Drug development encompasses multiple processes,wherein protein subcellular localization is essential.It promotes target identification,treatment development,and the design of drug delivery systems.In this research,a deep learning framework called LocPro is presented for predicting protein subcellular localization.Specifically,LocPro is unique in(a)combining protein representations from the pre-trained large language model(LLM)ESM2 and the expert-driven tool PROFEAT,(b)implementing a hybrid deep neural network architecture that integrates convolutional neural network(CNN),fully connected(FC)layer,and bidirectional long short-term memory(BiLSTM)blocks,and(c)developing a multi-label framework for predicting protein subcellular localization at multiple granularity levels.Additionally,a dataset was curated and divided using a homology-based strategy for training and validation.Compar-ative analyses show that LocPro outperforms existing methods in sequence-based multi-label protein subcellular localization prediction.The practical utility of this framework is further demonstrated through case studies on drug target subcellular localization.All in all,LocPro serves as a valuable complement to existing protein localization prediction tools.The web server is freely accessible at https://idrblab.org/LocPro/.

In the context of the development of transplant oncology,it is of great clinical significance to find a drug with both antitumor and immunosuppressive effects for liver transplantation patients with hepatocellular carcinoma(HCC).The antitumor effect of ginkgolic acid(GA)has been confirmed,and some studies suggest that GA may also have an immunosuppressive effect.The immunosuppressive effect of GA was evaluated by histopathology,T-cell subpopulation,and cytokine detection in rat liver transplantation and mouse cardiac transplantation models,and transcriptomic and metabolomic analysis was used to explore the underlying mechanism of the GA immunosuppressive effect.Metabolites,activation,and ferroptosis markers of CD8+T cells were detected in vivo and in vitro.Based on rat liver transplantation and mouse cardiac transplantation models,the immunosuppressive effect of GA was first confirmed by histopathology,T-cell subpopulation,and cytokine detection.In the mouse cardiac transplantation model,transcriptomics combined with metabolomics demonstrated for the first time that GA inhibited lactate dehydrogenase A(LDHA)expression and pyruvate metabolism in CD8+T cells.It was confirmed in vivo and in vitro that GA inhibited pyruvate metabolism of CD8+T cells through LDHA,inhibiting their activation and inducing ferroptosis.Over-expression of LDHA partially reversed the effect of GA on the metabolism,activation,and ferroptosis of CD8+T cells in vitro.GA mediates metabolic reprogramming through LDHA to inhibit the activation and induce ferroptosis of CD8+T cells to exert an immunosuppressive effect,which lays an experimental foundation for the future clinical application of its immunosuppressive effect.

Objective:To examine the dynamic interactive relationships between cyberbullying perpetration,anxiety,and depression in college students.Methods:A total of 4 148 college students from a comprehensive uni-versity in Guangxi Zhuang Autonomous Region completed self-report measures including Cyberbullying Scale(CS),Self-Rating Anxiety Scale(SAS),and Self-Rating Depression Scale(SDS)at two time points,18-month a-part.Results:Correlation analysis showed significant concurrent(r=0.11-0.17,Ps＜0.001)and lagged(r=0.06-0.44,Ps＜0.001)positive associations among CS,SAS,and SDS scores.Cross-lagged analysis,controlling for gender and age,indicated that T1 CS scores were positively associated with T2 SAS and SDS scores(β=0.13,0.14,Ps＜0.001).Additionally,SAS scores at T1 and SDS scores at T1 were positively associated CS scores at T2,respectively(β=0.07,0.05,Ps＜0.001).Conclusion:Cyberbullying perpetration,anxiety,and depression in-fluence each other over time among college students,suggesting a reinforcing negative cycle.

Objective: To explore the intervention effects of high intensity interval training (HIIT) on glucose metabolism, cortisol (Cor), and sleep quality among college students with comorbid depressive symptoms and obesity, so as to provide a reference for improving sleep quality among college students with comorbid depressive symptoms and obesity. Methods: In March 2023, 45 college students with comorbid depressive symptoms and obesity were recruited and randomly assigned to an exercise group ( n =23) and a control group ( n =22) by random number table method. The exercise group received HIIT intervention for 12 weeks, three times a week, while the control group received no intervention. Blood samples were collected from participants to measure fasting insulin (FINS), fasting blood glucose (FBG), homeostatic model assessment of insulin resistance (HOMA-IR), Cor, and Pittsburgh Sleep Quality Index (PSQI) before and after intervention. Statistical analysis was performed using t-test, repeated measures analysis of variance (ANOVA), simple effect analysis. Results: The repeated measures ANOVA revealed statistically significant time×group interaction effects for body composition (weight, body mass index, percentage of body fat, fat mass, waist to hip ratio), depressive symptoms, PSQI scores and its subdimensions (subjective sleep quality, sleep onset time, sleep efficiency, sleep disorders, daytime dysfunction), as well as FBG, FINS, and HOMA-IR between the exercise group and control group before and after intervention ( F =7.10-53.38, all P <0.05). Simple effect analysis showed that compared to the control group, the exercise group demonstrated significant improvements in body composition (body mass index, fat mass, waist to hip ratio), depressive symptoms, PSQI scores and its sub dimensions (subjective sleep quality, sleep onset time, sleep efficiency, sleep disorders, daytime dysfunction), FBG, FINS, HOMA-IR, and Cor (all P <0.05). Conclusion HIIT can improve the sleep quality of college students with comorbid depressive symptoms and obesity by enhancing glucose metabolism and regulating Cor levels.

Objective: To establish a risk prediction model for diabetic peripheral neuropathy (DPN) among patients with type 2 diabetes mellitus (T2DM), so as to provide a basis for DPN prevention and control. Methods: T2DM inpatients aged 18-65 years admitted to the department of endocrinology and metabolism at Affiliated Hospital Shandong Second Medical University from April to December 2024 were selected as study subjects. Age, T2DM duration, hypertension history, 25-hydroxyvitamin D, serum C-peptide, and high density lipoprotein cholesterol (HDL-C) were collected through electronic medical records. Risk predictors of DPN among T2DM patients were screened using multivariable logistic regression model, and a nomogram was established. The receiver operating characteristic (ROC) curve, calibration curve and decision curve analysis were employed to evaluate the discrimination, calibration and clinical utility of the nomogram, respectively. Results: A total of 598 T2DM patients were enrolled, including 359 (60.03%) males and 239 (39.97%) females. The median age was 54.50 (interquartile range, 15.00) years, the median T2DM duration was 6.00 (interquartile range, 9.00) years. There were 262 cases of T2DM patients with DPN, accounting for 43.81%. Multivariable logistic regression identified hypertension history (OR=3.260, 95%CI: 2.220-4.790), alcohol use history (OR=2.150, 95%CI: 1.390-3.310), diabetes complications (OR=0.430, 95%CI: 0.270-0.680), T2DM duration (OR=1.040, 95%CI: 1.010-1.070), body mass index (OR=1.130, 95%CI: 1.070-1.200), 25-hydroxyvitamin D (OR=0.930, 95%CI: 0.910-0.960), and HDL-C (OR=0.400, 95%CI: 0.230-0.720) as risk predictors for DPN among T2DM patients. The area under the ROC curve of the established risk prediction model was 0.774 (95%CI: 0.737-0.812), with a sensitivity of 0.710 and a specificity of 0.723. The calibration curve after repeated sampling calibration approached the standard curve. Decision curve analysis showed that when the risk threshold probability was 0.2 to 0.4, the model demonstrates favorable clinical applicability. Conclusion The risk prediction model established in this study has favorable discrimination, calibration, and clinical utility, can effectively predict the risk of DPN among T2DM patients aged 18-65 years.

In the context of the development of transplant oncology, it is of great clinical significance to find a drug with both antitumor and immunosuppressive effects for liver transplantation patients with hepatocellular carcinoma (HCC). The antitumor effect of ginkgolic acid (GA) has been confirmed, and some studies suggest that GA may also have an immunosuppressive effect. The immunosuppressive effect of GA was evaluated by histopathology, T-cell subpopulation, and cytokine detection in rat liver transplantation and mouse cardiac transplantation models, and transcriptomic and metabolomic analysis was used to explore the underlying mechanism of the GA immunosuppressive effect. Metabolites, activation, and ferroptosis markers of CD8⁺ T cells were detected in vivo and in vitro. Based on rat liver transplantation and mouse cardiac transplantation models, the immunosuppressive effect of GA was first confirmed by histopathology, T-cell subpopulation, and cytokine detection. In the mouse cardiac transplantation model, transcriptomics combined with metabolomics demonstrated for the first time that GA inhibited lactate dehydrogenase A (LDHA) expression and pyruvate metabolism in CD8⁺ T cells. It was confirmed in vivo and in vitro that GA inhibited pyruvate metabolism of CD8⁺ T cells through LDHA, inhibiting their activation and inducing ferroptosis. Overexpression of LDHA partially reversed the effect of GA on the metabolism, activation, and ferroptosis of CD8⁺ T cells in vitro. GA mediates metabolic reprogramming through LDHA to inhibit the activation and induce ferroptosis of CD8⁺ T cells to exert an immunosuppressive effect, which lays an experimental foundation for the future clinical application of its immunosuppressive effect.

Drug development encompasses multiple processes, wherein protein subcellular localization is essential. It promotes target identification, treatment development, and the design of drug delivery systems. In this research, a deep learning framework called LocPro is presented for predicting protein subcellular localization. Specifically, LocPro is unique in (a) combining protein representations from the pre-trained large language model (LLM) ESM2 and the expert-driven tool PROFEAT, (b) implementing a hybrid deep neural network architecture that integrates convolutional neural network (CNN), fully connected (FC) layer, and bidirectional long short-term memory (BiLSTM) blocks, and (c) developing a multi-label framework for predicting protein subcellular localization at multiple granularity levels. Additionally, a dataset was curated and divided using a homology-based strategy for training and validation. Comparative analyses show that LocPro outperforms existing methods in sequence-based multi-label protein subcellular localization prediction. The practical utility of this framework is further demonstrated through case studies on drug target subcellular localization. All in all, LocPro serves as a valuable complement to existing protein localization prediction tools. The web server is freely accessible at https://idrblab.org/LocPro/.

Primary liver cancer,particularly hepatocellular carcinoma,is one of the most common malignancies in China,and hepatectomy remains the primary curative treatment.However,the efficacy of hepatectomy is significantly limited due to the heterogeneity of liver cancer,its high recurrence rate,and the fact that most patients are diagnosed at advanced stages.In recent years,the development of precision medicine has brought new hope to liver cancer treatment,especially with notable advancements in preoperative assessment,systemic therapy,minimally invasive surgery,and personalized treatment strategies.Preoperative assessment,including imaging technologies such as three-dimensional visualization and molecular imaging,helps physicians accurately evaluate tumor characteristics and liver function,guiding the choice of treatment plan.The combined application of immunotherapy and targeted therapy has significantly improved survival rates for patients with advanced liver cancer.The strategy of combining systemic therapy with local treatment has provided new pathways for translational therapy,expanding the indications for hepatectomy.The optimal selection of patients based on tumor biological characteristics,especially molecular subtyping and liver function status,to maximize patient benefit still requires further exploration.The"seven-step"modular laparoscopic hepatectomy,by achieving scientific hepatectomy,demonstrates the clinical practice of maximizing patient benefit,further elucidating a multidisciplinary,personalized treatment model centered on surgical therapy.

Objective:To examine the dynamic interactive relationships between cyberbullying perpetration,anxiety,and depression in college students.Methods:A total of 4 148 college students from a comprehensive uni-versity in Guangxi Zhuang Autonomous Region completed self-report measures including Cyberbullying Scale(CS),Self-Rating Anxiety Scale(SAS),and Self-Rating Depression Scale(SDS)at two time points,18-month a-part.Results:Correlation analysis showed significant concurrent(r=0.11-0.17,Ps＜0.001)and lagged(r=0.06-0.44,Ps＜0.001)positive associations among CS,SAS,and SDS scores.Cross-lagged analysis,controlling for gender and age,indicated that T1 CS scores were positively associated with T2 SAS and SDS scores(β=0.13,0.14,Ps＜0.001).Additionally,SAS scores at T1 and SDS scores at T1 were positively associated CS scores at T2,respectively(β=0.07,0.05,Ps＜0.001).Conclusion:Cyberbullying perpetration,anxiety,and depression in-fluence each other over time among college students,suggesting a reinforcing negative cycle.

Primary liver cancer,particularly hepatocellular carcinoma,is one of the most common malignancies in China,and hepatectomy remains the primary curative treatment.However,the efficacy of hepatectomy is significantly limited due to the heterogeneity of liver cancer,its high recurrence rate,and the fact that most patients are diagnosed at advanced stages.In recent years,the development of precision medicine has brought new hope to liver cancer treatment,especially with notable advancements in preoperative assessment,systemic therapy,minimally invasive surgery,and personalized treatment strategies.Preoperative assessment,including imaging technologies such as three-dimensional visualization and molecular imaging,helps physicians accurately evaluate tumor characteristics and liver function,guiding the choice of treatment plan.The combined application of immunotherapy and targeted therapy has significantly improved survival rates for patients with advanced liver cancer.The strategy of combining systemic therapy with local treatment has provided new pathways for translational therapy,expanding the indications for hepatectomy.The optimal selection of patients based on tumor biological characteristics,especially molecular subtyping and liver function status,to maximize patient benefit still requires further exploration.The"seven-step"modular laparoscopic hepatectomy,by achieving scientific hepatectomy,demonstrates the clinical practice of maximizing patient benefit,further elucidating a multidisciplinary,personalized treatment model centered on surgical therapy.