measures in middle-aged adults identified distinct patterns of cardiac remodeling that were associated with risk of premature CVD. Premature CVD risk was highest with the pattern of suboptimal diastolic function. This suggests potential utility of a composite echocardiography-based index for prioritizing prevention strategies earlier in the life course.

There are various types of initial defects in weld joints of orthotropic steel deck, which significantly impact the fatigue crack growth behavior of the welds. The traditional single-crack growth model is not appropriate to be utilized for the fatigue investigation of welds containing multiple defects. This study analyzed the dynamic evolution behavior of crack-inclusion interactions in the weld toe of rib-to-deck joints based on the fracture mechanics theory. The influence of defect-related parameters on crack fatigue life was discussed. Numerical simulations and fatigue test were conducted to investigate the dynamic interaction behavior of the crack-inclusion under fatigue loading. A merging criterion for multiple inclusions was proposed to simply the fatigue life analysis of coupled cracks. The numerical results show that the stiff inclusion has a shielding effect on the crack growth. The effect is affected by the relative depth of the crack and inclusion (a0/R), the interaction spacing of defects (S), and the burial depth of the inclusion (h). The shielding effect becomes negligible under the condition a0/R≥3. The fatigue life of the interactive crack increases by up to 23 % compared to single crack. Fully buried inclusions have a limited impact on crack growth. The fatigue life of the interactive crack shows a non-monotonic increase with the quantities of inclusions, with double inclusion clusters. It is demonstrated that the highest shielding effect will increase the fatigue life by 26 %. The proposed merging criterion simplifies the prediction of the fatigue life for welded joint containing multiple inclusions. The numerical result provides a referencing database for fatigue life evaluation of steel structures with multiple welding defects.

Systemic lupus erythematosus (SLE) is an autoimmune inflammatory tissue disease. In view of the explosive growth in research on SLE, bibliometrics was performed to evaluate the 100 top-cited papers in this realm. We performed the search with terms “systemic lupus erythematosus” the Web of Science Core Collection database on May 3, 2023. Relevant literatures were screened. Data were extracted and analyzed by SPSS. The citations of 100 top-cited SLE studies spanned from 472 to 13,557. Most studies (60 out of 100) were conducted in the United States. Total citation times were positively associated with ACY, which was negatively correlated with the length of time since publication. Approximately half of the studies focused on the underlying mechanisms of SLE. New biologic therapies garnered attention and development. Our findings provide valuable insights into the developments in crucial areas of SLE and shed contributions to future studies.

Background Minichromosome maintenance complex component 3 (MCM3) plays a key role in various tumours. However, it remains largely unknown what the specific role and clinical significance of MCM3 in pancreatic adenocarcinoma (PAAD) are.Materials and Methods We integrated high-throughput data from PAAD worldwide to analyse the expression level of MCM3 mRNA. We used immunohistochemistry to analyse MCM3 protein expression levels in 145 cases in the PAAD group and 29 cases in the non-PAAD group. We also mainly analysed the necessity of MCM3 for PAAD growth based on CRISPR screen data. In addition, we used enrichment analysis and protein–protein interaction networks to explore the molecular mechanism of MCM3 in PAAD. We also analysed the correlation between MCM3 expression, components of the immune microenvironment in PAAD tissue and clinical prognosis.Results In PAAD, we observed for the first time that MCM3 was significantly highly expressed at both the mRNA (SMD = 0.67, 95% CI: 0.38 ∼ 0.96) and the protein level (p

Abstract Spatially resolved transcriptomics integrates high-throughput transcriptome measurements with preserved spatial cellular organization information. However, many technologies cannot reach single-cell resolution. We present STdGCN, a graph model leveraging single-cell RNA sequencing (scRNA-seq) as reference for cell-type deconvolution in spatial transcriptomic (ST) data. STdGCN incorporates expression profiles from scRNA-seq and spatial localization from ST data for deconvolution. Extensive benchmarking on multiple datasets demonstrates that STdGCN outperforms 17 state-of-the-art models. In a human breast cancer Visium dataset, STdGCN delineates stroma, lymphocytes, and cancer cells, aiding tumor microenvironment analysis. In human heart ST data, STdGCN identifies changes in endothelial-cardiomyocyte communications during tissue development.

Abstract Background Stroke is a globally dangerous disease capable of causing irreversible neuronal damage with limited therapeutic options. Meldonium, an inhibitor of carnitine-dependent metabolism, is considered an anti-ischemic drug. However, the mechanisms through which meldonium improves ischemic injury and its potential to protect neurons remain largely unknown. Methods A rat model with middle cerebral artery occlusion (MCAO) was used to investigate meldonium’s neuroprotective efficacy in vivo. Infarct volume, neurological deficit score, histopathology, neuronal apoptosis, motor function, morphological alteration and antioxidant capacity were explored via 2,3,5-Triphenyltetrazolium chloride staining, Longa scoring method, hematoxylin and eosin staining, terminal deoxynucleotidyl transferase-mediated dUTP-biotin nick end labeling assay, rotarod test, transmission electron microscopy and Oxidative stress index related kit. A primary rat hippocampal neuron model subjected to oxygen–glucose deprivation reperfusion was used to study meldonium’s protective ability in vitro. Neuronal viability, mitochondrial membrane potential, mitochondrial morphology, respiratory function, ATP production, and its potential mechanism were assayed by MTT cell proliferation and cytotoxicity assay kit, cell-permeant MitoTracker® probes, mitochondrial stress, real-time ATP rate and western blotting. Results Meldonium markedly reduced the infarct size, improved neurological function and motor ability, and inhibited neuronal apoptosis in vivo. Meldonium enhanced the morphology, antioxidant capacity, and ATP production of mitochondria and inhibited the opening of the mitochondrial permeability transition pore in the cerebral cortex and hippocampus during cerebral ischemia–reperfusion injury (CIRI) in rats. Additionally, meldonium improved the damaged fusion process and respiratory function of neuronal mitochondria in vitro. Further investigation revealed that meldonium activated the Akt/GSK-3β signaling pathway to inhibit mitochondria-dependent neuronal apoptosis. Conclusion Our study demonstrated that meldonium shows a neuroprotective function during CIRI by preserving the mitochondrial function, thus prevented neurons from apoptosis.

Abstract In recent years, two-dimensional (2D) van der Waals materials have emerged as a focal point in materials research, drawing increasing attention due to their potential for isolating and synergistically combining diverse atomic layers. Atomically thin transition metal dichalcogenides (TMDs) are one of the most alluring van der Waals materials owing to their exceptional electronic and optical properties. The tightly bound excitons with giant oscillator strength render TMDs an ideal platform to investigate strong light-matter coupling when they are integrated with optical cavities, providing a wide range of possibilities for exploring novel polaritonic physics and devices. In this review, we focused on recent advances in TMD-based strong light-matter coupling. In the foremost position, we discuss the various optical structures strongly coupled to TMD materials, such as Fabry-Perot cavities, photonic crystals, and plasmonic nanocavities. We then present several intriguing properties and relevant device applications of TMD polaritons. In the end, we delineate promising future directions for the study of strong light-matter coupling in van der Waals materials.

Abstract Missed appointments, or no-shows, disrupt healthcare delivery, exacerbating chronic disease management and leading to worse health outcomes. Telehealth has surged as a viable solution to reduce no-shows and improve healthcare accessibility, especially during the COVID-19 pandemic. However, telehealth disparities and its long-term efficacy across various medical specialties remain understudied. To address this, we performed a retrospective analysis of electronic health records from a heterogenous network of hospitals in Illinois, examining telehealth use and no-shows across among 444,752 adult patients with 1,973,098 outpatient encounters across nine specialties during the sustained pandemic phase (i.e., January 1, 2021 to July 1, 2022). Among them, 84,290 (4.27%) were no-shows, and telehealth constituted 202,933 (10.3%) of the total encounters. Telehealth use during the sustained phase varied significantly by specialty type. Overall, telehealth encounters were associated with reduced no-show odds compared to in-person encounters (OR, 0.28; 95% CI, 0.26–0.29). Black and Hispanic patients, as well as those with Medicaid, had higher no-show odds relative to their counterparts, even when using telehealth. Mental health specialty had the highest telehealth usage rate and the highest no-show odds (OR, 2.99; 95% CI, 2.84–3.14) relative to other specialties included in the study. Moreover, specialty type had differential effects on no-shows for telehealth. These results underscore the variability in telehealth use by specialty type and pervasive disparities telehealth use and no-shows. As we move beyond the pandemic, our findings can inform policymakers to tailor policies and incentives to reach different patient groups as well as specialties, with varying needs, to promote equitable telehealth utilization.

The food concept of "healthy low-fat" had attracted significant public attention in recent years. Oxidized konjac glucomannan (OKGM) was introduced as a fat replacer to address the issue of low gel strength in low-fat yoghurt. The effects of different oxidation degrees of OKGM on the quality of yoghurt were investigated by analyzing the physicochemical properties, texture, rheological properties and microstructure of yoghurt. The research demonstrated that yoghurt containing high oxidation degrees of OKGM (OK-60, OK-90, OK-120) increased the hardness from 375.90 g to 436.67 g and the water holding capacity from 28.03% to 31.89% compared to yoghurt without added OKGM. In addition, it improved the rheological properties of yoghurt including apparent viscosity and viscoelasticity. The results of scanning electron microscopy and confocal laser microscopy revealed that high oxidation degrees of OKGM reduced integral pores in yoghurt, promoted casein micelles aggregation, and increased average particle size from 88.27 nm to 125.33 nm. In contrast, natural konjac glucomannan (KGM) and lower levels of OKGM oxidation (OK-30) reduced texture, apparent viscosity, and viscoelasticity of the yoghurt, disrupted yoghurt gel structure and compromised its stability. The results of sensory evaluation showed that high oxidation levels improved the glossiness, uniformity, and overall taste of yoghurt, providing a theoretical basis for the development of high-quality, low-fat, and healthy yoghurt products.

Abstract Background Acute hypobaric hypoxia-induced brain injury has been a challenge in the health management of mountaineers; therefore, new neuroprotective agents are urgently required. Meldonium, a well-known cardioprotective drug, has been reported to have neuroprotective effects. However, the relevant mechanisms have not been elucidated. We hypothesized that meldonium may play a potentially novel role in hypobaric hypoxia cerebral injury. Methods We initially evaluated the neuroprotection efficacy of meldonium against acute hypoxia in mice and primary hippocampal neurons. The potential molecular targets of meldonium were screened using drug-target binding Huprot™ microarray chip and mass spectrometry analyses after which they were validated with surface plasmon resonance (SPR), molecular docking, and pull-down assay. The functional effects of such binding were explored through gene knockdown and overexpression. Results The study clearly shows that pretreatment with meldonium rapidly attenuates neuronal pathological damage, cerebral blood flow changes, and mitochondrial damage and its cascade response to oxidative stress injury, thereby improving survival rates in mice brain and primary hippocampal neurons, revealing the remarkable pharmacological efficacy of meldonium in acute high-altitude brain injury. On the one hand, we confirmed that meldonium directly interacts with phosphoglycerate kinase 1 (PGK1) to promote its activity, which improved glycolysis and pyruvate metabolism to promote ATP production. On the other hand, meldonium also ameliorates mitochondrial damage by PGK1 translocating to mitochondria under acute hypoxia to regulate the activity of TNF receptor-associated protein 1 (TRAP1) molecular chaperones. Conclusion These results further explain the mechanism of meldonium as an energy optimizer and provide a strategy for preventing acute hypobaric hypoxia brain injury at high altitudes.

Abstract Alzheimer’s disease (AD) is a serious brain disorder characterized by the presence of beta-amyloid plaques, tau pathology, inflammation, neurodegeneration, and cerebrovascular dysfunction. The presence of chronic neuroinflammation, breaches in the blood-brain barrier (BBB), and increased levels of inflammatory mediators are central to the pathogenesis of AD. These factors promote the penetration of immune cells into the brain, potentially exacerbating clinical symptoms and neuronal death in AD patients. While microglia, the resident immune cells of the central nervous system (CNS), play a crucial role in AD, recent evidence suggests the infiltration of cerebral vessels and parenchyma by peripheral immune cells, including neutrophils, T lymphocytes, B lymphocytes, NK cells, and monocytes in AD. These cells participate in the regulation of immunity and inflammation, which is expected to play a huge role in future immunotherapy. Given the crucial role of peripheral immune cells in AD, this article seeks to offer a comprehensive overview of their contributions to neuroinflammation in the disease. Understanding the role of these cells in the neuroinflammatory response is vital for developing new diagnostic markers and therapeutic targets to enhance the diagnosis and treatment of AD patients.

High-entropy alloys (HEAs) are of more chemical inhomogeneity than traditional alloys. How thermal effects perturb entropy and chemical fluctuations is a major scientific issue. In this study, selected benchmark phenomena revealed by in-situ synchrotron X-ray mapping demonstrates how local chemical gradients control temperature-dependent behaviors in HEAs. Specifically, the gradient chemical layers in the core/shell structure play an important role in the thermally-induced phase transformation of Cu15Ni35Ti50-x(HfZr)x high-entropy shape-memory alloys (HESMAs). The dendritic microstructure in Cu15Ni35Ti20(HfZr)30 exhibits the mixing enthalpy-driven pronounced composition inhomogeneity of an Ni-Hf-rich core and Cu-Zr-Ti-rich shell, demonstrated by thermodynamics calculations and second nearest-neighbor modified embedded atom method (2NN MEAM) formalism. The shell acts as an interfacial energy barrier for the stable martensitic transformation that occurred primarily in the effective Ni-Hf-rich core of the Cu15Ni35Ti20(HfZr)30. The temperature-dependent Gibbs-free energy agrees with the Cu movement, which widens the thermally-induced gradient layer of the elemental redistribution. Our findings are conducive to a potential design strategy of tailoring gradient chemical core/shell HEAs for stable high-temperature shape-memory applications.

Previous studies have demonstrated that intrahepatic cholangiocarcinoma (ICC) may derive from transdifferentiation of hepatocytes, so transforming ICC cells into hepatocytes could be a potential strategy for treating ICC. Hepatocyte nuclear factor 4α (HNF4α), a master transcription factor in the liver, has been demonstrated to induce the differentiation of hepatocellular carcinoma, while its effects on ICC remains unclear. Ivosidenib, an isocitrate dehydrogenase 1 (IDH1) inhibitor, is a novel targeted drug for ICC patients. However, drug resistance limits its clinical efficacy. In the present study, we found that the downregulation of HNF4α expression was associated with aggressive tumor behavior and poor prognosis in ICC patients. Upregulation of HNF4α inhibited proliferation, migration, invasion and colony-formation ability, increased the expression of hepatocyte functional genes in ICC cells in vitro, and suppressed the growth of subcutaneous tumors in vivo. Importantly, HNF4α adenovirus treatment significantly reduced the tumor burden of Akt/NICD-induced primary ICC in mice. Furthermore, HNF4α enhanced the sensitivity of ICC cells to ivosidenib both in vitro and in vivo. RNA sequencing revealed that HNF4α suppressed several cancer-related pathways, including Wnt signaling pathway. The agonist of Wnt signaling pathway partially blocked the inhibitory effect of HNF4α on the proliferation and resistance to ivosidenib of ICC cells. These results identify HNF4α as a tumor suppressor for ICC and a potential sensitizer to ivosidenib in ICC patients. The reintroduction of HNF4α might help achieve more effective and precise targeted therapy, benefiting the survival of patients with ICC.

Sulfur mustard (2,2′-dichloroethylsulfide; SM) is a bifunctional alkylating agent that can easily penetrate skin and cause persistent pain and damage. Effective biological dressings are required to treat wounds caused or poisoned by SM. Though the use of SM is regulated under the Chemical Weapons Convention, it is still a threat during wars and terrorist attacks. Herein, we present a photothermal-enhanced detoxification microneedles array (MNA) encapsulated with ZnIn2S4@UiO-66-NH2 (ZnInS/UIO) catalysts for the treatment of 2-chloroethyl ethyl sulfide (CEES, SM analog)-poisoned wounds under simulated sunlight (SSL) irradiation. Due to the excellent photothermal detoxification capability possessed by ZnInS/UIO, the conversion rate of CEES can be significantly increased under SSL exposure. When encased in a polyvinyl alcohol (PVA) MNA and piercing into the skin, ZnInS/UIO catalysts can be released quickly from MNA for detoxification. After applying the resultant ZnInS/UIO-MNA to the CEES-poisoned wound bed, acceleration of the wound healing process and a reduced inflammatory response can be confirmed. In conclusion, ZnInS/UIO-MNA has encouraging potential as a first-aid dressing for CEES-poisoned wound healing in battlefields and injuries related to acts of terrorism.

BackgroundTransfusion-dependent β-thalassemia (TDT) is a hereditary blood disorder that often leads to complications affecting growth, nutritional status, and muscle mass in children. This study aims to investigate the associations between age, nutritional status, and muscle mass in children with TDT, providing insights into the progressive impact of age on these parameters.MethodsOne hundred twenty-two children with TDT from March 2023 to March 2024 were enrolled in this cross-sectional study. Their nutritional status was assessed using the 2006–2007 World Health Organization (WHO) Growth Charts, and their muscle mass was measured using bioelectrical impedance analysis (BIA). Data related to age, gender, weight, height, fat-free mass (FFM), skeletal muscle mass (SMM), and soft lean mass (SLM) of children were collected for comparative analysis from the hospital record room. Furthermore, Spearman’s rank correlation coefficients and regression analyses were utilized to investigate the associations between age and both nutritional status and muscle mass.ResultsThe results of this study revealed negative correlations between age and wasting (r = −0.26, p

The aim of this study was to evaluate the performance of a computable phenotype for systemic lupus erythematosus (SLE) patients when it is ported from a local data warehouse to the i2b2, OMOP, and PCORnet CDMs. We adapted the SLE phenotype to the Northwestern Medicine (NM) Enterprise Data Warehouse (EDW) and NU i2b2, OMOP, and PCORnet instances. Each of the phenotype’s 17 components were determined by a rules-based algorithm built on diagnosis, medication, lab, and procedure codes. We assessed the phenotype over 168 clinician-confirmed SLE patients and 100 healthy controls and calculated agreement between the EDW and CDMs for overall SLE classification, individual SLICC criteria membership, and individual code occurrence level aggregates - count, first and last date, lab values using Cohen’s kappa (Cκ) and intraclass correlation coefficients (ICC). For overall SLE classification, agreement of the OMOP and PCORnet datamarts with the EDW was high (Cκ 0.928, 0.802, respectively) while that of i2b2 was low (Cκ 0.328). For the panel of SLICC criteria, OMOP had high agreement for 14/17 SLICC criteria (Cκ 0.792-1.000). PCORnet had high agreement for 13/17 criteria (Cκ 0.708-1.000). i2b2 had high agreement for 10/17 criteria (Cκ 0.813-1.000) with the exception of lab-based criteria. While agreement between the EDW and CDMs at the level of individual codes was relatively poor, agreement at the levels of overall SLE classification and individual SLICC criteria were reasonably high for OMOP and PCORnet, suggesting that inconsistencies at a micro, code level become less apparent at the macro, phenotype level.

In the treatment of central nervous system disease, the blood–brain barrier (BBB) is a major obstruction to drug delivery that must be overcome. In this study, we propose a brain-targeted delivery strategy based on selective opening of the BBB. This strategy allows some simple bare nanoparticles to enter the brain when mixed with special opening material; however, the BBB still maintains the ability to completely block molecules from passing through. Based on the screening of BBB opening and matrix delivery materials, we determined that phospholipase A2-catalyzed 1-palmitoyl-2-oleoyl-sn-glycero-3-phosphoserine liposomes can efficiently carry drugs into the brain immediately. At an effective dose, this delivery system is safe, especially with its effect on the BBB being reversible. This mix & act delivery system has a simple structure and rapid preparation, making it a strong potential candidate for drug delivery across the BBB.

Abstract Background Intestinal barrier is a dynamic interface between the body and the ingested food components, however, dietary components or xenobiotics could compromise intestinal integrity, causing health risks to the host. Gossypol, a toxic component in cottonseed meal (CSM), caused intestinal injury in fish or other monogastric animals. It has been demonstrated that probiotics administration benefits the intestinal barrier integrity, but the efficacy of probiotics in maintaining intestinal health when the host is exposed to gossypol remains unclear. Here, a strain (YC) affiliated to Pediococcus pentosaceus was isolated from the gut of Nile tilapia (Oreochromis niloticus) and its potential to repair gossypol-induced intestinal damage was evaluated. Results A total of 270 Nile tilapia (2.20 ± 0.02 g) were allotted in 3 groups with 3 tanks each and fed with 3 diets including CON (control diet), GOS (control diet containing 300 mg/kg gossypol) and GP (control diet containing 300 mg/kg gossypol and 108 colony-forming unit (CFU)/g P. pentosaceus YC), respectively. After 10 weeks, addition of P. pentosaceus YC restored growth retardation and intestinal injury induced by gossypol in Nile tilapia. Transcriptome analysis and siRNA interference experiments demonstrated that NOD-like receptors (NLR) family caspase recruitment domain (CARD) domain containing 3 (Nlrc3) inhibition might promote intestinal stem cell (ISC) proliferation, as well as maintaining gut barrier integrity. 16S rRNA sequencing and gas chromatography-mass spectrometry (GC-MS) revealed that addition of P. pentosaceus YC altered the composition of gut microbiota and increased the content of propionate in fish gut. In vitro studies on propionate’s function demonstrated that it suppressed nlrc3 expression and promoted wound healing in Caco-2 cell model. Conclusions The present study reveals that P. pentosaceus YC has the capacity to ameliorate intestinal barrier injury by modulating gut microbiota composition and elevating propionate level. This finding offers a promising strategy for the feed industry to incorporate cottonseed meal into fish feed formulations. Graphical Abstract

On the afternoon of 4 April, 2023, an intense downburst event (referred to as the '4.04' strong downburst) occurred in Hengyang, Hunan, causing serious disasters. This study analyzes the key warning indicators and environmental conditions for extreme strong winds using conventional meteorological observations, Doppler weather radar, and NCEP 1°×1° reanalysis data. The results are as follows: (1) The '4.04' strong downburst occurred under a 'baroclinic frontogenesis' weather system configuration, with a surface cold front providing the triggering conditions. The Skew-T plots clearly showed characteristics of high-level dryness and low-level moisture, unstable atmospheric convective parameters, and corrected convective available potential energy (CAPE), along with strong vertical wind shear, all conducive to the occurrence of extreme strong winds. (2) A mesoscale convective system (MCS) with bow echoes swept through Hengyang, causing regional downbursts. Extreme strong winds occurred when the corresponding storm cells moved rapidly, with maximum reflectivity factors reaching 60 dBz. The vertical integrated liquid water content and centroid height rapidly decreased. The reflectivity factors displayed a noticeable tilted structure, with clear features of bow echoes, rear-inflow jets, and radial velocity ambiguity. The low elevation asymmetric high-speed regions and velocity convergence are key warning indicators for extreme strong winds. (3) The strong downburst exhibited significant convective potential, with strong low-level moisture flux convergence and high specific humidity. There were conditions of thermal instability and vertical upward motion, along with low-level convergence and upper-level divergence. Cold air played an important role in the occurrence and development of this downburst event.

Abstract Background Systemic lupus erythematosus (SLE) is a rare autoimmune disorder characterized by an unpredictable course of flares and remission with diverse manifestations. Lupus nephritis, one of the major disease manifestations of SLE for organ damage and mortality, is a key component of lupus classification criteria. Accurately identifying lupus nephritis in electronic health records (EHRs) would therefore benefit large cohort observational studies and clinical trials where characterization of the patient population is critical for recruitment, study design, and analysis. Lupus nephritis can be recognized through procedure codes and structured data, such as laboratory tests. However, other critical information documenting lupus nephritis, such as histologic reports from kidney biopsies and prior medical history narratives, require sophisticated text processing to mine information from pathology reports and clinical notes. In this study, we developed algorithms to identify lupus nephritis with and without natural language processing (NLP) using EHR data from the Northwestern Medicine Enterprise Data Warehouse (NMEDW). Methods We developed five algorithms: a rule-based algorithm using only structured data (baseline algorithm) and four algorithms using different NLP models. The first NLP model applied simple regular expression for keywords search combined with structured data. The other three NLP models were based on regularized logistic regression and used different sets of features including positive mention of concept unique identifiers (CUIs), number of appearances of CUIs, and a mixture of three components (i.e. a curated list of CUIs, regular expression concepts, structured data) respectively. The baseline algorithm and the best performing NLP algorithm were externally validated on a dataset from Vanderbilt University Medical Center (VUMC). Results Our best performing NLP model incorporated features from both structured data, regular expression concepts, and mapped concept unique identifiers (CUIs) and showed improved F measure in both the NMEDW (0.41 vs 0.79) and VUMC (0.52 vs 0.93) datasets compared to the baseline lupus nephritis algorithm. Conclusion Our NLP MetaMap mixed model improved the F-measure greatly compared to the structured data only algorithm in both internal and external validation datasets. The NLP algorithms can serve as powerful tools to accurately identify lupus nephritis phenotype in EHR for clinical research and better targeted therapies.

Abstract Progress in sequencing technologies and clinical experiments has revolutionized immunotherapy on solid and hematologic malignancies. However, the benefits of immunotherapy are limited to specific patient subsets, posing challenges for broader application. To improve its effectiveness, identifying biomarkers that can predict patient response is crucial. Machine learning (ML) play a pivotal role in harnessing multi-omic cancer datasets and unlocking new insights into immunotherapy. This review provides an overview of cutting-edge ML models applied in omics data for immunotherapy analysis, including immunotherapy response prediction and immunotherapy-relevant tumor microenvironment identification. We elucidate how ML leverages diverse data types to identify significant biomarkers, enhance our understanding of immunotherapy mechanisms, and optimize decision-making process. Additionally, we discuss current limitations and challenges of ML in this rapidly evolving field. Finally, we outline future directions aimed at overcoming these barriers and improving the efficiency of ML in immunotherapy research.

Structural damage identification based on structural health monitoring (SHM) data and machine learning (ML) is currently a rapidly developing research area in structural engineering. Traditional machine learning techniques rely heavily on feature extraction, where weak feature extraction can lead to suboptimal features and poor classification performance. In contrast, ML-based methods, particularly deep learning approaches like convolutional neural networks (CNNs), automatically extract relevant features from raw data, improving the accuracy and adaptability of the damage identification process. This study developed a time–frequency-based data-driven approach aiming to improve the effectiveness of traditional data-driven structural damage identification approaches for large complex structures. Firstly, the structural acceleration signals in the time domain were converted into two-dimensional images via the Gram angle difference field (GADF). Subsequently, the characteristic feature in the image data was studied by convolutional neural networks (CNNs) to predict the structural damage conditions. An experimental study on a scale model of a cable-stayed bridge was conducted to identify the damage of stay cables under the moving vehicle load on the main girders. The CNN was employed to extract the characteristic features from the time-varying monitoring data of vehicle–bridge interactions. The CNN parameters were optimized to conduct the structural damage classification task. The performance of the proposed method was evaluated by comparing it with various traditional pre-trained networks. The effect of environmental noise on the prediction accuracy was also investigated. The numerical results show that the ResNet model has the best performance in terms of damage identification accuracy and convergence speed, achieving higher accuracy and faster convergence compared to the other four traditional networks. The method can accurately identify damage on bridges using insufficient sensors on the bridge deck, which has valuable potential for application to real-world bridges with monitoring data. As the Signal-to-Noise Ratio (SNR) decreases from 20 dB to 2.5 dB, the prediction accuracy of ResNet decreases from 86.63% to 62.5%, which demonstrates the robustness and reliability in identifying structural damage.

We illustrated a rare case of malignant solitary fibrous tumor (MSFT) with epithelioid morphology in the occipital region of a 59-year-old female, in which a rare NAB2ex7-STAT6 exon15/16 double fusion subtype was detected by the Next-generation sequencing (NGS) and STAT6 immunohistochemistry (IHC) was diffusely and strongly positively expressed, without recurrence after 20 months of postoperative follow-up. The morphological and molecular genetic aspects and the differential diagnosis are described, and the relevant literature was assessed in order to broaden our understanding and diagnostic capability of this malignancy.

BackgroundNonadherence to medication among patients with cardiovascular diseases undermines the desired therapeutic outcomes. eHealth interventions emerge as promising strategies to effectively tackle this issue. ObjectiveThe aim of this study was to conduct a network meta-analysis (NMA) to compare and rank the efficacy of various eHealth interventions in improving medication adherence among patients with cardiovascular diseases (CVDs). MethodsA systematic search strategy was conducted in PubMed, Embase, Web of Science, Cochrane, China National Knowledge Infrastructure Library (CNKI), China Science and Technology Journal Database (Weipu), and WanFang databases to search for randomized controlled trials (RCTs) published from their inception on January 15, 2024. We carried out a frequentist NMA to compare the efficacy of various eHealth interventions. The quality of the literature was assessed using the risk of bias tool from the Cochrane Handbook (version 2.0), and extracted data were analyzed using Stata16.0 (StataCorp LLC) and RevMan5.4 software (Cochrane Collaboration). The certainty of evidence was evaluated using the Grading of Recommendations, Assessment, Development, and Evaluations (GRADE) approach. ResultsA total of 21 RCTs involving 3904 patients were enrolled. The NMA revealed that combined interventions (standardized mean difference [SMD] 0.89, 95% CI 0.22-1.57), telephone support (SMD 0.68, 95% CI 0.02-1.33), telemonitoring interventions (SMD 0.70, 95% CI 0.02-1.39), and mobile phone app interventions (SMD 0.65, 95% CI 0.01-1.30) were statistically superior to usual care. However, SMS compared to usual care showed no statistical difference. Notably, the combined intervention, with a surface under the cumulative ranking curve of 79.3%, appeared to be the most effective option for patients with CVDs. Regarding systolic blood pressure and diastolic blood pressure outcomes, the combined intervention also had the highest probability of being the best intervention. ConclusionsThe research indicates that the combined intervention (SMS text messaging and telephone support) has the greatest likelihood of being the most effective eHealth intervention to improve medication adherence in patients with CVDs, followed by telemonitoring, telephone support, and app interventions. The results of these network meta-analyses can provide crucial evidence-based support for health care providers to enhance patients’ medication adherence. Given the differences in the design and implementation of eHealth interventions, further large-scale, well-designed multicenter trials are needed. Trial RegistrationINPLASY 2023120063; https://inplasy.com/inplasy-2023-12-0063/

BackgroundMore and more evidence supports the association between myocardial infarction (MI) and osteoarthritis (OA). The purpose of this study is to explore the shared biomarkers and pathogenesis of MI complicated with OA by systems biology.MethodsGene expression profiles of MI and OA were downloaded from the Gene Expression Omnibus (GEO) database. The Weighted Gene Co-Expression Network Analysis (WGCNA) and differentially expressed genes (DEGs) analysis were used to identify the common DEGs. The shared genes related to diseases were screened by three public databases, and the protein-protein interaction (PPI) network was built. GO and KEGG enrichment analyses were performed on the two parts of the genes respectively. The hub genes were intersected and verified by Least absolute shrinkage and selection operator (LASSO) analysis, receiver operating characteristic (ROC) curves, and single-cell RNA sequencing analysis. Finally, the hub genes differentially expressed in primary cardiomyocytes and chondrocytes were verified by RT-qPCR. The immune cell infiltration analysis, subtypes analysis, and transcription factors (TFs) prediction were carried out.ResultsIn this study, 23 common DEGs were obtained by WGCNA and DEGs analysis. In addition, 199 common genes were acquired from three public databases by PPI. Inflammation and immunity may be the common pathogenic mechanisms, and the MAPK signaling pathway may play a key role in both disorders. DUSP1, FOS, and THBS1 were identified as shared biomarkers, which is entirely consistent with the results of single-cell RNA sequencing analysis, and furher confirmed by RT-qPCR. Immune infiltration analysis illustrated that many types of immune cells were closely associated with MI and OA. Two potential subtypes were identified in both datasets. Furthermore, FOXC1 may be the crucial TF, and the relationship of TFs-hub genes-immune cells was visualized by the Sankey diagram, which could help discover the pathogenesis between MI and OA.ConclusionIn summary, this study first revealed 3 (DUSP1, FOS, and THBS1) novel shared biomarkers and signaling pathways underlying both MI and OA. Additionally, immune cells and key TFs related to 3 hub genes were examined to further clarify the regulation mechanism. Our study provides new insights into shared molecular mechanisms between MI and OA.

Abstract Introduction The rapid development of artificial intelligence (AI) in healthcare has exposed the unmet need for growing a multidisciplinary workforce that can collaborate effectively in the learning health systems. Maximizing the synergy among multiple teams is critical for Collaborative AI in Healthcare. Methods We have developed a series of data, tools, and educational resources for cultivating the next generation of multidisciplinary workforce for Collaborative AI in Healthcare. We built bulk‐natural language processing pipelines to extract structured information from clinical notes and stored them in common data models. We developed multimodal AI/machine learning (ML) tools and tutorials to enrich the toolbox of the multidisciplinary workforce to analyze multimodal healthcare data. We have created a fertile ground to cross‐pollinate clinicians and AI scientists and train the next generation of AI health workforce to collaborate effectively. Results Our work has democratized access to unstructured health information, AI/ML tools and resources for healthcare, and collaborative education resources. From 2017 to 2022, this has enabled studies in multiple clinical specialties resulting in 68 peer‐reviewed publications. In 2022, our cross‐discipline efforts converged and institutionalized into the Center for Collaborative AI in Healthcare. Conclusions Our Collaborative AI in Healthcare initiatives has created valuable educational and practical resources. They have enabled more clinicians, scientists, and hospital administrators to successfully apply AI methods in their daily research and practice, develop closer collaborations, and advanced the institution‐level learning health system.

Background White adipose tissue (WAT) has a key role in maintaining energy balance throughout the body, and their dysfunction take part in the regulation of diabetes mellitus. However, the internal regulatory mechanisms underlying are still unknown. Methods and results We generated adipocyte‐specific FAK KO (FAK‐AKO) mice and investigated their phenotype. The cascade of adipocyte, macrophage in adipocyte tissues, and pancreatic β‐cells were proposed in FAK‐AKO mice and validated by cell line studies using 3T3‐L1, Raw264.7 and Min6. The FAK‐AKO mice exhibited glucose intolerance, reduced adipose tissue mass and increased apoptosis, lipolysis and inflammatory response in adipose tissue. We further demonstrate that adipocyte FAK deletion increases β cell apoptosis and inflammatory infiltrates into islets, which is potentiated if mice were treated with STZ. In the STZ‐induced diabetes model, FAK AKO mice exhibit less serum insulin content and pancreatic β cell area. Moreover, serum pro‐inflammatory factors increased and insulin levels decreased after glucose stimulation in FAK AKO mice. In a parallel vitro experiment, knockdown or inhibition of FAK during differentiation also increased apoptosis, lipolysis and inflammatory in 3T3‐L1 adipocytes, whereas the opposite was observed upon overexpression of FAK. Moreover, coculturing LPS‐treated RAW264.7 macrophages with knockdown FAK of 3T3‐L1 adipocytes increased macrophage pro‐inflammatory response. Furthermore, conditioned medium from above stimulated Min6 cells apoptosis (with or without STZ), whereas the opposite was observed upon overexpression of FAK. Mechanistically, FAK protein interact with TRAF6 in adipocytes and knockdown or inhibition of FAK activated TRAF6/TAK1/NF‐κB signaling, which exacerbates inflammation of adipocytes themselves. Conclusion Adipocyte FAK deletion promotes both adipocyte apoptosis and adipose tissue inflammation. Pro‐inflammatory factors released by the FAK‐null adipose tissue further trigger apoptosis in pancreatic islets induced by the administration of STZ, thereby exacerbating the diabetes mellitus. This study reveals a link between FAK‐mediated adipose inflammation and diabetes mellitus, a mechanism that has not been previously recognized.

Abstract Energy transfer is a ubiquitous phenomenon that delivers energy from a blue-shifted emitter to a red-shifted absorber, facilitating wide photonic applications. Two-dimensional (2D) semiconductors provide unique opportunities for exploring novel energy transfer mechanisms in the atomic-scale limit. Herein, we have designed a planar optical microcavity-confined MoS2/hBN/WS2 heterojunction, which realizes the strong coupling among donor exciton, acceptor exciton, and cavity photon mode. This configuration demonstrates an unconventional energy transfer via polariton relaxation, brightening MoS2 with a record-high enhancement factor of ~440, i.e., two-order-of-magnitude higher than the data reported to date. The polariton relaxation features a short characteristic time of ~1.3 ps, resulting from the significantly enhanced intra- and inter-branch exciton-exciton scattering. The polariton relaxation dynamics is associated with Rabi energies in a phase diagram by combining experimental and theoretical results. This study opens a new direction of microcavity 2D semiconductor heterojunctions for high-brightness polaritonic light sources and ultrafast polariton carrier dynamics.