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.

Integrin α _v β ₃ is overexpressed in various tumor cells and angiogenesis. To date, no drug has been proven to target it for therapy. A first-in-human study was designed to investigate the safety, pharmacokinetics, and dosimetry of ¹⁷⁷Lu-AB-3PRGD2, a novel integrin α _v β ₃-targeting radionuclide drug with an albumin-binding motif to optimize the pharmacokinetics. Ten patients (3 men, 7 women; aged 45 ± 16 years) with integrin α _v β ₃-avid tumors were recruited to accept ¹⁷⁷Lu-AB-3PRGD2 injection in a dosage of 1.57 ± 0.08 GBq (42.32 ± 2.11 mCi), followed by serial scans to obtain its dynamic distribution in the body. Safety tests were performed before and every 2 weeks after the treatment for 6-8 weeks. No adverse event over grade 3 was observed. ¹⁷⁷Lu-AB-3PRGD2 was excreted mainly through the urinary system, with intense radioactivity in the kidneys and bladder. Moderate distribution was found in the liver, spleen, and intestines. The estimated blood half-life was 2.85 ± 2.17 h. The whole-body effective dose was 0.251 ± 0.047 mSv/MBq. The absorbed doses were 0.157 ± 0.032 mGy/MBq in red bone marrow and 0.684 ± 0.132 mGy/MBq in kidneys. This first-in-human study of ¹⁷⁷Lu-AB-3PRGD2 treatment indicates its promising potential for targeted radionuclide therapy of integrin α _v β ₃-avid tumors. It merits further studies in more patients with escalating doses and multiple treatment courses.

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 analyze the current quality of treatment for hospitalized cancer patients in Bei-jing,identify major issues in treatment practices,and propose improvements.Methods Nine hospitals in Beijing were selected for examination.Expert on-site interviews and medical record sampling were conducted.The"Bei-jing Cancer Diagnosis and Treatment Quality Control Checklist"was used to assess the hardware,management,anti-cancer drug therapy,radiation therapy,and surgical treatment during cancer treatment at these hospitals from January to October 2023.The relevant problems were analyzed.Results Among the nine hospitals,two(22.2％)were equipped with laminar flow rooms,and three(33.3％)had intravenous drug preparation centers.In terms of institutional management,seven hospitals(77.8％)had standardized anti-cancer drug prescription authority management,eight(88.9％)had complete emergency plans,and five(55.6％)had oncology specialist pharmacists.Regarding anti-cancer drug therapy,the areas with higher completion rates included pathology diag-nosis support(97.6％),routine pre-treatment examinations(96.3％),adverse reaction evaluation(92.7％),discharge summaries(95.1％),and admission records(91.5％).However,the accuracy of tumor staging before treatment(70.7％)and the evaluation of therapeutic efficacy after drug treatment(76.9％)needed improvement.The oncology specialty significantly outperformed the non-oncology specialty in terms of the accuracy rate of TNM staging(86.0％vs.46.9％,P＜0.001),the completeness of informed consent forms(100％vs.68.8％,P＜0.001),the completeness of drug indication evaluation(96.0％vs.78.1％,P=0.025),the completeness of admission medical history records(98.0％vs.81.3％,P=0.008),the rationality of drug dosage(96.0％vs.75.0％,P=0.005),the rationality of drug infusion time(100％vs.62.5％,P＜0.001),and the rationality of the order of drug infusion(100％vs.87.5％,P=0.010).Although the quality of radiation therapy was high,the subsequent evaluation of therapeutic efficacy(39.3％)requires enhancement.In surgical treatment,the preoper-ative pathology diagnosis support rate(78.1％)and the accuracy of tumor staging(37.5％)were relatively low,indicating issues with incomplete preoperative evaluation and the absence of multidisciplinary discussions.Conclusions There remains significant room for improvement in the quality of cancer treatment in China.It is recommended to standardize tumor staging assessment processes,strengthen entry assessments for non-oncology departments,promote the implementation of multidisciplinary treatment models,and establish a multi-department collaborative management model.Continuous monitoring of cancer diagnosis and treatment quality indicators is es-sential to promote ongoing improvements in cancer treatment quality.

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

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.