To develop and validate a model for predicting intensive care unit (ICU) mortality risk in patients with malignant tumors complicated by acute respiratory failure (ARF) based on an explainable machine learning framework.

OBJECTIVE To explore and predict the quality markers （Q-markers） of Compound xiebai capsules for the treatment of chronic obstructive pulmonary disease （COPD） by constituents analysis combined with network pharmacology and molecular docking studies， and to establish the quality standard of Compound xiebai capsules. METHODS UHPLC-TOF-MS was used for qualitative analysis of Compound xiebai capsules， and the candidate Q-markers of Compound xiebai capsules were screened by combining network pharmacology and molecular docking technology. Further， HPLC was applied to establish the fingerprints of 15 batches of Compound xiebai capsules and to conduct quantitative analysis of the main components. RESULTS A total of 51 components were identified from Compound xiebai capsules. Among them， 15 components， namely oxyberberine， methylworenine， coptisine， tetrahydroberberine， epiberberine， berberine， magnoflorine， gandensin， cucurbitacin D， hydroxygenkwan， jatrorrhizine， columbamine， quercetin， cucurbitacin R， and palmatine， were determined as the candidate Q-markers for Compound xiebai capsules in the treatment of COPD. A total of 13 common peaks were calibrated in the fingerprints of 15 batches of Compound xiebai capsules for COPD treatment， with similarity values ranging from 0.976 to 0.999 compared to the reference fingerprint. Seven components were identified among these peaks， namely peak 5 （magnoflorine）， peak 8 （jatrorrhizine）， peak 9 （epiberberine）， peak 10 （columbamine）， peak 11 （coptisine）， peak 12 （palmatine）， and peak 13 （berberine）. Their respective contents were （0.267±0.048）， （0.453±0.084）， （0.572±0.160）， （0.392±0.074）， （1.076±0.273）， （1.477±0.271）， and （6.664±1.249） mg/g （ n ＝3）. CONCLUSIONS This study predicted 15 candidate Q-markers of Compound xiebai capsules in the treatment of COPD and established the fingerprint along with a quantitative determination method for seven major components.

BACKGROUND:The repair process of myocardial injury involves complex cellular and molecular mechanisms,especially mitochondrial calcium homeostasis,macrophage autophagy and pyroptosis pathways.Traditional Chinese medicine(TCM)has shown significant clinical efficacy in improving myocardial injury,but its mechanism of action needs to be thoroughly investigated. OBJECTIVE:To investigate the role of mitochondrial calcium homeostasis-mediated macrophage autophagy and pyroptosis pathways in myocardial injury,and to summarize the progress of TCM in this field. METHODS:A computerized search was performed for relevant literature from the database inception to March 2024 in the Web of Science,PubMed and CNKI.The search terms were"mitochondrial calcium homeostasis,macrophage autophagy,macrophage pyroptosis,traditional Chinese medicine,myocardial injury,myocardial injury reperfusion"in Chinese and English.Through literature review,we analyzed the relationship between mitochondrial calcium homeostasis and macrophage autophagy and pyroptosis,explored the mechanism of their roles in myocardial injury,and summarized the pathways of multi-targeted,multi-pathway effects of TCM. RESULTS AND CONCLUSION:The maintenance of mitochondrial calcium homeostasis has been found to be closely related to the normal function of cardiomyocytes.Macrophages can participate in the repair process of myocardial injury through autophagy and pyroptosis pathways.Autophagy contributes to cell clearance and regulation of inflammatory response,while pyroptosis affects myocardial repair by releasing inflammatory factors.TCM regulates mitochondrial calcium homeostasis and macrophage function through multiple mechanisms.For example,astragalosid regulates calcium homeostasis by lowering mitochondrial membrane potential and inhibiting cytochrome C,and epimedium glycoside plays a role in reducing β-amyloid deposition.In addition,herbal compounds and single drugs promote myocardial repair by activating or inhibiting specific signaling pathways,such as PI3K/AKT and nuclear factor-κB signaling pathways.Future studies should focus on the interactions between mitochondrial calcium homeostasis,autophagy and pyroptosis pathways,as well as how TCM can exert therapeutic effects through these pathways to provide new strategies and drugs for the treatment of myocardial injury.

Objective: To analyze the heterogeneity of mobile phone dependence development trajectory in middle school students and its association with loneliness and selfcontrol ability, so as to provide reference for the prevention of mobile phone dependence in middle school students. Methods: A total of 941 grade 1 students from 4 public middle schools in Xiangxi Autonomous Prefecture, Hunan Province were selected for the followup survey by random cluster sampling from October 2023 to April 2024 and October 2024. Mobile Phone Addiction Index (MPAI), University of California, Los Angeles Loneliness Scale-20 (UCLA-20) and Selfcontrol Scales (SCS) were used for questionnaire survey. The heterogeneity of the developmental trajectory of middle school students mobile phone dependence was analyzed by the latent growth curve model (LGMM), and the influencing factors of the developmental trajectory of middle school students mobile phone dependence were explored by multiple Logistic regression analysis. Results: The development trajectory of middle school students mobile phone dependence could be divided into four categories: C1 "low risk slow decline group (n=438,44.6%)", C2 "medium risk slow rise group (n=272,29.7%)", C3 "high risk rapid decline group (n=73,8.6%)" and C4 "high risk rapid rise group (n=158,17.1%)". There were significant differences in the distribution of mobile phone dependence development track heterogeneity subgroups among sex, only child, lodging, and leftbehind students (χ2=117.79, 44.88, 37.09, 130.50, P <0.01). The results of the multinomial Logistic regression model analysis showed that, with C1 group as the reference, C2, C3, and C4 were positively correlated with students loneliness [OR(95%CI)=1.04 (1.02-1.06), 1.11(1.08-1.14), 1.12(1.09-1.14)]; C2 and C4 groups were negatively correlated with students selfcontrol [OR(95%CI)=0.97(0.96-0.99), 0.95(0.93-0.97)] (P<0.01). Conclusions The development trajectory of mobile phone dependence among middle school students is heterogeneous. Reducing the loneliness of individuals and cultivating good selfcontrol ability are helpful to alleviate mobile phone dependence behavior among middle school students.

Objective: To analyze the status of outdoor activities on weekends among children and adolescents of different educational stages in Shanghai and their impact on myopia, so as to provide a basis for formulating more specific prevention and control protocol of myopia. Methods: From September to October 2022, a stratified cluster random sampling method was employed to select 84 schools (27 kindergartens, 21 primary schools, 15 junior high schools and 21 high schools) across Shanghai, enrolling a total of 28 654 children and adolescents aged 4 to 18 for the study. Ophthalmic examinations were conducted to ascertain the prevalence of myopia among children and adolescents. Additionally, a questionnaire survey was administered to collect data on outdoor activity duration and associated factors. Multivariate Logistic regression analysis was utilized to investigate the associated factors of outdoor activity levels on weekends. Results: The overall myopia detection rate among children and adolescents was 58.4%, with a higher rate observed in girls (59.2%) compared to boys (57.6%). The myopia detection rates for children and adolescents with an average daily outdoor activity duration of ≥2 h and <2 h on weekends were 54.6% and 68.8%, and the differences were statistically significant ( χ 2=8.12,460.89, P <0.01). Multivariable Logistic regression analysis revealed that girls ( OR =0.80), those with a myopic parent ( OR =0.68), schools from urban districts ( OR =0.72), higher education stages (primary school: OR =0.65, junior high school: OR =0.24, high school: OR =0.14) and spending≥2 h/d on homework during weekends ( OR =0.57) among children and adolescents were less likely to engage in outdoor activities for ≥2 h on weekends ( P <0.01). After incorporating gender, parental myopia status, educational stage, school location, average daily duration on weekends for spending on homework, electronic product usage and outdoor activities as dependent variables in a multivariate Logistic regression analysis, the results showed that children and adolescents with an average outdoor activity duration for ≥2 h on weekends had a lower risk of myopia ( OR =0.86, P < 0.01). Conclusions The level of outdoor activity among children and adolescents on weekends needs to be improved. Outdoor activities on weekends is an associated factor for myopia among children and adolescents. Particularly, girls, those with myopic parents, schools from urban districts, and spending long hours on homework during weekends among children and adolescents require increased attention.

Objective: To investigate the visual acuity and correction conditions of children and adolescents in Shanghai, so as to provide a scientific basis for developing intervention measures to prevent myopia and protect vision among children and adolescents. Methods: From October to December 2022, a stratified cluster random sampling survey was conducted, involving 47 034 students from 16 municipal districts in Shanghai, covering kindergartens (≥5 years), primary schools, middle schools, general high schools and vocational high schools. According to the Guidelines for Screening Refractive Errors in Primary and Secondary School Students, the Standard Logarithmic Visual acuity Chart was used to examine naked vision and corrected vision of students, and general information was collected. The distribution and severity of visual impairment in different age groups were analyzed, and χ 2 tests and multivariate Logistic regression were used to explore factors associated with visual impairment. Results: The detection rate of visual impairment among children and adolescents was 76.2%, with a higher rate among females (78.8%) than males ( 73.8 %), higher among Han ethic students ( 76.2 %) than minority students (71.2%), and higher among urban students (76.7%) than suburban students (75.8%), all with statistically significant differences ( χ 2=162.6, 10.4, 5.5, P <0.05). The rate of visual impairment initially decreased and then increased with age, reaching its lowest at age 7 (53.8%) and peaking at age 17 (89.6%) ( χ 2 trend = 3 467.0 , P <0.05). Severe visual impairment accounted for the majority, at 56.6%, and there was a positive correlation between the severity of visual impairment and age among children and adolescents ( r =0.45, P <0.05). Multivariate Logistic regression showed that age, BMI, gender, ethnicity and urban suburban status were associated with visual impairment ( OR =1.18, 1.01, 1.38 , 0.79, 0.88, P <0.05). Among those with moderate to severe visual impairment, the rate of spectacle lens usage was 62.8%, yet only 44.8 % of those who used spectacle lens had fully corrected visual acuity. Females (64.9%) had higher spectacle lens usage rates than males (60.6%), and general high school students had the highest spectacle lens usage (83.9%), and there were statistically significant differences in gender and academic stages ( χ 2=57.7, 4 592.8, P <0.05). Conclusions The rate of spectacle lens usage among students with moderate to severe visual impairment is relatively low, and even after using spectacle lens, some students still do not achieve adequate corrected visual acuity. Efforts should focus on enhancing public awareness of eye health and refractive correction and improving the accessibility of related health services.

In protein engineering, while computational models are increasingly used to predict mutation effects, their evaluations primarily rely on high-throughput deep mutational scanning (DMS) experiments that use surrogate readouts, which may not adequately capture the complex biochemical properties of interest. Many proteins and their functions cannot be assessed through high-throughput methods due to technical limitations or the nature of the desired properties, and this is particularly true for the real industrial application scenario. Therefore, the desired testing datasets, will be small-size (∼10-100) experimental data for each protein, and involve as many proteins as possible and as many properties as possible, which is, however, lacking. Here, we present VenusMutHub, a comprehensive benchmark study using 905 small-scale experimental datasets curated from published literature and public databases, spanning 527 proteins across diverse functional properties including stability, activity, binding affinity, and selectivity. These datasets feature direct biochemical measurements rather than surrogate readouts, providing a more rigorous assessment of model performance in predicting mutations that affect specific molecular functions. We evaluate 23 computational models across various methodological paradigms, such as sequence-based, structure-informed and evolutionary approaches. This benchmark provides practical guidance for selecting appropriate prediction methods in protein engineering applications where accurate prediction of specific functional properties is crucial.

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.

Osteoporosis is a prevalent skeletal condition characterized by reduced bone mass and strength, leading to increased fragility. Buqi-Tongluo (BQTL) decoction, a traditional Chinese medicine (TCM) prescription, has yet to be fully evaluated for its potential in treating bone diseases such as osteoporosis. To investigate the mechanism by which BQTL decoction inhibits osteoclast differentiation in vitro and validate these findings through in vivo experiments. We employed MTS assays to assess the potential proliferative or toxic effects of BQTL on bone marrow macrophages (BMMs) at various concentrations. TRAcP experiments were conducted to examine BQTL's impact on osteoclast differentiation. RT-PCR and Western blot analyses were utilized to evaluate the relative expression levels of osteoclast-specific genes and proteins under BQTL stimulation. Finally, in vivo experiments were performed using an osteoporosis model to further validate the in vitro findings. This study revealed that BQTL suppressed receptor activator of NF-κB ligand (RANKL)-induced osteoclastogenesis and osteoclast resorption activity in vitro in a dose-dependent manner without observable cytotoxicity. The inhibitory effects of BQTL on osteoclast formation and function were attributed to the downregulation of NFATc1 and c-fos activity, primarily through attenuation of the MAPK, NF-κB, and Calcineurin signaling pathways. BQTL's inhibitory capacity was further examined in vivo using an ovariectomized (OVX) rat model, demonstrating a strong protective effect against bone loss. BQTL may serve as an effective therapeutic TCM for the treatment of postmenopausal osteoporosis and the alleviation of bone loss induced by estrogen deficiency and related conditions.
Animals ; NFATC Transcription Factors/genetics* ; Drugs, Chinese Herbal/pharmacology* ; Ovariectomy ; Osteoclasts/metabolism* ; Female ; Osteogenesis/drug effects* ; Rats, Sprague-Dawley ; Rats ; NF-kappa B/genetics* ; Osteoporosis/genetics* ; Signal Transduction/drug effects* ; Bone Resorption/genetics* ; Cell Differentiation/drug effects* ; Humans ; RANK Ligand/metabolism* ; Mitogen-Activated Protein Kinases/genetics* ; Transcription Factors

Objective To analyze the burden of atherosclerotic cardiovascular disease(ASCVD)attributable to lifestyle and metabolic factors in China from 1990 to 2021 and predict its future trends,providing evidence for public health prevention and control.Methods Based on the Global Burden of Disease(GBD)2021 database,we extracted disability-adjusted life years(DALYs)of ASCVD attributable to 7 key risk factors:dietary,low physical activity,smoking,high body mass index(BMI),high fasting blood glucose,high low-density lipid cholesterol(LDL-C),and hypertension.Age-standardized DALY rates(ASDR)were also calculated to account for variations in age structure,and an ARIMA model was applied to predict future trends.Results ① Overall trend analysis showed the leading risk factor for ischemic heart disease(IHD)shifted from dietary risks in 1990 to hypertension in 2021,while hypertension and smoking remained the primary risk factors for ischemic stroke(IS)and peripheral artery disease(PAD),respectively.② In BRICS nations,hypertension was the dominant risk factor for both IHD and IS,whereas in China,smoking and high fasting blood glucose continued to be the major drivers of PAD burden.③ Sex-specific analysis revealed that hypertension was the leading risk factor for IHD and IS in both males and females.For PAD,smoking was the main risk factor in males,while high fasting blood glucose was for females.Age-stratified analysis revealed that in the 15-49 age group,dietary,high LDL-C and smoking were the primary factor for IHD,IS and PAD,respectively;in the 50-69 group,dietary predominated IHD risk,hypertension IS,and smoking PAD;and among those aged≥70 years,hypertension was the leading risk factor for both IHD and IS,and high fasting blood glucose was for PAD.④ ARIMA model forecasted that by 2035,DALYs for IHD,IS,and PAD will increase to 82.816 million,46.808 million,and 0.192 million person-years,respectively.Conclusion The ASCVD burden attributable to lifestyle and metabolic factors continues to rise in China from 1990 to 2021,and is projected to further increase by 2035.Countermeasures We suggest that current prevention and control priorities are enhanced management of metabolic factors,tailored interventions by sex and age,promotion of healthy lifestyles and tobacco control,and establishment of an integrated system of prevention-treatment-rehabilitation-research.