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.

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.

The effect of Huanglian Jiedu Decoction on the intestinal flora of type 2 diabetes mellitus(T2DM) was investigated using 16S rRNA sequencing technology. Sixty rats were randomly divided into a normal group(10 rats) and a modeling group(50 rats). After one week of adaptive feeding, a high-fat diet + streptozotocin was given for modeling, and fasting blood glucose >16.7 mmol·L~(-1) was considered a sign of successful modeling. The modeling group was randomly divided into the model group, high-, medium-, and low-dose groups of Huanglian Jiedu Decoction, and metformin group. After seven days of intragastric treatment, the feces, colon, and pancreatic tissue of each group of rats were collected, and the pathological changes of the colon and pancreatic tissue of each group were observed by hematoxylin-eosin staining. The changes in the intestinal flora structure of each group were observed by the 16S rRNA sequencing method. The results showed that compared with the model group, the high-, medium-, and low-dose of Huanglian Jiedu Decoction reduced fasting blood glucose levels to different degrees and showed no significant changes in body weight. The number of islet cells increased, and intestinal mucosal damage attenuated. Alpha diversity analysis revealed that Huanglian Jiedu Decoction reduced the abundance and diversity of intestinal flora in rats with T2DM; at the phylum level, low-and mediam-dose of Huanglian Jiedu Decoction reduced the abundance of Bacteroidota, Proteobacteria, and Desulfobacterota and increased the abundance of Firmicute and Bacteroidota/Firmicutes, while the high-dose of Huanglian Jiedu Decoction increased the relative abundance of Proteobacteria and Bacteroidota/Firmicutes ratio, and decreaseal the relative; abundance of Firmicute; at the genus level, Huanglian Jiedu Decoction increased the relative abundance of Allobaculum, Blautia, and Lactobacillus; LEfse analysis revealed that the biomarker of low-and medium-dose groups of Huanglian Jiedu Decoction was Lactobacillus, and the structure of the intestinal flora of the low-dose group of Huanglian Jiedu Decoction was highly similar to that of the metformin group. PICRUSt2 function prediction revealed that Huanglian Jiedu Decoction mainly affected carbohydrate and amino acid metabolic pathways. It suggested that Huanglian Jiedu Decoction could reduce fasting blood glucose and increase the number of islet cells in rats with T2DM, and its mechanism of action may be related to increasing the abundance of short-chain fatty acid-producing strains and Lactobacillus and affecting carbohydrate and amino acid metabolic pathways.
Animals ; Drugs, Chinese Herbal/administration & dosage* ; Diabetes Mellitus, Type 2/metabolism* ; Gastrointestinal Microbiome/drug effects* ; Rats ; Male ; Rats, Sprague-Dawley ; Humans ; Bacteria/drug effects* ; Blood Glucose/metabolism*

Medical institutions, with their clinical practice foundation and abundant human use experience data, have become important carriers for the inheritance and innovation of traditional Chinese medicine(TCM) and the "cradles" of the preparation of new TCM. To effectively promote the transformation of new TCM originating from the TCM clinical practice in medical institutions and establish an effective evaluation index system for the transformation of new TCM conforming to the characteristics of TCM, consensus experts adopted the literature research, questionnaire survey, Delphi method, etc. By focusing on the policy and technical evaluation of new TCM originating from the TCM clinical practice in medical institutions, a comprehensive evaluation from the dimensions of drug safety, efficacy, feasibility, and characteristic advantages was conducted, thus forming a comprehensive evaluation system with four primary indicators and 37 secondary indicators. The expert consensus reached aims to encourage medical institutions at all levels to continuously improve the high-quality research and development and transformation of new TCM originating from the TCM clinical practice in medical institutions and targeted at clinical needs, so as to provide a decision-making basis for the preparation, selection, cultivation, and transformation of new TCM for medical institutions, improve the development efficiency of new TCM, and precisely respond to the public medication needs.
Medicine, Chinese Traditional/standards* ; Humans ; Consensus ; Drugs, Chinese Herbal/therapeutic use* ; Surveys and Questionnaires

Lumbar disc (LD) herniation and aging are prevalent conditions that can result in substantial morbidity. This study aimed to clarify the mechanisms connecting the LD aging and herniation, particularly focusing on cellular senescence and molecular alterations in the nucleus pulposus (NP). We performed a detailed analysis of NP samples from a diverse cohort, including individuals of varying ages and those with diagnosed LD herniation. Our methodology combined histological assessments with single-nucleus RNA sequencing to identify phenotypic and molecular changes related to NP aging and herniation. We discovered that cellular senescence and a decrease in nucleus pulposus progenitor cells (NPPCs) are central to both processes. Additionally, we found an age-related increase in NFAT1 expression that promotes NPPC senescence and contributes to both aging and herniation of LD. This research offers fresh insights into LD aging and its associated pathologies, potentially guiding the development of new therapeutic strategies to target the root causes of LD herniation and aging.
Intervertebral Disc Displacement/metabolism* ; Humans ; Aging/pathology* ; Nucleus Pulposus/pathology* ; Male ; Female ; Transcriptome ; Middle Aged ; Lumbar Vertebrae/pathology* ; Adult ; Cellular Senescence ; Stem Cells/pathology* ; Aged ; Intervertebral Disc Degeneration/metabolism*