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.

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 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:To analyze the HIV antibody positivity of new type drug users in drug abuse monitoring sites in Zhejiang Province from 2017 to 2023 and its influencing factors.Methods:From 2017 to 2023, a continuous cross-sectional survey was carried out in HIV monitoring posts among new type drug users in Zhejiang Province,the sample size was 400 people per site of 9 drug abuse surveillance sites in 7 cities. Questionnaires were conducted to investigate their social demographic information, related behavioral information, AIDS awareness, and acceptance of intervention measures, and blood was collected for HIV and syphilis antibody detection, with new type drugs users in the monitoring population as the research object. SPSS 22.0 software was used for statistical analysis.Results:A total of 13 955 new drug users were surveyed, and the number of new drug users was 2 518, 2 292, 2 526, 2 119, 2 161, 1 064 and 1 275 from 2017 to 2023, respectively, the HIV antibody positive rate of new type drugs users was 0.44%, 1.09%, 2.06%, 1.09%, 1.39%, 1.50%, 2.90%, respectively, and the HIV antibody positive standardized rate showed an increasing trend (all P<0.001). The results of multivariate logistic regression analysis showed that marital status: unmarried/divorced/widowed (a OR=3.92, 95% CI: 2.46-6.25), provincial household registration (a OR=3.54, 95% CI: 2.34-5.35), high school education or above (a OR=5.42, 95% CI: 3.68-7.98), sexual activity within the last 1 year after drug use (a OR=1.84, 95% CI: 1.19-2.84), and knowledge that the use of new drugs increases the risk of HIV infection (a OR=2.27, 95% CI: 1.17-4.39) were associated with increased HIV antibody favorable rates among new type drugs users. Conclusions:During 2017-2023, the HIV antibody-positive rate of new type drug users in Zhejiang Province showed an upward trend. It is necessary to strengthen the monitoring and intervention of this population.

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 analyze the epidemiological and clinical characteristics of respiratory pathogens in China.Methods:This study was a cross-sectional study, which encompassed 19 core units of the clinical pathogen network and established a three-tiered clinical pathogen surveillance system. Thirty respiratory samples were collected every two weeks from various units from January to December 2024, and the clinical and pathogen diagnostic information were gathered. A total of 11 864 samples were tested using this system. The tier-1 clinical pathogen surveillance system covered influenza A virus (Flu-A), influenza B virus (Flu-B), respiratory syncytial virus (RSV), and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The tier-2 clinical pathogen surveillance system focused on 18 key respiratory pathogens. The tier-3 clinical pathogen surveillance system further clarified whether any emerging infectious diseases had occurred.Results:The tier-1 clinical pathogen surveillance system showed Flu-A predominated in December, Flu-B predominated in January, SARS-CoV-2 peaked in March and August, whereas RSV circulated sporadically throughout the year. Geographic trends were broadly consistent across the seven major regions, although Flu-A detection in December was notably higher in Northeast China (48.1%(111/231)) and East China (36.2%(148/409)), and RSV detection was concentrated in the Northwest and South China from January to March. Data from the tier-2 clinical pathogen surveillance system indicated that Streptococcus pneumoniae, Mycoplasma pneumoniae, rhinovirus, and adenovirus were detected year-round, of these, Streptococcus pneumoniae and rhinovirus showed elevated positive detection rates from August to September, while adenovirus peaked in January. Legionella pneumophila was not detected throughout the year, and other pathogens fluctuated throughout the year without a consistent pattern. The predominant etiologic agents of pediatric pneumonia were Mycoplasma pneumoniae (35.0%(105/300)), rhinovirus (25.7%(77/300)), and adenovirus (17.3%(52/300)), whereas adult pneumonia was mainly caused by Streptococcus pneumoniae (10.5%(29/277)), Staphylococcus aureus (6.9%(19/277)), Mycoplasma pneumoniae (6.9%(19/277)), and Flu-A (6.1%(17/277)). The tier-3 clinical pathogen surveillance system did not identify any emerging respiratory pathogens. Conclusion:Respiratory pathogens in China in 2024 exhibit distinct temporal and spatial distribution patterns and vary among different populations.