OBJECTIVE To study the improvement effect and mechanism of Tripterygium wilfordii multiglucoside （TWM） on nephrotic syndrome in rats. METHODS The nephrotic syndrome model was established by intravenous injection of adriamycin via the tail vein. The modeling rats were randomly divided into the model group （distilled water）， prednisone group （10 mg/kg）， and TWM high- and low-dose groups （10 and 5 mg/kg， respectively）. Additionally， blank group （distilled water） without model induction was established. Each group consisted of 9 rats. Rats in each group were administered the corresponding drugs or distilled water by gavage， once a day， for 6 consecutive weeks. The histopathological morphology of kidney tissues in rats was observed； the levels of 24-hour urinary protein （24 h-UTP） and serum biochemical indicators [albumin （ALB）， blood urea nitrogen （BUN）， serum creatinine （SCr）， cholesterol （CHOL）， and triglyceride （TG）] in rats were determined； the levels of oxidative stress indicators [superoxide dismutase （SOD）， malondialdehyde （MDA）] in kidney tissue of rats were determined； expressions of hypoxia-inducible factor-1α （HIF-1α）/microRNA-155-5p （miR-155-5p）/nuclear factor erythriod 2- related factor 2 （Nrf2） signaling pathway-related mRNA and protein in the renal tissues of rats were detected. RESULTS Compared with the blank group， the rats in the model group exhibited disordered renal tissue structure， with a small amount of glomerular necrosis and edema of the renal tubular epithelial cells. 24 h-UTP， serum levels of SCr， BUN， CHOL and TG， MDA content， mRNA and protein expressions of HIF-1α and Keap1 as well as the expression of miR-155-5p in renal tissues were increased significantly （ P ＜0.05）. Serum level of ALB， SOD level in renal tissue as well as mRNA and protein expressions of Nrf2 were decreased significantly （ P ＜0.05）. Compared with the model group， TWM high-dose and low-dose groups exhibited significant improvements in renal injury， with notable reversals in the levels of the above quantitative indicators （ P ＜0.05）. CONCLUSIONS TWM can alleviate oxidative stress-induced damage and thereby improve nephrotic syndrome in rats by regulating the HIF-1α/miR-155-5p/Nrf2 signaling pathway.

ObjectiveDiscriminating atypical hepatocellular carcinoma (HCC) from other malignancies in liver nodules classified as Liver Imaging Reporting and Data System category M (LR-M) remains a significant diagnostic challenge on conventional ultrasound examination. The LR-M category, originally intended to capture non-HCC malignancies, paradoxically contains up to 63% of atypical HCCs that deviate from classic enhancement patterns, leading to potential misdiagnosis and suboptimal treatment planning. While deep learning has shown promise in HCC diagnosis, most existing models rely exclusively on single-modality ultrasound, overlooking the diagnostic benefits of integrating complementary information from multiple imaging sources. To address this gap, we propose a novel attention-weighted tri-modal ultrasound network (TUS-Net) that integrates contrast-enhanced ultrasound (CEUS), B-mode ultrasound (BUS), and time-intensity curves (TICs) to improve diagnostic accuracy for these clinically challenging lesions. MethodsOur framework incorporates a three-dimensional convolutional neural network (C3D) backbone to extract spatiotemporal features from CEUS videos, capturing dynamic vascular patterns critical for lesion characterization. To effectively fuse complementary modalities, we introduce a dual-channel feature fusion module (DCFFM) that adaptively combines features from CEUS and BUS through channel-wise attention mechanisms, allowing the model to dynamically weigh the contribution of each modality based on diagnostic relevance. Additionally, we propose a temporal intensity feature fusion module (TIFFM) that leverages quantitative hemodynamic information from TICs to guide the model’s attention toward diagnostically critical temporal phases, such as arterial wash-in and portal venous washout. The model is further enhanced by automated lesion localization using YOLOX and class activation mapping for interpretability, ensuring that predictions align with clinically meaningful imaging features. ResultsEvaluated on a tri-modal ultrasound dataset comprising 161 patients with pathologically confirmed LR-M nodules (131 atypical HCC and 30 non-HCC malignancies), our model achieved an accuracy of 86.83%, a sensitivity of 92.50%, a specificity of 75.50%, and an AUC of 89.32% in screening atypical HCC. Compared to single-modality baselines, TUS-Net demonstrated superior specificity, a clinically critical metric given the higher risk associated with misclassifying non-HCC malignancies. Ablation studies confirmed the contribution of each module, with the full model outperforming both standard C3D and 3D ResNet backbones integrated with attention mechanisms. A reader study involving junior and senior radiologists further validated the clinical utility of AI assistance, showing consistent improvements in specificity and inter-reader consistency, particularly for less experienced clinicians. ConclusionThese results surpass existing benchmark models and demonstrate the potential of our approach to enhance diagnostic precision in clinically specific cases. By intelligently fusing multi-modal ultrasound data with attention-guided mechanisms, TUS-Net offers a reliable and interpretable tool that holds promise for improving the non-invasive diagnosis of atypical HCC in challenging LR-M liver nodules.

ObjectiveDiscriminating atypical hepatocellular carcinoma (HCC) from other malignancies in liver nodules classified as Liver Imaging Reporting and Data System category M (LR-M) remains a significant diagnostic challenge on conventional ultrasound examination. The LR-M category, originally intended to capture non-HCC malignancies, paradoxically contains up to 63% of atypical HCCs that deviate from classic enhancement patterns, leading to potential misdiagnosis and suboptimal treatment planning. While deep learning has shown promise in HCC diagnosis, most existing models rely exclusively on single-modality ultrasound, overlooking the diagnostic benefits of integrating complementary information from multiple imaging sources. To address this gap, we propose a novel attention-weighted tri-modal ultrasound network (TUS-Net) that integrates contrast-enhanced ultrasound (CEUS), B-mode ultrasound (BUS), and time-intensity curves (TICs) to improve diagnostic accuracy for these clinically challenging lesions. MethodsOur framework incorporates a three-dimensional convolutional neural network (C3D) backbone to extract spatiotemporal features from CEUS videos, capturing dynamic vascular patterns critical for lesion characterization. To effectively fuse complementary modalities, we introduce a dual-channel feature fusion module (DCFFM) that adaptively combines features from CEUS and BUS through channel-wise attention mechanisms, allowing the model to dynamically weigh the contribution of each modality based on diagnostic relevance. Additionally, we propose a temporal intensity feature fusion module (TIFFM) that leverages quantitative hemodynamic information from TICs to guide the model’s attention toward diagnostically critical temporal phases, such as arterial wash-in and portal venous washout. The model is further enhanced by automated lesion localization using YOLOX and class activation mapping for interpretability, ensuring that predictions align with clinically meaningful imaging features. ResultsEvaluated on a tri-modal ultrasound dataset comprising 161 patients with pathologically confirmed LR-M nodules (131 atypical HCC and 30 non-HCC malignancies), our model achieved an accuracy of 86.83%, a sensitivity of 92.50%, a specificity of 75.50%, and an AUC of 89.32% in screening atypical HCC. Compared to single-modality baselines, TUS-Net demonstrated superior specificity, a clinically critical metric given the higher risk associated with misclassifying non-HCC malignancies. Ablation studies confirmed the contribution of each module, with the full model outperforming both standard C3D and 3D ResNet backbones integrated with attention mechanisms. A reader study involving junior and senior radiologists further validated the clinical utility of AI assistance, showing consistent improvements in specificity and inter-reader consistency, particularly for less experienced clinicians. ConclusionThese results surpass existing benchmark models and demonstrate the potential of our approach to enhance diagnostic precision in clinically specific cases. By intelligently fusing multi-modal ultrasound data with attention-guided mechanisms, TUS-Net offers a reliable and interpretable tool that holds promise for improving the non-invasive diagnosis of atypical HCC in challenging LR-M liver nodules.

Introduction: Polypharmacy and potentially inappropriate prescribing (PIP) are increasingly recognised as important contributors to medication -related harm among older adults. However, published national data on the prevalence of polypharmacy and PIP in Brunei Darussalam are unavailable. The objectives of this study was to determine the prevalence of polypharmacy and PIP among adults aged 65 years and above in Brunei Darussalam using Screening Tool of Older Persons ’ potentially inappropriate Prescriptions (STOPP) version 2 criteria. Materials and Methods: A retrospective cross -sectional study was conducted using data from the Brunei Healthcare Information Management System (Bru -HIMS). A stratified proportional random sample of 2,000 older adults with documented medical encounters in 2016 was selected. Polypharmacy was defined as the concurrent use of five or more medications. PIP was assessed using STOPP version 2 criteria. Descriptive statistics were used to summarise prescribing patterns. Results: Of the 2000 older adults included (mean age 73.0 ± 6.6 years), 1,181 (59.1%) had polypharmacy. Among those with polypharmacy, 1,057 (89.5%) could be assessed using STOPP criteria, and 206 (19.5%) had at least one PIP. The most frequently identified PIPs involved prolonged use of proton pump inhibitors for uncomplicated peptic ulcer disease at full therapeutic dosage for more than eight weeks, where dose reduction, discontinuation, or transition to maintenance therapy (e.g. H2 receptor antagonists) is indicated, prescribing without evidence -based indication, duplication of drug classes, use of ACE inhibitors or angiotensin receptor blockers in patients with hyperkalaemia, and use of first -generation antihistamines. Conclusion: Polypharmacy and PIP are common among older adults in Brunei Darussalam. The findings highlight the need for systematic medication review and deprescribing strategies to optimise pharmacotherapy and improve medication safety in older populations

OBJECTIVE: The relationship between fish consumption and stroke is inconsistent, and it is uncertain whether this association varies across predicted stroke risks. METHODS: A cohort study comprising 95,800 participants from the Prediction for Atherosclerotic Cardiovascular Disease Risk in China project was conducted. A standardized questionnaire was used to collect data on fish consumption. Participants were stratified into low- and moderate-to-high-risk categories based on their 10-year stroke risk prediction scores. Hazard ratios ( HRs) and 95% confidence intervals ( CIs) were estimated using Cox proportional hazard models and additive interaction by relative excess risk due to interaction (RERI), attributable proportion (AP), and synergy index (SI). RESULTS: During 703,869 person-years of follow-up, 2,773 incident stroke events were identified. Higher fish consumption was associated with a lower risk of stroke, particularly among moderate-to-high-risk individuals ( HR = 0.53, 95% CI: 0.47-0.60) than among low-risk individuals ( HR = 0.64, 95% CI: 0.49-0.85). A significant additive interaction between fish consumption and predicted stroke risk was observed (RERI = 4.08, 95% CI: 2.80-5.36; SI = 1.64, 95% CI: 1.42-1.89; AP = 0.36, 95% CI: 0.28-0.43). CONCLUSION Higher fish consumption was associated with a lower risk of stroke, and this beneficial association was more pronounced in individuals with moderate-to-high stroke risk.
Humans ; China/epidemiology* ; Male ; Female ; Stroke/etiology* ; Middle Aged ; Prospective Studies ; Incidence ; Aged ; Animals ; Fishes ; Risk Factors ; Diet ; Seafood ; Adult ; Cohort Studies

OBJECTIVE: To establish and validate a novel diabetic retinopathy (DR) risk-prediction model using a whole-exome sequencing (WES)-based machine learning (ML) method. METHODS: WES was performed to identify potential single nucleotide polymorphism (SNP) or mutation sites in a DR pedigree comprising 10 members. A prediction model was established and validated in a cohort of 420 type 2 diabetic patients based on both genetic and demographic features. The contribution of each feature was assessed using Shapley Additive explanation analysis. The efficacies of the models with and without SNP were compared. RESULTS: WES revealed that seven SNPs/mutations ( rs116911833 in TRIM7, 1997T>C in LRBA, 1643T>C in PRMT10, rs117858678 in C9orf152, rs201922794 in CLDN25, rs146694895 in SH3GLB2, and rs201407189 in FANCC) were associated with DR. Notably, the model including rs146694895 and rs201407189 achieved better performance in predicting DR (accuracy: 80.2%; sensitivity: 83.3%; specificity: 76.7%; area under the receiver operating characteristic curve [AUC]: 80.0%) than the model without these SNPs (accuracy: 79.4%; sensitivity: 80.3%; specificity: 78.3%; AUC: 79.3%). CONCLUSION Novel SNP sites associated with DR were identified in the DR pedigree. Inclusion of rs146694895 and rs201407189 significantly enhanced the performance of the ML-based DR prediction model.
Diabetic Retinopathy/diagnosis* ; Humans ; Machine Learning ; Male ; Female ; Polymorphism, Single Nucleotide ; Middle Aged ; Exome Sequencing ; Aged ; Adult ; Pedigree ; Diabetes Mellitus, Type 2/complications* ; Genetic Predisposition to Disease ; Mutation

OBJECTIVE: To investigate hypertension (HTN) trends, key risk factors, and gender disparities in rural China, and to propose targeted strategies for improving HTN control in resource-limited settings. METHODS: This longitudinal study used data from the Henan Rural Cohort Study, including baseline (2015-2017; n = 39,224) and follow-up (2018-2022; n = 28,621) participants. HTN was defined as systolic/diastolic blood pressure ≥ 140/90 mmHg, self-reported diagnosis, or use of antihypertensive medication. Severity was classified using a 7-tier blood pressure (BP) staging system (optimal, normal, high normal, and HTN stages 1-4). A generalized linear mixed-effects model (GLMM) identified associated risk factors. RESULTS: HTN prevalence increased modestly from 32.7% (95% CI: 32.2-33.2) to 33.9% (95% CI: 33.3%-34.4%). Awareness and treatment improved from 20.1% to 25.3%, and from 18.8% to 24.4%, respectively, but control rates remained low (6.2% to 12.3%). After adjustment, women had a 1.53-fold higher HTN risk than men ( OR = 1.53, 95% CI: 1.43-1.63), revealing gender-specific trends. Key risk factors included alcohol use ( OR = 1.37, 95% CI: 1.27-1.47) and overweight status ( OR = 1.76, 95% CI: 1.66-1.86). BP staging showed an increase in optimal BP (42.3% to 45.8%), but stagnant management of advanced HTN stages. CONCLUSION Hypertension in rural China is shaped by behavioral risk factors and healthcare access gaps. Gender-sensitive, community-based interventions, including task-shifting models, are necessary to mitigate the growing burden of hypertension.
Humans ; Hypertension/etiology* ; China/epidemiology* ; Female ; Male ; Rural Population/statistics & numerical data* ; Prevalence ; Risk Factors ; Middle Aged ; Adult ; Aged ; Longitudinal Studies ; Sex Factors ; Cohort Studies ; East Asian People

Pregnancy complicated with acute myeloid leukemia is uncommon,requiring the collaborative management by specialists from departments of hematology,obstetrics,anesthesiology,and neonatology for both the parturient and the neonate.This article reports an anesthesic management process of a parturient woman with acute myeloid leukemia and reviews relevant literature published in recent years to systematically summarize the approach for anesthesia-related perinatal management of such patients.
Humans ; Female ; Pregnancy ; Leukemia, Myeloid, Acute/complications* ; Pregnancy Complications, Neoplastic ; Adult ; Anesthesia, Obstetrical/methods*

Objective:The influencing factors of intraoperative hypoxemia in patients with benign central airway stenosis were investigated by machine learning algorithm, and the prediction model of hypoxemia was constructed and verified.Methods:A case-control study was used in this study. The clinical data of 650 patients with benign central airway stenosis who who received surgical treatment in the First Affiliated Hospital of PLA Naval Medical University from June 2022 to April 2024 were retrospectively analyzed. And they were divided into a training set ( n=455) and a test set ( n=195) according to 7:3. The training set was used for establishing Logistic regression model and conducting internal verification, and the test set was used for external verification. The least absolute shrinkage and selection operator (LASSO) regression and Boruta algorithm were used to select the factors affecting intraoperative hypoxemia in patients with benign central airway stenosis. A Logistic regression prediction model was constructed, and the model was evaluated using area under the receiver operating characteristic curve (AUC), decision curve analysis (DCA) and calibration curve. Shapley additive interpretation (SHAP) were used to analyze the importance of influencing factors. Results:Among 650 patients, 279 were males and 371 were females, aged (37.86 ± 8.82) years. Nine feature variables were screened by LASSO regression, while 7 feature variables were screened by Boruta algorithm, the intersection of the two was operation time, complications, degree of airway stenosis, thermal ablation therapy, balloon dilation, and airway stent, respectively, based on this, a logistic regression prediction model was constructed.The AUC values of the training set, validation set and test set of the model were 0.928 (95% CI 0.903-0.954), 0.922 (95% CI 0.843-0.995) and 0.919 (95% CI 0.872-0.965), respectively. The calibration curve showed that the predicted results of the model were in good agreement with the actual results, and the DCA curve showed that the model had clinical application value. SHAP analysis showed that the importance of variables affecting intraoperative hypoxemia in benign central airway stenosis patients was ranked as operation time, thermal ablation therapy, degree of airway stenosis, comorbidification, balloon dilation, and airway stent. Conclusions:The Logistic regression prediction model of intraoperative hypoxemia built based on machine learning algorithm has good prediction efficiency, which is helpful to early identification of risk groups and prevention of hypoxemia.