Osteoporosis is a common senile bone metabolism disease， clinically characterized by decreased bone mass， destruction of bone microstructure， increased bone fragility， and easy fracture. It tends to occur in the elderly and postmenopausal women， seriously threatening the quality of life and physical and mental health of the elderly. At present， the treatment of osteoporosis is mainly based on oral western medicines， such as calcium， Vitamin D， and bisphosphonates. Still， there are drawbacks such as a long medication cycle and many adverse reactions. In recent years， due to the advantages of multi-component， multi-pathway， and multi-target， some traditional Chinese medicines and effective ingredients can regulate the osteogenic and osteoclastic differentiation process in both directions and are widely used in the prevention and treatment of osteoporosis. Hippophae rhamnoides is a commonly used herbal medicine， and its fruits are rich in flavonoids， polyphenols， fatty acids， vitamins， and trace elements， which have been proven to have a good anti-osteoporosis effect. Isorhamnetin is the main effective ingredient of Hippophae rhamnoides fruits， which has many pharmacological effects such as anti-inflammation， anti-oxidative stress， anti-aging， and anti-tumor. Studies have shown that isorhamnetin can participate in the regulation of bone metabolism and has a good anti-osteoporosis effect. However， the pharmacological effects and related mechanisms of isorhamnetin against osteoporosis have not been systematically summarized. Therefore， this paper reviewed the pharmacological effects and related mechanisms of isorhamnetin against osteoporosis by referring to relevant literature to provide more basis for the development and application of isorhamnetin.

ObjectiveTo investigate the distribution of traditional Chinese medicine （TCM） syndrome elements in type 2 diabetes mellitus （T2DM） patients based on maximum body mass index （maxBMI） and explore their association with complication risks. MethodsA retrospective cross-sectional study was used to collect clinical data from hospitalized T2DM patients， extracting age， gender， smoking history， alcohol consumption history， duration of disease， HbA1c level， complications， and TCM syndromes， and extracting the syndrome elements of disease location and disease nature based on their TCM syndromes. MaxBMI was calculated by telephone survey of patients' self-reported maximum body weight； patients with maxBMI ≥24 kg/m² were classified into spleen-heat syndrome group， and those with maxBMI ＜24 kg/m² were classified into consumptive-heat syndrome group. The distribution of TCM syndrome types and syndrome elements of patients in the two groups were analysed. Then the propensity score matching method was used to balance the baseline characteristics between the two groups and compare the differences in the distribution of syndrome types and syndrome elements and the risk of macrovascular and microvascular complications between the two groups. ResultsAmong the 1178 T2DM patients， syndrome elements in spleen-heat patients （1034 cases） were primarily located in the spleen （351 cases， 33.95%）， liver （240 cases， 23.21%）， and stomach （139 cases， 13.44%）， while in consumptive-heat patients （144 cases）， they were concentrated in the spleen （57 cases， 39.58%）， liver （34 cases， 23.61%）， and kidneys （17 cases， 11.81%）； regarding syndrome elements of disease nature， spleen-heat patients were predominantly characterized by qi deficiency （481 cases， 46.52%）， phlegm （353 cases， 22.73%）， and dampness （241 cases， 23.31%）， whereas consumptive-heat patients showed more qi deficiency （84 cases， 58.33%） and yin deficiency （44 cases， 30.56%）. After propensity score matching， 132 cases were included in each group， and no statistically significant differences were observed in the distribution of syndrome elements of disease location between the two groups （P＞0.05）， but the phlegm element was significantly more prevalent in spleen-heat patients than in consumptive-heat patients （P = 0.006）. Regarding the risk of complications， spleen-heat patients had a significantly higher risk of developing macrovascular complications compared to consumptive-heat patients （OR=2.04， P=0.010）， while no significant differences were found between groups in the occurrence of microvascular complications （P＞0.05）. ConclusionThe spleen-heat T2DM patients show a more frequent syndrome element of disease nature of phlegm， and a higher risk of developing macrovascular complications compared to consumptive-heat patients.

Accurate prediction of drug responses in cancer cell lines (CCLs) and transferable prediction of clinical drug responses using CCLs are two major tasks in personalized medicine. Despite the rapid advancements in existing computational methods for preclinical and clinical cancer drug response (CDR) prediction, challenges remain regarding the generalization of new drugs that are unseen in the training set. Herein, we propose a multimodal fusion deep learning (DL) model called drug-target and single-cell language based CDR (DTLCDR) to predict preclinical and clinical CDRs. The model integrates chemical descriptors, molecular graph representations, predicted protein target profiles of drugs, and cell line expression profiles with general knowledge from single cells. Among these features, a well-trained drug-target interaction (DTI) prediction model is used to generate target profiles of drugs, and a pretrained single-cell language model is integrated to provide general genomic knowledge. Comparison experiments on the cell line drug sensitivity dataset demonstrated that DTLCDR exhibited improved generalizability and robustness in predicting unseen drugs compared with previous state-of-the-art baseline methods. Further ablation studies verified the effectiveness of each component of our model, highlighting the significant contribution of target information to generalizability. Subsequently, the ability of DTLCDR to predict novel molecules was validated through in vitro cell experiments, demonstrating its potential for real-world applications. Moreover, DTLCDR was transferred to the clinical datasets, demonstrating satisfactory performance in the clinical data, regardless of whether the drugs were included in the cell line dataset. Overall, our results suggest that the DTLCDR is a promising tool for personalized drug discovery.

As the cornerstone of modern medical diagnosis,pathology is facing multiple challenges such as workforce shortages,strong diagnostic subjectivity,and inefficient workflows.With advantages in image recognition,pattern analysis,and big data processing,artificial intelligence(AI)is increasingly being integrated into the field of pathological diagnosis,driving its transition toward digitization and intelligence.This article systematically reviews the development of AI in pathology,from early supervised learning validation to weakly supervised learning overcoming annotation bottlenecks,and the recent rise of self-supervised and multimodal foundation models.It demonstrates the broad applications of AI in improving diagnostic consistency,optimizing workflows,and predicting molecular features and prognoses.AI not only enhances the objectivity and efficiency of pathological diagnosis but also promotes the development of emerging interdisciplinary fields such as computational pathomics,providing strong support for precision medicine.Although challenges such as data standardization and regulatory approval remain in clinical implementation,the deep integration of AI and pathology is ushering in a new era of human-machine collaboration and intelligent diagnostics.

Objective To identify co-expressed genes and potential comorbidity mechanisms between Alzheimer's disease(AD)and epilepsy with publicly available single-cell transcriptome sequencing data from human brains,fol-lowed by functional validation in APP/PS1 double transgenic AD mouse models expressing the chimerical Mo/HuAPP695swe amyloid precursor protein and mutant PS1-dE9 presenilin 1.Methods The single-cell transcriptome sequencing data of brain tissue from AD and epilepsy patients were collected from gene expression omnibus(GEO)database followed by cell clustering,differential expression analysis and gene ontology(GO)func-tional enrichment analysis using R-based tools such as Seurat and cluster Profiler and video electroencephalogram (vEEG)monitoring and Western blot experiments.Results A total of eight major brain cell types were identified,with neurons and glial cells exhibiting shared differentially expressed genes between AD and epilepsy.These co-ex-pressed genes were significantly clustered in pathways related to metal ion homeostasis,synaptic transmission,oxi-dative stress,and immune activation,which suggested common pathological mechanisms involving in synaptic dys-function and neuro-inflammation in both disorders.The vEEG recordings of APP/PS1 mouse model of AD showed 30％of mice exhibited high-frequency epileptic seizures,while 70％showed low-frequency seizure activity.Subse-quent validation in the prefrontal cortex of AD mice confirmed up-regulated expression of key molecular markers(HES5,c-FOS,and RPL10A)identified through single-cell sequencing analysis.Conclusions AD and epilepsy share gene co-expression profiles and functional pathways in specific cell types.The results of research provide a theoretical support for further elucidating their comorbidity mechanisms and developing targeted therapeutic strategy.

Accurate prediction of drug responses in cancer cell lines(CCLs)and transferable prediction of clinical drug responses using CCLs are two major tasks in personalized medicine.Despite the rapid advancements in existing computational methods for preclinical and clinical cancer drug response(CDR)prediction,chal-lenges remain regarding the generalization of new drugs that are unseen in the training set.Herein,we propose a multimodal fusion deep learning(DL)model called drug-target and single-cell language based CDR(DTLCDR)to predict preclinical and clinical CDRs.The model integrates chemical descriptors,mo-lecular graph representations,predicted protein target profiles of drugs,and cell line expression profiles with general knowledge from single cells.Among these features,a well-trained drug-target interaction(DTI)prediction model is used to generate target profiles of drugs,and a pretrained single-cell language model is integrated to provide general genomic knowledge.Comparison experiments on the cell line drug sensitivity dataset demonstrated that DTLCDR exhibited improved generalizability and robustness in predicting unseen drugs compared with previous state-of-the-art baseline methods.Further ablation studies verified the effectiveness of each component of our model,highlighting the significant contribution of target information to generalizability.Subsequently,the ability of DTLCDR to predict novel molecules was validated through in vitro cell experiments,demonstrating its potential for real-world applications.Moreover,DTLCDR was transferred to the clinical datasets,demonstrating satisfactory performance in the clinical data,regardless of whether the drugs were included in the cell line dataset.Overall,our results suggest that the DTLCDR is a promising tool for personalized drug discovery.

Objective:To understand the situation and epidemic characteristics of injury deaths among children aged 5 to 24 years in Jiangsu Province from 2012 to 2021 and the trend of annual changes.Methods:The main injury mortality data of children and adolescents was collected, and the crude and standardized mortality rates of road traffic accidents, drowning, suicide, and accidental falls among children and adolescents over a decade and the annual average percentage of change (AAPC) were calculated. The main injury mortality characteristics and trends of children and adolescents of different age groups and genders were analyzed.Results:The total number of injury deaths among 5 to 24 adolescents in Jiangsu Province was 16 052, with a standardized mortality rate of 9.58/100 000. There was no significant trend in the overall standardized mortality rate of injuries (AAPC=-3.450%, P=0.055). The standardized mortality rate of road traffic injuries among children and adolescents showed a decreasing trend over the past decade, with statistical significance (AAPC=-9.406%, P<0.001). The standardized suicide mortality rate showed an upward trend over the past decade, with statistical significance (AAPC=9.000%, P=0.001). The overall injury mortality rate showed an upward trend with age. Suicide rates in males and females were on the rise and both have statistical significance (AAPC=9.420% and AAPC=9.607%, both P<0.05). The standardized mortality rates of female traffic accidents, drowning, and male traffic accidents showed a decreasing trend and were statistically significant (AAPC for female traffic accidents=-7.364%, AAPC for female drowning=-5.352%, and AAPC for male traffic accidents=-10.242%, all P<0.05). The standardized mortality rate of urban and rural traffic accidents showed a decreasing trend and was statistically significant(AAPC=-7.899% and AAPC=-9.421%, both P<0.001). The standardized suicide mortality rate showed an upward trend and statistical significance (AAPC=11.009% and AAPC=7.528%, both P<0.05). Conclusions:The overall injury situation of children and adolescents in Jiangsu Province improved in the past decade from 2012 to 2021, but the suicide mortality rate was on the rise. It is necessary to focus on the mental health issues of this age group and to strengthen the prevention and control of suicide among children and adolescents, in Jiangsu.

Objective:To analyze the dynamic development of physical, psychological, and cognitive degenerative multimorbidity among middle-aged and older Chinese adults (≥45 years old) while estimating the longitudinal association between socioeconomic status (SES) and the progression of multimorbidity.Methods:Based on data from the China Health and Retirement Longitudinal Study (2011-2020), the Sankey diagram was used to show the dynamic development of physical, psychological, and cognitive degenerative multimorbidity from 2011 to 2020. SES was constructed based on the level of education and total household wealth. Logistic regression was used to estimate OR and 95% CI to evaluate the association between SES and the progression of multimorbidity. Results:Of the 5 393 participants included, 4 484 (83.14%) of them developed new diseases, and the prevalence of physical, psychological, and cognitive degenerative multimorbidity increased from 38.04% to 74.23%. Compared to those with no reported disorders at baseline, participants with psychological disorder (for newly developed physical-cognitive multimorbidity: OR=4.59,95% CI: 2.89-7.29), cognitive disorder (for newly developed physical-psychological multimorbidity: OR=2.24,95% CI: 1.40-3.60), or their multimorbidity at baseline were more likely to progress to physical, psychological, and cognitive degenerative multimorbidity. After adjusting covariates, individuals with low SES were more likely to develop physical diseases ( OR=1.45, 95% CI: 1.11-1.89), cognitive disorder ( OR=1.84, 95% CI: 1.16-2.91), physical-psychological multimorbidity ( OR=1.87, 95% CI: 1.37-2.56), physical-cognitive multimorbidity ( OR=3.58, 95% CI: 2.54-5.06), psychological-cognitive multimorbidity ( OR=5.66, 95% CI: 3.04-10.55), and physical-psychological-cognitive multimorbidity ( OR=3.21, 95% CI: 2.06-5.01) in comparison to those with high SES. There is a dose-response relationship between SES and the multimorbidity progression (all trend P<0.001). Conclusions:The prevalence of physical, psychological, and cognitive degenerative multimorbidity increased significantly among middle-aged and older Chinese adults. Lower SES was associated with multiple patterns of physical, psychological, and cognitive disorders progression.

This article explores the application of Professor Zhou Zhongying's"focus on the core pathogenesis"concept in the con-text of epidemic hemorrhagic fever and examines how Academician Tong Xiaolin has inherited and developed Professor Zhou's experi-ences.Influenced by Professor Zhou Zhongying's academic thoughts and considering the contemporary context,Academician Tong Xia-olin,drawing on years of clinical experience,has proposed a new set of Nineteen Pathogenic Factors.Building upon the foundation of the Nineteen Pathogenic Factors in the The Yellow Emperor's Inner Classic,this new framework enriches and expands the understanding of disease location,etiology and pathogenesis,disease classification,and pays attention to a comprehensive understanding of diseases.It emphasizes that the process of seeking the underlying mechanisms should be approached from three aspects:dynamic,state,and condition,rather than solely focusing on the immediate clinical manifestations.This comprehensive approach to understand-ing disease development offers a fresh perspective and contributes to the application of traditional Chinese Medicine in the diagnosis and treatment of modern diseases.

Objective To compare the efficacy of TightRope loop plate and Endobutton plate in the treatment of acromioclavicular joint dislocation.Methods A retrospective analysis was conducted on 94 patients with acromioclavicular joint dislocation who were treated at this center from March 2021 to February 2023.They were divided into two groups based on different admission date.The Group E(n =47)received Endobutton plate treatment between March 2021 and February 2022,while the Group T(n =47)received TightRope loop plate treatment between March 2022 and February 2023.At the last follow-up,the perioperative indicators,Visual Analogue Scale(VAS),Constant-Murley shoulder joint function scores,and surgical complications were compared between the two groups.Results The surgical time,intraoperative bleeding,incision length,and VAS scores at 7 days after surgery in the Group T were shorter or lower than those in the Group E(P<0.05).There were no statistical differences in the incidence of perioperative nerve injury,internal fixation displacement,clavicle fracture,vascular injury,and infection between the two groups(P>0.05).The subjective and objective scores of Constant-Murley shoulder joint function in both groups at9 months after surgery showed significant improvement compared to preoperative scores(all P =0.000).There was no significant difference in the subjective and objective scores of Constant-Murley shoulder joint function between the two groups at 9 months after surgery(P>0.05).Conclusions The treatment of acromioclavicular joint dislocation with TightRope loop plate ot or Endobutton plate has a significant effect and can effectively improve shoulder joint function.Compared with Endobutton plate,use of TightRope loop plate has minor surgical trauma,less bleeding,and significantly reduced postoperative pain,being more conducive to early functional exercise for patients.