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Background The role of systemic inflammation in promoting cardiovascular diseases has attracted attention, but its correlation with various arrhythmias remains to be clarified. We aimed to comprehensively assess the association between various indicators of systemic inflammation and atrial fibrillation/flutter (AF), ventricular arrhythmia (VA), and bradyarrhythmia in the UK Biobank cohort. Methods After excluding ineligible participants, a total of 478,524 eligible individuals (46.75% male, aged 40–69 years) were enrolled in the study to assess the association between systemic inflammatory indicators and each type of arrhythmia. Results After covariates were fully adjusted, CRP levels were found to have an essentially linear positive correlation with the risk of various arrhythmias; neutrophil count, monocyte count, and NLR showed a non-linear positive correlation; and lymphocyte count, SII, PLR, and LMR showed a U-shaped association. VA showed the strongest association with systemic inflammation indicators, and it was followed sequentially by AF and bradyarrhythmia. Conclusions Multiple systemic inflammatory indicators showed strong associations with the onset of AF, VA, and bradyarrhythmia, of which the latter two have been rarely studied. Active systemic inflammation management might have favorable effects in reducing the arrhythmia burden and further randomized controlled studies are needed.

The emergence of the first three lineages during development is orchestrated by a network of transcription factors, which are best characterized in mice. However, the role and regulation of these factors are not completely conserved in other mammals, including human and cattle. Here, we establish a gene inactivation system with a robust efficiency by introducing premature codon with cytosine base editors in bovine early embryos. By using this approach, we have determined the functional consequences of three critical lineage-specific genes ( SOX2 , OCT4 and CDX2 ) in bovine embryos. In particular, SOX2 knockout results in a failure of the establishment of pluripotency in blastocysts. Indeed, OCT4 level is significantly reduced and NANOG barely detectable. Furthermore, the formation of primitive endoderm is compromised with few SOX17 positive cells. RNA-seq analysis of single blastocysts (day 7.5) reveals dysregulation of 2074 genes, among which 90% are up-regulated in SOX2 -null blastocysts. Intriguingly, more than a dozen lineage-specific genes, including OCT4 and NANOG , are down-regulated. Moreover, SOX2 level is sustained in the trophectoderm in absence of CDX2. However, OCT4 knockout does not affect the expression of SOX2. Overall, we propose that SOX2 is indispensable for OCT4 and NANOG expression and CDX2 represses the expression of SOX2 in the trophectoderm in cattle, which are all in sharp contrast with results in mice.

Dynamometer card-based fault diagnosis confronts critical challenges including imbalanced data distributions, noise contamination, and constrained generalization capacity. This paper proposes a novel diagnostic framework integrating similarity-guided data augmentation with wavelet-optimized lightweight neural networks to address these limitations. The methodology employs kinematic feature recombination for minority class expansion, resolving data imbalance through physically consistent sample generation. Concurrently, vertical load disparities, temporal load variations, and auxiliary operational parameters are fused into standardized dynamometer card representations, enhancing feature discriminability without architectural modifications. We integrate Discrete Wavelet Transform (DWT) layers into stride-2 inverted residual modules ofMobileNet-V2, strategically embedding spectral noise suppression during feature downsampling while preserving diagnostically critical low-frequency patterns. Experimental validation confirms the framework’s superior accuracy and noise robustness, achieving significant performance improvements over conventional approaches while maintaining computational efficiency. The proposed solution establishes a deployable framework for industrial fault diagnosis, balancing diagnostic precision with operational practicality in oilfield monitoring applications.

Visceral adiposity has been proposed to be closely linked to cognitive impairment. This cross-sectional study aimed to evaluate the predictive value of Chinese Visceral Adiposity Index (CVAI) for mild cognitive impairment (MCI) in patients with type 2 diabetes mellitus (T2DM) and to develop a quantitative risk assessment model. A total of 337 hospitalized patients with T2DM were included and randomly assigned to a training cohort (70%, n  = 236) and a validation cohort (30%, n  = 101). Demographic, clinical, and neuropsychological data were collected. CVAI levels were compared between patients with MCI and those with normal cognition. Associations between CVAI and cognitive performance were assessed using Spearman correlation and multivariable linear regression. Predictors of MCI were identified through Lasso regression followed by univariate and multivariate logistic regression analyses. A nomogram incorporating age, gender, education level, and CVAI was constructed and validated using calibration plots, ROC curve analysis, and decision curve analysis (DCA). Patients with MCI exhibited significantly higher CVAI values and lower MoCA and MMSE scores compared to those with normal cognition (all P  < 0.001). CVAI was independently and negatively associated with MoCA and MMSE scores (β = -0.22, P  < 0.001 for both) after adjustment. Multivariate logistic regression confirmed CVAI as an independent risk factor for MCI ( P  = 0.002). The nomogram demonstrated good discrimination, with an AUC of 0.765 in the training cohort and 0.690 in the validation cohort, and exhibited favorable clinical utility based on DCA. These findings suggest that CVAI is a valuable biomarker for the early identification and risk stratification of MCI in T2DM, and that the CVAI-based nomogram provides a practical tool for individualized clinical decision-making.

Microbiome abnormalities (dysbiosis) significantly contribute to the progression of Alzheimer’s disease (AD). However, the therapeutic efficacy of microbiome modulators in protecting against these ailments remains poorly studied. Herein, we tested a cocktail of unique probiotics, including 5 Lactobacillus and 5 Enterococcus strains isolated from infant gut with proven microbiome modulating capabilities. We aimed to determine the probiotics cocktail’s efficacy in ameliorating AD pathology in a humanized AD mouse model of APP/PS1 strains. Remarkably, feeding mice with 1 × 10 11 CFU per day in drinking water for 16 weeks significantly reduced cognitive decline (measured by the Morris Water Maze test) and AD pathology markers, such as Aβ aggregation, microglia activation, neuroinflammation, and preserved blood-brain barrier (BBB) tight junctions. The beneficial effects were linked to a reduced inflammatory microbiome, leading to decreased gut permeability and inflammation in both systemic circulation and the brain. Although both male and female mice showed overall improvements in cognition and biological markers, females did not exhibit improvements in specific markers related to inflammation and barrier permeability, suggesting that the underlying mechanisms may differ depending on sex. In conclusion, our results suggest that this unique probiotics cocktail could serve as a prophylactic agent to reduce the progression of cognitive decline and AD pathology. This is achieved by beneficially modulating the microbiome, improving intestinal tight junction proteins, reducing permeability in both gut and BBB, and decreasing inflammation in the gut, blood circulation, and brain, ultimately mitigating AD pathology and cognitive decline.

The gut bacteria producing metabolites like short-chain fatty acids (SCFAs; e.g., acetate, propionate and butyrate), are frequently reduced in Patients with diabetes, obesity, autoimmune disorders, and cancers. Hence, microbiome modulators such as probiotics may be helpful in maintaining or even restoring normal gut microbiome composition to benefit host health. Herein, we developed a human-origin probiotic cocktail with the ability to modulate gut microbiota to increase native SCFA production. Following a robust protocol of isolation, characterization and safety validation of infant gut-origin Lactobacillus and Enterococcus strains with probiotic attributes (tolerance to simulated gastric and intestinal conditions, adherence to intestinal epithelial cells, absence of potential virulence genes, cell-surface hydrophobicity, and susceptibility to common antibiotics), we select 10 strains (5 from each genera) out of total 321 isolates. A single dose (oral gavage) as well as 5 consecutive doses of this 10-strain probiotic cocktail in mice modulates gut microbiome and increases SCFA production (particularly propionate and butyrate). Inoculation of these probiotics in human feces also increases SCFA production along with microbiome modulation. Results indicate that human-origin probiotic lactobacilli and enterococci could ameliorate gut microbiome dysbiosis and hence may prove to be a potential therapy for diseases involving reduced SCFAs production in the gut.

Background Amid growing concerns over healthcare workforce shortages in aging societies, delayed retirement has emerged as a strategic policy response. However, little is known about the determinants of retirement attitudes across different healthcare professions in middle-income countries. Objectives Guided by Role Theory and the Push-Pull Model, this study aimed to identify demographic, occupational, and psychosocial predictors of support for delayed retirement among healthcare workers in China, with attention to inter-professional variation. Methods A cross-sectional survey was conducted among 1,200 full-time healthcare workers in Sichuan Province, including doctors, nurses, technicians, and administrative staff. A structured questionnaire captured data on demographics, work conditions, job satisfaction, occupational fatigue, self-rated health, and chronic illness. Univariate and multivariate logistic regression analyses were used to identify independent predictors of support for delayed retirement. Results Support for delayed retirement was positively associated with older age (OR: 1.06), male gender (OR: 1.34), higher education (OR: 1.42–1.65), longer working hours, more frequent night shifts, and higher job satisfaction (OR: 1.55), while greater occupational fatigue was negatively associated (OR: 0.82; all p  < 0.01). Supporters reported better health, lower fatigue, and greater career engagement. Subgroup comparisons revealed marked differences in predictors and attitudes across professional roles, reflecting distinct role identities and workplace demands. Conclusions By applying retirement theory to a diverse healthcare sample, this study highlights the need for differentiated workforce retention strategies. Findings suggest that policies should account for occupational fatigue, gendered caregiving burdens, and role-based professional motivations to ensure sustainable retirement planning in resource-constrained health systems.

Quantifying forage nutritional quality and pool at various spatial and temporal scales are major challenges in quantifying global nitrogen and phosphorus cycles, and the carrying capacity of grasslands. In this study, we modeled forage nutrition quality and storage using climate data under fencing conditions, and using climate data and a growing-season maximum normalized-difference vegetation index under grazing conditions based on four different methods (i.e., multiple linear regression, random-forest models, support-vector machines and recursive-regression trees) in the alpine grasslands of Tibet. Our results implied that random-forest models can have greater potential ability in modeling forage nutrition quality and storage than the other three methods. The relative biases between simulated nutritional quality using random-forest models and the observed nutritional quality, and between simulated nutrition storage using random-forest models and the observed nutrition storage, were lower than 2.00% and 6.00%, respectively. The RMSE between simulated nutrition quality using random-forest models and the observed nutrition quality, and between simulated nutrition storage using random-forest models and the observed nutrition storage, were no more than 0.99% and 4.50 g m−2, respectively. Therefore, random-forest models based on climate data and/or the normalized-difference vegetation index can be used to model forage nutrition quality and storage in the alpine grasslands of Tibet.

Silent information regulator 1 (SIRT1) exerts neuroprotection in many neurodegenerative diseases. However, it is not clear if SIRT1 has protective effects after intracerebral hemorrhage (ICH)-induced brain injury in rats. Thus, our goal was to examine the influence of SIRT1 on ICH injuries and any underlying mechanisms of this influence. Brain injury was induced by autologous arterial blood (60 μL) injection into rat brains, and data show that activation of SIRT1 with SRT1720 (5 mg/kg) restored nuclear SIRT1, deacetylation of PGC-1α, and mitochondrial biogenesis and decreased mortality, behavioral deficits, and brain water content without significant changes in phosphorylated AMP-activated protein kinase (pAMPK) induced by ICH. Activation of SIRT1 with SRT1720 also restored mitochondrial electron transport chain proteins and decreased apoptotic proteins in ICH; however, these changes were reversed after ICH. In contrast, treatment with PGC-1α siRNA yielded opposite effects. To explore the protective effects of SIRT1 after ICH, siRNAs were used to knockdown SIRT1. Treatment with SIRT1 siRNA increased mortality, behavioral deficits, brain water content, mitochondrial dysfunction, and neurocyte apoptosis after ICH. Thus, activation of SIRT1 promotes recovery of mitochondrial protein and function by increasing mitochondrial biogenesis and reduces apoptosis after ICH via the PGC-1α mitochondrial pathway. These data may suggest a new therapeutic approach for ICH injuries.

Rheumatoid arthritis (RA) is a chronic autoimmune disorder that causes joint inflammation and damage. Extra-articular manifestations occur in many patients and can include lung involvement in the form of airway or parenchymal inflammation and fibrosis. Although the pathophysiology of articular RA has been extensively investigated, the mechanisms causing airway and parenchymal lung disease are not well defined. Infections, cigarette-smoking, mucosal dysbiosis, host genetics and premature senescence are all potentially important contributors to the development of lung disease in patients with RA. RA-associated lung disease (which can predate the onset of articular disease by many years) probably originates from chronic airway and alveolar epithelial injury that occurs in an individual with a genetic background that permits the development of autoimmunity, leading to chronic inflammation and subsequent airway and lung parenchymal remodelling and fibrosis. Further investigations into the specific mechanisms by which lung disease develops in RA will be crucial for the development of effective therapies. Identifying mechanisms by which environmental and host factors cooperate in the induction of autoimmunity in the lung might also help to establish the order of early events in RA.