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.

Background and aims The mechanism of cholestatic liver injury (CLI) is unclear, and effective therapies are lacking. While peroxisome proliferator-activated receptor alpha (PPARα) agonists show potential hepatoprotective effect and pyroptosis is implicated in hepatocellular damage, how PPARα activation mitigates lithocholic acid (LCA)-induced pyroptosis remains unknown. Methods The hepatoprotective effect of PPARα agonists was evaluated in a mouse model of intrahepatic cholestasis induced by LCA. Liver injury was assessed via serum biochemistry, hematoxylin and eosin and TUNEL staining, and electron microscopy. Pyroptosis pathways were analyzed using real-time quantitative polymerase chain reaction, Western blot, and co-immunoprecipitation. Results Combined morphological, histopathological, and biochemical analyses confirmed that PPARα activation protects against CLI. Compared with LCA treatment alone, PPARα activation significantly attenuated the elevation of serum lactate dehydrogenase (LDH), the increased TUNEL-positive cells, and the formation of hepatocyte membrane pores. Mechanistically, PPARα activation suppressed both NOD-like receptor protein 3 (NLRP3) inflammasome-mediated pyroptosis and apoptosis protease-activating factor-1 (APAF-1)/CASPASE-3/GSDME-mediated pyroptosis. Furthermore, PPARα agonist pretreatment inhibited activation of the nuclear factor-kappa B (NF-κB) and forkhead box O1 (FOXO1) signaling pathways. Conclusions PPARα protects against LCA-induced CLI by inhibiting both NLRP3 inflammasome-mediated pyroptosis associated with NF-κB and APAF-1/CASPASE-3/GSDME-mediated pyroptosis associated with the FOXO1 signaling pathway.

AIM To study the chemical constituents from the water fraction of rhizoma of Smilax trinervula Miq.and their biological activities.METHODS Polyamide,silica gel,Sephadex LH-20,ODS and semi-preparative HPLC were used for isolation and purification,then the structures of obtained compounds were identified by physicochemical properties and spectral data.The antitumor activities were determined by MTT mothod,and the inhibitory activities on α-glucosidase were determined by PNPG method.RESULTS Eleven compounds were isolated and identified as tyrosine(1),uridine(2),2-(2',3',4'-trihydroxybutyl)-6-(2",3",4"-trihydroxybutyl)-pyrazine(3),2-(1',2',3',4'-tetrahydroxybutyl)-6-(2",3",4"-trihydroxybutyl)-pyrazine(4),2-(1',2',3',4'-tetrahydroxybutyl)-5-(2",3",4"-trihydroxybutyl)-pyrazine(5),uracil(6),2-(1',2',3',4'-tetrahydroxybutyl)-5-(1",2",3",4"-tetrahydroxybutyl)-pyrazine(7),dioscin(8),shikimic acid(9),pyrazine(10),3,4-dihydroxyphenyethyl alcohol 8-O-β-D-glycopyranoside(11).The IC50 values of compounds 8 to human breast cancer cell MCF-7 was(2.36±0.26)μg/mL,and the IC50 values of compounds 3-5 and 7 to α-glucosidase were(1.54±0.15)-(10.53±0.38)μg/mL.CONCLUSION Compounds 1-7,10 are isolated from Smilax genus for the first time,and compound 9,11 are first isolated from this plant.Compound 8 has anti-tumor activity,and compounds 3-5,7 have α-glucosidase inhibitory activities.

Objective:To develop and validate machine learning models for predicting the cumulative live birth rate (CLBR) following in vitro fertilization (IVF) and to analyze key predictive features using SHAP values. Methods:This retrospective study included data from patients who underwent IVF-embryo transfer at the Department of Reproductive Medicine, Baoding Maternal and Child Health Hospital, between January 2017 and December 2022. Patients were categorized into two groups based on live birth outcome: the live birth group ( n=1 036) and the non-live birth group ( n=756). The dataset was randomly divided into a training set and a validation set in a ratio of 7∶3. Five algorithms were utilized for model development: logistic regression, random forest, extreme gradient boosting (XGBoost), support vector machine, and neural networks. Model performance was assessed using the area under the receiver operating characteristic (AUC) curve, F1 score, and calibration curves. Clinical decision curve analysis (DCA) was employed to evaluate the clinical utility of the models. SHAP values were used to interpret feature importance in the XGBoost model and enhance its explainability. Results:The XGBoost model demonstrated the best performance in predicting CLBR,with accuracy of 72.44%, AUC of 0.775, and F1 score of 0.654, accuracy and F1 score outperforming logistic regression (accuracy was 70.02%, F1 score was 0.585), random forest (accuracy was 71.69%, F1 score was 0.606), support vector machine (accuracy was 70.20%, F1 score was 0.607), and neural network (accuracy was 68.72%, F1 score was 0.560). The calibration curve of XGBoost closely aligned with the diagonal line, indicating that the predicted probabilities were very close to the actual outcomes, demonstrating good calibration. DCA indicated that the XGBoost model provided higher net benefits across a wide range of clinical decision thresholds. SHAP value analysis identified number of previous IVF failures, antral follicle count, anti-Müllerian hormone level, percentage of normal sperm morphology, and sperm DNA fragmentation index as key predictors of CLBR.Conclusion:The XGBoost model exhibits excellent predictive performance and calibration for CLBR, with SHAP values providing important insights into feature importance. This model has the potential to support the development of personalized treatment strategies in clinical practice. However, its generalizability needs to be validated using external datasets to ensure its applicability to diverse populations.

The success of"New Medical Sciences"in higher education requires effective tools in evalua-ting students'performance in courses.Previously,we reported a teamwork(T),critique(C)and ap-preciation(A)(TCA)ideological and political model,a teaching model widely applied in Basic Medical courses.TCA is an abbreviation derived from Tricarboxylic Acid Cycle in Biochemistry as an analogy for nurturing the abilities of thinking and teamwork(T),critique(C)and appreciation(A),which hope-fully could provide students with moral norms for cognition,science and life.This paper further explores the tools to assess the educational outcomes of the TCA model,by which teachers can collect feedback and reflect on teaching quality and effectiveness.Addressing the challenges of individual differences in large classes,fragmented learning feedback,and the difficulty of measuring meta-cognition in educational evaluation,this study employs strategies of value-added assessment,matrix assessment and norm-transfer-able assessment to evaluate the TCA abilities from the aspects of thinking quality,thinking creativity,co-operation ability,iterative thinking,dialectical thinking and job responsibility.By modifying/using 18 e-valuation tools in Education and Psychology,we have established a rubric system composed of 30 primary indicators(with 11 newly designed,10 partly modified and 9 directly adopted),along with 49 secondary indicators and 98 tertiary indicators to enhance the feasibility of the evaluation process.This rubric sys-tem was applied to Biochemistry teaching among the five-year-program undergraduates at Air Force Medi-cal University.Specifically,thinking and teamwork are evaluated by creative works from"the magic bio-chemical-circle",while critiques are assessed in large classes under the guidance of basic and clinical teachers,coupled with appreciation measured by job responsibility in a task-driven virtual reality(VR)project.The results indicate that Biochemistry teaching not only accumulates knowledge in students,but also achieves the goals in nurturing values and cognition.The inclusion of creative performance evalua-tion,cooperative learning and clinical case studies,can enhance students'interpersonal skills,coopera-tion,quality of thinking,creative thinking,iterative thinking and dialectical thinking to varying degrees.TCA-based Biochemistry teaching has a long-lasting impact on character education,and is capable of in-ducing positive long-term changes in students'cognition and lifestyle.Taken together,with the help of this rubric system,teachers can promptly acknowledge the effectiveness of their teaching,thereby facilita-ting their teaching strategies.

Abnormal expression of CMTM4 protein is closely related to tumour occurrence,development and prognosis.Although the important role of CMTM4 in tumours has been gradually manifested,its spe-cific mechanism of action in cervical cancer remains unclear.The aim of this study was to investigate the relationship between CMTM4 expression and clinicopathological features in cervical cancer and study its mechanism.Immunohistochemistry and Western blot were used to detect the expression level of CMTM4 in cancer tissues and paracancerous tissues,and it was found that CMTM4 was significantly under-ex-pressed in cervical cancer tissues(P＜0.001).The chi-square test analysed the relationship between high and low CMTM4 expression and the clinical and pathological characteristics of cervical cancer pa-tients and results found that CMTM4 expression was correlated with the number of births,HPV infection status,pathological type,FIGO stage and lymph node metastasis.Data from Western blot and RT-qPCR found that CMTM4 protein and mRNA levels in HaCaT cells were significantly higher than that of C-33A cells,HeLa cells,U14 cells,and HT-3 cells.Among them,the most significant change in CMTM4 ex-pression was observed in C-33A cells,so the C-33A cell line was selected for subsequent overexpression experiments.CCK-8 analysis found that the proliferation ability of C-33A cervical cancer cells in the pcDNA-CMTM4 group was significantly lower than that in the pcDNA-NC group(P＜0.001).Flow cy-tometry and Western blot results indicated that CMTM4 overexpression promoted apoptosis(P＜0.001),significantly increased Bax(P＜0.001)and cleaved caspase 3(P＜0.05)protein levels,and significant-ly decreased Bcl-2 protein level(P＜0.01).Western blot results further found that CMTM4 overexpres-sion significantly reduced the protein levels of p-PI3K(P＜0.001)and p-AKT(P＜0.01),but did not affect the protein levels of PI3K and AKT(P＞0.05).The above findings indicated that CMTM4 gene expression was down-regulated in cervical cancer,and CMTM4 overexpression inhibited cervical cancer cell proliferation and induced apoptosis by inhibiting the PI3K/AKT pathway.Therefore,CMTM4 may be used as a biological marker for screening cervical cancer.

Objective To explore the association between psychological resilience and internet addiction among senior primary school students,so as to provide a scientific basis for formulating strategies for preventing internet addiction and enhancing psychological resilience in this group.Methods A stratified cluster sampling method was employed.In May 2021,a total of 1 618 fourth-and fifth-grade students from 5 primary schools in Jiading District,Shanghai were surveyed on psychological resilience and internet addiction through questionnaires.Independent sample t-tests,chi-square tests,and Logistic regression models were used for data analysis.Results Among the 1 618 students,the prevalence rate of internet addiction was 8.8%(142 students).The total score of psychological resilience(t=6.215,P<0.001)and the scores of three dimensions,namely family support(t=3.509,P<0.001),goal focus(t=6.965,P<0.001),and positive perception(t=5.887,P<0.001),of those reported with internet addiction were all significantly lower than those without internet addiction.Multivariate logistic regression analysis showed that compared with individuals with low psychological resilience,those with moderate(aOR=0.395,95%CI:0.267-0.584,P<0.001)and high psychological resilience(aOR=0.167,95%CI:0.077-0.365,P<0.001)had a lower probability of internet addiction.The three sub-dimensions of psychological resilience,namely goal focus,positive perception and family support,also showed a statistically significant negative association with internet addiction in these students.Conclusion Nearly one-tenth senior primary school students in Jiading District self-reported internet addiction.Higher levels of psychological resilience were associated with a lower probability of internet addiction among senior primary school students.Focusing on enhancing the goal focus,positive perception and family support dimensions in psychological resilience may be of great significance for preventing internet addiction among senior primary school students.

Metabolites produced as intermediaries or end-products of microbial metabolism provide crucial signals for health and diseases, such as metabolic dysfunction-associated steatotic liver disease (MASLD). These metabolites include products of the bacterial metabolism of dietary substrates, modification of host molecules (such as bile acids [BAs], trimethylamine-N-oxide, and short-chain fatty acids), or products directly derived from bacteria. Recent studies have provided new insights into the association between MASLD and the risk of developing chronic kidney disease (CKD). Furthermore, alterations in microbiota composition and metabolite profiles, notably altered BAs, have been described in studies investigating the association between MASLD and the risk of CKD. This narrative review discusses alterations of specific classes of metabolites, BAs, fructose, vitamin D, and microbiota composition that may be implicated in the link between MASLD and CKD.

Metabolites produced as intermediaries or end-products of microbial metabolism provide crucial signals for health and diseases, such as metabolic dysfunction-associated steatotic liver disease (MASLD). These metabolites include products of the bacterial metabolism of dietary substrates, modification of host molecules (such as bile acids [BAs], trimethylamine-N-oxide, and short-chain fatty acids), or products directly derived from bacteria. Recent studies have provided new insights into the association between MASLD and the risk of developing chronic kidney disease (CKD). Furthermore, alterations in microbiota composition and metabolite profiles, notably altered BAs, have been described in studies investigating the association between MASLD and the risk of CKD. This narrative review discusses alterations of specific classes of metabolites, BAs, fructose, vitamin D, and microbiota composition that may be implicated in the link between MASLD and CKD.