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.

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.

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.

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.

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 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 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 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.