Diabetic Peripheral Neuropathy （DPN） is one of the most common and harmful complications of type 2 diabetes. DPN's pathogenesis include high blood sugar-induced oxidative stress， inflammation， and mitochondrial dysfunction. These factors are combined to damage nerve fibers， leading to sensory issues， pain， and numbness. Through a coordinated effect， these factors trigger nerve fiber damage and lead to sensory abnormalities， pain and numbness in limbs， and other symptoms， seriously restricting patients' activities of daily living and mobility. Recent research highlights that cellular senescence plays a critical role in DPN. Cellular senescence is manifested by the loss of cell proliferation ability， and further aggravates nerve damage via oxidative stress， mitochondrial dysfunction， autophagy impairment， inflammatory reaction， and other mechanisms， accelerating DPN occurrence and progression. In terms of medical treatment， current methods focus on blood sugar control， pain relief medicine， and microcirculation improvement， while no therapy has been developed based on cellular senescence. In contrast， traditional Chinese medicine （TCM） shows a unique advantage in DPN prevention and treatment via cellular senescence modulation. TCM emphasizes a holistic approach， as well as syndrome differentiation and treatment， effective in anti-aging and nerve damage repair. Recent studies show that TCM active ingredients， including puerarin， ginsenosides， and berberine， can reduce inflammation， oxidative stress， and apoptosis via signaling pathway regulation， thereby slowing cellular senescence to alleviate nerve damage. Furthermore， TCM compounds such as Buyang Huanwutang， Taohong Siwutang， and Huangqi Guizhi Wuwutang exert synergistic effects on cellular senescence-related pathways to improve nerve health and reduce DPN clinical symptoms. Therefore， this paper reviews the literature related to the interaction between cellular senescence and DPN from the perspective of cellular senescence， summarizing the mechanism of DPN and TCM intervention strategies.

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.

Health economics evidence plays an important role in linking clinical value evidence with health resource allocation decisions in the development of clinical practice guidelines. It can not only effectively balance clinical effectiveness and economic feasibility but also avoid forming "idealized" recommendations that are detached from the affordability of the healthcare system or the burden-bearing capacity of patients. To promote guideline developers to use health economics evidence more standardizedly and fully, this paper conducts an in-depth analysis of the current application status, existing challenges, access channels, and application processes of health economics evidence in current guidelines, and on this basis, puts forward considerations and suggestions for strengthening and standardizing the application of health economics evidence in China's clinical practice guidelines.

The growth and development of children is an important stage for health, and its monitoringand intervention are related to the long-term development of individuals. The construction of a standardized and multi-dimensional database of pediatric growth and development-related diseases is an important basis for realizing precise diagnosis and treatment and health management. Based on the needs of clinical practice, this study proposes to establish a specialized database of pediatric growth and development-related diseases that integrates facial phenotypes and clinical diagnosis and treatment information. This study elaborates on the construction process, including data sources, data collection content, and the operation and management of the database; and proposes key points for quality control, including the establishment of quality control nodes, database construction standards, and a full-process quality control framework. The above ensure the integrity, logic and effectiveness of the data, so that the database can provide an objective basis for the screening and diagnosis of pediatric growth and development-related diseases. On the basis of scientific data management and strict quality control, the database will help reveal the patterns of children's growth and development, and promote the level of children's health management.

To investigate the mediating role of vitamin D in the association between exerciseand triglyceride among adolescents, as well as its potential molecular mechanisms.

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.

The auxin/indole-3-acetic acid (Aux/IAA) gene family is an important regulator for plant growth hormone signaling, involved in plant growth, development, as well as response to environmental stresses. In the present study, we identified SmIAA7 which is potentially associated with Salvia miltiorrhiza leaf development through comparatively analyzed the transcriptome data from different pinnate leaves. SmIAA7 was successfully isolated from S. miltiorrhiza using the specific primers. Then subsequent bioinformatic analysis, prokaryotic expression and purification, subcellular localization, and induction expression analysis under auxin and abiotic stress were performed. The full-length of SmIAA7 contained an ORF of 684 bp encoding a protein of 227 amino acid with a molecular weight of 25.3 kD. Conserved domain analysis showed that SmIAA7 contains the conserved Aux_IAA domain (pfam02309). Sequence analysis and phylogenetic tree analysis results indicated SmIAA7 was phylogenetically close to IAA7 and IAA14 from other plants, suggesting SmIAA7 involved in plant growth and development as well as response to environmental stresses. The prokaryotic expression vector pET28a-SmIAA7 was constructed and SmIAA7 recombinant protein was successfully expressed in E. coli Rosetta (DE3) strain. Subcellular localization experiment demonstrated that SmIAA7 localized in the nucleus of plant cells. Real-time fluorescence quantitative PCR results showed that the expression level of SmIAA7 was upregulated in response to auxin. Drought, low temperature, and salt stress significantly increased the transcript level of SmIAA7 gene. This study lays a foundation for further elucidating the role of SmIAA7 in leaf development, signal transduction, and stress defense in S. miltiorrhiza.

Background: and Purpose Symptomatic intracranial hemorrhage (sICH) following endovascular thrombectomy (EVT) is a severe complication associated with adverse functional outcomes and increased mortality rates. Currently, a reliable predictive model for sICH risk after EVT is lacking. Methods: This study used data from patients aged ≥20 years who underwent EVT for anterior circulation stroke from the nationwide Taiwan Registry of Endovascular Thrombectomy for Acute Ischemic Stroke (TREAT-AIS). A predictive model including factors associated with an increased risk of sICH after EVT was developed to differentiate between patients with and without sICH. This model was compared existing predictive models using nationwide registry data to evaluate its relative performance. Results: Of the 2,507 identified patients, 158 developed sICH after EVT. Factors such as diastolic blood pressure, Alberta Stroke Program Early CT Score, platelet count, glucose level, collateral score, and successful reperfusion were associated with the risk of sICH after EVT. The TREAT-AIS score demonstrated acceptable predictive accuracy (area under the curve [AUC]=0.694), with higher scores being associated with an increased risk of sICH (odds ratio=2.01 per score increase, 95% confidence interval=1.64–2.45, P<0.001). The discriminatory capacity of the score was similar in patients with symptom onset beyond 6 hours (AUC=0.705). Compared to existing models, the TREAT-AIS score consistently exhibited superior predictive accuracy, although this difference was marginal. Conclusions The TREAT-AIS score outperformed existing models, and demonstrated an acceptable discriminatory capacity for distinguishing patients according to sICH risk levels. However, the differences between models were only marginal. Further research incorporating periprocedural and postprocedural factors is required to improve the predictive accuracy.

With the improvement in the accuracy and portability of three-dimensional facial imaging de-vices,and the rapid development of medical image recognition technology in artificial intelligence,the analysis and automatic recognition of three-dimensional facial characteristics of diseases have been widely applied in multiple fields such as endocrine metabolic disorders,chronic respiratory diseases,neuromuscular diseases,ge-netic syndromes,and plastic surgery.We aim to systematically review and summarize the current research status and development trends of three-dimensional facial photogrammetry and image analysis techniques in disease di-agnosis,assessment of prognosis and treatment efficacy,in order to provide references and insights for scientific research and clinical applications of this field.