OBJECTIVE To establish a Chinese drug-related problem （DRP） classification system applicable to pharmacist-led pharmaceutical care in China， providing pharmacists with an effective and practical tool for pharmaceutical care. METHODS A multi-stage process was employed to construct the DRP classification system， including literature review and analysis， comparison of existing classification systems， refinement of classification items and framework development， two rounds of standard case validation， expert discussion， and system revision. The Fleiss′ kappa test was used to calculate the consistency coefficient κ， assessing the reliability of pharmacists participating in evaluating the classification system. An electronic questionnaire comprising six items was employed to evaluate the system’s applicability. RESULTS The constructed Chinese DRP classification system comprised six sections [problem（including potential problems）， DRP evaluation， cause （including possible causes of potential problems）， intervention， acceptance of intervention and DRP status]， with 24 primary codes and 96 secondary codes. In the first round of case validation， κ values exceeded 0.4 for all sections except “intervention” and “DRP status”. In the second round， κ values exceeded 0.4 for all sections. In the applicability evaluation of the classification system， positive ratings （“strongly agree” or “agree”） exceeded 85% for all items. Specifically， positive ratings for“the classification system can provide appropriate category selection”，“ the classification system is comprehensive”，“ the classification system is convenient to use” and “the classification system is highly satisfactory” exceeded 92%. CONCLUSIONS The Chinese DRP classification system developed demonstrates both high reliability and applicability， providing an effective and practical classification tool for pharmacists in China to conduct pharmaceutical care.

Objective To explore the prevalence of depressive symptoms in postoperative patients with ovarian cancer and to analyze its influencing factors from multiple dimensions, including clinical characteristics, psychological factors, and laboratory indicators. Methods A cross-sectional study was conducted, which enrolled 235 postoperative patients with ovarian cancer. Depressive status was assessed using the patient health questionnaire, and the demographic, pathological, and medical record data of the patients were collected using the generalized anxiety disorder scale, Pittsburgh sleep quality index, European organization for research and treatment of cancer quality of life questionnaire core 30, and ECOG performance status score. Peripheral blood tumor marker (CA125), routine blood test, lymphocyte subsets, and serum cytokine levels were measured. Univariate and multivariate binary logistic regression analysis were used for statistical analysis. Results The prevalence of depression in postoperative patients with ovarian cancer was 39.15% (92/235). Univariate analysis showed that ECOG score ≥ 2 points, pain, anxiety, poor sleep quality, low quality of life, low life satisfaction, tumor recurrence, six or more cycles of chemotherapy, as well as higher levels of CA125, NLR, and NAR, and lower hemoglobin levels were significantly associated with depression (all P<0.05). Multivariate binary Logistic regression analysis showed that anxiety (OR=1.975, 95%CI: 1.231-3.170), sleep efficiency (OR=4.181, 95%CI: 1.211-14.43), sleep latency (OR=34.806, 95%CI: 4.258-284.542), ECOG performance status score, cognitive function (OR=0.918, 95%CI: 0.868-0.97), and life satisfaction were independent risk factors for depression (all P<0.05). Laboratory indicators were not independent influencing factors in the multivariate Logistic regression model. Conclusion Depression in postoperative patients with ovarian cancer is influenced by physiological, psychological, and social factors. Clinical management should focus on patients with anxiety, sleep disorders, poor physical condition, and low life satisfaction, and a comprehensive prevention and treatment strategy centered on psychological intervention and taking into account symptom management and social support should be implemented.

BACKGROUND: Neoadjuvant chemoradiotherapy followed by radical surgery has been a common practice for patients with locally advanced rectal cancer, but the response rate varies among patients. This study aimed to develop a ResNet-Vision Transformer based magnetic resonance imaging (MRI)-endoscopy fusion model to precisely predict treatment response and provide personalized treatment. METHODS: In this multicenter study, 366 eligible patients who had undergone neoadjuvant chemoradiotherapy followed by radical surgery at eight Chinese tertiary hospitals between January 2017 and June 2024 were recruited, with 2928 pretreatment colonic endoscopic images and 366 pelvic MRI images. An MRI-endoscopy fusion model was constructed based on the ResNet backbone and Transformer network using pretreatment MRI and endoscopic images. Treatment response was defined as good response or non-good response based on the tumor regression grade. The Delong test and the Hanley-McNeil test were utilized to compare prediction performance among different models and different subgroups, respectively. The predictive performance of the MRI-endoscopy fusion model was comprehensively validated in the test sets and was further compared to that of the single-modal MRI model and single-modal endoscopy model. RESULTS: The MRI-endoscopy fusion model demonstrated favorable prediction performance. In the internal validation set, the area under the curve (AUC) and accuracy were 0.852 (95% confidence interval [CI]: 0.744-0.940) and 0.737 (95% CI: 0.712-0.844), respectively. Moreover, the AUC and accuracy reached 0.769 (95% CI: 0.678-0.861) and 0.729 (95% CI: 0.628-0.821), respectively, in the external test set. In addition, the MRI-endoscopy fusion model outperformed the single-modal MRI model (AUC: 0.692 [95% CI: 0.609-0.783], accuracy: 0.659 [95% CI: 0.565-0.775]) and the single-modal endoscopy model (AUC: 0.720 [95% CI: 0.617-0.823], accuracy: 0.713 [95% CI: 0.612-0.809]) in the external test set. CONCLUSION The MRI-endoscopy fusion model based on ResNet-Vision Transformer achieved favorable performance in predicting treatment response to neoadjuvant chemoradiotherapy and holds tremendous potential for enabling personalized treatment regimens for locally advanced rectal cancer patients.
Humans ; Rectal Neoplasms/diagnostic imaging* ; Magnetic Resonance Imaging/methods* ; Male ; Female ; Middle Aged ; Neoadjuvant Therapy/methods* ; Aged ; Adult ; Chemoradiotherapy/methods* ; Endoscopy/methods* ; Treatment Outcome

OBJECTIVE: To screen out the key metabolites related to diabetic foot (DF) by integrating genome-wide association studies (GWAS) and metabolome genome-wide association studies (mGWAS). METHODS: The literature databases such as PubMed and China national knowledge infrastructure(CNKI), as well as genomics databases such as PAN UKBB, FinnGen, and IEU Open GWAS were systematically retrieved from database estobilishment to November 2024 on DF-related single nucleotide polymorphisms and genome-wide association studies. DF-single nucleotide polymorphism-metabolite network was constructed by mGWAS package and mGWAS-Explorer platform. The causal relationship between key factors was evaluated by two-sample Mendelian randomization. The genetic correlation between DF and 575 metabolites (source:IEU Open GWAS) was evaluated by linkage disequilibrium score regression. In vitro experiments were conducted to induce injury of human umbilical vein endothelial cells with 30 mM glucose and intervene with 20 μM γ-tocopherol. Changes in cell migration, scratch healing and tube formation function were detected. RESULTS: Twenty-senen literatures on single nucleotide polymorphism literatures and 3 studies on GWAS were included. Genetic analysis results showed DF-related single nucleotide polymorphisms were enriched in vascular endothelial dysfunction-related pathways (such as fluid shear stress and atherosclerosis). The results of metabolic network analysis screened out 19 associated metabolites, among which 12 such as γ -tocopherol and pyruvate had significant genetic correlations with DF. Mendelian randomization suggested matrix metalloproteinase-9(MMP-9) might be a potential driver of DF (β=0.658, P=0.063 8), and the occurrence of DF could reduce the level of high-density lipoprotein (β=-0.002, P=0.015 2). The results of in vitro experiments confirmed that γ -tocopherol could improve endothelial dysfunction induced by high glucose, specifically manifested as an increase in the number of cell migrations, improvement in the scratch healing rate, and recovery of tubule formation ability (P<0.05). CONCLUSION DF has a genetic basis centered on vascular endothelial dysfunction, and its occurrence can lead to further metabolic disorders. The key single nucleotide polymorphism loci integrated provided molecular markers for the risk stratification of foot ulcers in diabetic patients. In addition, γ -tocopherol has demonstrated clinical application potential as a therapeutic drug for DF by significantly improving the function of vascular endothelial cells in a high-glucose environment.
Humans ; Diabetic Foot/drug therapy* ; Polymorphism, Single Nucleotide ; Genome-Wide Association Study ; Genomics ; Metabolomics ; Metabolome

Biomolecular condensates are formed through phase separation of biomacromolecules such as proteins and RNAs. These condensates exhibit liquid-like properties that can futher transition into more stable material states. They form complex internal structures via multivalent weak interactions, enabling precise spatiotemporal regulations. However, the use of inconsistent and non-standardized terminology has become increasingly problematic, hindering academic exchange and the dissemination of scientific knowledge. Therefore, it is necessary to discuss the terminology related to biomolecular condensates in order to clarify concepts, promote interdisciplinary cooperation, enhance research efficiency, and support the healthy development of this field.

AIM To invesigate the quantitative transmitting rules of benchmark samples of Ganjiang Lingzhu Decoction.METHODS HPLC fingerprints were established,after which principal component analysis and partial least squares discriminant analysis were performed.The contents of 6-gingerol,liquiritin and glycyrrhizic acid were determined,after which their transfer rates and dry paste rate were calculated.RESULTS There were 22 common peaks in the fingerprints for 15 batches of benchmark samples with the similarities of more than 0.9.Various batches of benchmark samples were clustered into 3 categories,4 principal components demonstrated the accumulative variance contribution rate of 92.676％,and liquiritin made a great contribution to grouping.In various batches of medicinal materials-decoction pieces,decoction pieces-standard decoctions and standard decoctions-benchmark samples,the transfer rates of 6-gingerol were 84.72％-100.00％,14.29％-24.14％,88.60％-99.36％,those of liquiritin were 74.38％-100.00％,38.54％-64.20％,96.91％-100.30％,and those of glycyrrhizic acid were 68.28％-103.27％,28.48％-49.79％,92.93％-100.49％,the dry paste rate was 13.75％-16.41％.CONCLUSION The preparation process for benchmark samples of Ganjiang Lingzhu Decoction is stable,6-gingerol,liquiritin and glycyrrhizic acid can be taken as their quality markers.Fingerprint combined with content determination can provides a reference for the quality control of Ganjiang Lingzhu Decoction and their related preparations.

PURPOSE: To comprehensively analyze the geographic and temporal trends of foot fracture, understand its health burden by age, sex, and sociodemographic index (SDI), and explore its leading causes from 1990 to 2019. METHODS: The datasets in the present study were generated from the Global Burden of Diseases Study 2019, which included foot fracture data from 1990 to 2019. We extracted estimates along with the 95% uncertainty interval (UI) for the incidence and years lived with disability (YLDs) of foot fracture by location, age, gender, and cause. The epidemiology and burden of foot fracture at the global, regional, and national level was exhibited. Next, we presented the age and sex patterns of foot fracture. The leading cause of foot fracture was another focus of this study from the viewpoint of age, sex, and location. Then, Pearson's correlations between age-standardized rate (ASR), SDI, and estimated annual percentage change were calculated. RESULTS: The age-standardized incidence rate was 138.68 (95% UI: 104.88 - 182.53) per 100,000 persons for both sexes, 174.24 (95% UI: 134.35 - 222.49) per 100,000 persons for males, and 102.19 (95% UI: 73.28 - 138.00) per 100,000 persons for females in 2019. The age-standardized YLDs rate was 5.91 (95% UI: 3.58 - 9.25) per 100,000 persons for both genders, 7.35 (95% UI: 4.45 - 11.50) per 100,000 persons for males, and 4.51 (95% UI: 2.75 - 7.03) per 100,000 persons for females in 2019. The global incidence and YLDs of foot fracture increased in number and decreased in ASR from 1990 to 2019. The global geographical distribution of foot fracture is uneven. The incidence rate for males peaked at the age group of 20 - 24 years, while that for females increased with advancing age. The incidence rate of older people was rising, as younger age incidence rate declined from 1990 to 2019. Falls, exposure to mechanical forces, and road traffic injuries were the 3 leading causes of foot fracture. Correlations were observed between ASR, estimated annual percentage change, and SDI. CONCLUSIONS The burden of foot fracture remains high globally, and it poses an enormous public health challenge, with population aging. It is necessary to allocate more resources to the high-risk populations. Targeted realistic intervention policies and strategies are warranted.
Humans ; Male ; Female ; Incidence ; Fractures, Bone/epidemiology* ; Middle Aged ; Adult ; Global Health ; Aged ; Global Burden of Disease ; Adolescent ; Child ; Young Adult ; Foot Injuries/epidemiology* ; Cost of Illness ; Child, Preschool ; Aged, 80 and over ; Infant

PURPOSE: This study aims to identify the prevalence and risk factors of military training-related abdominal injuries and help plan and conduct training properly. METHODS: This questionnaire survey study was conducted from October 2021 to May 2022 among military personnel from 6 military units and 8 military medical centers and participants' medical records were consulted to identify the training-related abdominal injuries. All the military personnel who ever participated in military training were included. Those who refused to participate in this study or provided an incomplete questionnaire were excluded. The questionnaire collected demographic information, type of abdominal injury, frequency, training subjects, triggers, treatment, and training disturbance. Chi-square test and t-test were used to compare baseline information. Univariate and multivariate regression analyses were used to explore the risk factors associated with military training-related abdominal injuries. RESULTS: A total of 3058 participants were involved in this study, among which 1797 (58.8%) had suffered training-related abdominal injuries (the mean age was 24.3 years and the service time was 5.6 years), while 1261 (41.2%) had no training-related abdominal injuries (the mean age was 23.1 years and the service time was 4.3 years). There were 546 injured patients (30.4%) suspended the training and 84 (4.6%) needed to be referred to higher-level hospitals. The most common triggers included inadequate warm-up, fatigue, and intense training. The training subjects with the most abdominal injuries were long-distance running (589, 32.8%). Civil servants had the highest rate of abdominal trauma (17.1%). Age ≥ 25 years, military service ≥ 3 years, poor sleep status, and previous abdominal history were independent risk factors for training-related abdominal injury. CONCLUSION More than half of the military personnel have suffered military training-related abdominal injuries. Inadequate warm-up, fatigue, and high training intensity are the most common inducing factors. Scientific and proper training should be conducted according to the factors causing abdominal injuries.
Humans ; Military Personnel ; Risk Factors ; Prevalence ; Male ; Abdominal Injuries/etiology* ; Female ; Adult ; Surveys and Questionnaires ; Young Adult

BACKGROUND: Based on the China-VHD database, this study sought to develop and validate a Valvular Heart Disease- specific Age-adjusted Comorbidity Index (VHD-ACI) for predicting mortality risk in patients with VHD. METHODS & RESULTS: The China-VHD study was a nationwide, multi-centre multi-centre cohort study enrolling 13,917 patients with moderate or severe VHD across 46 medical centres in China between April-June 2018. After excluding cases with missing key variables, 11,459 patients were retained for final analysis. The primary endpoint was 2-year all-cause mortality, with 941 deaths (10.0%) observed during follow-up. The VHD-ACI was derived after identifying 13 independent mortality predictors: cardiomyopathy, myocardial infarction, chronic obstructive pulmonary disease, pulmonary artery hypertension, low body weight, anaemia, hypoalbuminaemia, renal insufficiency, moderate/severe hepatic dysfunction, heart failure, cancer, NYHA functional class and age. The index exhibited good discrimination (AUC, 0.79) and calibration (Brier score, 0.062) in the total cohort, outperforming both EuroSCORE II and ACCI (P < 0.001 for comparison). Internal validation through 100 bootstrap iterations yielded a C statistic of 0.694 (95% CI: 0.665-0.723) for 2-year mortality prediction. VHD-ACI scores, as a continuous variable (VHD-ACI score: adjusted HR (95% CI): 1.263 (1.245-1.282), P < 0.001) or categorized using thresholds determined by the Yoden index (VHD-ACI ≥ 9 vs. < 9, adjusted HR (95% CI): 6.216 (5.378-7.184), P < 0.001), were independently associated with mortality. The prognostic performance remained consistent across all VHD subtypes (aortic stenosis, aortic regurgitation, mitral stenosis, mitral regurgitation, tricuspid valve disease, mixed aortic/mitral valve disease and multiple VHD), and clinical subgroups stratified by therapeutic strategy, LVEF status (preserved vs. reduced), disease severity and etiology. CONCLUSION The VHD-ACI is a simple 13-comorbidity algorithm for the prediction of mortality in VHD patients and providing a simple and rapid tool for risk stratification.