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.

Renal fibrosis is the common pathological endpoint of many forms of chronic kidney disease (CKD), yet the mechanisms determining effective repair versus persistent fibrotic remodeling remain incompletely understood. Peritubular capillaries (PTCs) are not merely passive targets of fibrotic injury, but form a dynamic vascular microenvironment linking regional perfusion, oxygen delivery, tubular metabolism, endothelial barrier integrity, pericyte support, immune-cell trafficking, and extracellular matrix (ECM) remodeling. We propose that PTC abnormalities should not be interpreted simply as reduced capillary density, but as a multidimensional disturbance of microvascular structure and function. Structural vascular preservation does not necessarily indicate effective microcirculation: anatomically detectable capillaries may still exhibit impaired perfusion, barrier dysfunction, abnormal endothelial-pericyte interactions, disturbed oxygen metabolism, or reduced network connectivity. Conversely, increased angiogenesis does not necessarily signify successful vascular repair if vessels remain immature, poorly perfused, or inadequately integrated into the existing network. To organize these abnormalities, we conceptualize PTC remodeling into four predominant but nonexclusive states: endothelial barrier disruption with inflammatory adhesion; endothelial-pericyte uncoupling with no-reflow; dysregulated angiogenesis with network simplification; and capillary rarefaction with fibrotic matrix locking. These states are not sequential stages of CKD. They may coexist within the same kidney, overlap within the same anatomical region, and vary with disease etiology, injury phase, vascular compartment, and treatment exposure. We further propose six dimensions for evaluating PTC status—structural presence, functional perfusion, barrier integrity, pericyte coupling, oxygen metabolism, and network connectivity—so that vascular quantity, function, and reparative capacity are assessed separately rather than inferred from a single endothelial marker or capillary-density measurement. Mechanistically, these states converge on a bidirectional microvascular-fibrotic feedback loop. Barrier disruption facilitates leukocyte recruitment and alters vascular paracrine signaling. Endothelial-pericyte uncoupling compromises capillary stability, whereas abnormal pericyte contraction may increase local vascular resistance and contribute to no-reflow before overt capillary loss. Dysregulated angiogenesis may generate vessels that fail to mature or connect to an effective perfusion network. Progressive capillary rarefaction compromises oxygen delivery, while sustained tubular transport demand and mitochondrial dysfunction may aggravate regional oxygen supply-demand mismatch. Injured tubules, immune cells, endothelial cells, pericytes, and fibroblasts communicate through soluble mediators, extracellular vesicles, direct cell contact, and cell-matrix signaling. Importantly, fibrosis is not merely a downstream consequence of microvascular injury. Altered ECM may increase diffusion distance, modify mechanotransduction, impair endothelial survival and migration, and restrict capillary reopening and regeneration. We use the term matrix locking to describe this sustained constraint on vascular repair, while emphasizing that current evidence does not establish irreversible disease in humans. This framework also has therapeutic implications. PTC-directed treatment should not be reduced to increasing vessel number or uniformly promoting angiogenesis. Instead, intervention should be matched to the dominant and potentially reversible abnormality: preserving endothelial barrier function; restoring endothelial-pericyte stability and reducing pathological microvascular resistance; normalizing angiogenesis so that vascular growth is accompanied by maturation, barrier competence, network integration, and effective perfusion; and, in advanced rarefaction, relieving matrix-related constraints while supporting vascular regeneration. The context- and time-dependent effects of signaling through VEGF, Tie2, PDGF-B/PDGFRβ, eNOS/NO, and related pathways indicate that the same molecular signal may exert different effects depending on disease context and timing. Therapeutic efficacy should therefore be evaluated by restoration of effective microcirculation and tissue function rather than by isolated molecular changes or capillary counts alone. Despite increasing mechanistic evidence, the translational basis for this framework remains limited. Most causal and interventional data derive from animal or cell models, whereas human evidence is largely limited to pathological observations, patient-sample associations, and prognostic studies. Currently available circulating and urinary candidate biomarkers generally lack sufficient specificity for PTC injury or state assignment, and no standardized quantitative thresholds have been externally validated. The four-state model should therefore be regarded as a hypothesis-generating analytical framework rather than a clinicopathological classification system. Future studies should integrate spatially resolved tissue phenotyping, blood and urine biomarkers, and dynamic microcirculatory assessment in longitudinal cohorts. They should test reproducibility, assess prognostic value beyond conventional pathology, estimated glomerular filtration rate, and proteinuria, and determine whether changes in PTC status precede or mediate therapeutic benefit. Linking reproducible microvascular phenotypes to defined mechanisms, treatment response, and long-term renal outcomes will be essential to determine whether state-directed assessment and restoration of the PTC microenvironment can ultimately inform patient stratification and therapeutic decision-making.

Diabetes is a very complex endocrine disease whose common feature is the increase in blood glucose concentration. Persistent hyperglycemia can lead to blindness, kidney and heart disease, neurodegeneration, and many other serious complications that have a significant impact on human health and quality of life. The number of people with diabetes is increasing yearly. The global diabetes prevalence in 20-79 year olds in 2021 was estimated to be 10.5% (536.6 million), and it will rise to 12.2% (783.2 million) in 2045. The main modes of intervention for diabetes include medication, dietary management, and exercise conditioning. Medication is the mainstay of treatment. Marketed diabetes drugs such as metformin and insulin, as well as GLP-1 receptor agonists, are effective in controlling blood sugar levels to some extent, but the preventive and therapeutic effects are still unsatisfactory. Peptide drugs have many advantages such as low toxicity, high target specificity, and good biocompatibility, which opens up new avenues for the treatment of diabetes and other diseases. Currently, insulin and its analogs are by far the main life-saving drugs in clinical diabetes treatment, enabling effective control of blood glucose levels, but the risk of hypoglycemia is relatively high and treatment is limited by the route of delivery. New and oral anti-diabetic drugs have always been a market demand and research hotspot. Inhibitor cystine knot (ICK) peptides are a class of multifunctional cyclic peptides. In structure, they contain three conserved disulfide bonds (C3-C20, C7-C22, and C15-C32) form a compact “knot” structure, which can resist degradation of digestive protease. Recent studies have shown that ICK peptides derived from legume, such as PA1b, Aglycin, Vglycin, Iglycin, Dglycin, and aM₁, exhibit excellent regulatory activities on glucose and lipid metabolism at the cellular and animal levels. Mechanistically, ICK peptides promote glucose utilization by muscle and liver through activation of IR/AKT signaling pathway, which also improves insulin resistance. They can repair the damaged pancrease through activation of PI3K/AKT/Erk signaling pathway, thus lowering blood glucose. The biostability and hypoglycemic efficacy of the ICK peptides meet the requirements for commercialization of oral drugs, and in theory, they can be developed into natural oral anti-diabetes peptide drugs. In this review, the structural properties, activity and mechanism of ICK pattern peptides in regulating glucose and lipid metabolism were summaried, which provided a reference for the development of new oral peptides for diabetes.

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.

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.

ObjectivePrimary liver cancer, predominantly hepatocellular carcinoma (HCC), is a significant global health issue, ranking as the sixth most diagnosed cancer and the third leading cause of cancer-related mortality. Accurate and early diagnosis of HCC is crucial for effective treatment, as HCC and non-HCC malignancies like intrahepatic cholangiocarcinoma (ICC) exhibit different prognoses and treatment responses. Traditional diagnostic methods, including liver biopsy and contrast-enhanced ultrasound (CEUS), face limitations in applicability and objectivity. The primary objective of this study was to develop an advanced, light-weighted classification network capable of distinguishing HCC from other non-HCC malignancies by leveraging the automatic analysis of brightness changes in CEUS images. The ultimate goal was to create a user-friendly and cost-efficient computer-aided diagnostic tool that could assist radiologists in making more accurate and efficient clinical decisions. MethodsThis retrospective study encompassed a total of 161 patients, comprising 131 diagnosed with HCC and 30 with non-HCC malignancies. To achieve accurate tumor detection, the YOLOX network was employed to identify the region of interest (ROI) on both B-mode ultrasound and CEUS images. A custom-developed algorithm was then utilized to extract brightness change curves from the tumor and adjacent liver parenchyma regions within the CEUS images. These curves provided critical data for the subsequent analysis and classification process. To analyze the extracted brightness change curves and classify the malignancies, we developed and compared several models. These included one-dimensional convolutional neural networks (1D-ResNet, 1D-ConvNeXt, and 1D-CNN), as well as traditional machine-learning methods such as support vector machine (SVM), ensemble learning (EL), k-nearest neighbor (KNN), and decision tree (DT). The diagnostic performance of each method in distinguishing HCC from non-HCC malignancies was rigorously evaluated using four key metrics: area under the receiver operating characteristic (AUC), accuracy (ACC), sensitivity (SE), and specificity (SP). ResultsThe evaluation of the machine-learning methods revealed AUC values of 0.70 for SVM, 0.56 for ensemble learning, 0.63 for KNN, and 0.72 for the decision tree. These results indicated moderate to fair performance in classifying the malignancies based on the brightness change curves. In contrast, the deep learning models demonstrated significantly higher AUCs, with 1D-ResNet achieving an AUC of 0.72, 1D-ConvNeXt reaching 0.82, and 1D-CNN obtaining the highest AUC of 0.84. Moreover, under the five-fold cross-validation scheme, the 1D-CNN model outperformed other models in both accuracy and specificity. Specifically, it achieved accuracy improvements of 3.8% to 10.0% and specificity enhancements of 6.6% to 43.3% over competing approaches. The superior performance of the 1D-CNN model highlighted its potential as a powerful tool for accurate classification. ConclusionThe 1D-CNN model proved to be the most effective in differentiating HCC from non-HCC malignancies, surpassing both traditional machine-learning methods and other deep learning models. This study successfully developed a user-friendly and cost-efficient computer-aided diagnostic solution that would significantly enhances radiologists’ diagnostic capabilities. By improving the accuracy and efficiency of clinical decision-making, this tool has the potential to positively impact patient care and outcomes. Future work may focus on further refining the model and exploring its integration with multimodal ultrasound data to maximize its accuracy and applicability.

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.

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.

Hypoxia-inducible factor (HIF-1α), a core transcription factor responding to changes in cellular oxygen levels, is closely associated with a wide range of physiological and pathological conditions. However, its differential impacts on vascular cell types and molecular programs modulating human vascular homeostasis and regeneration remain largely elusive. Here, we applied CRISPR/Cas9-mediated gene editing of human embryonic stem cells and directed differentiation to generate HIF-1α-deficient human vascular cells including vascular endothelial cells, vascular smooth muscle cells, and mesenchymal stem cells (MSCs), as a platform for discovering cell type-specific hypoxia-induced response mechanisms. Through comparative molecular profiling across cell types under normoxic and hypoxic conditions, we provide insight into the indispensable role of HIF-1α in the promotion of ischemic vascular regeneration. We found human MSCs to be the vascular cell type most susceptible to HIF-1α deficiency, and that transcriptional inactivation of ANKZF1, an effector of HIF-1α, impaired pro-angiogenic processes. Altogether, our findings deepen the understanding of HIF-1α in human angiogenesis and support further explorations of novel therapeutic strategies of vascular regeneration against ischemic damage.
Humans ; Vascular Endothelial Growth Factor A/metabolism* ; Endothelial Cells/metabolism* ; Transcription Factors/metabolism* ; Gene Expression Regulation ; Hypoxia/metabolism* ; Cell Hypoxia/physiology*