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

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

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.

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.

Objective:To explore the predictive value of baseline complete blood count derivative marker levels for the occurrence of the first peritonitis in patients undergoing peritoneal dialysis (PD).Methods:This study was a retrospective cohort study. The data of inpatients who underwent PD catheterization in Tongji Hospital Affiliated to Tongji Medical College of Huazhong University of Science and Technology from April 1, 2005 to February 29, 2024 were collected and followed up until June 1, 2024. According to the 2022 International Society for Peritoneal Dialysis guidelines for peritonitis prevention and treatment, the patients were divided into the peritonitis group and the non-peritonitis group. Basic demographic data and laboratory parameters of the patients were collected, and inflammatory markers derived from complete blood count were calculated, including the comprehensive index of systemic inflammation, the systemic inflammation response index (SIRI), the ratio of hemoglobin to platelets (HPR), and the ratio of monocytes to lymphocytes (MLR). Cox regression analysis was conducted to identify factors associated with the occurrence of peritonitis.Results:A total of 824 PD patients aged ≥18 years were included in this study. Among them, there were 398 males (48.30%), with an age of 42.06 (33.04, 52.01) years, and the follow-up time was 595.00 (173.50, 1 158.00) d. The proportion of conversion to hemodialysis or death in the peritonitis group was higher than that in the non-peritonitis group (40.91% vs. 13.58%, χ 2=56.173, P<0.001). The age of the peritonitis group was greater than that of the non-peritonitis group [45.05(34.92, 52.99) year old vs. 41.11(32.89, 51.46) year old, Z=-1.978, P=0.048], and the follow-up time was lower than that in the non-peritonitis group [529.50(146.25, 861.00) d vs. 627.00(177.00, 1 222.50)d, Z=-2.260, P=0.024]. A multivariate Cox analysis model was constructed based on the univariate Cox analysis. After adjusting for covariates, the results showed the comprehensive index of systemic inflammation ( HR=0.997, 95% CI 0.995-0.998, P<0.001), HPR ( HR=0.520, 95% CI 0.271-0.995, P=0.048), MLR ( HR=7.027, 95% CI 1.468-33.636, P=0.015) and SIRI ( HR=2.673, 95% CI 1.302-5.488, P=0.007) were the related factors for the first occurrence of peritonitis. Conclusion:The levels of inflammatory markers derived from baseline complete blood count, especially MLR, SIRI and HPR, are the independent influencing factors for the occurrence of the first peritonitis in patients with PD.

Objective:To investigate the feasibility of deep learning based on computed tomography(CT)radiomics in improving diagnostic accuracy for pulmonary nodules.Methods:A total of 500 patients with pulmonary nodules who admitted to our hospital from January 2023 to January 2024 were selected as study subjects,and they were randomly divided into a training set(350 patients)and a test set(150 patients)as 7:3 ratio.All patients underwent CT examination,and pathological diagnosis was used as gold standard to record pulmonary nodules that were judged by clinical judgment.The radiomics features were screened from the CT images of the patients,and these features were used to construct multiple machine learning models.The predictive value of different models in diagnosing pulmonary nodules was analyzed through confusion matrices and receiver operating characteristic(ROC)curve.Results:A total of 1,594 radiomics features,including 1,195 texture features(74.97%)that was the largest ratio,334 first-order histograms(20.95%),and 65 second-order histograms(4.08%),were extracted in this study.After least absolute shrinkage and selection operator(LASSO)regression analysis and ten-fold cross-validation processing,a total of six radiomics features were screened out.The screened radiomics features were incorporated respectively into four assembled models with machine learning,including ResNet50,DenseNet121,Inception_V3 and VGG19.The constructed models were evaluated respectively using the training set and the test set.The results showed that the assembled model had the highest accuracies in both training set and the test set(96.57%and 95.33%),which area under curve(AUC)values were 0.934 and 0.923,and specificities were 81.64%and 80.52%,and sensitivities were 90.25%and 88.71%,respectively.The results of consistency test indicated that the assembled model had the best classification consistency(Kappa=0.856,P<0.001)in the constructed diagnostic model for pulmonary nodule,which was the best-performing model.Conclusion:The deep learning technique based on CT radiomics has a certain feasibility in improving the diagnostic accuracy for pulmonary nodules,and the machine learning model that is included in this study has favorable predictive value in diagnosing pulmonary nodules.In them,the assembled model that is constructed on the basis of ResNet50,DenseNet121,Inception_V3,and VGG19 has better classification ability.

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.

Articular cartilage is a crucial tissue structure in humans.With ongoing exploration of joint tissue structure and the emergence of innovative biotechnological organoids,various sources of stem cells can be selected for induction to differentiate into articular cartilaginous organoids based on the articular cartilage tissue structure,which can be applied to the treatment of cartilage defects,drug testing,and precision medicine and biological development.This article presents a review of the research progress concerning articular cartilaginous organoids,in order to provide a reference for clinical practice.

Objective To characterize the expression patterns of serum microRNA-497(miR-497)in patients with colorectal cancer(CRC)and to investigate its associations with clinicopathological characteristics,diagnostic performance,and long-term prognostic outcomes.Methods This study retrospectively analyzed data from 122 patients with CRC admitted to the hospital between March 2020 and March 2022(CRC group),and enrolled 100 healthy individuals undergoing routine physical examinations(healthy control group)for comparison.Serum samples were collected from all participants prior to any surgical intervention,and the expression levels of miR-497 in serum were quantified using real-time quantitative polymerase chain reaction(qRT-PCR).Simulta-neously,the levels of carcinoembryonic antigen(CEA)and carbohydrate antigen 199(CA199)were measured.To investigate the association between miR-497 expression and clinicopathological characteristics,we evaluated its diagnostic performance using receiver operating characteristic(ROC)curve analysis.Furthermore,Kaplan-Meier survival analysis and Cox proportional hazards regression models were employed to assess its impact on patient prognosis.Results Compared to healthy individuals,CRC patients exhibited significantly lower serum miR-497 expression levels(P<0.001).Notably,miR-497 expression was strongly correlated with TNM stage progression and lymph node metastasis(P<0.001),but showed no significant association with tumor location,patient sex,or age.Diagnostic evaluation using ROC curves demonstrated that miR-497 achieved an AUC of 0.845 for CRC detection,outperforming CEA(AUC=0.748)and CA19-9(AUC=0.702),with DeLong's test confirming the statistically significant differences(P<0.05).Kaplan-Meier survival analysis revealed a significantly higher 3-year DFS rate among patients with high miR-497 expression(84.06%)compared to those with low expression(64.58%),with median DFS not reached in the high-expression group versus 36 months in the low-expression group(P=0.015).Multivariate Cox regression analysis confirmed that reduced miR-497 expression(HR=1.923,95%CI:1.184～3.125),advanced TNM stage(HR=2.511,95%CI:1.421～4.437),and lymph node metastasis(HR=1.753,95%CI:1.151～2.664)were independently associated with poorer disease-free survival outcomes.Conclusions Serum miR-497 is downregulated in patients with CRC and is significantly associated with tumor progression and poor prognosis.It demonstrates high diagnostic accuracy and strong potential for prognostic evalua-tion,highlighting its promise as a biomarker for auxiliary diagnosis and outcome prediction in CRC.