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 analyze the effect of releasing the lower pulmonary ligament on right residual lung expansion after right upper lobe resection under different body mass index (BMI) levels. Methods The clinical data of patients who underwent thoracoscopic right upper lobe resection in the First Affiliated Hospital with Nanjing Medical University from 2021 to 2022 were retrospectively analyzed. Patients were divided into a group A (17 kg/m2＜BMI≤23 kg/m2), a group B (23 kg/m2＜BMI≤29 kg/m2) and a group C (BMI＞29 kg/m2) according to BMI. The presence of residual cavity was judged by chest X-ray at 7-10 days after operation, the degree of compensation change of the right main bronchus angle was measured, and the changes in lung volume were determined by CT three-dimensional reconstruction. Results A total of 157 patients who underwent thoracoscopic right upper lobe resection were included, including 71 males and 86 females, with an average age of (59.7±11.2) years. There were 50 patients in the group A, 75 patients in the group B, and 32 patients in the group C. In the group A, compared with those without releasing the lower pulmonary ligament, patients with releasing had a lower incidence of postoperative residual cavity (P=0.016), greater changes in bronchus angle (P<0.001), and smaller changes in lung volume (P<0.001). In the group B and C, there was no significant effect of releasing the lower pulmonary ligament on postoperative residual cavity, bronchus angle, and lung volume changes (P>0.05). Conclusion For patients with thin and long body shape and low BMI, releasing the lower pulmonary ligament is helpful to promote the expansion of the residual lung after right upper lobe resection and reduce the occurrence of postoperative residual cavity in patients.

Pulmonary fibrosis（PF） is a progressive and life-threatening disease with limited therapeutic options， highlighting the urgent need for innovative drug discovery strategies. To address this challenge， the authors propose the formula-originated rational intelligent screening&translation（FIRST）， a systematic framework for developing anti-fibrotic monomers derived from classical traditional Chinese medicine（TCM）. The strategy integrates three key dimensions， including tissue-oriented intelligent screening of active compounds， structural optimization based on drug-target spatial interactions and plant biosynthetic pathways， and cross-scale validation of drug. We further highlight its applications in discovering tissue-oriented novel drugs from clinically validated TCM， the development and mechanistic elucidation of anti-fibrotic therapeutics， as well as the clinical translation and secondary development of candidate drugs. This strategy paves the way for first-in-class， formula-derived monomeric drugs with defined structures， clarified mechanisms， and proven safety， offering a transformative avenue to meet the urgent therapeutic needs of PF and setting a new paradigm for TCM-based drug innovation.

Pulmonary fibrosis（PF） is a progressive and life-threatening disease with limited therapeutic options， highlighting the urgent need for innovative drug discovery strategies. To address this challenge， the authors propose the formula-originated rational intelligent screening&translation（FIRST）， a systematic framework for developing anti-fibrotic monomers derived from classical traditional Chinese medicine（TCM）. The strategy integrates three key dimensions， including tissue-oriented intelligent screening of active compounds， structural optimization based on drug-target spatial interactions and plant biosynthetic pathways， and cross-scale validation of drug. We further highlight its applications in discovering tissue-oriented novel drugs from clinically validated TCM， the development and mechanistic elucidation of anti-fibrotic therapeutics， as well as the clinical translation and secondary development of candidate drugs. This strategy paves the way for first-in-class， formula-derived monomeric drugs with defined structures， clarified mechanisms， and proven safety， offering a transformative avenue to meet the urgent therapeutic needs of PF and setting a new paradigm for TCM-based drug innovation.

ObjectiveIn the context of the digital transformation of education, this study aims to construct a triadic interactive teaching model for surgical animal surgery in clinical medicine using modern information technology. It explores the effectiveness of different teaching methods in improving students' practical skills, aseptic awareness, and teamwork abilities, providing a reference for the reform of clinical practice education. MethodsA quasi-experimental research design was adopted. A total of 80 students from the eight-year clinical medicine program at Nanjing University were selected, including the Class of 2020 (control group, n=40) and the Class of 2021 (experimental group, n=40). The control group received traditional teaching methods, while the experimental group implemented the "Teacher-Student-AI" triadic interactive teaching model. This model utilized a smart teaching platform for personalized pre-class preparation , as well as data-driven post-class review and feedback throughout the entire teaching process. The "assessment indicators and scoring criteria for the surgical animal surgery course" were used to evaluate teaching effectiveness, with independent samples t-tests used for statistical analysis. ResultsPre-course assessments revealed no statistically significant differences in baseline theoretical knowledge or practical skills between the two groups (P>0.05). Upon completion of the course, the experimental group achieved higher scores than the control group across three key dimensions: practical skills (47.98±1.34 vs 46.92±2.51, P=0.022), aseptic awareness (17.84±1.16 vs 16.94±2.29, P=0.029), and teamwork (16.82±1.44 vs 15.95±1.22, P=0.004). However, no statistically significant difference was observed in the scores for humane care awareness between the two groups (8.24±0.70 vs 8.16±0.53, P=0.589). ConclusionThe AI-based triadic interactive teaching model can, to some extent, address the limitations of traditional surgical animal surgery education. It plays a positive role in enhancing medical students' surgical skills, aseptic awareness, and collaborative abilities. This model facilitates the transition from traditional to personalized teaching and offers a practical framework for the digital reform of clinical practice education.

Objective To analyze the status of teaching occupational health and occupational medicine course under the background of new medical science, and to construct a semantic knowledge graph to realize the digital teaching reform of the course. Methods i) The literature on teaching research of occupational health and occupational medicine courses published between January 1, 2005 to December 31, 2025 was retrieved through the China National Knowledge Infrastructure. CiteSpace 6.4.R2 and VOSviewer 1.6.20 were used for bibliometric visualization. ii) A knowledge graph system based on Web front-end was constructed using the textbook of Occupational Health and Occupational Medicine (9th ed.), published by People's Medicine Publishing House, as the core data source. Results i) A total of 240 publications related to the teaching in occupational health and occupational medicine courses were included. The overall number of publications showed an upward trend. The research focused on curriculum teaching reform, curriculum-based political education, and digital technology-empowered teaching, forming an interdisciplinary and multi-dimensional research system. The evolution of keywords included three stages: the foundation period (2005-2015), the growth period (2016-2020), and the innovation period (2021-2025). ii) The constructed semantic knowledge graph realizes structural visualization and a dual-mode intelligent question answering agent. A total of four independent navigation modules, including chapter overview, core concept exploration, learning progress details, and relationship network visualization, were used to achieve multidimensional learning pathways. The system requires no installation and supports cross-platform operation, meeting the needs of both teachers and students. It effectively helps students establish systematic knowledge frameworks and enhances interdisciplinary integration ability and learning initiative. Conclusion In the context of new medical science, research on teaching in occupational health and occupational medicine has evolved from a niche field into a research hotspot and entered a stage of high-quality development. The constructed semantic knowledge graph for the Occupational Health and Occupational Medicine course facilitates digital teaching reform in this field and lays a solid foundation for cultivating high-level interdisciplinary occupational health professionals.

ObjectiveTo investigate the differential expression of LEFTY1 in peritoneal dialysis （PD）-associated peritoneal fibrosis （PF） and its clinical value. MethodsLevels of LEFTY1， transforming growth factor-β （TGF-β）， and fibronectin （FN） in peritoneal dialysis effluent were measured in 72 PD patients， and clinical data were collected. Differences in these indicators were compared among groups with different dialysis durations， and the correlation between LEFTY1 and clinical parameters was analyzed to evaluate its predictive value for ultrafiltration insufficiency. ResultsLEFTY1 levels decreased progressively with longer dialysis duration： short-term group ［（2 262.49±270.47） ng/mL］ > mid-term group ［（1 853.52±226.06） ng/mL］ > long-term group ［（1 767.24±264.57） ng/mL］， with significant differences among groups （P<0.001）. TGF-β and FN levels increased with prolonged dialysis duration （P<0.05）. Correlation analysis revealed that LEFTY1 was significantly negatively correlated with FN （r=-0.327， P=0.004）， dialysis duration （r=-0.496，P<0.001）， peritoneal Kt/V （r=-0.333， P=0.004）， peritoneal creatinine clearance （r=-0.239， P=0.043）， and serum calcium （r=-0.410， P<0.001）， while positively correlated with total creatinine clearance （r=0.283， P=0.016）. The area under the ROC curve for LEFTY1 combined with peritoneal Kt/V and peritoneal Ccr in predicting ultrafiltration insufficiency was 0.739. Multivariate regression analysis identified dialysis duration as an independent influencing factor for LEFTY1 expression （P=0.001）. ConclusionThe expression of LEFTY1 in the peritoneal dialysis effluent of PD patients decreases with prolonged dialysis duration and is negatively correlated with pro-fibrotic indicators， suggesting that it may play a potential anti-fibrotic role in the progression of PF. The combined detection of LEFTY1 and solute removal indicators holds value for the early identification of ultrafiltration dysfunction.

This study investigated the association between a proximal promoter polymorphism of IFNGR1 (interferon-γ receptor α chain, IFNGR-α) and breast cancer susceptibility, as well as the prognostic value of its expression variation in breast cancer patients. A case-control study was conducted at the Sixth Medical Center of PLA General Hospital from June 2020 to June 2022. The study included 182 pathologically confirmed breast cancer patients as the breast cancer group, 177 non-tumor patients with benign breast lesions as the benign breast lesions group, and 229 healthy individuals as the normal control group. 2-3 ml EDTA anticoagulant whole blood samples were collected from all participants, and genomic DNA was extracted and stored for further analysis. Basic patient information was retrieved from the hospital′s electronic medical records by patients′ ID number. The proximal promoter sequence of IFNGR1 was obtained from NCBI, and sequencing primers were designed using Primer Premier 6.0. Sanger sequencing was employed to analyze the IFNGR1 promoter sequence in the three groups, and the results were compared with the Eukaryotic Promoter Database (EPD) sequence using Bioedit software. Statistical analysis was performed on single nucleotide polymorphisms (SNPs) in the IFNGR1 promoter. The TCGA database was utilized to assess the relationship between IFNGR1 expression levels and breast cancer patient survival. The findings revealed that the -56 TG genotype of the IFNGR1 promoter was significantly associated with increased breast cancer risk ( Z=2.73, P<0.05). Notably, IFNGR1 expression was lower in breast cancer group compared to normal control group ( P<0.05). Analysis of the TCGA database indicated that patients with high IFNGR1 expression had longer survival times than those with low expression ( HR=0.87, 95% CI:0.77-0.98, P<0.05). In summary, the IFNGR1 -56 TG genotype is associated with an increased risk of breast cancer, and there is a positive correlation between IFNGR1 expression levels and the survival of breast cancer patients.

Bismuth iodide(BiOI)with different crystal plane ratios of(110)to(001)was synthesized,and typeⅡheterojunction formed between(001)and(110)crystal planes of BiOI was used to improve the separation efficiency of photogenerated electrons and holes.Then the BiOI(001)/(110)was composited with nitrogen-doped graphene quantum dots(N-GQDs)to prepare a ternary composites,which could enhance the range and intensity of light absorption,and prolonged the lifetime of photogenerated electrons due to the formation of Z-scheme heterojunctions between BiOI and N-GQDs,thereby leading to the excellent photoelectric performance of the BiOI/N-GQDs for generating sensitive photoelectric response signals.A photoelectrochemical sensor for sensitive detection of chlorpyrifos(CPF)was designed with BiOI/N-GQDs-modified FTO electrode as a photocathode.The S and N atoms contained in CPF were coordinated with Bi(Ⅲ)on the surface of BiOI,which reduced the photocurrent of BiOI/N-GQDs.The photocurrent change was linear with logarithm of concentration of CPF in the range of 1.5×10-12-5.0×10-9 mol/L,and the detection limit was 1.5×10-12 mol/L.The sensor was highly sensitive,selective and stable,and could be used for determination of trace CPF in environmental and food samples.