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.

Toxoplasma gondii is an intracellular protozoan pathogen with a global distribution,and dogs are considered a potential risk factor for human toxoplasmosis.This study was aimed at systematically analyzing the epidemiological characteris-tics of canine T.gondii infection in China from 1987 to 2023,to provide a scientific basis for the prevention and control of T.gondii in the country.Epidemiological data on canine T.gondii infections in China from 1987 to 2023 were retrieved from PubMed,China National Knowledge Infrastructure(CNKI),Wanfang Database,and Baidu Scholar.A database was established with Excel,and the data were visualized with ArcGIS 10.2 software.Statistical analysis was performed in SPSS 26.0 software,and group differences were analyzed with the X2 test.A P-value of＜0.05 was considered statistically significant.From 1987 to 2023,the overall seroprevalence of T.gondii antibodies in dogs in China remained stable,and the overall sero-prevalence rate was 13.97％.Yunnan Province had the highest seroprevalence,at 27.65％,whereas Shaanxi Province had the lowest seroprevalence,at 0.56％.Significant differences were observed among provinces(P＜0.05).Epidemiological data on canine T.gondii infections were not available for some regions.The seroprevalence in southwestern China was significantly higher than that in other regions(P＜0.05).A comparison of the seroprevalence between 1987-2004 and 2005-2023 revealed significant differences(P＜0.05).Canine T.gondii infection is widespread in China and shows a stable epidemic cycle.Appro-priate prevention and control measures should be implemented,along with strengthened surveillance of T.gondii outbreaks.Public education on the prevention and control of toxoplasmosis should be enhanced to decrease transmission risk and safeguard public health.

Objective:The predictive value of oxidized low density lipoprotein(ox-LDL)and electrocardiogram(ECG)ischaemia grade for major adverse cardiovascular events(MACE)in patients with ST elevation myocardial infarction(STEMI)after percutaneous coronary intervention(PCI)was assessed by a retrospective cohort study de-sign.Methods:A total of 336 STEMI patients admitted to Cangzhou People's Hospital between October 2019 and May 2022 were selected,and the medical record information was obtained through the hospital medical record sys-tem,and all patients received PCI and physician-recommended basic treatment.With occurrence of MACE with in 12-month follow-up as the evaluation index,they were divided into MACE group(n=65)and no MACE group(n=271).Multifactorial Logistic regression model was used to study the influencing factors of MACE after PCI in STEMI patients,and Spearman test for association of ox-LDL level,ECG ischaemia grade with MACE after PCI.ROC curve was used to evaluate the predictive efficacy of ox-LDL,ECG ischaemia grade and their combination for MACE after PCI.Results:The overall MACE incidence was 19.35％.Compared with patients in no MACE group,those in MACE group had significant higher ox-LDL level[46.34(29.46,66.29)U/L vs.33.00(23.02,50.03)U/L]and proportion of ECG grade Ⅲ ischaemia(64.62％vs.42.80％)(P＜0.01 all).Multifactorial Logistic re-gression analysis showed that ox-LDL(OR=1.022,95％CI 1.011～1.033,P=0.001)and ECG grade Ⅲ ischae-mia(OR=1.878,95％CI 1.007～3.504,P=0.048)were the independent risk factors of post-PCI MACE in STEMI patients.Spearman test showed that ox-LDL and ECG grade Ⅲ ischaemia were positively correlated with post-PCI MACE(r=0.209,0.173,P＜0.001 all).ROC curve analysis showed that the AUCs of ox-LDL,ECG grade Ⅲ ischaemia and their combination in predicting post-PCI MACE were respectively 0.653(95％CI 0.599～0.704),0.609(95％CI 0.555～0.662)and 0.758(95％CI 0.709～0.803),in which the predictive value of the combination of the two was significantly higher than any single detection(Z=2.030,3.097,P=0.042,0.002).Conclusion:ox-LDL combined with ECG ischaemia grading has a high predictive value for the occurrence of MACE with in 12 months after PCI in STEMI patients.

Objective:To analyze the coupling and coordination relationship between multi-level medical security and medical resource allocation,and reveal the obstructive factors and dynamic evolution characteristics of spatial differences in the coordinated development of the two systems.Method:Based on the panel data of 31 provinces in China(excluding Hong Kong,Macao and Taiwan),a multi-level medical security and medical resource allocation index system was constructed.Entropy weight method,coupling coordination degree model,obstacle degree model,Dagum Gini coefficient and other methods were used to analyze the coupling coordination situation,obstacle factors and spatial distribution characteristics of the two systems.Result:The coupling and coordination degree of multi-level medical security and medical resource allocation in our country has steadily improved,but the overall level is still relatively low and is affected by multiple obstructive factors.The overall spatial difference in the coupling coordination degree of the two systems shows a downward trend,and this difference is mainly caused by the differences between regions.Conclusion:It is recommended to enhance policy coordination and departmental collaboration to improve the level of coupling and coordination.Build an efficient medical insurance payment mechanism and improve the quality of medical resource supply.Implement differentiated regional policies to promote balanced regional development.

Objective:Using ecological niche width and situation model,the operation and management of public Traditional Chinese Medicine(TCM)hospitals in 16 cities in Shandong Province were evaluated to put forward countermeasures.Methods:The relevant index data of public TCM hospitals in 16 cities in Shandong Province from 2019 to 2022 were collected to analyze the development status.The ecological niche width model and niche situation model were applied to evaluate the operation and management of public TCM hospitals in 16 cities in Shandong.Results:The top three public TCM hospitals in terms of average ecological niche width were City G,City P and City F.The ecological niche situation value of public TCM hospitals in City K,City A and City J ranked the top three.Conclusion:The dimensions of operation management structure of public TCM hospitals in Shandong need to be enhanced and the development of operation management space needs to be balanced.It is feasible to evaluate operation management by using ecological niche width and situation model.It is suggested to improve the allocation efficiency of health personnel,facilities,funds and other resources;promote the spatial balanced development of operation management;promote the application of ecological niche width and situation model in the field of medicine and health management.

Background:Bushen Zhuanggu Formula(BZF),derived from the classic Yougui Pills,has shown favorable clinical efficacy in treating advanced nontraumatic osteonecrosis of the femoral head(NONFH),particularly by promoting bone repair.However,its underlying mechanisms remain unclear.Objective:This study aimed to explore the mechanisms by which BZF promotes bone repair in advanced NONFH.Materials and methods:A total of 518 potential BZF targets were identified from the ETCM v2.0 database.Transcriptomic profiling of clinical cohorts revealed 485 differentially expressed genes in advanced NONFH patients compared to healthy controls.A drug target-disease gene interaction network was constructed to identify candidate BZF targets involved in NONFH pathogenesis.In vivo experiments were conducted to validate the effects of BZF in a rat model of advanced NONFH.Results:Network analysis identified key pathways associated with blood circulation obstruction,immune-inflammatory imbalance,and abnormal bone metabolism.Protein kinase C alpha(PKCA),Ras proto-oncogene(RAS),mitogen-activated protein kinase 3(ERK),ETS proto-oncogene 1(ETS1),and receptor activator of nuclear factor-κB ligand(RANKL)formed a signaling axis implicated in NONFH pathogenesis.BZF treatment alleviated joint inflammation,preserved trabecular bone morphology,reduced bone loss,and promoted bone repair.Mechanistically,BZF significantly downregulated the expression of PKCA,RAS,ERK,ETS1,and RANKL,improved blood circulation,and inhibited osteoclast activation while promoting osteoblast activation.Conclusion:BZF may promote bone repair in advanced NONFH by enhancing blood circulation and modulating the PKC-RAS-ERK-ETS1-RANKL signaling axis,thereby reversing dysregulated bone metabolism.

Objective:To analyze the coupling and coordination relationship between multi-level medical security and medical resource allocation,and reveal the obstructive factors and dynamic evolution characteristics of spatial differences in the coordinated development of the two systems.Method:Based on the panel data of 31 provinces in China(excluding Hong Kong,Macao and Taiwan),a multi-level medical security and medical resource allocation index system was constructed.Entropy weight method,coupling coordination degree model,obstacle degree model,Dagum Gini coefficient and other methods were used to analyze the coupling coordination situation,obstacle factors and spatial distribution characteristics of the two systems.Result:The coupling and coordination degree of multi-level medical security and medical resource allocation in our country has steadily improved,but the overall level is still relatively low and is affected by multiple obstructive factors.The overall spatial difference in the coupling coordination degree of the two systems shows a downward trend,and this difference is mainly caused by the differences between regions.Conclusion:It is recommended to enhance policy coordination and departmental collaboration to improve the level of coupling and coordination.Build an efficient medical insurance payment mechanism and improve the quality of medical resource supply.Implement differentiated regional policies to promote balanced regional development.

Obstructive sleep apnea (OSA) is an increasingly widespread sleep-breathing disordered disease, and is an independent risk factor for many high-risk chronic diseases such as hypertension, coronary heart disease, stroke, arrhythmias and diabetes, which is potentially fatal. The key to the prevention and treatment of OSA is early diagnosis and treatment, so the assessment and diagnostic technologies of OSA have become a research hotspot. This paper reviews the research progresses of severity assessment parameters and diagnostic technologies of OSA, and discusses their future development trends. In terms of severity assessment parameters of OSA, apnea hypopnea index (AHI), as the gold standard, together with the percentage of duration of apnea hypopnea (AH%), lowest oxygen saturation (LSpO₂), heart rate variability (HRV), oxygen desaturation index (ODI) and the emerging biomarkers, constitute a multi-dimensional evaluation system. Specifically, the AHI, which measures the frequency of sleep respiratory events per hour, does not fully reflect the patients’ overall sleep quality or the extent of their daytime functional impairments. To address this limitation, the AH%, which measures the proportion of the entire sleep cycle affected by apneas and hypopneas, deepens our understanding of the impact on sleep quality. The LSpO₂ plays a critical role in highlighting the potential severe hypoxic episodes during sleep, while the HRV offers a different perspective by analyzing the fluctuations in heart rate thereby revealing the activity of the autonomic nervous system. The ODI provides a direct and objective measure of patients’ nocturnal oxygenation stability by calculating the number of desaturation events per hour, and the biomarkers offers novel insights into the diagnosis and management of OSA, and fosters the development of more precise and tailored OSA therapeutic strategies. In terms of diagnostic techniques of OSA, the standardized questionnaire and Epworth sleepiness scale (ESS) is a simple and effective method for preliminary screening of OSA, and the polysomnography (PSG) which is based on recording multiple physiological signals stands for gold standard, but it has limitations of complex operations, high costs and inconvenience. As a convenient alternative, the home sleep apnea testing (HSAT) allows patients to monitor their sleep with simplified equipment in the comfort of their own homes, and the cardiopulmonary coupling (CPC) offers a minimal version that simply analyzes the electrocardiogram (ECG) signals. As an emerging diagnostic technology of OSA, machine learning (ML) and artificial intelligence (AI) adeptly pinpoint respiratory incidents and expose delicate physiological changes, thus casting new light on the diagnostic approach to OSA. In addition, imaging examination utilizes detailed visual representations of the airway’s structure and assists in recognizing structural abnormalities that may result in obstructed airways, while sound monitoring technology records and analyzes snoring and breathing sounds to detect the condition subtly, and thus further expands our medical diagnostic toolkit. As for the future development directions, it can be predicted that interdisciplinary integrated researches, the construction of personalized diagnosis and treatment models, and the popularization of high-tech in clinical applications will become the development trends in the field of OSA evaluation and diagnosis.

This study aims to explore the mechanism of lung and gut protection by Tanreqing Capsules on the mice infected with influenza virus based on "the lung-gut axis". A total of 110 C57BL/6J mice were randomized into control group, model group, oseltamivir group, and low-and high-dose Tanreqing Capsules groups. Ten mice in each group underwent body weight protection experiments, and the remaining 12 mice underwent experiments for mechanism exploration. Mice were infected with influenza virus A/Puerto Rico/08/1934(PR8) via nasal inhalation for the modeling. The lung tissue was collected on day 3 after gavage, and the lung tissue, colon tissue, and feces were collected on day 7 after gavage for subsequent testing. The results showed that Tanreqing Capsules alleviated the body weight reduction and increased the survival rate caused by PR8 infection. Compared with model group, Tanreqing Capsules can alleviate the lung injury by reducing the lung index, alleviating inflammation and edema in the lung tissue, down-regulating viral gene expression at the late stage of infection, reducing the percentage of neutrophils, and increasing the percentage of T cells. Tanreqing Capsules relieved the gut injury by restoring the colon length, increasing intestinal lumen mucin secretion, alleviating intestinal inflammation, and reducing goblet cell destruction. The gut microbiota analysis showed that Tanreqing Capsules increased species diversity compared with model group. At the phylum level, Tanreqing Capsules significantly increased the abundance of Firmicutes and Actinobacteria, while reducing the abundance of Bacteroidota and Proteobacteria to maintain gut microbiota balance. At the genus level, Tanreqing Capsules significantly increased the abundance of unclassified＿f＿Lachnospiraceae while reducing the abundance of Bacteroides, Eubacterium, and Phocaeicola to maintain gut microbiota balance. In conclusion, Tanreqing Capsules can alleviate mouse lung and gut injury caused by influenza virus infection and restore the balance of gut microbiota. Treating influenza from the lung and gut can provide new ideas for clinical practice.
Animals ; Drugs, Chinese Herbal/administration & dosage* ; Mice ; Lung/metabolism* ; Mice, Inbred C57BL ; Capsules ; Orthomyxoviridae Infections/virology* ; Gastrointestinal Microbiome/drug effects* ; Male ; Humans ; Female ; Influenza A virus/physiology* ; Influenza, Human/virology*