A 51-year-old male presented with nasal obstruction, followed by progressive hearing loss and blurred vision. Imaging identified space-occupying lesions in the paranasal sinuses, orbits, and paraspinal regions, while laboratory tests confirmed positive anti-proteinase 3 anti-neutrophil cytoplasmic antibody(PR3- ANCA) immunoglobulin G (IgG)and markedly elevated serum IgG4. Despite treatment with corticosteroids, immunosuppressants, and radiotherapy, the patient exhibited steroid dependency with relentless disease progression. Following multidisciplinary consultation, a diagnosis of inflammatory myofibroblastic tumor (IMT) coexisting with ANCA- associated vasculitis (AAV) was favored, though IgG4-related disease remained a critical differential. Ultimately, profound immunosuppression precipitated a severe herpesvirus infection, leading to disseminated intravascular coagulation and multiple organ dysfunction syndrome. This case underscores the rarity and diagnostic complexity of concurrent IMT and AAV, highlights the therapeutic dilemma of balancing primary disease control against fatal opportunistic infections, and emphasizes the critical role of multidisciplinary collaboration in the diagnosis and treatment of complex diseases.

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.

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.

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 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 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.