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.

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.

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.

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: 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.

Objectives: Bipolar hemiarthroplasty is commonly performed to treat displaced femoral neck fractures in osteo porotic patients. This study aimed to assess the occurrence and outcomes of unplanned return visits to the emergency department (ED) within 90 days following bipolar hemiarthroplasty for displaced femoral neck fractures. Methods: The clinical data of 1322 consecutive patients who underwent bipolar hemiarthroplasty for osteoporotic femoral neck fractures at a tertiary medical center were analyzed. Data from the patients’ electronic medical records, including demographic information, comorbidities, and operative details, were collected. The risk factors and mortality rates were analyzed. Results: Within 90 days after surgery, 19.9% of patients returned to the ED. Surgery-related reasons accounted for 20.2% of the patient’s returns. Older age, a high Charlson comorbidity index score, chronic kidney disease, and a history of cancer were identified as significant risk factors for unplanned ED visits. Patients with uncemented implants had a significantly greater risk of returning to the ED due to periprosthetic fractures than did those with cemented implants (P = 0.04). Patients who returned to the ED within 90 days had an almost fivefold greater 1-year mortality rate (15.2% vs 3.1%, P < 0.001) and a greater overall mortality rate (26.2% vs 10.5%, P < 0.001). Conclusions This study highlights the importance of identifying risk factors for unplanned ED visits after bipolar hemiarthroplasty, which may contribute to a better prognosis. Consideration should be given to the use of cemented implants for hemiarthroplasty, as uncemented implants are associated with a greater risk of peri prosthetic fractures.

Objectives: This study aimed to present the Asia-Pacific consensus on long-term and sequential therapy for osteoporosis, offering evidence-based recommendations for the effective management of this chronic condition.The primary focus is on achieving optimal fracture prevention through a comprehensive, individualized approach. Methods: A panel of experts convened to develop consensus statements by synthesizing the current literature and leveraging clinical expertise. The review encompassed long-term anti-osteoporosis medication goals, first-line treatments for individuals at very high fracture risk, and the strategic integration of anabolic and anti resorptive agents in sequential therapy approaches. Results: The panelists reached a consensus on 12 statements. Key recommendations included advocating for anabolic agents as the first-line treatment for individuals at very high fracture risk and transitioning to anti resorptive agents following the completion of anabolic therapy. Anabolic therapy remains an option for in dividuals experiencing new fractures or persistent high fracture risk despite antiresorptive treatment. In cases of inadequate response, the consensus recommended considering a switch to more potent medications. The consensus also addressed the management of medication-related complications, proposing alternatives instead of discontinuation of treatment. Conclusions This consensus provides a comprehensive, cost-effective strategy for fracture prevention with an emphasis on shared decision-making and the incorporation of country-specific case management systems, such as fracture liaison services. It serves as a valuable guide for healthcare professionals in the Asia-Pacific region, contributing to the ongoing evolution of osteoporosis management.

Background/Aims: Despite the high efficacy of direct-acting antivirals (DAAs), approximately 1–3% of hepatitis C virus (HCV) patients fail to achieve a sustained virological response. We conducted a nationwide study to investigate risk factors associated with DAA treatment failure. Machine-learning algorithms have been applied to discriminate subjects who may fail to respond to DAA therapy. Methods: We analyzed the Taiwan HCV Registry Program database to explore predictors of DAA failure in HCV patients. Fifty-five host and virological features were assessed using multivariate logistic regression, decision tree, random forest, eXtreme Gradient Boosting (XGBoost), and artificial neural network. The primary outcome was undetectable HCV RNA at 12 weeks after the end of treatment. Results: The training (n=23,955) and validation (n=10,346) datasets had similar baseline demographics, with an overall DAA failure rate of 1.6% (n=538). Multivariate logistic regression analysis revealed that liver cirrhosis, hepatocellular carcinoma, poor DAA adherence, and higher hemoglobin A1c were significantly associated with virological failure. XGBoost outperformed the other algorithms and logistic regression models, with an area under the receiver operating characteristic curve of 1.000 in the training dataset and 0.803 in the validation dataset. The top five predictors of treatment failure were HCV RNA, body mass index, α-fetoprotein, platelets, and FIB-4 index. The accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of the XGBoost model (cutoff value=0.5) were 99.5%, 69.7%, 99.9%, 97.4%, and 99.5%, respectively, for the entire dataset. Conclusions Machine learning algorithms effectively provide risk stratification for DAA failure and additional information on the factors associated with DAA failure.

Background/Aims: Chronic hepatitis C (CHC) patients who failed antiviral therapy are at increased risk for hepatocellular carcinoma (HCC). This study assessed the potential role of metformin and statins, medications for diabetes mellitus (DM) and hyperlipidemia (HLP), in reducing HCC risk among these patients. Methods: We included CHC patients from the T-COACH study who failed antiviral therapy. We tracked the onset of HCC 1.5 years post-therapy by linking to Taiwan’s cancer registry data from 2003 to 2019. We accounted for death and liver transplantation as competing risks and employed Gray’s cumulative incidence and Cox subdistribution hazards models to analyze HCC development. Results: Out of 2,779 patients, 480 (17.3%) developed HCC post-therapy. DM patients not using metformin had a 51% increased risk of HCC compared to non-DM patients, while HLP patients on statins had a 50% reduced risk compared to those without HLP. The 5-year HCC incidence was significantly higher for metformin non-users (16.5%) versus non-DM patients (11.3%; adjusted sub-distribution hazard ratio [aSHR]=1.51; P=0.007) and metformin users (3.1%; aSHR=1.59; P=0.022). Statin use in HLP patients correlated with a lower HCC risk (3.8%) compared to non-HLP patients (12.5%; aSHR=0.50; P<0.001). Notably, the increased HCC risk associated with non-use of metformin was primarily seen in non-cirrhotic patients, whereas statins decreased HCC risk in both cirrhotic and non-cirrhotic patients. Conclusions Metformin and statins may have a chemopreventive effect against HCC in CHC patients who failed antiviral therapy. These results support the need for personalized preventive strategies in managing HCC risk.