Autoimmune liver diseases （AILD） are a group of chronic liver diseases caused by abnormal activation of the immune system， mainly including autoimmune hepatitis， primary biliary cholangitis， primary sclerosing cholangitis， IgG4-related sclerosing cholangitis， and overlap syndrome. Clinical studies have shown that patients with AILD are often comorbid with thyroid diseases， especially autoimmune thyroid diseases （AITD）， such as Graves’ disease and Hashimoto’s thyroiditis. This article systematically reviews the epidemiological association， potential shared pathogenesis， and overlapping features between AILD and thyroid diseases. A deeper understanding of the immunological links between AILD and AITD may provide a theoretical basis for precision medicine and future research.

Objective To explore the risk prediction model of major adverse cardiovascular events (MACE) in community patients with hypertension based on remote ambulatory blood pressure parameters. Methods From November 2023 to October 2024, 486 community patients with hypertension who received standardized management in Nanjing Medical University Affiliated to Suzhou Hospital were retrospectively selected. All patients wore remote ambulatory blood pressure monitor to obtain 24-hour ambulatory blood pressure data. Clinical data were collected and remote ambulatory blood pressure parameters [24-hour systolic blood pressure variability (SBPV), 24-hour diastolic blood pressure variability (DBPV), nighttime SBPV, nighttime DBPV, daytime SBPV, daytime DBPV] were extracted. The patients were followed up for 12 months, and were classified into MACE group (n=42) and non-MACE group (n=444) according to whether MACE occurred during follow-up. Multivariate Logistic regression analysis was adopted to screen the influencing factors for MACE. Based on the above factors, a risk prediction model was constructed and verified by receiver operating characteristic (ROC) curve. Results MACE occurred in 42 cases among 486 patients, with an incidence rate of 8.64%. Multivariate Logistic regression analysis suggested that nighttime DBPV (OR=1.119, 95%CI: 1.030-1.214), 24h-SBPV (OR=1.115, 95%CI: 1.007-1.235), nighttime SBPV (OR=1.116, 95%CI: 1.016-1.226) and diabetes mellitus (OR=2.762, 95%CI: 1.059-7.203) were independent factors for MACE (P<0.05). The model validation results revealed that the area under the ROC curve was 0.905 (95%CI: 0.854-0.956 ), and the model had a good discrimination degree. Conclusion Nighttime DBPV, 24h-SBPV, nighttime SBPV and diabetes mellitus are independent risk factors for MACE in community patients with hypertension. The clinical prediction model based on these variables exhibits certain predictive value on MACE risk.

Purpose: Our previous study showed that circulating tumor cell (CTC) count combined with gene mutation detection might help differentiate benign and malignant pulmonary nodules (PNs). Herein, we aimed to expand the study cohort and conduct further sequencing analysis. Materials and Methods: Patients with PNs were included, and CTCs were identified before operation. Low-coverage whole-genome sequencing (LC-WGS) and lung cancer-related targeted gene sequencing were performed on CTCs. The diagnostic efficacy was evaluated by receiver operating characteristic (ROC) curve. The differences in CTC counts among subgroups classified by demographic–clinical characteristics were analyzed. LC-WGS–based copy number variation (CNV) analysis and targeted gene mutation analysis were conducted. Results: A total of 172 patients were included. CTC count of 2.5 was identified by the ROC curves as the optimal diagnostic cutoff. The sensitivity and specificity of CTC count for differentiating benign and malignant PNs were 54.2% and 78.6%, respectively. The diagnostic sensitivity and specificity of combined CTC count, radiological nodule type, and any malignant imaging features were 84.7% and 71.4%, respectively. The CTC counts were significantly greater in patients with aggressive tumors, later stage, and spread through air spaces. CTCs from malignant cases had more CNVs than those from benign cases. Conclusion CTC count can be used in identifying malignant PNs. The diagnostic efficacy can be improved if combined with computed tomography imaging characteristics. Further CNV analysis might help differential diagnosis. Greater CTC count might suggest more aggressive tumors. CTC detection can provide important information and guidance for subsequent management of PNs.

ObjectiveTo investigate the patterns of chronic disease comorbidity among permanent elderly residents in Xuhui District of Shanghai, and to analyze the impact of different comorbidity patterns on the elderly’s ability to perform activities of daily living. MethodsBased on data from the 2015 and 2021 Surveys on Health Status and Health Service Utilization among Permanent Residents in Xuhui District of Shanghai, the study included permanent residents who fully participated in both surveys, were aged 60 years or older at the 2015 survey, self-reported having a chronic disease, and reported no significant changes in their chronic disease status between the two surveys. A prospective cohort study design was adopted, using the 2015 data as the baseline and the 2021 survey data as follow-up data. Physical examinations and questionnaire surveys were conducted by the Shanghai Xuhui District Center for Disease Control and Prevention in the subdistricts and towns within the jurisdiction from April to July in both 2015 and 2021. Latent class analysis (LCA) was used to classify comorbidity patterns across 11 types of chronic diseases. The smaller the values of the Akaike information criterion (AIC), the Bayesian information criterion (BIC), and the sample-corrected Bayesian information criterion (aBIC), the better the model fits. The Barthel index (BI) and the Lawton and Brody Instrumental Activities of Daily Living Scale (LB-IADL) were used to assess the participants’ activities of daily living (ADL) and instrumental activities of daily living (IADL). Multiple linear regression and multivariate logistic regression models were used to analyze the association between different comorbidity patterns and ADL and IADL. ResultsA total of 1 608 study participants were enrolled. Among them, 671 (41.73%) self-reported having no chronic diseases. The prevalence of 11 types of chronic diseases was as follows: hypertension in 743 participants (46.21%), heart disease in 148 participants (9.20%), cerebrovascular disease in 37 participants (2.30%), diabetes or abnormal blood glucose in 310 participants (19.28%), other endocrine system diseases (non-diabetic) in 39 participants (2.43%), malignant tumors in 37 participants (2.30%), chronic lung disease in 16 participants (1.00%), gout in 8 participants (0.50%), musculoskeletal disorders in 34 participants (2.11%), gastric or digestive system diseases in 27 participants (1.68%), and dyslipidemia in 15 participants (0.93%). LCA was used to classify comorbidity patterns of chronic diseases among older adults into 1 to 9 latent category combinations. Model 5 (i.e., 5 latent categories) had the lowest BIC (7 112.69) and aBIC (6 909.38), with a relatively low AIC (6 768.20), indicating the best model fit; thus, five latent categories represented the optimal combination in this study. The five latent categories (and their respective proportions of study participants) were the healthy control group (41.73%), the hypertension group (31.28%), the diabetes-hypertension group (19.28%), the severe illness group (5.29%), and the other metabolic diseases group (2.43%). According to multiple linear regression analyses, compared with the healthy control group, the diabetes-hypertension group had significantly lower BI-20 scores (b=-0.952, 95%CI: -1.317‒ -0.587, P<0.001) and lower IADL scores (b=-0.744, 95%CI: -0.999‒ -0.489, P<0.001); the severe illness group (b=-0.644, 95%CI: -1.072‒ -0.217, P=0.003) and the hypertension group (b=-0.344, 95%CI: -0.562‒ -0.125, P=0.002) had lower IADL scores. According to multivariate logistic regression analyses, compared with the healthy control group, older adults in the diabetes-hypertension group had a higher risk of disability (OR=2.835, 95%CI: 1.392‒5.774), impaired ADL function (OR=2.470, 95%CI: 1.637‒3.726), and impaired IADL function (OR=1.739, 95%CI: 1.264‒2.392); older adults in the severe illness group had a higher risk of impaired ADL function (OR=2.206, 95%CI: 1.171‒4.155). ConclusionAmong the elderly participating in the Survey on Health Status and Health Service Utilization of Permanent Residents in Xuhui District of Shanghai, diabetes-hypertension is one of the key chronic disease comorbidity combinations and has adverse effects on both ADL and IADL. Community health management should prioritize the elderly with diabetes-hypertension comorbidity, and strengthen early screening and comprehensive interventions to slow the decline in ADL and IADL.

BACKGROUND: Postoperative pulmonary complications (PPCs) are major adverse events in neurosurgical patients. This study aimed to develop and validate machine learning models predicting PPCs after neurosurgery. METHODS: PPCs were defined according to the European Perioperative Clinical Outcome standards as occurring within 7 postoperative days. Data of cases meeting inclusion/exclusion criteria were extracted from the anesthesia information management system to create three datasets: The development (data of Huashan Hospital, Fudan University from 2018 to 2020), temporal validation (data of Huashan Hospital, Fudan University in 2021) and external validation (data of other three hospitals in 2023) datasets. Machine learning models of six algorithms were trained using either 35 retrievable and plausible features or the 11 features selected by Lasso regression. Temporal validation was conducted for all models and the 11-feature models were also externally validated. Independent risk factors were identified and feature importance in top models was analyzed. RESULTS: PPCs occurred in 712 of 7533 (9.5%), 258 of 2824 (9.1%), and 207 of 2300 (9.0%) patients in the development, temporal validation and external validation datasets, respectively. During cross-validation training, all models except Bayes demonstrated good discrimination with an area under the receiver operating characteristic curve (AUC) of 0.840. In temporal validation of full-feature models, deep neural network (DNN) performed the best with an AUC of 0.835 (95% confidence interval [CI]: 0.805-0.858) and a Brier score of 0.069, followed by Logistic regression (LR), random forest and XGBoost. The 11-feature models performed comparable to full-feature models with very close but statistically significantly lower AUCs, with the top models of DNN and LR in temporal and external validations. An 11-feature nomogram was drawn based on the LR algorithm and it outperformed the minimally modified Assess respiratory RIsk in Surgical patients in CATalonia (ARISCAT) and Laparoscopic Surgery Video Educational Guidelines (LAS VEGAS) scores with a higher AUC (LR: 0.824, ARISCAT: 0.672, LAS: 0.663). Independent risk factors based on multivariate LR mostly overlapped with Lasso-selected features, but lacked consistency with the important features using the Shapley additive explanation (SHAP) method of the LR model. CONCLUSIONS: The developed models, especially the DNN model and the nomogram, had good discrimination and calibration, and could be used for predicting PPCs in neurosurgical patients. The establishment of machine learning models and the ascertainment of risk factors might assist clinical decision support for improving surgical outcomes. TRIAL REGISTRATION ChiCTR 2100047474; https://www.chictr.org.cn/showproj.html?proj=128279 .
Adult ; Aged ; Female ; Humans ; Male ; Middle Aged ; Algorithms ; Lung Diseases/etiology* ; Machine Learning ; Neurosurgical Procedures/adverse effects* ; Postoperative Complications/diagnosis* ; Risk Factors ; ROC Curve

[Purpose]To evaluate the liver and kidney safety of traditional Chinese medicine(TCM)compounds in the treatment of gastric cancer.[Methods]The clinical data of gastric cancer patients who attended in Longhua Hospital Affiliated to Shanghai University of Traditional Chinese Medicine from January 2012 to February 2022 were collected.The results of liver and renal func-tion tests were rated according to National Cancer Institute-Common Terminology Criteria for Ad-verse Events(NCI-CTCAE)V5.0.The patients were divided into 4 groups:TCM group,TCM+chemotherapy/targeted/antivascular therapy group,TCM+TCM preparation group and TCM+chemotherapy/targeted/antivascular therapy+TCM preparation group,and the effects of TCM on liver and renal functions were analyzed.[Results]A total of 7 943 patients were included in the analysis,of which 2 941 cases receiving TCM ≥12 months,1 468 months ≥36 months,and 687 months≥60 months.The highest incidence rate of liver function abnormality was 13.71％,the highest in-cidence rate of grade 3/4 abnormality was 2.58％;the highest incidence of creatinine abnormality was 2.32％,the highest incidence rate of grade 3/4 abnormality was 0.37％in patients with differ-ent duration of taking TCM.Most of liver and renal function abnormalities occurred in the early stage of drug taking.The incidence of liver and renal function abnormalities in the TCM group was lower than that of the other three groups.The incidence of grade 3/4 abnormality for direct biliru-bin(DBIL)in the TCM group was 0.14％,for aspartate transaminase(AST)was 0.11％,for alka-line phosphatase(ALP)was 0.16％,for alanine aminotransferase(ALT)was 0.06％and for total bilirubin(TBIL)was 0.07％,and there was no grade 3/4 creatinine abnormality observed.The ab-normal liver and renal function indexes were not increased with the increase of the length of herbal medicine taking.[Conclusion]The study shows that long term taking TCM drugs and stan-dardized prescriptions for gastric cancer patients are safe.

Objective:To investigate the prevalence of and risk factors associated with Neisseria gonorrhoeae (NG), Chlamydia trachomatis (CT) and high-risk human papillomavirus (HPV) coinfections among patients with newly diagnosed syphilis at a hospital in Nanjing. Methods:A cross-sectional study was conducted on patients with newly diagnosed syphilis at the STD Clinic, Hospital of Dermatology, Chinese Academy of Medical Sciences in Nanjing, China from May 2023 to April 2024. Urethral, cervical or rectal swabs were collected according to the gender and types of sexual behavior of the patients. Screening tests for CT, NG and HPV infections were then performed by nucleic acid amplification testing. High-risk HPV testing was performed only in female patients. Univariate analysis was carried out to investigate risk factors associated with CT infection. The chi-square test, chi-square test with continuity correction, or Fisher's exact test was chosen based on the sample size and expected values.Results:A total of 111 newly diagnosed syphilis patients with test specimens were collected, including 71 males (64%) and 40 females (36%), and their ages ranged from 16 to 87 years; there were 82 patients with early syphilis and 29 with late latent syphilis; the number of sexual partners in the past 3 months ranged from 0 to 3. Among the 60 male patients with early syphilis, 1 (1.7%) was co-infected with NG, and 12 (20%) with CT; among the 16 men who have sex with men, 7 (43.8%) were co-infected with CT, while 5 (11.4%) of the 44 heterosexual patients were co-infected with CT, showing a significant difference in CT infection rates between the two groups ( χ2 = 5.80, P = 0.016). Among the 22 female patients with early syphilis, 1 (4.5%) was co-infected with NG, and 8 (36.4%) with CT; among the 12 female patients aged < 25 years, 8 (66.7%) were infected with CT, while none of the 7 patients aged 25-44 years or the 3 patients aged ≥ 45 years were infected with CT, showing a significant difference in CT infection rates among the 3 age groups ( P = 0.005) ; among the 16 female patients with 1 sexual partner in the past 3 months, 3 were infected with CT, while 5 were infected with CT in the 6 female patients with 2-3 sexual partners in the past 3 months, with a significant difference in CT infection rates between the two groups ( P = 0.011). Out of the 40 female patients with syphilis, 16 (40%) were co-infected with high-risk HPV; the HPV infection rates did not differ among different age groups (age groups of < 25 years, 25-44 years, ≥ 45 years: 8/14, 2/9, 6/17, respectively; P = 0.265) . Conclusion:The CT infection rate was relatively high in patients with newly diagnosed early syphilis, and a relatively high infection rate of high-risk HPV was observed in female patients with syphilis aged < 25 years.

Objective:This study aimed to analyze the genetic subtypes and drug resistance transmission characteristics of HIV-1 among the preoperative population in Ningxia from 2018 to 2023, to provide a scientific basis for the prevention and control of the AIDS epidemic.Methods:Plasma samples and demographic information of HIV/AIDS patients receiving antiviral treatment in Ningxia from 2018 to 2023 were collected. Blood samples with a viral loads >200 copies/ml from preoperative testing were amplified, sequenced, and subjected to genotypic resistance testing to analyze their genetic subtypes and drug resistance characteristics. The TN93 model in MEGA11 software was used to calculate the genetic distance between each pair of all sequences, and a molecular transmission network was constructed in Cytoscape 3.10.0 with 1.9% as the genetic threshold.Results:Among 101 preoperative HIV/AIDS patients, CRF07_BC and CRF01_AE were the predominant subtypes. The majority were male (85.15%, 86/101), aged 41-60 years (45.54%, 46/101), residing in Yinchuan city (61.39%, 62/101), and infected via heterosexual transmission (71.29%, 72/101), with most cases being late-detected. Of 39 drug-resistant sequences, resistance to non-nucleoside reverse transcriptase inhibitors (NNRTIs) alone (18.81%, 19/101) and dual resistance to nucleoside reverse transcriptase inhibitors (NRTIs)-NNRTIs (13.86%, 14/101) were most common. Among 44 sequences forming 13 transmission clusters, nine clusters harbored drug-resistant mutations. Four subtypes entered the molecular network, primarily involving heterosexual transmission, individuals with junior high school education or below, and men aged≥50 years.Conclusions:From 2018 to 2023, the preoperative HIV/AIDS patients had diversified genetic subtypes, with higher rates of overall drug resistance and late detection, stronger drug resistance and higher mortality rate. Strengthening molecular epidemiological research and developing targeted screening strategies are critical to improve early detection and reduce transmission risks.

Objective To explore the risk factors associated with the development of acute respir-atory distress syndrome(ARDS)in patients with severe traumatic brain injury(sTBI)and to construct and validate a risk prediction model for ARDS in these patients.Methods Clinical data from 371 sTBI patients admitted to Yangzhou Affiliated Hospital of Yangzhou University between January 2017 and December 2023 were retrospectively collected.Patients were randomly divided into modeling group(n=259)and validation group(n=112)at a 7-to-3 ratio.A nomogram model was constructed after screening for risk factors using the Least Absolute Shrinkage and Selection Operator(LASSO)and multivariate Logistic regression analysis.Model performance was evaluated using the receiver operating characteristic(ROC)curve,area under the curve(AUC),Hosmer-Lemeshow test,calibration curve,and deci-sion curve analysis(DCA).Results Statistically significant differences were observed in heart rate,respiratory rate,pupil size,percutaneous oxygen saturation(SpO2),Glasgow Coma Scale(GCS)score,Acute Physiology and Chronic Health Evaluation Ⅱ(APACHE Ⅱ)score,head Ab-breviated Injury Scale(AIS)score,chest AIS score,emergency intubation,pulmonary infection,associated chest trauma,midline shift,blood transfusion within 12 hours of admission,fluid intake within 24 hours of admission,shock,mechanical ventilation,hemoglobin level,hematocrit,white blood cell count,prothrombin time,international normalized ratio,total protein,albumin,serum calcium,oxygenation index,and base excess between the two groups(P＜0.05).Multivariate Lo-gistic regression analysis revealed that SpO2,pulmonary infection,and fluid intake within 24 hours of admission were predictors of ARDS in sTBI patients.The Hosmer-Lemeshow test results for the modeling and validation groups showed good fit(x2=10.373,P=0.240;x2=13.21,P=0.105).DCA results for both groups indicated net benefit at threshold probabilities ranging from 0％to 72％and 0％to 50％,respectively.Conclusion SpO2,pulmonary infection,and fluid in-take within 24 hours of admission are risk factors for ARDS in sTBI patients.The model constructed using these factors demonstrates good performance and provides a reliable tool for clinical screening of high-risk ARDS populations among sTBI patients.

Objective:In critical care medicine, extubation is a pivotal step in the management of mechanically ventilated patients. Accurately determining the optimal timing for extubation is essential for minimizing complications and improving patient survival rates. However, reliable indicators to predict clinical outcomes following extubation remain scarce. This study aims to identify a novel and robust predictor of extubation success in critically ill patients, thereby providing clinicians with more precise decision-making support.Methods:This retrospective study analyzed data from adult patients who underwent mechanical ventilation and were evaluated for extubation across six intensive care units (ICUs) at Xiangya Third Hospital of Central South University between January 2019 and December 2021. Patients with a history of difficult airway, upper airway obstruction, or neuromuscular disorders affecting respiratory function were excluded. The primary outcome was the reintubation rate within 24 hours post-extubation. Categorical variables were analyzed using the chi-square test or Fisher’s exact test, while between-group differences were assessed with the Mann-Whitney U test. Significant predictors identified in univariate analysis were further evaluated via multivariate logistic regression. The diagnostic accuracy of the maximum inspiratory pressure/body mass index (MIP/BMI) ratio was determined using receiver operating characteristic (ROC) curve analysis, with the Youden index employed to establish the optimal cutoff value. Kaplan-Meier analysis and log-rank tests were used to compare extubation success rates between groups. Statistical analyses were performed using SPSS V28.0 and Stata v.16.0. Results:Diabetes comorbidity ( OR: 8.181, 95% CI: 1.659–40.338) and MIP/BMI ( OR: 0.140, 95% CI: 0.042–0.469) were identified as independent predictors of reintubation. The area under the ROC curve (AUROC) for MIP/BMI was 0.753, demonstrating good predictive accuracy. The optimal cutoff value for MIP/BMI was 1.26 cmH 2O/(kg·m 2), with a sensitivity of 55.3% and specificity of 92.3%. Kaplan-Meier analysis revealed a significantly higher reintubation rate in the low MIP/BMI group compared to the high MIP/BMI group ( P = 0.009), further validating its predictive utility. Conclusions:This study establishes MIP/BMI as a novel and clinically valuable predictor of extubation outcomes in critically ill patients. A cutoff value of 1.26 cmH 2O/(kg·m 2) was found to best predict successful extubation.