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.

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.

Objective To investigate the relationship between serum cystatin C(CysC)level and the risk of Parkinson's disease(PD)in middle-aged and elderly people in China.Methods Based on the baseline survey data from the China Health and Retirement Longitudinal Study(CHARLS)in 2011,participants who were not diagnosed with PD at the time of the baseline survey were recruited.The onset of PD was tracked and followed up until 2020,and the participants were divided into PD group and non-PD group according to whether they were newly diagnosed with PD in 2020.Multivariable Logistic regression analysis was performed to assess the association between serum CysC level and the risk of PD.Subgroup and interaction analyses were performed to assess effect modifications by age,gender and depression.Additionally,restricted cubic spline(RCS)was used to explore the linear or non-linear relationship between serum CysC level and the risk of PD in different subgroups.Results We included a total of 3 339 subjects in this study,who consisted of 1 495 males(44.77％)and 1 844 females(55.23％).While baseline participants were followed until 2020,32 subjects had a new PD,and the incidence of PD was 0.96％.The median age of PD group was 63.00 years.Multivariable Logistic regression analysis found that CysC was an independent risk factor for the risk of PD,and CysC was positive significantly associated with the risk of PD(OR=2.34,95％ CI:1.14-4.82,P=0.021).Subgroup analysis showed that CysC was positively associated with PD in females(OR=2.70,95％ CI:1.30-5.58,P=0.007)and subjects aged 60 years or older(OR=5.29,95％ CI:1.69-16.53,P=0.004).RCS model indicated a linear relationship between serum CysC level and the risk of PD in females(Ptotal=0.018,Pnon-linear=0.062)and subjects aged 60 years or older(Ptotal=0.024,Pnon-linear=0.379).Conclusion High level of CysC may increase the risk of PD in middle-aged and elderly people,especially in females and those aged 60 years or older.

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 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 observe the value of machine learning model based on MR T2WI and diffusion weighted imaging(DWI)radiomics for predicting perineural invasion(PNI)of rectal cancer.Methods Totally 343 patients with rectal cancer were retrospectively collected and divided into training set(n=275,92 PNI[+]and 183 PNI[-])and test set(n=68,23 PNI[+]and 45 PNI[-])at the ratio of 8∶2.Univariate and multivariate logistic regression(LR)were used to analyze clinical data and screen the independent predictors of PNI in rectal cancer,so as to construct a clinical model.The best radiomics features were extracted and screened based on preoperative T2WI and DWI.Then extremely randomized trees,multilayer perceptron,light gradient boosting machine,extreme gradient boosting,support vector machine(SVM),LR,K-nearest neighbor and random forest algorithms were used to construct ML models,respectively,and the optimal ML model was selected to establish a clinical-radiomics ML model combined with clinical relevant independent predictors.The predictive efficacy and clinical value of each model were evaluated.Results Patients' age was the independent predictor of PNI of rectal cancer(OR=0.988,P＜0.001),and the area under the curve(AUC)of the clinical model constructed based on it was 0.435 and 0.458 in training and test sets,respectively.SVM model was the best one among 8 ML models,with AUC in training and test set of 0.887 and 0.854,respectively.The AUC of clinical-radiomics ML model in training and test sets was 0.887 and 0.860,respectively,not different with AUC of SVM model(both P＞0.05).Decision curve analysis showed that when the threshold value was 0.20-0.45,clinical net benefit of SVM model was higher than that of other models.Conclusion SVM model based on T2WI and DWI radiomics could effectively predict PNI of rectal cancer.

Objective To investigate the role of Toll-like receptor 4(TLR4)in the regulation of homocys-teine(Hcy)-induced ferroptosis in macrophages.Methods Mouse macrophage cells RAW264.7 were cultured and divided into control group,Hcy intervention group(Hcy group),and Hcy plus ferroptosis inhibitor group(Hcy+Fer-1 group).After transfection with interference fragments,macrophages were treated with Hcy,and then divided into control group,Hcy intervention group(Hcy group),TLR4 interference negative control plus Hcy intervention group(si-NC+Hcy group),and TLR4 interference plus Hcy intervention group(si-TLR4+Hcy group).Macrophages were transfected with overexpression lentivirus and treated with Hcy,then were divided into control group,Hcy intervention group(Hcy group),a TLR4 overexpression negative control plus Hcy intervention group(OE-NC+Hcy group),and a TLR4 overexpression plus Hcy intervention group(OE-TLR4+Hcy group).After 48 hours of intervention,real-time fluorescent quantitative PCR and western blot were used to detect the expression levels of TLR4 in macrophages treated with Hcy;western blot was used to detect the expression levels of ferroptosis-related proteins ACSL4,GPX4,and FTH1 in macrophages,and ferrous ion assay kit to detect the concentration of Fe2+in macrophages;reactive oxygen species(ROS)assay kit and laser confocal microscopy were used to detect the content of intracellular reactive oxygen species.Results Compared with those in the control group,the expression level of the pro-ferroptosis protein ACSL4 was increased in the Hcy group(P<0.05),while the expression levels of anti-ferroptosis proteins GPX4 and FTH1 were decreased(P<0.05);the concentration of Fe2+was increased(P<0.05),and the content of ROS was increased.Meanwhile,the protein and mRNA expres-sion levels of TLR4 were both increased in the Hcy group(P<0.05).After macrophages were transfected with TLR4 interference fragments,compared with those in the si-NC+Hcy group,the expression levels of GPX4 and FTH1 were increased(P<0.05);the expression level of ACSL4 was decreased(P<0.05);the concentration of Fe2+was decreased(P<0.05),and the content of ROS was reduced in the si-TLR4+Hcy group.After macro-phages were transfected with TLR4 overexpression lentivirus,compared with those in the OE-NC+Hcy group,the expression levels of GPX4 and FTH1 were decreased(P<0.05),and the expression level of ACSL4 was increased(P<0.05)in the OE-TLR4+Hcy group.Conclusion Hcy induces the occurrence of ferroptosis in macrophages,and Toll-like receptor 4 has a positive feedback regulatory effect on ferroptosis in macrophages.

Objective To investigate the relationship between serum cystatin C(CysC)level and the risk of Parkinson's disease(PD)in middle-aged and elderly people in China.Methods Based on the baseline survey data from the China Health and Retirement Longitudinal Study(CHARLS)in 2011,participants who were not diagnosed with PD at the time of the baseline survey were recruited.The onset of PD was tracked and followed up until 2020,and the participants were divided into PD group and non-PD group according to whether they were newly diagnosed with PD in 2020.Multivariable Logistic regression analysis was performed to assess the association between serum CysC level and the risk of PD.Subgroup and interaction analyses were performed to assess effect modifications by age,gender and depression.Additionally,restricted cubic spline(RCS)was used to explore the linear or non-linear relationship between serum CysC level and the risk of PD in different subgroups.Results We included a total of 3 339 subjects in this study,who consisted of 1 495 males(44.77％)and 1 844 females(55.23％).While baseline participants were followed until 2020,32 subjects had a new PD,and the incidence of PD was 0.96％.The median age of PD group was 63.00 years.Multivariable Logistic regression analysis found that CysC was an independent risk factor for the risk of PD,and CysC was positive significantly associated with the risk of PD(OR=2.34,95％ CI:1.14-4.82,P=0.021).Subgroup analysis showed that CysC was positively associated with PD in females(OR=2.70,95％ CI:1.30-5.58,P=0.007)and subjects aged 60 years or older(OR=5.29,95％ CI:1.69-16.53,P=0.004).RCS model indicated a linear relationship between serum CysC level and the risk of PD in females(Ptotal=0.018,Pnon-linear=0.062)and subjects aged 60 years or older(Ptotal=0.024,Pnon-linear=0.379).Conclusion High level of CysC may increase the risk of PD in middle-aged and elderly people,especially in females and those aged 60 years or older.