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.

BACKGROUND: The contraction behaviors of cardiomyocytes (CMs), especially contraction synchrony, are crucial factors reflecting their maturity and response to drugs. A wider field of view helps to observe more pronounced synchrony differences, but the accompanied greater computational load, requiring more computing power or longer computational time. METHODS: We proposed a method that directly correlates variations in optical field brightness with cardiac tissue contraction status (CVB method), based on principles from physics and photometry, for rapid video analysis in wide field of view to obtain contraction parameters, such as period and contraction propagation direction and speed. RESULTS: Through video analysis of human induced pluripotent stem cell (hiPSC)-derived CMs labeled with green fluorescent protein (GFP) cultured on aligned and random nanofiber scaffolds, the CVB method was demonstrated to obtain contraction parameters and quantify the direction and speed of contraction within regions of interest (ROIs) in wide field of view. The CVB method required less computation time compared to one of the contour tracking methods, the LucasKanade (LK) optical flow method, and provided better stability and accuracy in the results. CONCLUSION This method has a smaller computational load, is less affected by motion blur and out-of-focus conditions, and provides a potential tool for accurate and rapid analysis of cardiac tissue contraction synchrony in wide field of view without the need for more powerful hardware.

The general models for intermediates quality analysis in the production process of Yaobitong capsule were established by near infrared spectroscopy (NIRS) combined with chemometrics, realizing the rapid determination of notoginsenoside R1, ginsenoside Rg1, ginsenoside Re, ginsenoside Rb1, ginsenoside Rd and moisture. The spray-dried fine powder and total mixed granule were selected as research objects. The contents of five saponins were determined by high performance liquid chromatography and the moisture content was determined by drying method. The measured contents were used as reference values. Meanwhile, NIR spectra were collected. After removing abnormal samples by Monte Carlo cross validation (MCCV), Monte Carlo uninformative variables elimination (MC-UVE) and competitive adaptive reweighted sampling (CARS) were used to select feature variables respectively. Based on the feature variables, quantitative models were established by partial least squares regression (PLSR), extreme learning machine (ELM) and ant lion optimization least squares support vector machine (ALO-LSSVM). The results showed that CARS-ALO-LSSVM model had the optimum effect. The correlation coefficients of the six index components were greater than 0.93, and the relative standard errors were controlled within 6%. ALO-LSSVM was more suitable for a large number of samples with rich information, and the prediction effect and stability of the model were significantly improved. The general models with good predicting effect can be used for the rapid quality determination of Yaobitong capsule intermediates.

This paper explores the therapeutic approach for glaucoma based on the concept of "jueyin （厥阴） as the closing phase" from the perspectives of time and space. In traditional Chinese medicine， jueyin governs inward， converging aspect of qi， representing the crucial turning point between the end of yin and the emergence of yang， as well as the transformation between yin and yang. When the closing and descending function of jueyin operates smoothly， it promotes the inward convergence and smooth descent of qi， enabling the internal retention of blood， spirit， and emotions， which nourishes the internal organs and moistens the meridian-sinews. Conversely， dysfunction of this "closing" mechanism results in a disturbance of yin and yang， a mixture of cold and heat， and disharmony of qi and blood. It is proposed that "failure of jueyin to properly close and descend" is a core pathomechanism of glaucoma. From the perspective of spatiotemporal theory， clinical treatment should focus on "regulating the closing function of jueyin and harmonizing yin and yang". The modified Wumei Pill （乌梅丸） is recommended to adjust the ascending-descending and entering-exiting dynamics of jueyin qi transformation， thereby restoring its free flow， achieving yin and yang balance， and ensuring nourishment to the ocular system.

Objective:To describe epidemiological characteristics and their influencing factors of diabetes and pre-diabetes among adult residents in Hainan Province and provide a theoretical basis to develop epidemic prevention and control strategies for diabetes.Methods:This study used a two-stage unequal proportion cluster sampling method, and 32 857 subjects (≥18 years old) were collected from 24 cities/counties/districts in Hainan Province. All the subjects were investigated with questionnaires, physical examination, and laboratory tests from January to June 2023. The χ2 and Mantel-Haenszel trend χ2 tests were used to analyze the data. Multivariate logistic regression was used to analyze the factors influencing diabetes and pre-diabetes. SPSS 23.0 software was used to analyze the data. Results:The crude prevalence of diabetes and pre-diabetes in adult residents of Hainan Province were 18.1% and 22.8%, while the weighted rates were 13.7% and 20.7%, respectively. The results of multivariate logistic regression analysis showed that: aging (30-39 years old: OR=2.65, 95% CI: 2.06-3.41; 40-49 years old: OR=5.64, 95% CI: 4.40-7.24; 50- 59 years old: OR=9.88, 95% CI: 7.71-12.67; 60-69 years old: OR=18.34, 95% CI: 14.28-23.55; 70-79 years old: OR=21.30, 95% CI: 16.41-27.65; 80 years old and above: OR=24.13, 95% CI: 17.94-32.46), nationality (Li minority group: OR=1.50, 95% CI: 1.38-1.63; other ethnic groups: OR=1.53, 95% CI: 1.20-1.94), urban ( OR=1.12, 95% CI: 1.04-1.21), central obesity ( OR=2.14, 95% CI: 2.01-2.29), higher frequency of alcohol consumption (5-7 day/week: OR=1.24, 95% CI: 1.11-1.38), physical inactivity ( OR=1.09, 95% CI: 1.02-1.17) were risk factors for diabetes, while aging (30-39 years old: OR=1.53, 95% CI: 1.31-1.79; 40-49 years old: OR=2.36, 95% CI: 2.01-2.76; 50-59 years old: OR=3.03, 95% CI: 2.58-3.55; 60-69 years old: OR=4.22, 95% CI: 3.58-4.97; 70-79 years old: OR=5.05, 95% CI: 4.23-6.04; 80 years old and above: OR=6.08, 95% CI: 4.86-7.61), nationality: (Li minority group: OR=1.18, 95% CI: 1.10-1.28; other ethnic groups: OR=1.40, 95% CI: 1.14-1.71), urban ( OR=1.12, 95% CI: 1.04-1.19), central obesity ( OR=1.72, 95% CI: 1.62-1.83), higher frequency of alcohol consumption (1-4 day/week: OR=1.12, 95% CI: 1.01-1.23; 5-7 day/week: OR=1.35, 95% CI: 1.22-1.49) were risk factors for pre-diabetes. Conclusions:The epidemic situation of diabetes and pre-diabetes among adult residents in Hainan Province was not optimistic. In order to control the development of abnormal blood glucose, measures and targeted health education should be carried out to strengthen the screening, treatment, and management of people with abnormal blood glucose among different populations.

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.

Retinopathy caused by myopia is the first cause of irreversible blinding eye disease in China.The TCM methods to prevent and control myopia mainly include Chinese materia medica and TCM appropriate techniques,which have the advantages of good efficacy,simple operation,and few adverse reactions.This paper believed that internal and external pathogenic factors act on the brain and eyes,breaking their homeostasis,leading to rhythmic disorders,and imbalance of essence,qi and blood is the main pathogenesis of myopia.Based on the idea of"nature and human in the same rhythm"and"the same treatment for common diseases",targeting the above pathogenesis,the method of"brain-eye synchronization"was proposed to restore the homeostasis of the brain and eyes to prevent and control myopia in children and adolescents,and the treatment rules were to regulate the rhythms of the time,harmonize the qi and blood,nourish the blood to soften the tendons,and replenish the essence and blood,so as to achieve the effect of brain-eye synchronization and the treatment of the spirit and the body together.This article summarized the theoretical basis of"brain-eye synchronization"and its clinical application in traditional Chinese and Western medicine,with a view to providing new ideas for the prevention and control of myopia in children and adolescents.

Hepatocellular carcinoma is the most common type of primary liver cancer. Many patients who have been diagnosed with hepatocellular carcinoma are already at an advanced stage, leading to very limited treatment options and poor prognosis. Therefore, the key to improving prognosis is early-stage diagnosis. In recent years, with deeper analysis of the underlying biological mechanisms of HCC, new diagnostic methods have emerged, including emerging serum markers, liquid biopsy, molecular diagnostics, and imaging techniques. This article reviews and analyzes the research progress on early-stage screening and explores their value and application prospects in the early predictive diagnosis of HCC..

Objective To investigate the changing antibiotic resistance profiles of E.coli isolated from patients in the 52 hospitals participating in the CHINET program from 2015 to 2021.Methods Antimicrobial susceptibility was tested for clinical isolates of E.coli according to the unified protocol of CHINET program.WHONET 5.6 and SPSS 20.0 software were used for data analysis.Results Atotal of 289 760 nonduplicate clinical strains ofE.coli were isolated from 2015 to 2021,mainly from urine samples(44.7±3.2)％.The proportion of E.coli strains isolated from urine samples was higher in females than in males(59.0％vs 29.5％).The proportion of E.coli strains isolated from respiratory tract and cerebrospinal fluid samples was significantly higher in children than in adults(16.7％vs 7.8％,0.8％vs 0.1％,both P＜0.05).The isolates from internal medicine department accounted for the largest proportion(28.9±2.8)％with an increasing trend over years.Overall,the prevalence of ESBLs-producing E.coli and carbapenem resistant E.coli(CREco)was 55.9％and 1.8％,respectively during the 7-year period.The prevalence of ESBLs-producing E.coli was the highest in tertiary hospitals each year from 2015 to 2021 compared to secondary hospitals.The prevalence of CREco was higher in children's hospitals compared to secondary and tertiary hospitals each year from 2015 to 2021.The prevalence of ESBLs-producing E.coli in tertiary hospitals and children's hospitals and the prevalence of CREco in children's hospitals showed a decreasing trend over the 7-year period.The prevalence of CREco in secondary and tertiary hospitals increased slowly.Antibiotic resistance rates changed slowly from 2015 to 2021.Carbapenem drugs(imipenem,meropenem)were the most active drugs amongβ-lactams against E.coli(resistance rate≤2.1％).The resistance rates of E.coli to β-lactam/β-lactam inhibitor combinations(piperacillin-tazobactam,cefoperazone-sulbactam),aminoglycosides(amikacin),nitrofurantoin and fosfomycin(for urinary isolates only)were all less than 10％.The resistance rate of E.coli strains to antibiotics varied with the level of hospitals and the departments where the strains were isolated,especially for cefazolin and ciprofloxacin,to which the resistance rate of E.coli strains from children in non-ICU departments was significantly lower than that of the strains isolated from other departments(P＜0.05).The E.coli isolates from ICU showed higher resistance rate to most antimicrobial agents tested(excluding tigecycline)than the strains isolated from other departments.The E.coli strains isolated from tertiary hospitals showed higher resistance rates to the antimicrobial agents tested(excluding tigecycline,polymyxin B,cefepime and carbapenems)than the strains from secondary hospitals and children's hospitals.Conclusions E.coli is an important pathogen causing clinical infection.More than half of the clinical isolates produced ESBL.The prevalence of CREco is increasing in secondary and tertiary hospitals over the 7-year period even though the overall prevalence is still low.This is an issue of concern.