OBJECTIVES:Post-transplantation diabetes mellitus (PTDM) is a common complication after kidney transplantation and contributes to graft dysfunction and adverse cardiovascular outcomes. We conducted a preliminary evaluation of an integrated perioperative glycemic management model combining Healthcare Failure Mode and Effect Analysis (HFMEA), continuous glucose monitoring (CGM), and artificial intelligence (AI)-driven prediction in routine clinical practice. DESIGN:Single-center retrospective cohort study with historical controls. METHODS:We included 120 adult kidney transplant recipients from 2021-2024 and divided them into a historical control group (n = 60, conventional intermittent capillary glucose monitoring) and an intervention group (n = 60, HFMEA-CGM-AI integrated management). Primary outcomes were perioperative CGM-derived glycemic metrics over 14 days (time in range TIR, coefficient of variation CV, time below range TBR, mean amplitude of glycemic excursions MAGE) and 3-month PTDM incidence. Secondary outcomes included hospital length of stay and postoperative complications; patient satisfaction was assessed descriptively in the intervention group. RESULTS:The intervention group was associated with substantially tighter perioperative glycemic control, with higher TIR (80.0 ± 9.0% vs 63.8 ± 11.6%) and lower CV, TBR, and MAGE (all p < 0.001). TIR in the intervention group exceeded recommended CGM targets (>70%). Three-month PTDM incidence was numerically lower (10.0% vs 20.0%; p = 0.201; 18 events), consistent with limited power for this endpoint and an exploratory role of PTDM in this study. Hospital stay was shorter with the integrated model (12.4 ± 2.2 vs 14.2 ± 3.3 days, p < 0.001), and postoperative complications were numerically reduced but non-significant. Patient satisfaction in the intervention group was high (mean 8.4 ± 1.1 on a 10-point scale). CONCLUSION:In this retrospective historical cohort, the HFMEA-CGM-AI model was feasibly implemented and was associated with improved perioperative CGM metrics and shorter hospital stay. PTDM and complication outcomes remained exploratory due to limited power and the non-randomized design. Prospective, adequately powered randomized trials are required to determine the causal impact and cost-effectiveness of this integrated HFMEA-CGM-AI management approach.
OBJECTIVES:We found earlier increased antibody reactivity against the insulin receptor-A and insulin-like growth factor 1 receptor and their ligands (insulin and insulin-like growth factor 1) in patients with schizophrenia or related psychosis. Hypothetically, a newly-identified insulin-insulin-like growth factor 2 (INS-IGF2) protein may be a ligand to these receptors too, and recently, significantly higher antibody reactivity was found against a part of this protein in patients with schizophrenia or related psychosis in partial than full symptom remission. The aim of this study was to map the region of the INS-IGF2 transcript that codes this part of the protein in schizophrenia and related psychosis susceptibility. MATERIAL AND METHODS:The present INS-IGF2 region was DNA-sequenced by the Sanger method in a study population, consisting of 94 patients with schizophrenia, 11 patients with schizoaffective disorder and 58 controls. RESULTS:Two novel SNPs were identified solely in patients, but not in controls. Tendencies towards overall differences in genotype- and allele frequencies between patients with schizophrenia or schizoaffective disorder and controls were found for SNP no 1 (p = 0.067 and p = 0.067, respectively), with the G/A-genotype and G-allele appearing more frequently in patients with schizoaffective disorder. The patient with SNP no 1 was diagnosed with both schizoaffective disorder and polycystic ovary syndrome, and the patient with SNP no 2 was diagnosed with schizophrenia, paranoid type with affective features. CONCLUSION:The findings in this exploratory study suggest that SNPs in the INS-IGF2 transcript may be associated with psychosis, specifically with schizoaffective disorder, and warrant replication in larger samples.
OBJECTIVE:This study aimed to identify pyroptosis related genes (pyroptosis RGs) in osteoarthritis (OA) and to evaluate their potential as candidate biomarkers by integrating bulk transcriptomic analysis, machine learning, and single cell RNA sequencing. METHODS:Bulk RNA sequencing datasets (GSE55235 and GSE55457) and a single cell RNA sequencing dataset (GSE216651) related to OA were obtained from the Gene Expression Omnibus database, and gene sets associated with OA and pyroptosis were retrieved from GeneCards. Differential expression analysis and weighted gene co expression network analysis (WGCNA) of GSE55235 were used to derive differentially expressed pyroptosis related genes (DEPRGs). Candidate hub genes were screened by intersecting rankings from five cytoHubba algorithms with three supervised machine learning models (least absolute shrinkage and selection operator, support vector machine-recursive feature elimination, and random forest), and their diagnostic performance was evaluated using receiver operating characteristic curve analysis in the training and validation cohorts. The potential biological roles of the identified hub genes were further examined through functional enrichment analysis, immune cell infiltration assessment, and single-cell transcriptomic profiling. RESULTS:Twenty-one DEPRGs were identified. Among these, interleukin 8 (CXCL8) and NF kappa B inhibitor alpha (NFKBIA) emerged as central hub genes with consistent diagnostic performance across cohorts. Functional enrichment analyses indicated that these genes are primarily implicated in inflammatory signaling and immune regulation pathways, including responses to tumor necrosis factor and lipopolysaccharide, chemokine and cytokine activity, and Toll like receptor, TNF, and IL 17 signaling. Single-cell transcriptomic analysis demonstrated predominant expression of CXCL8 and NFKBIA in synovial macrophages. In these cells, expression of both genes was positively correlated with pyroptosis scores, and macrophages displayed the highest pyroptosis activity among the analyzed synovial cell populations in OA. CONCLUSION:CXCL8 and NFKBIA were identified as synovium associated pyroptosis related genes in OA with promising diagnostic performance in public datasets. These in silico findings refine current understanding of OA related inflammatory pathways and generate hypotheses for future mechanistic and experimental studies.
OBJECTIVE:This study aimed to quantitatively characterize microbial contamination patterns in dental unit waterlines (DUWLs) and to evaluate a centralized, data-driven cleaning and disinfection system in a university dental hospital. METHODS:We conducted a cross-sectional survey combined with longitudinal monitoring of DUWL samples from 56 dental chairs in three clinical departments of a university hospital. One-way analysis of variance, Kruskal-Wallis tests, paired t tests, linear regression, correlation analysis, and multiple linear regression were used to evaluate interdepartmental differences, sampling site-specific contamination risk, temporal trends, and independent predictors. Based on the statistical findings, a centralized cleaning and disinfection system was designed and subsequently validated. RESULTS:A total of 726 water samples were analyzed. Statistically significant differences in microbial colony counts were observed between departments (p < 0.001), with the Optics Valley Teeth Cleaning Center exhibiting the highest contamination levels. Significant differences were also detected among sampling points (p < 0.001), with the handpiece water outlet identified as the highest-risk location. Active dental procedures were associated with a significant increase in microbial colony counts (p < 0.001). Under conventional disinfection, contamination levels increased progressively from April to July (p < 0.01). Significant positive correlations were identified among contamination levels at different sampling points. Multiple linear regression identified month, sampling site, treatment status, and department as independent predictors of microbial contamination. Implementation of the centralized cleaning and disinfection system significantly extended the duration of compliance with established water quality standards from 1.2 ± 0.4 days to 4.1 ± 0.7 days (p < 0.001). CONCLUSION:We observed significant interdepartmental variation, site-specific contamination risk profiles, and clear temporal patterns in DUWL microbial contamination. The centralized cleaning and disinfection system, developed based on these statistical findings, significantly improved microbial control and prolonged compliance with water quality standards.
OBJECTIVE:This study examined the associations among self-perceived burden (SPB), medical coping strategies, and acceptance of disability (AOD) and evaluated whether avoidance coping mediated the relationship between SPB and AOD in patients with Parkinson's disease (PD). METHODS:A cross-sectional survey of 180 patients with PD admitted to the Department of Neurology, Lishui Hospital of Traditional Chinese Medicine between September 2024 and July 2025 was conducted to collect data on SPB, coping strategies, and AOD using validated questionnaires. Data were collected using a general information questionnaire, the SPB scale, the Medical Coping Modes Questionnaire, and the Acceptance of Disability Scale‑Revised. RESULTS:Correlation analysis demonstrated significant associations between SPB, coping strategies, and AOD (all p < 0.01). Mediation analysis indicated that avoidance coping partially mediated the relationship between SPB and AOD, accounting for 33.84% of the total effect of SPB on AOD in the structural equation model. CONCLUSION:SPB and avoidance coping demonstrated predictive value for AOD in patients with PD-related disability, and avoidance coping partially mediated the association between SPB and AOD. These findings suggest that psychosocial and rehabilitation interventions should aim to reduce reliance on avoidance coping and strengthen active coping to improve disability acceptance.
