T-cell acute lymphoblastic leukemia (T-ALL) is a highly aggressive hematologic malignancy with a poor prognosis, despite advancements in treatment. Many patients struggle with relapse or refractory disease. Investigating the role of the super-enhancer (SE) regulated gene ubiquitin-specific protease 20 (USP20) in T-ALL could enhance targeted therapies and improve clinical outcomes. Analysis of histone H3 lysine 27 acetylation (H3K27ac) chromatin immunoprecipitation sequencing (ChIP-seq) data from six T-ALL cell lines and seven pediatric samples identified USP20 as an SE-regulated driver gene. Utilizing the Cancer Cell Line Encyclopedia (CCLE) and BloodSpot databases, it was found that USP20 is specifically highly expressed in T-ALL. Knocking down USP20 with short hairpin RNA (shRNA) increased apoptosis and inhibited proliferation in T-ALL cells. In vivo studies showed that USP20 knockdown reduced tumor growth and improved survival. The USP20 inhibitor GSK2643943A demonstrated similar anti-tumor effects. Mass spectrometry, RNA-Seq, and immunoprecipitation revealed that USP20 interacted with hypoxia-inducible factor 1 subunit alpha (HIF1A) and stabilized it by deubiquitination. Cleavage under targets and tagmentation (CUT&Tag) results indicated that USP20 co-localized with HIF1A, jointly modulating target genes in T-ALL. This study identifies USP20 as a therapeutic target in T-ALL and suggests GSK2643943A as a potential treatment strategy.

This study examines the progress and application of Artificial Intelligence (AI) in the prevention and control of infectious diseases within Chinese healthcare institutions. It analyzes the difficulties and challenges encountered during implementation to promote the intelligent transformation and upgrading of infectious disease prevention and control. The results indicate that AI technology has made progress in areas such as infectious disease surveillance and early warning, risk assessment and emergency response, screening and detection, image-based diagnosis and analysis, and health management. Nevertheless, significant challenges remain, including limited application depth and breadth, issues with data quality and privacy protection, insufficient technological maturity and interpretability, potential legal risks, and a shortage of interdisciplinary professionals. To advance the application of AI technology in infectious disease prevention and control and support the modernization of China′s relevant systems, recommendations include strengthening policy support, establishing data standards and robust privacy protection mechanisms, increasing R&D investment, refining laws and regulations, and enhancing the training of interdisciplinary talent.

Objective:To investigate the influence of serum C-X-C motif chemokine ligand 9 (CXCL9) and C-X3-C motif chemokine ligand 1 (CX3CL1) on the development of preeclampsia in patients with gestational diabetes mellitus (GDM).Methods:A retrospective analysis was conducted on the clinical data of 398 GDM patients admitted to Huangshi Aikang Hospital from January 2021 to August 2024. Based on the occurrence of preeclampsia, patients were divided into the GDM-preeclampsia group (51 cases) and the simple GDM group (347 cases). The baseline data, blood glucose indicators, four lipid items, platelet count (PLT), aspartate aminotransferase (AST), alanine aminotransferase (ALT), blood urea nitrogen, serum creatinine, and 24-hour urinary protein quantification were compared between the two groups. The influencing factors for GDM complicated by preeclampsia were analyzed, and the predictive value of serum CXCL9 and CX3CL1 for the onset of preeclampsia in GDM patients was assessed. Measurement data with a normal distribution were expressed as Mean ± SD, and the t'-test was used for intergroup comparisons when variances were unequal; measurement data with a skewed distribution were expressed as M ( Q1, Q3), and the Wilcoxon rank-sum test was used for intergroup comparisons; counting data were expressed as case (%), and the χ2 test was used for intergroup comparisons. Unconditional logistic regression models were used to analyze the risk factors for preeclampsia in GDM patients. The predictive value of serum CXCL9 and CX3CL1 levels for preeclampsia in GDM patients was analyzed using the receiver operator characteristic (ROC) curve. Results:Pre-pregnancy body mass index, glycated hemoglobin, and 24-hour urinary protein quantification in the GDM-preeclampsia group [(24.50±3.74) kg/m 2, (5.68±0.52)%, 0.42 (0.17, 0.69) g] were all higher than those in the simple GDM group [(22.70±2.97) kg/m 2, (5.42±0.44)%, 0.30 (0.10, 0.44) g], with statistically significant differences between groups (statistic values: t'=3.90, t'=3.85, U=2.70; P values: <0.001, <0.001, 0.007, respectively). Serum CXCL9 levels in the GDM-preeclampsia group [(111.69±36.65) ng/L] were lower than those in the simple GDM group [(200.16±85.57) ng/L], while CX3CL1 levels [(2.22±0.29) μg/L] were higher than those in the simple GDM group [(1.83±0.35) μg/L], with statistically significant differences ( t' values: 7.28 and 7.58, respectively; both P<0.001). Multivariate logistic regression analysis showed that increased CX3CL1 ( OR=1.562, 95% CI: 1.237-1.972), decreased CXCL9 ( OR=0.979, 95% CI: 0.970-0.989), increased pre-pregnancy body mass index ( OR=1.226, 95% CI: 1.060-1.417), and increased glycated hemoglobin ( OR=3.474, 95% CI: 1.192-10.122) were associated with an increased risk of developing preeclampsia in GDM patients ( P values: <0.001, <0.001, 0.006, 0.023, respectively). The ROC curve showed that the area under the curve for serum CXCL9 (sensitivity: 88.24%, specificity: 70.89%) and CX3CL1 (sensitivity: 78.43%, specificity: 69.16%) in predicting preeclampsia in GDM patients were both >0.50 ( P values: 0.015, 0.034, respectively), indicating that both have high predictive efficacy, with CXCL9 being slightly superior to CX3CL1. Conclusion:Decreased serum CXCL9 and increased CX3CL1 are associated with an increased risk of preeclampsia in GDM patients. Both can serve as auxiliary predictive indicators for preeclampsia in GDM patients.

The prevalence of chronic constipation among the elderly is significant, exerting adverse effects on both their physical and mental health.Presently, pharmacological therapy remains the predominant treatment modality for elderly patients with chronic constipation; however, prolonged use can lead to drug dependence, tolerance, and adverse effects.This article systematically reviews non-pharmacological interventions for chronic constipation in elderly patients, both domestically and internationally, aiming to provide a comprehensive reference for clinical practice.

Objective To investigate the relationship between tooth loss and the occurrence of esophageal cancer in a natural village in Wenfeng District, Anyang City, Henan Province. Methods A prospective cohort study was conducted to observe the occurrence of tooth loss and esophageal cancer among the asymptomatic residents of the natural village for 16 years from January 2008 to July 2024. Data were analyzed by chi-square test, binary logistic regression, and restricted cubic spline. Results Among the total population of 711 cases, 136 cases were lost to follow-up and 575 cases were included in the final statistics, including 45 cases with esophageal cancer. Significant statistical difference was found between esophageal cancer patients with and without tooth loss (P<0.05). Logistic regression analysis showed that tooth loss was associated with the occurrence of esophageal cancer (OR=3.977, 95%CI: 1.543-10.255). After the adjustment for confounders, tooth loss remained significantly associated with the occurrence of esophageal cancer (OR=3.038, 95%CI: 1.035-8.914). A nonlinear dose-response relationship was observed between the number of teeth lost and the incidence of esophageal cancer. When the number of teeth lost was less than 12, the risk of esophageal cancer increased with the number of teeth lost. When more than 12 teeth were lost, the risk of esophageal cancer decreased as the number of teeth lost increased. Conclusion Tooth loss is a risk factor for the occurrence of esophageal cancer in this natural village. When the number of teeth lost is less than 12, the risk of esophageal cancer increases with the number of teeth lost.