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Blood pressure levels are higher in individuals with type 1 diabetes mellitus compared to healthy subjects during exercise stress test
Association between temperature rise from climate normal and sleep quality
Association between neutrophil percentage to albumin ratio and cardiovascular disease in the metabolic syndrome population
Association between the psychological frailty index and stroke: a cohort study from CHARLS
Design of a liquid cooled battery thermal management system using neural networks, cheetah optimizer and salp swarm algorithm
PENC: a predictive-estimative nonlinear control framework for robust target tracking of fixed-wing UAVs in complex urban environments
Microbial and nutrient enrichments in Brine and Nilas during the first 24 h of open water lead refreezing
Functional discrimination of CSF from Alzheimer’s patients in a brain on chip platform
Deep nested U-structure network with frequency attention for building semantic segmentation
Protein hydrolysates derived from residual after polysaccharide extraction of Chlorella vulgaris biomass improves yield and quality of baby leaf lettuce
Research on the impact of transportation infrastructure construction on urban sustainable development
Integrating 7-day D-dimer exposure into deep vein thrombosis risk prediction after gastrointestinal surgery
Abstract Deep vein thrombosis (DVT) is a serious complication following gastrointestinal surgery. While D-dimer is a widely used biomarker for thrombosis, its postoperative specificity is limited due to inflammatory interference. This study introduces a novel cumulative metric—7-day D-dimer exposure (7dDDE)—to quantify perioperative coagulation burden. We retrospectively analyzed 525 patients undergoing gastrointestinal surgery, performed propensity score matching, and constructed a multivariable logistic regression model incorporating 7dDDE and other clinical variables. Model performance was evaluated using ROC curves, decision curve analysis, calibration plots, SHAP values, and a nomogram. Additionally, a linear mixed-effects model assessed D-dimer trajectories over time. The results demonstrated that 7dDDE was independently associated with postoperative DVT and was the most influential predictor in the model. The model showed good discrimination and clinical utility. Longitudinal analysis further revealed significant differences in D-dimer dynamics between DVT and non-DVT groups, even after adjustment for confounders. These findings support the use of 7dDDE as a robust biomarker for thrombotic risk stratification and highlight the importance of integrating temporal biomarker patterns into perioperative DVT prediction.
Isolation and characterization of novel bacteriophages targeting Stenotrophomonas maltophilia
Abstract Stenotrophomonas maltophilia is a bacterium often resistant to antibiotics and is a significant cause of nosocomial infections, particularly in immunocompromised patients. Phage therapy has shown promise as a potential treatment for such difficult-to-treat bacterial infections, but research on phages targeting this bacterium is very limited. In this study, we isolated 34 phages using four clinical strains of S. maltophilia and evaluated their infectivity and bactericidal activity. While some phages infected all four strains, many exhibited strain-specific infectivity. We investigated the bacterial growth curves in response to three phages, named Yut1, Yut2, and Yut4, and found that all phages exhibited potent lytic activity against the clinical strains even at low doses. Genome analysis found that the phages did not carry any lysogeny genes, virulence factors, or antibiotic resistance genes, suggesting their high potential as therapeutic phages. Furthermore, phylogenetic analysis suggested that Yut1 and Yut4 belong to a novel phage lineage. These results highlight the therapeutic potential of our novel phages to combat the growing antibiotic resistance problem.
Environmental cues rather than quality of supplemented pollen drive the foraging behaviour of honey bees during avocado pollination
Balancing complexity and accuracy for defect detection on filters with an improved RT-DETR
Lysosomal free sialic acid storage disorder iPSC-derived neural cells display altered glycosphingolipid metabolism
Abstract Lysosomal free sialic acid storage disorder (FSASD) is a rare neurodegenerative disease caused by biallelic mutations in SLC17A5, encoding the lysosomal sialic acid exporter, SLC17A5 (sialin). While the involvement of oligodendroglia in FSASD pathogenesis is established, the roles of other neural cell types remain elusive. In this study, we utilized radial glial cells (iRGCs), immature and mature astrocytes (iIAs and iMAs, respectively), and cortical neurons (iCNs) differentiated from induced pluripotent stem cells (iPSCs) derived from two individuals with FSASD, alongside two independent healthy donors for comparison. We employed a multifaceted profiling approach, including the assessment of cellular glycosphingolipids (GSLs), transcriptomics focused on GSL metabolism genes, and 4-methylumbelliferone-based lysosomal enzyme activity measurements. Our findings revealed significant elevations in free sialic acid levels across all FSASD cell types, indicating that iPSCs and derived iRGCs, iIAs, iMAs and iCNs may be used to model FSASD in vitro. We observed significant alterations in the abundance of specific GSL species, predominantly in mature astrocytes, with fewer changes in other cell types. Transcriptomic analyses uncovered differential expression of genes involved in GSL catabolism, including those encoding glycohydrolases. Enzyme assays corroborated the transcriptomic findings, showing heightened glycohydrolase activities, particularly in mature astrocytes. Collectively, these data may help refine our understanding of neural cell phenotypes and potential contributors to selective vulnerability in FSASD.