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.

Objective: To investigate the efficacy, safety, and traditional Chinese medicine (TCM) medication patterns of rituximab (RTX) combined with TCM on treating children with steroid-dependent nephrotic syndrome (SDNS). Methods: One hundred and forty-three children with SDNS who visited the Pediatric Nephrology Department of the First Affiliated Hospital of Henan University of Chinese Medicine from January 2018 to December 2022 were enrolled. A cohort study design was adopted, with " RTX treatment" as the exposure factor. Children who met this exposure factor were assigned to the RTX cohort (RTX, glucocorticoid, immunosuppressive agent, combined with traditional Chinese medicine syndrome differentiation treatment), whereas those who did not were assigned to the basic treatment cohort (glucocorticoid, immunosuppressive agent, combined with traditional Chinese medicine syndrome differentiation treatment ), and followed up for 6 months. The frequency of urinary protein recurrences, urinary protein remission duration, proportion and duration of steroid reduction and cessation, cumulative usage of steroids, proportion of recurrence, recurrence amount of steroid used, efficacy of TCM syndrome, and laboratory and safety indicators after treatment, and height and CD19+ B cell count before and after treatment were compared between the two cohorts. The medication patterns of TCM in the two cohorts were analyzed using frequency statistics, association rule analysis, and systematic clustering analysis. Results: Compared with the basic treatment cohort, the RTX cohort showed a decrease in the frequency of urinary protein recurrence, extended sustained remission of urinary protein, an increase in the proportion of steroid reduction and cessation, a shorter duration of steroid reduction and cessation, a decrease in cumulative steroid dosage, a lower recurrence rate, a decrease in CD19+ B cell count, and a decrease in 24-h urinary total protein quantification and the level of cholesterol (P<0.05). No significant difference in the recurrence amount of steroid used, height, TCM syndrome efficacy, albumin, aspartate transaminase, blood urea nitrogen, platelet count, and safety indicators between the two cohorts. Children with SDNS were mostly characterized by qi and yin deficiency syndrome, followed by spleen and kidney yang deficiency syndrome. A total of 175 TCMs were included, including 28 high-frequency drugs such as Huangqi, Fuling, Gancao, Baizhu, Dangshen, and Jiuyurou. The primary use of medication is to nourish the qi and spleen, nourish the kidney, and warm yang. The analysis of association rules yielded eight binary associations and ten three-phase associations, with Huangqi, Baizhu, Fuling, and Dangshen, being the most closely related. Cluster analysis identified four TCM combinations, primarily focusing on tonifying kidney and replenishing essence, benefiting qi and nourishing yin, and removing blood stasis. Conclusion RTX combined with TCM syndrome differentiation treatment can reduce the recurrence frequency of SDNS, prolong the remission period, reduce the glucocorticoid dosage, and have no marked effect on height growth. No apparent adverse reactions were observed. TCM should focus on nourishing qi and yin while removing blood stasis.

Abelmoschi Corolla, the dried corolla of Abelmoschus manihot, has anti-inflammatory, antioxidant, and anti-fibrosis activities. Its chemical constituents mainly include flavonoids, organic acids, steroids, and polysaccharides. This study reviewed the research progress in the chemical constituents and pharmacological activities of Abelmoschi Corolla in recent 20 years. According to the concept of quality marker(Q-marker), the Q-markers of Abelmoschi Corolla were predicted from plant phylogeny, chemical constituent specificity, traditional efficacy, chemical constituent measurability, and absorbed constituents. The primary Q-markers for Abelmoschi Corolla were anticipated to include quercetin-3'-O-β-D-glucopyranoside, gossypetin-8-O-β-D-glucuronide, isoquercetin, myricetin,quercetin, and hyperoside, with the aim of providing reference data for improving the quality evaluation system of Abelmoschi Corolla.
Abelmoschus/chemistry* ; Drugs, Chinese Herbal/pharmacology* ; Flowers/chemistry* ; Humans ; Animals ; Quality Control ; Flavonoids/chemistry*

Coronary heart disease is a cardiovascular disease that affects coronary arteries. It presents high incidence and high mortality worldwide, bringing a serious threat to human health and quality of life. Salviae Miltiorrhizae Radix et Rhizoma derived from Salvia miltiorrhiza is widely used in the treatment of cardiovascular diseases, such as coronary heart disease. Salvianolic acid B is an active component in Salviae Miltiorrhizae Radix et Rhizoma extracts, and studies have shown that it has anti-inflammatory, antioxidant, apoptosis-and autophagy-regulating, anti-fibrosis, and metabolism-modulating effects. This article reviews the research progress regarding the therapeutic effect of salvianolic acid B on coronary heart disease in the recent decade. It elaborates on the role and mechanism of salvianolic acid B in treating coronary heart disease from multiple perspectives, such as the inhibition of thrombosis, improvement of blood circulation, reduction of myocardial cell injury, and inhibition of cardiac remodeling. This article provides a theoretical basis for the application of Chinese medicinal materials and TCM prescriptions containing salvianolic acid B in the treatment of coronary heart disease.
Humans ; Benzofurans/administration & dosage* ; Coronary Disease/genetics* ; Drugs, Chinese Herbal/administration & dosage* ; Salvia miltiorrhiza/chemistry* ; Animals ; Depsides

Objective To analyze the epidemiological distribution,microbiological characteristics,drug-resistance status,and risk factors for mortality in adult intensive care unit(ICU)patients with Klebsiella pneumoniae infection.Methods This multi-center prospective cohort study included ICU patients with suspected infection from 67 hospitals across 16 Chinese provinces/municipalities between July 1,2021 and December 31,2022.Clinical data and microbiological results were collected,and patients were divided into survival and non-survival groups according to their survival status and drug-resistance situation.Risk factors for mortality and drug resistance in ICU patients with Klebsiella pneumonia infection were determined through univariate and multivariate logistic regression analyses.Results A total of 2964 ICU-infected patients were enrolled,with 12 175 microbial specimens submitted for testing.Among these,487 specimens tested positive for Klebsiella pneumoniae.Ultimately,314 patients with Klebsiella pneumoniae infection were identified,primarily from lung infections,with a drug-resistance rate of 78.3%.The in-hospital mortality rate of ICU patients infected with Klebsiella pneumoniae was 19.8%.Univariate and multivariate logistic regression analyses revealed that older age(P=0.027),high drug-resistance rate(P=0.028),and low clinical-effectiveness rate(P<0.001)were independent risk factors for mortality in ICU patients infected with Klebsiella pneumoniae.Drug-resistance analysis showed that,compared with non-resistant cases,ICU patients with drug-resistant Klebsiella pneumoniae infection had lower pathogen-clearance rates(P=0.003),clinical-effectiveness rates(P=0.004),and antibiotic-effectiveness rates(P<0.010),and higher mortality rates(P=0.006).Patients with Klebsiella pneumoniae abdominal infection(P=0.003)and urinary tract infection(P=0.007)had higher drug-resistance incidences.There were no statistically significant differences in clinical-effectiveness rate,Klebsiella pneumoniae clearance,drug-resistance incidence,mortality rate,or hospital-stay length between patients with lung infection and those with non-lung infection of Klebsiella pneumoniae(P>0.05).Compared with patients with non-bloodstream infection,patients with bloodstream infection of Klebsiella pneumoniae had lower clinical-effectiveness rates(P=0.027)and higher mortality rates(P=0.021).Conclusions Older age,high drug-resistance rate,and low clinical-effectiveness rate are independent risk factors for mortality in ICU patients infected with Klebsiella pneumoniae.ICU patients with bloodstream infection of Klebsiella pneumoniae may have lower clinical-effectiveness rates and higher mortality rates.ICU patients with abdominal and urinary tract infections caused by Klebsiella pneumoniae are more likely to develop drug resistance.

ObjectivePrimary liver cancer, predominantly hepatocellular carcinoma (HCC), is a significant global health issue, ranking as the sixth most diagnosed cancer and the third leading cause of cancer-related mortality. Accurate and early diagnosis of HCC is crucial for effective treatment, as HCC and non-HCC malignancies like intrahepatic cholangiocarcinoma (ICC) exhibit different prognoses and treatment responses. Traditional diagnostic methods, including liver biopsy and contrast-enhanced ultrasound (CEUS), face limitations in applicability and objectivity. The primary objective of this study was to develop an advanced, light-weighted classification network capable of distinguishing HCC from other non-HCC malignancies by leveraging the automatic analysis of brightness changes in CEUS images. The ultimate goal was to create a user-friendly and cost-efficient computer-aided diagnostic tool that could assist radiologists in making more accurate and efficient clinical decisions. MethodsThis retrospective study encompassed a total of 161 patients, comprising 131 diagnosed with HCC and 30 with non-HCC malignancies. To achieve accurate tumor detection, the YOLOX network was employed to identify the region of interest (ROI) on both B-mode ultrasound and CEUS images. A custom-developed algorithm was then utilized to extract brightness change curves from the tumor and adjacent liver parenchyma regions within the CEUS images. These curves provided critical data for the subsequent analysis and classification process. To analyze the extracted brightness change curves and classify the malignancies, we developed and compared several models. These included one-dimensional convolutional neural networks (1D-ResNet, 1D-ConvNeXt, and 1D-CNN), as well as traditional machine-learning methods such as support vector machine (SVM), ensemble learning (EL), k-nearest neighbor (KNN), and decision tree (DT). The diagnostic performance of each method in distinguishing HCC from non-HCC malignancies was rigorously evaluated using four key metrics: area under the receiver operating characteristic (AUC), accuracy (ACC), sensitivity (SE), and specificity (SP). ResultsThe evaluation of the machine-learning methods revealed AUC values of 0.70 for SVM, 0.56 for ensemble learning, 0.63 for KNN, and 0.72 for the decision tree. These results indicated moderate to fair performance in classifying the malignancies based on the brightness change curves. In contrast, the deep learning models demonstrated significantly higher AUCs, with 1D-ResNet achieving an AUC of 0.72, 1D-ConvNeXt reaching 0.82, and 1D-CNN obtaining the highest AUC of 0.84. Moreover, under the five-fold cross-validation scheme, the 1D-CNN model outperformed other models in both accuracy and specificity. Specifically, it achieved accuracy improvements of 3.8% to 10.0% and specificity enhancements of 6.6% to 43.3% over competing approaches. The superior performance of the 1D-CNN model highlighted its potential as a powerful tool for accurate classification. ConclusionThe 1D-CNN model proved to be the most effective in differentiating HCC from non-HCC malignancies, surpassing both traditional machine-learning methods and other deep learning models. This study successfully developed a user-friendly and cost-efficient computer-aided diagnostic solution that would significantly enhances radiologists’ diagnostic capabilities. By improving the accuracy and efficiency of clinical decision-making, this tool has the potential to positively impact patient care and outcomes. Future work may focus on further refining the model and exploring its integration with multimodal ultrasound data to maximize its accuracy and applicability.

Objective To explore the potential causality between visceral adipose tissue (VAT) and inflammatory bowel disease, peptic ulcer disease, irritable bowel syndrome and gastroesophageal reflux disease. Methods Two-sample Mendelian randomization (MR) investigations were carried out, employing genetic variants closely linked with VAT as instrumental variables. The summary data for aforementioned gastrointestinal disorders were obtained from the genome-wide association study (GWAS) in UK biobank and Finn Gen databases, respectively. The inverse variance-weighted method (IVW) was employed as the primary analytical method, bolstered by a sequence of sensitivity analyses to verify the robustness of MR. Subsequently, a meta-analysis was conducted to amalgamate the findings derived from both databases. Results This study included a total of 42 single nucleotide polymorphisms that were strongly associated with visceral adipose tissue as instrumental variables. Statistically significant correlations were found between VAT and peptic ulcer (OR=1.17, 95% CI: 1.08-1.27, P < 0.01), gastroesophageal reflux disease (OR=1.10, 95% CI: 1.03-1.18, P = 0.02), and irritable bowel syndrome (OR=1.12, 95% CI: 1.04-1.12, P = 0.03). There was no statistical significance between VAT and inflammatory bowel disease (OR=0.94, 95% CI: 0.79-1.12, P = 0.65). Conclusion VAT exhibits a causal linkage with peptic ulcer, gastroesophageal reflux disease, and irritable bowel syndrome. Accurately assessing VAT levels via imaging methods, screening individuals at high-risk, and actively controlling abdominal obesity bear important clinical significance.

INTRODUCTION: Trastuzumab deruxtecan (T-DXd) has revolutionised treatment for metastatic breast cancer (MBC). While effective, its high cost and toxicities, such as fatigue and nausea, pose challenges. METHOD: Medical records from the Joint Breast Cancer Registry in Singapore were used to study MBC patients treated with T-DXd (February 2021-June 2024). This study was conducted to address whether reducing dose intensity and density may have an adverse effect on treatment outcomes. RESULTS: Eighty-seven MBC patients were treated with T-DXd, with a median age of 59 years. At the time of data cutoff, 32.1% of patients were still receiving T-DXd. Over half (54%) of the patients received treatment with an initial relative dose intensity (RDI) of <;85%. Overall median real-world progression-free survival (rwPFS) was 8.1 months. rwPFS was similar between RDI groups (<85%: 8.7 months, <85%: 8.1 months, P=0.62). However, human epidermal growth receptor 2 (HER2)-positive patients showed significantly better rwPFS outcomes compared to HER2-low patients (8.8 versus 2.5 months, P<0.001). Only 16% with central nervous system (CNS) involvement had CNS progressive disease on treatment. No significant progression-free survival (PFS) differences were found between patients with or without CNS disease, regardless of RDI groups. Five patients (5.7%) developed interstitial lung disease (ILD), with 3 (3.4%) having grade 3 events. Two required high-dose steroids and none were rechallenged after ILD. There were no fatalities. CONCLUSION Our study demonstrated that reduced dose intensity and density had no significant impact on rwPFS or treatment-related toxicities. Furthermore, only 5.7% of patients developed ILD. T-Dxd provided good control of CNS disease, with 82% of patients achieving CNS disease control.
Humans ; Female ; Breast Neoplasms/mortality* ; Middle Aged ; Trastuzumab/adverse effects* ; Aged ; Adult ; Singapore/epidemiology* ; Antineoplastic Agents, Immunological/adverse effects* ; Camptothecin/adverse effects* ; Immunoconjugates/adverse effects* ; Retrospective Studies ; Progression-Free Survival ; Receptor, ErbB-2/metabolism* ; Neoplasm Metastasis ; Dose-Response Relationship, Drug ; Treatment Outcome ; Registries