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.

Accurate pain assessment is a prerequisite for pain management.However,pain is a relatively subjective feeling greatly affected by the environment,education,and emotional state.Therefore,in diagnosing and treating pain,it is necessary not only to assess the subjective feelings of patients,but also to have stable and reliable objective indicators to evaluate the pain status of different patients.At present,there are many clinical tools for assessing and monitoring pain.This paper introduces the commonly used subjective assessment tools(single-dimensional pain assessment scale,multi-dimensional pain assessment scale)and objective assessment tools(surgical pleth index,analgesia and nociception index,nociceptive level index,and biomarkers),and analyzes their advantages and disadvantages,so as to provide reference for future clinical pain assessment.

Accurate pain assessment is a prerequisite for pain management.However,pain is a relatively subjective feeling greatly affected by the environment,education,and emotional state.Therefore,in diagnosing and treating pain,it is necessary not only to assess the subjective feelings of patients,but also to have stable and reliable objective indicators to evaluate the pain status of different patients.At present,there are many clinical tools for assessing and monitoring pain.This paper introduces the commonly used subjective assessment tools(single-dimensional pain assessment scale,multi-dimensional pain assessment scale)and objective assessment tools(surgical pleth index,analgesia and nociception index,nociceptive level index,and biomarkers),and analyzes their advantages and disadvantages,so as to provide reference for future clinical pain assessment.

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*

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.

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

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.

A new method for simultaneous determination of 23 kinds of per-and polyfluoroalkyl substances(PFASs)(13 kinds of perfluoro carboxylic acids,4 kinds of perfluoro sulfonic acids,and 6 kinds of new substitutes)in plant leaf tissue by ultra-high performance liquid chromatography-tandem mass spectrometry(UHPLC-MS/MS)using automatic online solid phase extraction(SPE)to remove the matrix interference components in plant crude extracts was developed.The plant leaf samples were extracted twice with 1%formic acid-methanol solution,then evaporated to dry,redissolved with 70%methanol solution,and directly injected for analysis.After 23 kinds of target PFASs were purified automatically by online SPE with a WAX column,the six-way valve was switched to rinse PFASs onto an alkaline mobile phase system-compatible C18 analytical column.Then,the 23 kinds of target PFASs were separated within 16 min by gradient elution using a binary mobile phase system of methanol/water(Containing 0.4%ammonium hydroxide).Tandem mass spectrometry was performed in multiple reaction monitoring(MRM)mode for online detection of various PFASs,and quantification was carried out by internal standard method.The results of the method validation showed that satisfactory average recoveries of 23 kinds of PFASs in plant leaf samples(64.2%-125.5%),precision(relative standard deviations(RSDs)of 0.7%-12.8%),linearity(R2＞0.990),and sensitivity(the detection limits(S/N=3)were in the range of 0.02-0.50 μg/kg)were achieved.Finally,this method was used to detect PFASs in the marine green tide algae(Enteromorpha prolifera)and several tree leaves,and a total of 6 kinds of PFASs were detected,in which PFBA was the main contaminant.Compared with the reported offline SPE methods,the proposed online SPE technique significantly simplified the sample pretreatment process and provided an automatic,simple,and environment-friendly method for the routine monitoring of legacy and emerging PFASs in plant tissues.