Objective To explore the visiting behavior and health management strategy of patients with cardiovascular disease (CVD) and the industry management strategies under the family doctor contract service model. Methods A total of 351 patients with CVD from January 2022 to January 2025 were included in the case investigation by random sampling. Taking the number of visits in community health service institutions/total number of visits ≥90% as high compliance, the influencing factors of the visiting behavior of CVD patients were analyzed. Results Finally, 344 valid questionnaires were collected, with an effective recovery rate of 98.01%. The study subjects were categorized into high compliance group (271 cases) and low compliance group (73 cases). The scores of policy cognition understanding, institutional trust and medical service understanding in the high compliance group were higher compared with those in the low compliance group (P<0.05). There was no significant difference in the understanding of medical expenses between the two groups (P>0.05). Multivariate logistic regression model analysis revealed that CVD duration [OR (95% CI): 1.72 (1.21-2.44)], policy cognition understanding [OR (95% CI): 2.16 (1.20-3.89)] and institutional trust [OR (95% CI): 1.88 (1.08-3.25)] were independently associated with medical treatment behavior in CVD patients (P<0.05). Conclusion Enhancing the policy cognition understanding and institutional trust of CVD patients on family doctor contract service can promote patients to seek medical treatment and promote the high-quality development of family doctor contract service.

Objectives: Osteoporosis is a silent disease with low screening rates in many developing countries. This study aimed to evaluate the feasibility of using an artificial intelligence (AI)-based system to screen osteoporosis from pelvic and hip radiographs in Vietnam. Methods: We conducted a cross-sectional study at a tertiary medical center in Central Vietnam in 2023. A total of 2000 consecutive pelvic and hip radiographs from patients aged ≥ 40 years were collected. After excluding poorquality images, 1987 radiographs were analyzed using an AI-based software designed to estimate bone mineral density (BMD) from plain radiographs and derive T-scores. Osteoporosis was defined as a T-score ≤ − 2.5. Patient characteristics, radiographic findings, and risk factors for osteoporosis were analyzed. Results: Among 1987 patients (mean age 66.4 ± 15.1 years; 41.3% men), osteoporosis was identified in 872 patients (43.9%). The prevalence increased with age and was higher in women than in men (58.7% vs 22.8%, P < 0.001). Osteoporosis was associated with femoral neck (OR = 3.8, 95% CI: 2.7–5.2) and intertrochanteric fractures (OR = 7.0, 95% CI: 4.5–11.0). Patients with lower T-scores had a higher risk of hip fractures, especially those with T-scores ≤ − 3.0 (OR = 11.5, 95% CI: 5.5–24.5). Conclusions AI-based analysis of pelvic and hip radiographs is a feasible and effective tool for osteoporosis screening in Vietnam. The prevalence of osteoporosis in this hospital-based setting was high, particularly among elderly women.AI-assisted screening may offer an accessible strategy for early detection of osteoporosis in resource-limited settings.

Recently, diffusion models have emerged as a promising paradigm for molecular design and optimization. However, most diffusion-based molecular generative models focus on modeling 2D graphs or 3D geometries, with limited research on molecular sequence diffusion models. The International Union of Pure and Applied Chemistry (IUPAC) names are more akin to chemical natural language than the Simplified Molecular Input Line Entry System (SMILES) for organic compounds. In this work, we apply an IUPAC-guided conditional diffusion model to facilitate molecular editing from chemical natural language to chemical language (SMILES) and explore whether the pre-trained generative performance of diffusion models can be transferred to chemical natural language. We propose DiffIUPAC, a controllable molecular editing diffusion model that converts IUPAC names to SMILES strings. Evaluation results demonstrate that our model outperforms existing methods and successfully captures the semantic rules of both chemical languages. Chemical space and scaffold analysis show that the model can generate similar compounds with diverse scaffolds within the specified constraints. Additionally, to illustrate the model's applicability in drug design, we conducted case studies in functional group editing, analogue design and linker design.

OBJECTIVE: This study examines utilizes the advantages of machine learning algorithms to discern key determinants in prognosticate postoperative circulatory complications (PCCs) for older patients. METHODS: This secondary analysis of data from a randomized controlled trial involved 1,720 elderly participants in five tertiary hospitals in Beijing, China. Participants aged 60-90 years undergoing major non-cardiac surgery under general anesthesia. The primary outcome metric of the study was the occurrence of PCCs, according to the European Society of Cardiology and the European Society of Anaesthesiology diagnostic criteria. The analysis metrics contained 67 candidate variables, including baseline characteristics, laboratory tests, and scale assessments. RESULTS: Our feature selection process identified key variables that significantly impact patient outcomes, including the duration of ICU stay, surgery, and anesthesia; APACHE-II score; intraoperative average heart rate and blood loss; cumulative opioid use during surgery; patient age; VAS-Move-Median score on the 1st to 3rd day; Charlson comorbidity score; volumes of intraoperative plasma, crystalloid, and colloid fluids; cumulative red blood cell transfusion during surgery; and endotracheal intubation duration. Notably, our Random Forest model demonstrated exceptional performance with an accuracy of 0.9872. CONCLUSION We have developed and validated an algorithm for predicting PCCs in elderly patients by identifying key risk factors.
Aged ; Aged, 80 and over ; Female ; Humans ; Male ; Middle Aged ; Cardiovascular Diseases/etiology* ; Machine Learning ; Postoperative Complications/etiology* ; Risk Factors ; Randomized Controlled Trials as Topic ; Secondary Data Analysis

OBJECTIVE: To investigate the association between long-term glycemic control and cerebral infarction risk in patients with diabetes through a large-scale cohort study. METHODS: This prospective, community-based cohort study included 12,054 patients with diabetes. From 2006 to 2012, 38,272 fasting blood glucose (FBG) measurements were obtained from these participants. FBG trajectory patterns were generated using latent mixture modelling. Cox proportional hazards models were applied to assess the subsequent risk of cerebral infarction associated with different FBG trajectory patterns. RESULTS: At baseline, the mean age of the participants was 55.2 years. Four distinct FBG trajectories were identified based on FBG concentrations and their changes over the 6-year follow-up period. After a median follow-up of 6.9 years, 786 cerebral infarction events were recorded. Different trajectory patterns were associated with significantly varied outcome risks (Log-Rank P < 0.001). Compared with the low-stability group, Hazard Ratio ( HR) adjusted for potential confounders were 1.37 for the moderate-increasing group, 1.23 for the elevated-decreasing group, and 2.08 for the elevated-stable group. CONCLUSION Sustained high FBG levels were found to play a critical role in the development of ischemic stroke among patients with diabetes. Controlling FBG levels may reduce the risk of cerebral infarction.
Humans ; Cerebral Infarction/blood* ; Middle Aged ; Male ; Female ; Blood Glucose/analysis* ; Fasting/blood* ; Aged ; Prospective Studies ; Risk Factors ; Diabetes Mellitus/blood* ; Adult ; Proportional Hazards Models

Recently,diffusion models have emerged as a promising paradigm for molecular design and optimization.However,most diffusion-based molecular generative models focus on modeling 2D graphs or 3D geom-etries,with limited research on molecular sequence diffusion models.The International Union of Pure and Applied Chemistry(IUPAC)names are more akin to chemical natural language than the simplified molecular input line entry system(SMILES)for organic compounds.In this work,we apply an IUPAC-guided conditional diffusion model to facilitate molecular editing from chemical natural language to chemical language(SMILES)and explore whether the pre-trained generative performance of diffusion models can be transferred to chemical natural language.We propose DiffIUPAC,a controllable molecular editing diffusion model that converts IUPAC names to SMILES strings.Evaluation results demonstrate that our model out-performs existing methods and successfully captures the semantic rules of both chemical languages.Chemical space and scaffold analysis show that the model can generate similar compounds with diverse scaffolds within the specified constraints.Additionally,to illustrate the model's applicability in drug design,we conducted case studies in functional group editing,analogue design and linker design.

Osteoarthritis is a chronic degenerative joint disorder that adversely affects the quality of life of patients.Its pathological characteristics mainly involve the degeneration,destruction,and functional loss of articular cartilage.As one of the most crucial structures in the joint,articular cartilage is an avascular tissue composed of extracellular matrix synthesized by sparsely distributed chondrocytes.Chondrocyte apoptosis is one of the programmed cell death modalities of chondrocytes.During the normal physiological process,the proliferation and apoptosis of chondrocytes maintain a dynamic equilibrium,jointly safeguarding the homeostasis of articular cartilage.However,in the osteoarthritis process,the stimulation of related cytokines after tissue damage induces an imbalance between chondrocyte proliferation and apoptosis,resulting in excessive chondrocyte apoptosis,and subsequently leading to the degradation and degeneration of cartilage.Clinically,traditional Chinese medicine and its related therapeutic approaches remain one of the principal means for treating osteoarthritis in China.The traditional Chinese medicine,which are bioactive compounds extracted from traditional herbal medicines,has gradually gained recognition thanks to their convenient to take and remarkable therapeutic effect.Therefore,this review takes the signaling pathways involved in the inhibition of chondrocyte apoptosis by traditional Chinese medicine as the entry point,systematically retrieves and screens and summarizes relevant studies at home and abroad in the past five years,and analyzes the mechanism of action of related signaling pathways in the treatment of chondrocyte apoptosis in osteoarthritis by traditional Chinese Medicine,providing new options for the pharmacological treatment of osteoarthritis.

Osteoarthritis is a chronic degenerative joint disorder that adversely affects the quality of life of patients.Its pathological characteristics mainly involve the degeneration,destruction,and functional loss of articular cartilage.As one of the most crucial structures in the joint,articular cartilage is an avascular tissue composed of extracellular matrix synthesized by sparsely distributed chondrocytes.Chondrocyte apoptosis is one of the programmed cell death modalities of chondrocytes.During the normal physiological process,the proliferation and apoptosis of chondrocytes maintain a dynamic equilibrium,jointly safeguarding the homeostasis of articular cartilage.However,in the osteoarthritis process,the stimulation of related cytokines after tissue damage induces an imbalance between chondrocyte proliferation and apoptosis,resulting in excessive chondrocyte apoptosis,and subsequently leading to the degradation and degeneration of cartilage.Clinically,traditional Chinese medicine and its related therapeutic approaches remain one of the principal means for treating osteoarthritis in China.The traditional Chinese medicine,which are bioactive compounds extracted from traditional herbal medicines,has gradually gained recognition thanks to their convenient to take and remarkable therapeutic effect.Therefore,this review takes the signaling pathways involved in the inhibition of chondrocyte apoptosis by traditional Chinese medicine as the entry point,systematically retrieves and screens and summarizes relevant studies at home and abroad in the past five years,and analyzes the mechanism of action of related signaling pathways in the treatment of chondrocyte apoptosis in osteoarthritis by traditional Chinese Medicine,providing new options for the pharmacological treatment of osteoarthritis.

Aim To explore the action mechanism of isoquercitrin(IQ)in ameliorating anxiety based on network pharmacology,cellular transcriptomics,molecu-lar docking and animal experiments.Methods The common targets of anxiety disorders and IQ were ob-tained by using relevant databases.The protein-protein interaction network,the biological function and signa-ling pathway enrichment analysis were conducted by u-sing the common targets.Primary hippocampal neurons were cultured in vitro,and corticosterone was added to induce neurons to establish a corticosterone injury mod-el.IQ treatment was added to the culture system,and transcriptomics was used to screen for differentially ex-pressed genes and enrich for differentially expressed pathways.Subsequently,the results were validated by quantitative real-time polymerase chain reaction(qRT-PCR).Possible targets and signaling pathways for IQ treatment on anxiety were speculated and screened u-sing network pharmacology,transcriptomics and molec-ular docking.The anxiety rat model was constructed,and the anxiety state of rats was evaluated after IQ in-tervention,and the protein expression level of hippo-campus was detected to verify the relevant mechanism.Results Network pharmacology,cellular transcrip-tome,and molecular docking analyses revealed that the key mechanism of IQ for anxiety may be related to the BDKRB2/PI3K/Akt signaling pathway.Animal exper-iments showed that IQ was effective in improving anxie-ty behaviour and learning memory ability in rats.IQ increased the movement distance and residence time in the central area of the open field,the time and number percentage of entries into the open arm in the elevated plus maze,and the spontaneous alternations score in the Y maze in rats,and significantly elevated protein expression of BDKRB2,PI3K,Akt and decreased pro-tein expression of NF-κB in the hippocampus.Conclu-sions Isoquercitrin can effectively treat anxiety,and the mechanism of action may be related to the regula-tion of BDKRB2/PI3K/Akt signaling pathway in hip-pocampus.