ObjectiveThis study aims to explore the mechanism by which Yishen Huoxue Tongqiao Formula improves metabolism-neuroplasticity and treats unilateral vestibular labyrinth destruction by regulating the metabolic balance of glutamate （Glu）/γ-aminobutyric acid （GABA）. Methods48 Sprague-Dawley （SD） adult rats were randomly divided into the sham operation group， model group， Yishen Huoxue Tongqiao Formula groups with low， medium， and high doses （9.20， 18.39， 36.78 g·kg^-1）， and betahistine group （1.62 mg·kg^-1）. A unilateral vestibular labyrinth destruction （vestibular dysfunction） model was established by intratympanic injection of chloroform into the right ear， while the control group received intratympanic injection of normal saline. Drugs were administered once daily for seven consecutive days. During the period， behavioral tests were performed to evaluate the behaviors of rats after unilateral vestibular labyrinth destruction. Hematoxylin-eosin （HE） staining and Nissl staining were used to observe the neuronal morphology in the medial vestibular nucleus. Golgi staining was employed to assess the number of dendritic spines of neurons in the medial vestibular nucleus. Ultra-performance liquid chromatography-tandem mass spectrometry （LC-ESI-MS/MS） was utilized to detect Glu/GABA. Immunofluorescence and immunohistochemistry were used to detect the expressions of neuronal nuclei （NeuN）， growth-associated protein 43 （GAP-43）， and glial fibrillary acidic protein （GFAP）. Western blot and real-time fluorescent quantitative polymerase chain reaction （Real-time PCR） were applied to determine the expressions of glutamate-immunoreactive （Glu-IR）， GABA， GFAP， postsynaptic density protein 95 （PSD-95）， and GAP-43. ResultsCompared with the sham operation group， the model group presented with head deviation， balance disorder， increased tail suspension score， nuclear consolidation of medial vestibular nerve neurons， and decreased Nissl bodies （P<0.01）. The number of dendritic spines in neurons and NeuN-positive cells decreased. The content of Glu decreased. The content of GABA increased （Glu/GABA decreased）. The expression of GAP-43 was down-regulated， and GFAP was up-regulated （P<0.05， P<0.01）. The expressions of Glu-IR， PSD-95， and GAP-43 proteins， as well as Glu-IR mRNA decreased， while the expressions of GABA and GFAP proteins and mRNA increased （P<0.05， P<0.01）. Compared with those in the model group， the head deviation， imbalanced behavior， and tail suspension scores in each treatment group decreased， with alleviated neuronal injury and recovered Nissl bodies （P<0.01）. The number of dendritic spines of neurons increased， and the number of NeuN-positive cells rebounded. The content of Glu increased， and the content of GABA decreased （Glu/GABA increased）. GFAP was down-regulated， and GAP-43 was up-regulated （P<0.05， P<0.01）. The expressions of Glu-IR， PMD-95， and GAP-43 proteins， as well as Glu-IR mRNA increased， while the expressions of GABA and GFAP proteins and mRNA decreased. The effect was more significant in the high-dose group （P<0.01）. ConclusionThe Yishen Huoxue Tongqiao Formula can alleviate vestibular dysfunction， and its mechanism may be associated with regulating the metabolic balance of Glu/GABA， mitigating neural damage， improving synaptic plasticity （promoting GAP-43 expression and inhibiting GFAP expression）， and facilitating vestibular compensation.

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.

Objective The study aimed to elucidate the population composition and seasonal distribution characteristics of mosquitoes in Laoshan District, Qingdao City, providing a scientific basis for the prevention and control of mosquito-borne infectious diseases. Methods Mosquito surveillance was conducted in Laoshan District in Qingdao City using light traps from April to November 2020-2022. Dominant mosquito species were identified using the Berger-Parker dominance index(I)and mosquito density was compared using the Kruskal-Wallis H test. The seasonal distribution characteristics of adult mosquitoes were analyzed using concentration and circular distribution method. Spearman rank correlation analysis was used to study the relationship between mosquito populations and meteorological factors with mean monthly temperature, mean monthly relative humidity, and mean monthly rainfall. Results The mean mosquito density for the trapping period was 9.771 females/(trap·night)in 2020,9.771 in 2021, and 9.427 in 2022. There was no statistically significant difference in mosquito density between different years(H = 0.095, P = 0.954). In total, 2 781 adult female mosquitoes comprising five species from four genera were captured over three years. Culex pipiens(I = 0.654), Aedes albopictus(I = 0.202), and Aedes aegypti(I = 0.118)were identified as dominant species. Seasonal fluctuations in the adult mosquitoes were unimodal, and primarily concentrated from June to September. The peak period of adult mosquito varied between years with statistical significance. (F = 3.838, P ＜ 0.05). Mosquito density was highly correlated with mean monthly temperature, mean monthly relative humidity and mean monthly precipitation(P＜ 0.05). Conclusions Cx. pipiens pallens, Ae. albopictus, and Ar. inamoratus are the dominant mosquito species in the Laoshan District of Qingdao City. The activity of adult mosquitoes is seasonal, and primarily concentrated in summer and autumn, during which the risk of mosquito-borne diseases such as dengue fever is increased.

OBJECTIVE To screen and quantitatively analyze the quality marker （Q-Marker） associated with anti-renal fibrosis in Shenqi shenshuai mixture （SQSS）， so as to provide references for the analysis of pharmacodynamic substances and new drug transformation of SQSS. METHODS The chemical components of SQSS were characterized by untargeted metabolomics. Combined with network pharmacology， gene expression omnibus （GEO） and connectivity map （CMAP） databases， the anti-renal fibrosis Q-Markers of SQSS were screened. HPLC-MS/MS was applied to determine the contents of Q-Markers in 10 batches of SQSS. RESULTS A total of 1 319 compounds were identified from SQSS via untargeted metabolomics，and 84 active ingredients with anti-renal fibrosis activity were further screened out， such as formononetin. Nine anti-renal fibrosis Q-Markers were obtained by network pharmacology and GEO database， including berberine， quercetin， honokiol， nicotinamide， daidzein， coumarin， kaempferol， formononetin and amygdalin. The quantitative results showed that the average contents of the above components （excluding amygdalin） in 10 batches of SQSS were 2.523， 1.942， 26.848， 1.415， 0.692， 0.171， 0.374， 7.401 μg/mL， respectively. CONCLUSIONS Nine anti-renal fibrosis Q-Markers of SQSS were screened in this study， and the contents of eight among them were determined. The results can provide a basis for elucidating the pharmacodynamic material basis and promoting the new drug transformation of SQSS.

OBJECTIVE To screen and quantitatively analyze the quality marker （Q-Marker） associated with anti-renal fibrosis in Shenqi shenshuai mixture （SQSS）， so as to provide references for the analysis of pharmacodynamic substances and new drug transformation of SQSS. METHODS The chemical components of SQSS were characterized by untargeted metabolomics. Combined with network pharmacology， gene expression omnibus （GEO） and connectivity map （CMAP） databases， the anti-renal fibrosis Q-Markers of SQSS were screened. HPLC-MS/MS was applied to determine the contents of Q-Markers in 10 batches of SQSS. RESULTS A total of 1 319 compounds were identified from SQSS via untargeted metabolomics，and 84 active ingredients with anti-renal fibrosis activity were further screened out， such as formononetin. Nine anti-renal fibrosis Q-Markers were obtained by network pharmacology and GEO database， including berberine， quercetin， honokiol， nicotinamide， daidzein， coumarin， kaempferol， formononetin and amygdalin. The quantitative results showed that the average contents of the above components （excluding amygdalin） in 10 batches of SQSS were 2.523， 1.942， 26.848， 1.415， 0.692， 0.171， 0.374， 7.401 μg/mL， respectively. CONCLUSIONS Nine anti-renal fibrosis Q-Markers of SQSS were screened in this study， and the contents of eight among them were determined. The results can provide a basis for elucidating the pharmacodynamic material basis and promoting the new drug transformation of SQSS.

Background and aims The mechanism of cholestatic liver injury (CLI) is unclear, and effective therapies are lacking. While peroxisome proliferator-activated receptor alpha (PPARα) agonists show potential hepatoprotective effect and pyroptosis is implicated in hepatocellular damage, how PPARα activation mitigates lithocholic acid (LCA)-induced pyroptosis remains unknown. Methods The hepatoprotective effect of PPARα agonists was evaluated in a mouse model of intrahepatic cholestasis induced by LCA. Liver injury was assessed via serum biochemistry, hematoxylin and eosin and TUNEL staining, and electron microscopy. Pyroptosis pathways were analyzed using real-time quantitative polymerase chain reaction, Western blot, and co-immunoprecipitation. Results Combined morphological, histopathological, and biochemical analyses confirmed that PPARα activation protects against CLI. Compared with LCA treatment alone, PPARα activation significantly attenuated the elevation of serum lactate dehydrogenase (LDH), the increased TUNEL-positive cells, and the formation of hepatocyte membrane pores. Mechanistically, PPARα activation suppressed both NOD-like receptor protein 3 (NLRP3) inflammasome-mediated pyroptosis and apoptosis protease-activating factor-1 (APAF-1)/CASPASE-3/GSDME-mediated pyroptosis. Furthermore, PPARα agonist pretreatment inhibited activation of the nuclear factor-kappa B (NF-κB) and forkhead box O1 (FOXO1) signaling pathways. Conclusions PPARα protects against LCA-induced CLI by inhibiting both NLRP3 inflammasome-mediated pyroptosis associated with NF-κB and APAF-1/CASPASE-3/GSDME-mediated pyroptosis associated with the FOXO1 signaling pathway.

Radiogenomics, an extension of radiomics, explores the relationship between imaging features and underlying gene expression patterns. This field is instrumental in providing reliable imaging surrogates, thus potentially representing an alternative to genetic testing. The rapidly growing area of radiogenomics that utilizes ultrasound (US) imaging seeks to elucidate the connections between US image characteristics and genomic data. In this review, the authors outline the radiogenomics workflow and summarize the applications of US-based radiogenomics. These include the prediction of gene variations, molecular subtypes, and other biological characteristics, as well as the exploration of the relationships between US phenotypes and cancer gene profiles. Although the field faces various challenges, US-based radiogenomics offers promising prospects and avenues for future research.

Purpose: The aim of this study was to investigate the feasibility of an intelligent handheld ultrasound (US) device for assisting non-expert general practitioners (GPs) in detecting carotid plaques (CPs) in community populations. Methods: This prospective parallel controlled trial recruited 111 consecutive community residents. All of them underwent examinations by non-expert GPs and specialist doctors using handheld US devices (setting A, setting B, and setting C). The results of setting C with specialist doctors were considered the gold standard. Carotid intima-media thickness (CIMT) and the features of CPs were measured and recorded. The diagnostic performance of GPs in distinguishing CPs was evaluated using a receiver operating characteristic curve. Inter-observer agreement was compared using the intragroup correlation coefficient (ICC). Questionnaires were completed to evaluate clinical benefits. Results: Among the 111 community residents, 80, 96, and 112 CPs were detected in settings A, B, and C, respectively. Setting B exhibited better diagnostic performance than setting A for detecting CPs (area under the curve, 0.856 vs. 0.749; P<0.01). Setting B had better consistency with setting C than setting A in CIMT measurement and the assessment of CPs (ICC, 0.731 to 0.923). Moreover, measurements in setting B required less time than the other two settings (44.59 seconds vs. 108.87 seconds vs. 126.13 seconds, both P<0.01). Conclusion Using an intelligent handheld US device, GPs can perform CP screening and achieve a diagnostic capability comparable to that of specialist doctors.