Objective: To investigate the association between screen time (ST) during leisure time and anxiety-depression symptoms among middle school students, so as to provide a basis for formulating relevant intervention measures. Methods: From November to December 2023, a stratified cluster random sampling method was used to select 2 542 students from junior and senior high school in Taiyuan City. A self designed questionnaire, the Generalized Anxiety Disorder Scale (GAD-7), and the Patient Health Questionnaire (PHQ-9) were used to investigate ST and anxiety/depression symptoms among middle school students. The Logistic regression model was used to explore the association of ST with symptoms of anxiety and depression, as well as with anxiety and depression comorbiditles (CAD). Results: The detection rates of anxiety symptoms, depression symptoms, and CAD were 13.69%, 15.77%, and 10.11%, respectively. The median ST was 2.00 h/d [interquartile range ( IQR =2.38) for weekly averages], with 0.33 h/d ( IQR =1.67) on work days and 5.00 h/d ( IQR=5.50) on rest days. Logistic regression analysis indicated that the total ST of mobile phones during rest days ( OR =1.07, 1.10, 1.11) and the averages ST of mobile phones over a week ( OR = 1.20 , 1.22, 1.29), as well as the total ST of all screen types during rest days ( OR =1.04, 1.04, 1.05) and the averages ST of all screen types over a week ( OR =1.08, 1.09, 1.21) were positively correlated with anxiety symptoms, depression symptoms, and CAD (all P <0.01). Conclusions Among middle school students in Taiyuan City, screen time is positively correlated with symptoms of anxiety or depression and the comorbidity of anxiety and depression, especially smartphone screen time and weekend screen use. Therefore, measures should be implemented to reduce unnecessary screen time among middle school students, especially the use of mobile phones, in order to mitigate the occurrence of anxiety and depression.

In recent years, the ongoing development of transcranial electrical stimulation (TES) and transcranial magnetic stimulation (TMS) has demonstrated significant potential in the treatment and rehabilitation of various brain diseases. In particular, the combined application of TES and TMS has shown considerable clinical value due to their potential synergistic effects. This paper first systematically reviews the mechanisms underlying TES and TMS, highlighting their respective advantages and limitations. Subsequently, the potential mechanisms of transcranial electromagnetic combined stimulation are explored, with a particular focus on three combined stimulation protocols: Repetitive TMS (rTMS) with transcranial direct current stimulation (tDCS), rTMS with transcranial alternating current stimulation (tACS), and theta burst TMS (TBS) with tACS, as well as their clinical applications in brain diseases. Finally, the paper analyzes the key challenges in transcranial electromagnetic combined stimulation research and outlines its future development directions. The aim of this paper is to provide a reference for the optimization and application of transcranial electromagnetic combined stimulation schemes in the treatment and rehabilitation of brain diseases.
Humans ; Transcranial Magnetic Stimulation/methods* ; Transcranial Direct Current Stimulation/methods* ; Brain Diseases/therapy*

Traditional Chinese medicine (TCM) demonstrates distinctive advantages in disease prevention and treatment. However, analyzing its biological mechanisms through the modern medical research paradigm of "single drug, single target" presents significant challenges due to its holistic approach. Network pharmacology and its core theory of network targets connect drugs and diseases from a holistic and systematic perspective based on biological networks, overcoming the limitations of reductionist research models and showing considerable value in TCM research. Recent integration of network target computational and experimental methods with artificial intelligence (AI) and multi-modal multi-omics technologies has substantially enhanced network pharmacology methodology. The advancement in computational and experimental techniques provides complementary support for network target theory in decoding TCM principles. This review, centered on network targets, examines the progress of network target methods combined with AI in predicting disease molecular mechanisms and drug-target relationships, alongside the application of multi-modal multi-omics technologies in analyzing TCM formulae, syndromes, and toxicity. Looking forward, network target theory is expected to incorporate emerging technologies while developing novel approaches aligned with its unique characteristics, potentially leading to significant breakthroughs in TCM research and advancing scientific understanding and innovation in TCM.
Artificial Intelligence ; Medicine, Chinese Traditional ; Humans ; Network Pharmacology/methods* ; Drugs, Chinese Herbal/pharmacology* ; Animals ; Multiomics

Drug development remains a critical issue in the field of biomedicine. With the rapid advancement of information technologies such as artificial intelligence (AI) and the advent of the big data era, AI-assisted drug development has become a new trend, particularly in predicting drug-target associations. To address the challenge of drug-target prediction, AI-driven models have emerged as powerful tools, offering innovative solutions by effectively extracting features from complex biological data, accurately modeling molecular interactions, and precisely predicting potential drug-target outcomes. Traditional machine learning (ML), network-based, and advanced deep learning architectures such as convolutional neural networks (CNNs), graph convolutional networks (GCNs), and transformers play a pivotal role. This review systematically compiles and evaluates AI algorithms for drug- and drug combination-target predictions, highlighting their theoretical frameworks, strengths, and limitations. CNNs effectively identify spatial patterns and molecular features critical for drug-target interactions. GCNs provide deep insights into molecular interactions via relational data, whereas transformers increase prediction accuracy by capturing complex dependencies within biological sequences. Network-based models offer a systematic perspective by integrating diverse data sources, and traditional ML efficiently handles large datasets to improve overall predictive accuracy. Collectively, these AI-driven methods are transforming drug-target predictions and advancing the development of personalized therapy. This review summarizes the application of AI in drug development, particularly in drug-target prediction, and offers recommendations on models and algorithms for researchers engaged in biomedical research. It also provides typical cases to better illustrate how AI can further accelerate development in the fields of biomedicine and drug discovery.

Liver failure is a kind of acute and severe liver disease.Bone marrow-derived mesenchymal stem cells(BM-MSCs)have the function of differentiating into hepatocytes.Promotion of the regeneration of hepatocytes re-generation,inhibition of apoptosis,necrosis and inflammation of hepatocytes,may facilitate repairing damaged liver tissue and improving liver function.BM-MSCs have become a new choice with great application potential in the treatment of liver failure.

Inorganic polyphosphate(polyP)is wide spread in biology and exhibits striking procoagulant ef-fects in vivo.Long-chain polyphosphate(such as those present in infectious microorganisms)is a potent,natural patho-physiologic activator of the contact pathway of blood clotting and promotes thrombosis and inflammation,which suggest that antagonizing polyP function in vivo may be an attractive approach for identifying novel antithrombotic agents,perhaps with reduced bleeding side effects compared with conventional anticoagulant drugs.Short-chain polyphosphate(secreted from activated human platelets)remarkably accelerates factor V activation,absolutely abrogates the anticoagulant function of tissue factor pathway inhibitor,increase fibrin clot structure,and accelerates factor XI activation by thrombin.So polyP may be used as hemostatic agent.In this paper,the procoagulant role of polyP and the mechanism underlying polyP modu-lates blood clotting and the strategy of screening polyP inhibitors are described in detail.

Objective:To explore the efficacy and safety of ibrutinib for the treatment of newly treated and relapsed refractory (R/R) lymphoplasmacytic lymphoma (LPL) /Waldenstr?m macroglobulinemia (WM) .Methods:Retrospectively collected clinical data of 98 cases of newly treated and R/R LPL/WM patients who received ibrutinib treatment at the Hematology & Blood Diseases Hospital of the Chinese Academy of Medical Sciences from March 2016 to June 2023, and analyzed their efficacy and safety.Results:A total of 98 LPL/WM patients were included, which consisted of 45 newly treated patients and 53 R/R patients. Of these, 74 were males (75.5%) and the cohort had a median age of 64 (42-87) years. Eighty-eight patients were eligible for efficacy evaluation with a median treatment time of 20.8 (2.1-55.0) months, a major remission rate (MRR) of 78.4%, and an overall response rate (ORR) of 85.2%. The MRR and ORR of the newly treated patients were 78.4% and 86.5%, respectively, whereas the MRR and ORR of the R/R patients were 78.4% and 84.3%, respectively. There were no statistically significant differences in MRR and ORR between the initial treatment and R/R patients (all P values >0.05) . The median follow-up period was 29.1 (2.9-50.3) months and the median overall survival time for newly treated and R/R patients was not reached. The median progression-free survival time was 23.5 (95% CI 10.5-36.5) months and 45.0 (95% CI 34.0-56.0) months, respectively, with no statistically significant differences (all P values >0.05) . There were 25 deceased patients and no deaths were related to ibrutinib treatment. The main adverse reactions of ibrutinib were thrombocytopenia (5.1%) , pneumonia (8.1%) , and hyperuricemia (21.4%) . The incidence of atrial fibrillation was 2.0%. Conclusion:Ibrutinib exhibits good efficacy and safety for newly treated and R/R LPL/WM patients.

Objective:To investigate serum vitamin A and vitamin D status in children aged 2-<7 years in 20 cities in China.Methods:A cross-sectional study was conducted. A total of 2 924 healthy children aged 2-<7 years were recruited from September 2018 to September 2019 from 20 cities in China, categorized by age groups of 2-<3 years, 3-<5 years, and 5-<7 years. The demographic and economic characteristics and health-related information of the enrolled children were investigated. Body weight and height were measured by professional staff members. The serum vitamin A and vitamin D levels were detected by high-performance liquid chromatography-tandem mass spectrometry. Chi-square test and Logistic regression were applied to analyze the association between vitamin A and vitamin D deficiency and insufficiency as well as their underlying impact factors.Results:The age of the 2 924 enrolled children was 4.33 (3.42, 5.17) years. There were 1 726 males (59.03%) and 1 198 females (40.97%). The prevalences of vitamin A and vitamin D deficiency in enrolled children were 2.19% (64/2 924) and 3.52% (103/2 924), respectively, and the insufficiency rates were 29.27% (856/2 924) and 22.20% (649/2 924), respectively. Children with both vitamin A and vitamin D deficiencies or insufficiencies were found in 10.50% (307/2 924) of cases. Both vitamin A ( χ2=7.91 and 8.06, both P=0.005) and vitamin D ( χ2=71.35 and 115.10, both P<0.001) insufficiency rates were higher in children aged 3-<5 and 5-<7 years than those in children aged 2-<3 years. Vitamin A and vitamin D supplementation in the last 3 months was a protective factor for vitamin A and D deficiency and insufficiency, respectively ( OR=0.68 and 0.22, 95% CI 0.49-0.95 and 0.13-0.40, both P<0.05). The rates of vitamin A and D insufficiency was higher in children with annual household incomes <60 000 RMB than in those with annual household incomes ≥60 000 RMB ( χ2=34.11 and 10.43, both P<0.01). Northwest and Southwest had the highest rates of vitamin A and vitamin D insufficiency in children aged 2-<7 yeas, respectively ( χ2=93.22 and 202.54, both P<0.001). Conclusions:Among 20 cities in China, children aged 2-<7 years experience high rates of vitamin A and vitamin D insufficiency, which are affected by age, family economic level, vitamin A and vitamin D supplementation, and regional economic level. The current results suggest that high level of attention should be paid to vitamin A and vitamin D nutritional status of preschool children.

OBJECTIVE To investigate the effects of renally inappropriate medication （RIM） on the frailty of elderly patients with diabetes. METHODS The data of elderly patients with diabetes mellitus admitted to a third-grade class A hospital in Yunnan province from January to December 2022 were collected， and Beers criteria （2019 edition） and Chinese version of FRAIL scale were used to evaluate RIM and the frailty of the patients； the patients were divided into the trial group （with RIM） and the control group （without RIM） according to whether there was RIM. The propensity score matching was used to balance confounding factors between two groups， and the influence of RIM on the frailty of elderly diabetic patients was analyzed by the Logistic regression model. RESULTS Among the 367 patients， 80 patients （21.80%） had RIM， the drugs involved RIM were spironolactone （82.56%）， rivaroxaban （13.95%） and gabapentin （3.49%）. After reaching the balance between groups using the propensity score matching method， the incidence of frailty was 77.94% in trial group and 27.94% in control group （P＜0.001）； the difference was not statistically significant in other confounding factors between the two groups （P＞0.05）. Results of Logistic regression analysis showed that the risk of frailty in the experimental group was 3.118 times that of the control group （odds ratio was 3.118，95% confidence interval was 1.758-5.530， P＜0.001）. CONCLUSIONS RIM is a risk factor for the frailty of elderly patients with diabetes， which can be considered as an indicator for early identification and screening of the frailty of elderly diabetes patients.

Humans ; Alzheimer Disease ; Cross-Sectional Studies ; Creatine Kinase ; Brain/metabolism*