Objective To analyze the factors influencing occupational health literacy (OHL), work-related musculoskeletal disorders (WMSDs), and occupational stress in medical staff, and to investigate the potential associations between OHL and WMSDs as well as occupational stress. Methods A total of 880 medical staff members from 18 medical institutions were selected as the study subjects using a probability proportional method. Data on OHL, WMSDs, and occupational stress were investigated using the National Key Populations Occupational Health Literacy Surveillance Individual Questionnaire (including the Musculoskeletal Disorders Questionnaire and the Core Occupational Stress Scale), and the influencing factors and potential relationships were analyzed. Results The OHL level was 63.1%, the prevalence of WMSDs was 88.1%, and the detection rate of occupational stress was 24.0% in the study subjects. Multivariable logistic regression analysis showed that female medical staff had lower OHL levels than males (P<0.05). Medical staff with higher education levels and those working in higher-level medical institutions had higher OHL levels (all P<0.05). Medical staff working >40-<49 hours per week and those working ≥49 hours per week had higher OHL levels than those working ≤40 hours per week (all P<0.01). Medical staff with higher education levels or longer working hours had a higher risk of WMSDs (all P<0.05). Medical staff with lower monthly personal income, longer weekly working hours, and lower OHL levels had a higher risk of occupational stress (all P<0.05), and those working night shifts had a higher risk of occupational stress than those who did not (P<0.01). Conclusion The study participants had a relatively high level of OHL, a high prevalence of WMSDs, and a low detection rate of occupational stress. It is recommended that medical institutions effectively implement occupational health promotion and support measures to improve the OHL level of medical staff, to reduce the risk of WMSDs and occupational stress.

Objective:To explore the movement patterns and factors influencing lung tumors tracked using the M6 cyberknife stereotactic radiotherapy(SRT)system and to provide a reference for the implementation of precise stereotactic radiotherapy for lung tumors.Method:A retrospective analysis was conducted on 29 patients with lung tumors who were treated using x-sight lung tracking technology and the M6 cyberknife SRT system at Tianjin Medical University Cancer Institute&Hospital,from January 2022 to August 2024.The tumor location and volume,irradiation dose,isodose line,and number of divisions were recorded.Lung tumor location and SPSS 26.0 software were used to analyze the movement amplitude of tumors in the left and right(LFT/RGT,LR)directions,the anterior-posterior(ANT/POS,AP)direction,and the superior-inferior(SUP/INF,SI)direction.The results are expressed as the mean±standard deviation((x)±s)mm,and a t-test was used for inter-group comparisons.Multiple linear regression was used to analyze the effects of factors such as age,gender,tumor location(upper and lower lungs),and tumor volume on the amplitudes of the lung tumor movements.Result:The average motion amplitudes in the LR direc-tions,AP direction,and SI direction of the tumor target areas were(3.5±1.8)mm,(5.3±1.7)mm,and(7.3±5.4)mm for the upper lung,based on 19 cases,and(3.1±1.6)mm,(4.5±2.2)mm,and(12.2±4.4)mm for the lower lung,based on 10 cases,respectively.There was a statistic-ally significant difference(P=0.015 3)in the amplitude of movements between the lower and upper lung tumors in the SI direction.The lung tumor movement amplitude in the SI direction was influenced by tumor location(P=0.035),and the movement amplitudes in the LR direc-tions and the AP direction were not related to factors such as gender,age,tumor location,and tumor volume.Conclusions:The lung tumor movement amplitudes for the different locations varied depending on the respiratory movement shown by the patient.In the SI direction,the movement amplitude of the lower lung tumors was greater than that of upper lung tumors,and this was due to tumor location effects.The movement amplitudes of the lower and upper lung tumors were similar in the LR directions and AP directions.Furthermore,movement amplitude was not affected by gender,age,tumor location,and tumor volume.

Objective:To observe the effect of isokinetic sensorimotor training on the hand function of stroke survivors.Methods:Forty-two stroke survivors with hand dysfunction were randomly divided into an isokinetic group of 22 and a control group of 20. Both groups were given sensorimotor training in addition to routine drug treatment and rehabilitation therapy, but the isokinetic group was additionally provided with sensorimotor training based on isokinetic techniques for 45 minutes daily, 5 days a week for 4 consecutive weeks. Before and after the intervention, both groups were evaluated using the Semmes-Weinstein monofilament examination (SWME), their two-point discrimination (2-PD) was documented, proprioception of their wrist joints was quantified, and the Fugl-Meyer upper extremity assessment (FMA-UE) and the simplified upper limb function assessment (STEF) were applied.Results:In both groups after treatment, there was a significant improvement in the SWME scores and 2-PD distance of the index finger and the thenar, and there was a significant decrease in the angle of motion perception (at 30° of flexion). The average FMA-UE and STEF scores of both groups had improved. After the treatment, the SWME scores of the index finger and the thenar, as well as well as the average FMA-UE and STEF scores of the isokinetic group were significantly higher than the control group′s averages. Angle of motion perception was also significantly superior.Conclusions:Sensorimotor training based on isokinetic techniques can significantly improve touch, motion sense, gross motor function and the fine motor ability of stroke survivors.

Objective:To explore the association between uric acid and new-onset chronic kidney disease (CKD) in middle-aged and elderly hypertensive patients.Methods:This was a retrospective cohort study. Middle-aged and elderly hypertensive patients who had attended at least two annual health examinations at Yongshun Community Health Service Center in Tongzhou District, Beijing, from June 2016 to December 2020 were enrolled. The time interval between the two physical examinations was three years. The first physical examination time served as the baseline, and the second as the end of follow-up. Based on the uric acid level at baseline, the participants were divided into the normal uric acid group and the hyperuricemia group. The relevant clinical data of the participants were collected. The endpoint of the study was new-onset CKD. A multivariate logistic regression model was used to analyze the association between uric acid and new-onset CKD in hypertensive patients.Results:A total of 2 472 middle-aged and elderly hypertensive patients with an average age of (62.43±7.02) years were included. Of these, 733(29.7%) were male. There were 710 patients with hyperuricemia (hyperuricemia group) and 1 762 patients with normal uric acid levels (normal uric acid group).After adjusting for age, sex, body mass index (BMI), systolic blood pressure, diabetes mellitus, estimated glomerular filtration rate (eGFR), and uric acid-lowering treatment, multivariate logistic regression analysis showed that combined with hyperuricemia was an independent risk factor for new-onset CKD in middle-aged and elderly hypertensive patients ( OR=3.00, 95% CI: 1.87-4.80, P<0.001). The results of multivariate logistic analysis showed that elevated uric acid level was an independent risk factor for new-onset CKD in both male and female middle-aged and elderly hypertensive patients (both P<0.05), and there was no sex interaction ( P for interactio n>0.05). The results of multivariate logistic analysis showed that the combination of asymptomatic hyperuricemia was an independent risk factor for new-onset CKD in middle-aged and elderly hypertensive patients ( OR=3.00, 95% CI: 1.87-4.80, P<0.001), and there was no gender interaction ( P for interactio n>0.05). Conclusions:Hyperuricemia is an independent risk factor for new-onset CKD in middle-aged and elderly hypertensive patients, and elevated uric acid levels increase the risk of new-onset CKD in both male and female patients. Moreover, asymptomatic hyperuricemia may increase the risk of new-onset CKD.

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.

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.

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.

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.