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

Nigella sativa L. seeds have been traditionally utilized in Chinese folk medicine for centuries to treat vitiligo. This study revealed that the ethanolic extract of Nigella sativa L. (HZC) enhances melanogenesis and mitigates oxidative stress-induced cellular senescence and dysfunction in melanocytes. In accordance with established protocols, the ethanol fraction from Nigella sativa L. seeds was extracted, concentrated, and lyophilized to evaluate its herbal effects via 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assays, tyrosinase activity evaluation, measurement of cellular melanin contents, scratch assays, senescence-associated β-galactosidase (SA-β-gal) staining, enzyme-linked immunosorbent assay (ELISA), and Western blot analysis for expression profiling of experimentally relevant proteins. The results indicated that HZC significantly enhanced tyrosinase activity and melanin content while notably increasing the protein expression levels of Tyr, Mitf, and gp100 in B16F10 cells. Furthermore, HZC effectively mitigated oxidative stress-induced cellular senescence, improved melanocyte condition, and rectified various functional impairments associated with melanocyte dysfunction. These findings suggest that HZC increases melanin synthesis in melanocytes through the activation of the MAPK, PKA, and Wnt signaling pathways. In addition, HZC attenuates oxidative damage induced by H₂O₂ therapy by activating the nuclear factor E2-related factor 2-antioxidant response element (Nrf2-ARE) pathway and enhancing the activity of downstream antioxidant enzymes, thus preventing premature senescence and dysfunction in melanocytes.
Oxidative Stress/drug effects* ; Melanocytes/cytology* ; Cellular Senescence/drug effects* ; Nigella sativa/chemistry* ; Plant Extracts/pharmacology* ; Seeds/chemistry* ; Mice ; Animals ; Melanins/metabolism* ; Monophenol Monooxygenase/metabolism* ; Humans

Congenital orofacial cleft, the most common birth defect in the maxillofacial region, exhibits a wide range of prognosis depending on the severity of deformity and underlying etiology. Non-syndromic congenital orofacial clefts typically present with milder deformities and more favorable treatment outcomes, whereas syndromic congenital orofacial clefts often manifest with concomitant organ abnormalities, which pose greater challenges for treatment and result in poorer prognosis. This consensus provides an elaborate classification system for varying degrees of orofacial clefts along with corresponding diagnostic and therapeutic guidelines. Results serve as a crucial resource for families to navigate prenatal screening results or make informed decisions regarding treatment options while also contributing significantly to preventing serious birth defects within the development of population.
Humans ; Cleft Lip/diagnosis* ; Cleft Palate/diagnosis* ; Consensus ; Prenatal Diagnosis ; Female

Objective To explore the mechanism of Xiakucao Xiaoliu Mixture against non-small cell lung cancer （NSCLC）. Methods The effective components of Xiakucao Xiaoliu Mixture were screened by TCMSP, BATMAN-TCM database and literature reviews. The targets of effective components were predicted. NSCLC related targets were collected by GeneCards, OMIM, PharmGKB, TTD and Drugbank, combined with the differential genes of Xiakucao Xiaoliu Mixture against Lewis lung cancer mice. The intersection targets of Xiakucao Xiaoliu Mixture against NSCLC were obtained. Cytoscape software was used to construct the network diagram of traditional Chinese medicine-active ingredients-core targets. STRING database was used to construct PPI network diagram. GO function enrichment analysis and KEGG pathway enrichment analysis of the intersection targets were performed by Metascape database to predict the key targets and active components of Xiakucao Xiaoliu Mixture against NSCLC, and Schrodinger software was used to perform molecular docking verification. Results The 32 active components and 24 intersection targets of Xiakucao Xiaoliu Mixture against NSCLC were obtained. 11 core targets such as ESR1, MAPK1 were found. The mechanism of action may be related to 30 signaling pathways such as cellular senescence, receptor activation, prolactin signaling pathway. Conclusion The active components of Xiakucao Xiaoliu Mixture act on multiple targets and signaling pathways to regulate complex biological processes. By regulating ESR1 agsinst NSCLC, it may play an important role in improving the survival rate and prognosis of female patients.

Objective: To investigate the efficacy, nursing characteristics, and management of different desensitization strategies before allogeneic hematopoietic stem cell transplantation (allo-HSCT) among patients with high level of human leukocyte antigen (HLA) antibodies. Methods: A retrospective analysis was conducted on 82 patients with high levels of HLA antibodies who underwent allo-HSCT at the First Affiliated Hospital of Soochow University and Suzhou Hopes Hematonosis Hospital between January 2020 to November 2023. Patients were divided into two groups based on the desensitization strategy they received: the anti-CD20 monoclonal antibody combined with therapeutic plasma exchange (TPE) group (n=50) and the anti-CD20 monoclonal antibody combined with Protein A immunoabsorption group (n=32). The differences of efficacy between the desensitization strategies were analyzed. The safety of both desensitization strategies were assessed by close monitoring of adverse events throughout the treatment. The nursing characteristics and interventions specific to these strategies were comprehensively summarized. Results: There were no significant differences in age, gender, and diagnosis between the two groups of patients receiving different desensitization strategies (P>0.05). Following desensitization in the immunoadsorption group, the mean fluorescence intensity (MFI) levels of anti-HLA Class I antibody decreased significantly compared to initial screening (P=0.048), while the decrease in MFI values of anti-HLA Class II antibody was not statistically significant (P=0.173). In the TPE group, the MFI levels for both anti-HLA Class I and II antibodies after desensitization decreased significantly compared to initial screening (P=0.025 and 0.028, respectively). Monitoring of adverse events during desensitization treatment, found that patients in the immunoadsorption group experienced mild decreases in blood pressure during the process, with two patients developing severe hypotension. No allergic reactions occurred, and no damage of liver or kidney function was observed after the immunoadsorption. In the immunoadsorption group, a total of 19 patients underwent sera immunoglobulin assays before and after immunoadsorption. Compared to the initial screening, the immunoglobulin G (IgG) levels significantly decreased after immunoadsorption (P<0.001). In TPE group, 12 patients experienced mild hypotension during the plasma exchange process, but no severe hypotension was observed. One patient developed an allergic reaction. After the TPE treatment, no damage of liver or kidney function was observed, nor any decrease of IgG levels. In terms of safety of intravenous access, neither group experienced severe complications such as catheter-related bloodstream infections or deep vein thrombosis. In the TPE group, catheter occlusion occurred during the process of plasma exchange in 2 patients, while no such incident was observed in the immunoadsorption group. Patients of both groups exhibited anxiety and depression before treatment. After psychological care, the scores for anxiety and depression significantly decreased (P<0.001). Conclusion: Both desensitization strategies significantly decreased the HLA antibodies in highly sensitized patients with high level of HLA antibodies undergoing allo-HSCT. For patients receiving immunoabsorption, nursing care should focus on preventing and managing hypotension and implementing infection-prevention measures due to IgG depletion. In contrast, for those undergoing TPE, vigilant monitoring and prompt management of potential allergic reactions are essential components of nursing practice.

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.