Objective: To explore the relationship between non suicidal self injury (NSSI) behavior and serum lipid levels as well as thyroid function among college students with depression. Methods: A total of 169 college students with depression in the psychiatry departments of tertiary hospitals (grade 3A and 3B) in Ningbo from December 2023 to April 2025 were selected. The Adolescent Self injury Scale (ASIS) was used to assess the presence of NSSI, and participants were accordingly divided into a NSSI group ( n =51) and a non NSSI group ( n =118). General demographic data (including gender, age, and family situation) were collected from both groups. Blood tests were performed to measure lipid profiles [triglyceride (TG), total cholesterol (TC), high density lipoprotein cholesterol (HDL-C), low density lipoprotein cholesterol (LDL-C)] and thyroid hormones [triiodothyronine (T3), thyroxine (T4), free triiodothyronine (FT3), free thyroxine (FT4), thyroid stimulating hormone (TSH)]. Multivariate Logistic regression was employed to analyze risk factors for NSSI, and receiver operating characteristic (ROC) curve analysis was used to evaluate the predictive value of serum lipid and thyroid hormone levels for NSSI occurrence in college students with depression. Results: The levels of TC, LDL-C, and TSH in the NSSI group were (4.02±0.73) mmol/L, (2.32±0.36) mmol/L, and (6.57±1.95) mU/L , which were significantly higher than those in the non NSSI group [(3.41±0.56) mmol/L, (2.00±0.27) mmol/L, and ( 4.48± 1.09) mU/L, respectively] ( t =5.32, 5.60, 7.20, all P <0.05). Logistic regression analysis revealed that college students from single parent/reconstituted families, those who had experienced school bullying, and those with higher levels of TC, LDL-C, and TSH had a significantly increased risk of engaging in NSSI ( OR =5.22, 6.12, 5.90, 83.64, 3.64, all P <0.05). ROC curve analysis demonstrated that the combined detection of TC, LDL-C, and TSH had high diagnostic efficacy for predicting NSSI in college students with depression, with a sensitivity of 86.3% and a specificity of 94.9%. Conclusions NSSI behavior in college students with depression is associated with serum lipid levels and thyroid function. These biomarkers may serve as useful reference indicators for assessing the conditions of these patients.

Immunoglobulin G4-related disease (IgG4-RD) is a rare systemic immune-mediated disorder characterized by infiltration of IgG4-positive plasma cells and multiorgan fibrosis. Among them, cases with multiple pancreatic cystic lesions as the prominent manifestation are relatively rare, and they are easily misdiagnosed as pancreatic tumors or pancreatitis in clinical practice. Herein, we report one patient with IgG4-RD admitted to Peking Union Medical College Hospital. The patient initially presented with multiple pancreatic cystic lesions and aneurysmal dilatation of the abdominal aorta, accompanied by markedly elevated serum IgG4 levels. Combined with pathological findings from submandibular gland biopsy, the diagnosis of IgG4-RD was ultimately established. After treatment with a regimen of glucocorticoids combined with cyclophosphamide, the patient′s condition improved. Meanwhile, we performed a literature review of cases with IgG4-RD-related pancreatic cystic lesions, aiming to improve clinicians′ understanding of this rare disease phenotype.

Objective To investigate the relationship between inflammation index and the severity of cere-bral small vessel disease.Methods A total of 168 patients with cerebral small vessel disease who were admit-ted to the Eighth People's Hospital of Hebei from August 2019 to October 2022 were selected as the study ob-jects.The study subjects were divided into cerebral small vessel disease with light burden(light burden group)and cerebral small vessel disease with medium-heavy burden(medium-heavy burden group)according to the total imaging burden score of cerebral small vessel disease.The basic data and systemic immune-inflam-matory index(SII),neutrophil to lymphocyte ratio(NLR),platelet to lymphocyte ratio(PLR),and monocyte to lymphocyte ratio(MLR)were compared between the two groups.Spearman correlation analysis was used to evaluate the correlation between SII,NLR,PLR and MLR and the total imaging burden score of cerebral small vessel disease,and the influence of SII,NLR,PLR and MLR on the medium-heavy burden of cerebral small vessel disease was further analyzed by multivariate Logistic regression.Receiver operating characteristic(ROC)curves were plotted to analyze the predictive value of SII,NLR,PLR and MLR in the severity of cere-bral small vessel disease.Results The age,smoking history,hypertension history,neutrophil,platelet,SII,NLR,PLR and MLR in medium-heavy burden group were significantly higher than those in light burden group,and lymphocyte was significantly lower than that in light burden group,with statistical significance(P<0.05).Spearman correlation analysis showed that SII(r=0.377),NLR(r=0.516),PLR(r=0.486)and MLR(r=0.391)were positively correlated with the total imaging burden score of cerebral small vessel disease(P<0.001).Multivariate Logistic regression analysis showed that age,neutrophil,platelet,SII,NLR,PLR and MLR were independent risk factors for medium-heavy burden in cerebral small vessel disease,and lymphocyte was independent protective factor for medium-heavy burden in cerebral small vessel disease(P<0.05).ROC curve analysis results showed that the area under the curve of SII,NLR,PLR and MLR separately detected were 0.720(95%CI:0.646-0.787),0.802(95%CI:0.733-0.859),0.784(95%CI:0.714-0.843)and 0.728(95%CI:0.654-0.794),respectively.The area under the curve of the combined detection of the four indexes was higher than that of the single detection of each index(P<0.05).Conclusion The combined detection of SII,NLR,PLR and MLR has high predictive value for the severity of cerebral small vessel dis-ease,and is worthy of clinical promotion.

Current experimental and computational methods have limitations in accurately and efficiently classi-fying ion channels within vast protein spaces.Here we have developed a deep learning algorithm,GPT2 Ion Channel Classifier(GPT2-ICC),which effectively distinguishing ion channels from a test set con-taining approximately 239 times more non-ion-channel proteins.GPT2-ICC integrates representation learning with a large language model(LLM)-based classifier,enabling highly accurate identification of potential ion channels.Several potential ion channels were predicated from the unannotated human proteome,further demonstrating GPT2-ICC's generalization ability.This study marks a significant advancement in artificial-intelligence-driven ion channel research,highlighting the adaptability and effectiveness of combining representation learning with LLMs to address the challenges of imbalanced protein sequence data.Moreover,it provides a valuable computational tool for uncovering previously uncharacterized ion channels.

Objective To investigate the characteristics of left ventricular hypertrophy in patients with primary aldosteron-ism(PA)accompanied by varying severity of obstructive sleep apnea(OSA),and to further identify potential factors contributing to ventricular hypertrophy in this population.Methods A retrospective analysis was performed on 308 patients with PA who received treatment at Kunming Medical University Affiliated Cardiovascular Hospital from January 2021 to June 2022.For com-parison,309 hospitalized patients diagnosed with essential hypertension(EH)during the same period were included as the control group.According to polysomnography findings,the patients were categorized into PA/EH with OSA group and PA/EH without OSA group.PA patients with OSA were further stratified into mild,moderate,and severe OSA subgroups based on the apnea-hypopnea index(AHI).Demographic characteristics,biochemical profiles,and echocardiographic parameters were compared across the groups.Results Compared to the EH groups with or without OSA,interventricular septal diastolic thickness(IVS-DT),left ventricular mass index(LVM),and left ventricular mass index(LVMI)was increased in PA with OSA group(all P＜0.05).The body mass index(BMI)was increased in both the PA with OSA group and EH with OSA group compared to their counterparts without OSA(all P＜0.05).Within the three subgroups of PA patients with OSA,left ventricular end-diastolic di-mension(LVEDD),IVSDT,left ventricular posterior wall diastolic thickness(LVPWDT),and LVM measurements were in-creased in severe OSA group compared to the mild OSA group(all P＜0.05).Conclusion Aldosterone is an independent risk factor from OSA for left ventricular hypertrophy;In PA patients with OSA,cardiac impairment is severer in the moderate and severe OSA subgroups compared to those without OSA,and the severity of cardiac impairment escalates as OSA severity increa-ses.These findings highlight the necessity of screening for OSA in PA patients to stratify cardiac impairment risk and enable early therapeutic intervention.

Objective:To study the characteristics of metabolites in lower resPiratory tract between coal workers' pneumoconiosis patients and dust exposure patients, and compare the differences of metabolites and their main metabolic pathways.Methods:From December 2020 to February 2021, through a prospective cross-sectional study, a total of 26 patients with coal workers' pneumoconiosis (metabolic group of coal workers' pneumoconiosis) were selected from the bronchoalveolar lavage treatment of coal workers' pneumoconiosis and dust exposure in the Respiratory and Critical Care Medicine Department of the 920th Hospital of the Joint Logistics Support Force during the same period. With 19 cases of dust exposure as the control group (dust exposure metabolic group), samples of alveolar lavage fluid were collected from 2 groups. Metabolites of the two groups were quantitatively analyzed by metabonomics technology, and the characteristics of metabolites and their metabolic pathways were compared. The metabolites with potential predictive value were screened by receiver operating characteristic curve (ROC curve) .Results:Through metabolomic analysis of alveolar lavage fluid in the coal workers' pneumoconiosis group and the dust contact group, a total of 28 different metabolites were screened, including trihydroxybutyric acid, alanine, ethanolamine, L-osan, proline (carboxyl), leucine, 2-hydroxyglutaric acid, proline, lactic acid, serine, valine and threonine in the coal workers' pneumoconiosis group. The levels of differential metabolites such as ornithine, isoleucine, threitol, glucose and lysine were higher ( P<0.05). The levels of different metabolites such as sarcoine, pelanoic acid, palmitic acid, heptadecanoic acid, n-butylamine, tetradecanoic acid, isobutylamine, aminoadipic acid, phosphate, uracil and cytosine were higher in the dust exposure group ( P<0.05). Two major metabolic pathways include glycine, serine and threonine metabolism, arginine and proline metabolism, biotin metabolism, and aminoacyl biosynthesis metabolism. Among the 17 metabolites increased in the coal workers' pneumoconiosis group, the AUC of threitol and lactic acid was greater than 0.8, and the specificity and sensitivity of the working characteristic curves of the two metabolites were 80% and 70%, respectively. Conclusion:There were significant differences in the metabolites of lower respiratory tract between patients with coal workers' pneumoconiosis and those exposed to dust, and the differences were related to multiple metabolic pathways. Threitol and lactic acid may have potential predictive value for pneumoconiosis.

This paper reviews gamified mobile applications, summarizes their current application status in nurse training for pressure injury management, and analyzes the functions, uses, and limitations of pressure injury management mobile applications. The aim is to provide a reference for the development of gamified mobile applications for pressure injury management in China.

Objective:To explore the application value of machine learning prediction model for neural invasion in gallbladder cancer based on enhanced computed tomography (CT) and clinical characteristics.Methods:The retrospective cohort study was conducted. The clinical and imaging data of 502 patients with gallbladder cancer who were admitted to The First Affiliated Hospital of Xinxiang Medical University from January 2010 to June 2024 were collected. There were 171 males and 331 females, aged 65(range, 35?91)years. All patients underwent preoperative abdominal enhanced CT and radical resection. The 502 patients were randomly divided into a training set of 351 cases and a test set of 151 cases at a 7:3 ratio. The training set was used to construct prediction model, and the test set was used to validate prediction model. Observation indicators: (1)neural invasion in gallbladder cancer and influencing factor analysis; (2) construction and validation of machine learning prediction models for neural invasion in gallbladder cancer. Comparison of count data between groups was conducted using the chi-square test. Comparison of ordinal data between groups was conducted using the Mann-Whitney U test. Logistic regression model was performed for univariate and multivariate analyses. Independent influencing factors were incor-porated to construct machine learning models using the standard library modules based on Python 3.9. Receiver operating characteristic (ROC) curves were plotted, and the accuracy, sensitivity, specificity, area under the curve (AUC), precision, F1 score, positive predictive value, negative predic-tive value, and Kappa value were calculated to evaluate the predictive performance of the models. The Delong test was used to assess the differences in AUC among different models in the test set. The Hosmer-Lemeshow test and Brier score were used to evaluate the calibration of the models. Results:(1) Neural invasion in gallbladder cancer and influencing factor analysis. Of the 502 patients with gallbladder cancer, 131 cases had neural invasion, and 371 cases had no neural invasion. Results of multivariate analysis showed that total bilirubin, carcinoembryonic antigen, CA199, CA125, neutrophil-lymphocyte ratio, liver invasion detected by CT, vascular invasion detected by CT, hilar or retroperi-toneal lymph node metastasis detected by CT, and tumor stages T3 and T4 were independent influencing factors for neural invasion in patients with gallbladder cancer [ odds ratios=3.747, 2.395, 3.917, 3.596, 2.805, 2.377, 3.523, 2.774, 5.080, 6.809, 95% confidence interval ( CI) as 1.890?7.430, 1.154?4.971, 2.054?7.472, 1.807?7.155, 1.506?5.225, 1.241?4.553, 1.666?7.449, 1.483?5.189, 2.050?12.589, 2.552?18.168, P<0.05]. (2) Construction and validation of machine learning predic-tion models for neural invasion in gallbladder cancer. Based on the independent influencing factors, seven machine learning models were constructed, including logistic regression, K-nearest neighbors, support vector machine, random forest, decision tree, back-propagation neural network, and gradient boosting machine. The ROC curves of seven machine learning models in the test set were plotted, and the AUC were 0.900(95% CI as 0.851?0.948), 0.741(95% CI as 0.646?0.829), 0.836(95% CI as 0.762?0.895), 0.782(95% CI as 0.701?0.855), 0.839(95% CI as 0.770?0.901), 0.817(95% CI as 0.738?0.887), 0.843(95% CI as 0.770?0.909), respectively. Results of Delong test showed that the logistic regression model had the highest AUC. The sensitivity and specificity of the logistic regression model were 0.868 and 0.805 respectively, indicating the best balance. Results of Hosmer-Lemeshow test showed that the logistic regression model had a good goodness-of-fit ( χ2=5.320, P>0.05). The Brier score of the logistic regression model was relatively low, as 0.168, which verified its calibration advantage. Conclusion:Total bilirubin, carcinoembryonic antigen, CA199, CA125, neutrophil-to-lymphocyte ratio, liver invasion detected by enhanced CT, vascular invasion detected by enhanced CT, hilar or retroperitoneal lymph node metastasis detected by enhanced CT, and tumor stages T3 and T4 are independent influencing factors for nerve invasion in patients with gallbladder cancer. Seven machine learning models are constructed based on enhanced CT and clinical characteristics to predict neural invasion in gallbladder cancer, of which the logistic regression model demonstrates good predictive performance.

Current experimental and computational methods have limitations in accurately and efficiently classifying ion channels within vast protein spaces. Here we have developed a deep learning algorithm, GPT2 Ion Channel Classifier (GPT2-ICC), which effectively distinguishing ion channels from a test set containing approximately 239 times more non-ion-channel proteins. GPT2-ICC integrates representation learning with a large language model (LLM)-based classifier, enabling highly accurate identification of potential ion channels. Several potential ion channels were predicated from the unannotated human proteome, further demonstrating GPT2-ICC's generalization ability. This study marks a significant advancement in artificial-intelligence-driven ion channel research, highlighting the adaptability and effectiveness of combining representation learning with LLMs to address the challenges of imbalanced protein sequence data. Moreover, it provides a valuable computational tool for uncovering previously uncharacterized ion channels.

This paper reviews gamified mobile applications, summarizes their current application status in nurse training for pressure injury management, and analyzes the functions, uses, and limitations of pressure injury management mobile applications. The aim is to provide a reference for the development of gamified mobile applications for pressure injury management in China.