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 explore how to organically integrate the human anatomy curriculum with medical imaging,thereby enhancing medical students' spatial understanding and 3D reconstruction skills,and strengthening their anatomical foundation and clinical competence.This approach aims to bridge the gap between basic science and clinical practice while cultivating clinical thinking abilities.Methods In this study,the medical imaging knowledge was introduced into the anatomy curriculum in Peking University,enabling students to better understand the human body structure and its relationship to the clinical practice with aid of the ultrasound and MRI method.After the course concluded,we evaluated the examination result and learning satisfaction data from the anatomy course.Results The result showed that students provided positive feedback,showing increased interest in learning,enhanced initiative,significant improvement in their anatomy grades(P＜0.01),and a notable enhancement in their ability to apply basic knowledge to solve clinical problems(P＜0.05).Conclusion The integrated teaching approach of medical imaging and human anatomy courses provides innovative ideas and practical method for medical students to learn the basic medical course and enhance their clinical skills in the future.

Combined seawater Hf and Nd isotope compositions have been applied as reliable tracers of water mass mixing and weathering input changes.The establishment of standardized synchronous extraction methods for Fe-Mn oxyhydroxide in sediments,which serve as effective carriers for isotopic signals of ancient seawater,is a prerequisite and key for paleoceanography research.The research material of this study was the surface sediments of the Western Arctic Ocean obtained from China's 7th Arctic Scientific Expedition,and a reliable simultaneous Nd-Hf isotope extraction method in Fe-Mn oxyhydroxide fraction was established through systematic optimization of pretreatment procedures and key extraction parameters.This research focused on analyzing the effects of pretreatment(Milli-Q water pre-wash)and key extraction parameters(leaching time,solution/solid ratios,concentration of EDTA-2Na,and concentration of mixed solution of hydroxyamine hydrochloride(HH)and acetic acid(HAc))on element extraction efficiency and isotopic composition.The experimental results indicated that the Milli-Q water pre-wash was not necessary,Fe-Mn oxyhydroxide extraction could be directly performed.Nd extraction amount in Fe-Mn oxyhydroxide fraction was not sensitive to changes in extraction conditions,while the Hf extraction amount was significantly affected by leaching time,solution/solid ratios and concentration of EDTA-2Na.The optimal conditions for synchronously extracting Nd-Hf isotopes were as follows:adding 20 mL of a mixed solution of 0.005 mol/L HH+1.5%HAc+0.03 mol/L EDTA-2Na per gram of sample,and reaction at room temperature for 3 h under shaking.Through systematic verification of 87Sr/86Sr ratio,εNd value,εHf value,Al/Nd ratio and Al/Hf ratio,it was confirmed that this method could effectively avoid interferences from residual components and accurately obtain Nd-Hf isotope signals of ancient seawater preserved in sediments.The standardized method established in this work provided reliable technical support for reconstructing water mass mixing between the Arctic,North Atlantic,and North Pacific Oceans and riverine input histories.It also provided important reference for the extraction methods of Nd and Hf isotopes from marine sources in sediments from other sea areas around the world.

Phosgene is a highly reactive chemical substance and a prevalent chemical warfare agent,and it is vitally important for rapid and accurate detection of phosgene to counteract terrorist threats and industrial accidents.In this work,a phosgene probe,designated as SX-Pho,which incorporated benzimidazole and hydroxyl groups as recognition motifs,was prepared through Suzuki coupling and Debus-Radziszewski methodologies to incorporate an electron-donating carbazole moiety.This probe exhibited a large Stokes shift(Approximately 130 nm).Upon exposure to triphosgene/triethylamine conditions(in situ phosgene generation),the fluorescence emission of probe at 470 nm underwent significant quenching,with a 20-fold reduction in intensity,while the fluorescence lifetime decreased from 3.30 ns to 3.06 ns.Concentration titration experiments demonstrated that SX-Pho achieved a lower detection limit of 57.8 nmol/L with high specificity and interference resistance.Preton nuclear magnetic resonance spectroscopy(1H NMR),high-resolution mass spectrometry,and density functional theory(DFT)calculations confirmed the cyclization reaction between hydroxyl groups,imines,and phosgene.The extent of overlap between the highest occupied molecular orbital(HOMO)and the lowest unoccupied molecular orbital(LUMO)was notably decreased,leading to the suppression of radiative transitions.The energy gap underwent a reduction of 0.43 eV,while the non-radiative transition was augmented,resulting in fluorescence quenching and achieving rapid detection of phosgene.Based on this,probe-loaded test strips were prepared.The color change under 365 nm illumination allowed visual discrimination of phosgene at concentrations below 20 μL/L.Furthermore,using a smartphone's built-in RGB application to measure the intensity of the blue(B)channel after the test strips were exposed to phosgene enabled both qualitative and quantitative detection.The detection range was 1.82-50 μL/L,with a limit of detection(LOD)of 1.814 μL/L.

Insulin is an important protein hormone that participates in multiple metabolic pathways.Biosynthetic insulin has been widely used in the treatment of type 1 and type 2 diabetes.Currently,the number of reported cases of insulin overdose both at home and abroad is gradually increasing,and insulin homicide is no longer a means of"committing murder without leaving a trace".At present,there are no systematic protocols for the identification of insulin overdose in the field of forensic medi-cine in China.This article introduces the causes,toxicological characteristics,forensic examination,labo-ratory testing methods and indicator reference of insulin overdose.Based on the identification practice and research results and referring to relevant studies on insulin overdose at home and abroad,this pa-per aims to provide recommendations and references for the formulation of forensic identification guide-lines for insulin overdose cases.

Body weight abnormalities, including overweight, obesity, and underweight, have become a dual public health challenge in Chinese adults: overweight and obesity lead to a variety of chronic complications, while underweight increases the risks of malnutrition, sarcopenia, and organ dysfunction. To systematically address these issues, multidisciplinary experts in endocrinology, sports science, nutrition, and psychiatry from various regions have held multiple weight management seminars. Based on the latest epidemiological data and clinical evidence, they expanded the guideline to include assessment and intervention strategies for underweight, in addition to the core content of obesity management. This guideline outlines the etiological mechanisms, evaluation methods, and multidimensional management strategies for overweight and obesity, covering key areas such as diagnosis and assessment, medical nutrition therapy, exercise prescription, pharmacological intervention, and psychological support. It is intended to provide a scientific and standardized approach to weight management across the adult population, aiming to curb the rising prevalence of obesity, mitigate complications associated with abnormal body weight, and improve nutritional status and overall quality of life.

Turning to critical illness is a common stage of various diseases and injuries before death. Patients usually have complex health conditions, while the treatment process involves a wide range of content, along with high requirements for doctor′s professionalism and multi-specialty teamwork, as well as a great demand for time-sensitive treatments. However, this is not matched with critical care professionals and the current state of medical care in China. Telemedicine, which shortens the distance of medical professionals and the gap of disease diagnosis and treatments in various regions through electronic information, can effectively solve the current problem. Therefore, there is an urgent need to develop a standardized, high-quality visualization telemedicine round system .Therefore, experts have been organized to search domestic and foreign literature on telemedicine round for critically ill patients and to form this consensus based on clinical experiences so as to further improve the level of critical care treatments in regions.

Objective:To analyze the factors associated with long-term survival after radical resection of hepatitis B virus(HBV)-associated hepatocellular carcinoma(HCC),and to construct random forest and Cox regression models,to evaluate the two models.Methods:A total of 368 patients with HBV-infected HCC who underwent radical resection were selected retrospectively.These patients were categorized as having a good prognosis(n=266)or a poor prognosis(n=102)based on their survival and mortality status.Univariate and Cox regression analysis were used to identify fac-tors that predict poor prognosis in HCC patients after surgery,and Cox regression and random forest prediction models were constructed and evaluated.Results:There were significant differences in smoking history,Child-Pugh classifica-tion,cirrhosis,microvascular invasion,TNM staging,tumor capsule integrity,platelet-to-lymphocyte ratio(PLR),regular antiviral therapy,HBV-DNA load,alpha-fetoprotein(AFP),neutrophil-to-lymphocyte ratio(NLR),systemic immune in-flammatory index(SII),and albumin-to-globulin ratio(AGR)between the two groups(P<0.05);Cox regression showed that cirrhosis,microvascular invasion,regular antiviral treatment,HBV-DNA load,NLR,PLR,SII,and AGR were related factors that negatively affected the prognosis of patients with HBV-infected HCC after surgery(P<0.05),with an AUC of 0.870 for predicting prognosis;the importance ranking obtained by the random forest model was HBV-DNA load,cirrho-sis,regular antiviral therapy,microvascular invasion,NLR,PLR,AGR,and SII,with an AUC of 0.926 for predicting prog-nosis;the AUC predicted by the random forest model was greater than that predicted by the Cox regression model(Z=2.411,P=0.016).Conclusion:HBV-DNA load,cirrhosis,regular antiviral therapy,microvascular invasion,NLR,PLR,AGR,and SII are factors that affect the poor prognosis of patients with HBV-related HCC after surgery.The random for-est prediction model constructed based on these factors has high predictive value and is superior to the Cox regression prediction model.

To promote the effective implementation of China's Essential Public Health Services Program(EPHSP)and ensure the secure and efficient utilization of project funds,China officially initiated the performance evaluation of EPHSP in 2011.This performance evaluation has evolved through three distinct phases:initial exploration(2011-2014),steady advancement(2015-2018),and reform and enhancement(2019—present).The evaluation objectives have progressively expanded from an initial focus on fund security and service coverage to a broader emphasis on enhancing service quality,improving residents'health status and sense of benefit,and facilitating the refinement of policy frameworks and the implementation of primary responsibilities.Performance evaluation has emerged as a critical instrument for strengthening project governance and optimizing resource allocation,gradually establishing a performance-driven incentive mechanism that aligns rewards with both the quantity and quality of work.This approach has effectively contributed to the continuous improvement of service quality.To further advance the high-quality development of EPHSP,future efforts should optimize the performance evaluation system,prioritize the adoption of a full-cycle performance management approach and the integration of health outcome-based indicators.Additionally,it is essential to deepen the application of information technologies to enhance the precision and efficiency of evaluations,and to innovate mechanisms for utilizing evaluation results to reinforce accountability at the local level.These measures will collectively strengthen project performance management capabilities.