BackgroundPolychlorinated dibenzo‑p‑dioxins （PCDDs） is a type of global environmental pollutants， have been shown in numerous studies to exert neurotoxic effects. Nevertheless， existing research has predominantly focused on 2，3，7，8‑tetrachlorodibenzo‑p‑dioxin （2，3，7，8‑TCDD）. Whether low‑chlorinated PCDDs （LC‑PCDDs）， a subclass with fewer chlorine substitutions， can induce depression as well as the corresponding molecular mechanisms remains unclear. ObjectiveTo elucidate molecular mechanisms underlying depression induced by LC‑PCDDs， so as to provide evidence for environmental neuro-psychotoxicological risk assessment. Methods①Network toxicology. Four compounds， namely 2-monochlorodibenzo-p-dioxin （2-MCDD）， dichlorodibenzo-p-dioxin （DCDD）， 1，2，4‑trichlorodibenzo‑p‑dioxin （1，2，4-TrCDD）， and 2，3，7-TrCDD， were selected as research subjects. Their toxic profiles were predicted using public databases， and target genes associated with LC-PCDDs and depression were retrieved. The overlapping targets were imported into the STRING database to construct a protein-protein interaction （PPI） network. Topological analysis was subsequently performed via the CytoNCA plugin in Cytoscape to identify hub targets. Finally， Gene Ontology （GO） and Kyoto Encyclopedia of Genes and Genomes （KEGG） enrichment analyses for these hub targets were completed in DAVID. ②Molecular docking. Docking simulations were conducted using AutoDock Tools 1.5.7， and the binding poses and interaction profiles of the resultant protein-ligand complexes were visualized using PyMOL （TM） 3.1.6.1. ③Molecular dynamics simulation. A 100-ns production simulation for the top-ranked docked complex was carried out using GROMACS 2025. The protein was parameterized with the AMBER99SB-ILDN force field， the ligand topologies were generated using GAFF2， and the two were then merged to construct the protein-ligand complex system. The complex was solvated in TIP3P water， and the system was adjusted to physiological ionic strength by adding 0.15 mol/L Na⁺/Cl⁻. Following energy minimization and sequential 2-ns equilibrations in canonical and isothermal-isobaric ensembles， a 100-ns production run was executed at 310 K and 1 bar. Results①Network toxicology analysis identified 10 hub targets （ESR1， HSP90AA1， PPARG， EGFR， MAPK1， MAPK14， AR， PGR， CDK2， and TGFBR1）， which were enriched in multiple critical signaling pathways. ②Molecular docking revealed that all four compounds exhibited strong binding affinities for hub targets， with 2-MCDD showing the highest binding affinity for PPARG （ΔG=-8.836 kcal/mol）. ③Molecular dynamics simulations demonstrated that the ligand-receptor complex formed by 2-MCDD and PPARG maintained a stable binding conformation throughout the 100-ns simulation. ConclusionLC-PCDDs may serve as significant environmental risk factors for depression， participating in its pathogenesis by mediating a multidimensional regulatory network involving metabolism， oxidative damage and apoptosis. ［Funded by Hubei Province Traditional Chinese Medicine Clinical Excellent Talents Project］.

Objective To observe the value of whole-brain CT perfusion(CTP)parameters at different time and clinical data for predicting delayed cerebral ischemia(DCI)and 3-month poor prognosis in patients with aneurysmal subarachnoid hemorrhage(aSAH).Methods Totally 127 aSAH patients were retrospectively enrolled.Clinical and CTP data within 24 h of symptom onset and during DCI time window(DCITW)were collected.The patients were divided into DCI group(n=34)and non-DCI group(n=93)based on DCI occurred or not during hospitalization,also into poor outcome group(modified Rankin scale[mRS]≥3,n=36)and good outcome group(mRS≤2)based on 3-month's follow-up.Multivariate logistic regression was performed to select independent predictive factors among variates being significantly different between groups.Then receiver operating characteristic curve was drawn,and the area under the curve(AUC)was calculated to evaluate the predictive performance of logistic regression model.Results Patients'age,modified Fisher score(mFS),subarachnoid hemorrhage early brain edema score(SEBES)and mean flow extraction product(mFEP)within 24 h of onset were all identified as independent predictive factors of DCI,and the AUC of their combination for predicting DCI during hospitalization was 0.817.Patients' age and mFS within 24 h of onset,alternatively,World Federation of Neurosurgical Societies(WFNS)grade and mFEP during DCITW were all independent predictive predictors of 3 months' prognosis,and the combination of the latter two showed better predictive performance(AUC=0.922)tahn the former two(AUC=0.822,P＜0.05).Conclusion Whole-brain CTP parameters combined with clinical data within 24 h of onset of aSAH could be used to predict the occurrence of DCI during hospitalization,whole-brain CTP parameters during DCITW could be used to predict 3 months'poor prognosis.

Objective To observe the value of whole-brain CT perfusion(CTP)parameters at different time and clinical data for predicting delayed cerebral ischemia(DCI)and 3-month poor prognosis in patients with aneurysmal subarachnoid hemorrhage(aSAH).Methods Totally 127 aSAH patients were retrospectively enrolled.Clinical and CTP data within 24 h of symptom onset and during DCI time window(DCITW)were collected.The patients were divided into DCI group(n=34)and non-DCI group(n=93)based on DCI occurred or not during hospitalization,also into poor outcome group(modified Rankin scale[mRS]≥3,n=36)and good outcome group(mRS≤2)based on 3-month's follow-up.Multivariate logistic regression was performed to select independent predictive factors among variates being significantly different between groups.Then receiver operating characteristic curve was drawn,and the area under the curve(AUC)was calculated to evaluate the predictive performance of logistic regression model.Results Patients'age,modified Fisher score(mFS),subarachnoid hemorrhage early brain edema score(SEBES)and mean flow extraction product(mFEP)within 24 h of onset were all identified as independent predictive factors of DCI,and the AUC of their combination for predicting DCI during hospitalization was 0.817.Patients' age and mFS within 24 h of onset,alternatively,World Federation of Neurosurgical Societies(WFNS)grade and mFEP during DCITW were all independent predictive predictors of 3 months' prognosis,and the combination of the latter two showed better predictive performance(AUC=0.922)tahn the former two(AUC=0.822,P＜0.05).Conclusion Whole-brain CTP parameters combined with clinical data within 24 h of onset of aSAH could be used to predict the occurrence of DCI during hospitalization,whole-brain CTP parameters during DCITW could be used to predict 3 months'poor prognosis.

Objective:To investigate the association between quantified CT (QCT)-measured body composition and metabolic syndrome (MS) components in obese populations before bariatric surgery.Methods:A cross-sectional study. A retrospective analysis was conducted on a cohort of 97 obese patients scheduled for weight-loss surgery at the First Affiliated Hospital of Wannan Medical College from January 2021 to March 2023. The patients′ body mass index (BMI), biochemical parameters and body composition measurements obtained by QCT were recorded. The patients were stratified into groups based on gender, obesity severity and the number of MS components. Differences in body composition among the groups were compared. Additionally, the correlations between each body composition parameter and metabolic indicators were analyzed. The diagnostic efficacy of each body composition parameter for identifying obese individuals with different MS components was assessed using receiver operating characteristic (ROC) curve analysis.Results:There were 75 females (77.3%). Male obese patients had higher total abdominal fat area [(693.23±148.90) vs (574.99±114.89) cm 2, t=-3.958, P<0.001], visceral fat area [(289.65±57.67) vs (195.60±57.37) cm 2, t=-6.753, P<0.001], fat content of pancreatic head [27.45%(21.65%, 45.48%) vs 21.60%(17.6%, 26.9%), Z=-2.675, P=0.007], and skeletal muscle index [73.36(68.74, 81.26) vs 61.52(55.74, 66.41) cm 2/m 2, Z=-5.246, P<0.001]. With the increase of obesity, abdominal fat mainly increases in subcutaneous fat. With the increase of MS components (MS2 group, MS3 group, MS4 group, MS5 group), the abdominal fat area, abdominal fat/subcutaneous fat, liver fat content, pancreatic head fat content, and skeletal muscle index of patients all increased accordingly. In diagnosing the presence of two components of MS, area under the curve of visceral fat area was the largest (AUC=0.706, 95% CI=0.577-0.834). For diagnosing the presence of three, four and five components of MS, area under curve of liver fat content were all the largest (MS3=0.712, 95% CI=0.605-0.818; MS4=0.652, 95% CI=0.537-0.766; MS5=0.706, 95% CI=0.576-0.836). Conclusion:There are differences in QCT body composition among obese patients with different MS components, and there is a correlation between each body composition and MS component. Among them, intra-abdominal fat area and liver fat content are of great value in evaluating obese patients with different MS components.