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.

OBJECTIVES: To investigate the protective effects and underlying mechanisms of Gynostemma pentaphyllum (GP)ethanol extract on motor dysfunction in a mouse model of Parkinson's disease (PD). METHODS: Eighty C57BL/6 male mice were randomly divided into five groups: control group, model group, levodopa group (positive control group), low-dose GP group, and high-dose GP group, with 16 mice per group. The PD model was induced by injection of 6-hydroxydopamine into the substantia nigra pars reticulata of the mice. Two weeks after 6-hydroxydopamine, positive control group received intraperitoneal injection of levodopa 10 mg·kg^－1·d^－1, while low-dose GP and high-dose GP groups received GP extract 100 or 200 mg·kg^－1·d^－1 orally for three weeks. After a 3-week-treatment, the effects of GP on motor dysfunction in 6-hydroxydopamine-induced PD were assessed using open field and CatWalk gait tests, while the effects on muscle strength were evaluated by forelimb grip strength. Immunofluorescence staining was used to detect the number of tyrosine hydroxylase (TH) positive neurons. The levels of dopamine and serotonin in the midbrain were determined by enzyme-linked immunosorbent assay. In addition, Western blotting was performed to detect the expression of mitogen-activated protein kinase (MAPK) family proteins such as p-extracellular signal-regulated kinase (ERK)1/2, p-p38 and p-c-Jun N-terminal kinase (JNK)1/2, and mitochondrial apoptosis pathway proteins such as B-cell lymphoma (Bcl)-2, Bcl-2 associated X protein (Bax), and cleaved-cysteine aspartic acid specific protease (caspase)-3. RESULTS: Behavioral experiments showed that GP significantly improved the spontaneous activity and motor coordination of PD mice (P<0.05). The forelimb grip strength was also increased by GP treatment (P<0.05), compared to the PD model group. In addition, compared with the model group, the number of TH-positive neurons in substantia nigra pars reticulata region, the levels of dopamine and serotonin in midbrain and the expression of p-ERK1/2 were significantly increased by GP treatment (all P<0.05), whereas the expression of p-p38 and p-JNK1/2, the ratio of Bax/Bcl-2 and cleaved-caspase-3/caspase-3 were significantly decreased (all P<0.05). CONCLUSIONS The results indicate that GP might increase dopamine and serotonin levels in the midbrain and promote the survival of dopaminergic neurons in substantia nigra pars reticulata by regulating the expression of phosphorylation of MAPK family proteins and the expression of mitochondrial apoptosis-related proteins, thereby ameliorating motor deficits in PD mice.
Animals ; Mice ; Male ; Gynostemma/chemistry* ; Mice, Inbred C57BL ; Apoptosis/drug effects* ; Plant Extracts/therapeutic use* ; Parkinson Disease/metabolism* ; Disease Models, Animal ; Neurons/pathology*

Objective:To retrospectively analyze the results of auditory examination,vestibular function examination and laboratory examination of 63 patients diagnosed as vestibular neuritis.Methods:A total of 63 patients diagnosed with vestibular neuritis hospitalized in the Department of Otolaryngology, Head and Neck Surgery of the Third Affiliated Hospital of Sun Yat-sen University, from October 2012 to December 2022 were recruited. All patients met the diagnostic criteria for the 2022 Bárány association vestibular neuritis. Clinical data and the results of pure tone audiometry, electrocochleogram, video electronystagmogram, caloric test, cervical vestibuloevoked myogenic potential（cVEMP）, ocular vestibuloevoked myogenic potential（oVEMP）, video head impulse test（vHIT） was collected.A total of 63 age-and sex-matched healthy subjects in the physical examination center were randomly selected as the control group. The differences of blood indexs and lipid metabolism indexes between the two groups were compared. Results:In patients with vestibular neuritis, 50 out of 63 patients presented normal threshold in pure tone audiometry, 8 out of 63 patients had bilateral high-frequency sensorineural hearing loss and 5 out of 63 patients had unilateral mild high-frequency sensorineural hearing loss, 56 out of 63 cases completed the electrocochleogram, of which 3 cases had a binaural-SP/AP amplitude ratio≥0.4, 5 cases had monaural amplitude ratio ≥0.4. Fifty-five out of 63 patients completed the caloric test with CP values greater than 30% in all. The ratio of patients completed cVEMP, oVEMP and vHIT were 46 cases, 22 cases and 30 cases, respectively. 17 out of 63 cases completed all the four vestibular function tests. According to these tests, 49 patients could determine the extent of injury，including 27 cases with unilateral superior vestibular nerve injury, 21 cases with unilateral superior and inferior vestibular nerve injury and 1 case with unilateral inferior vestibular nerve injury. There were significant differences in neutrophil value（P<0.001）, lymphocyte value（P<0.005）, neutrophil/lymphocyte ratio（P<0.001） and apolipoprotein A1（P<0.001） between patient group and control group. Inflammatory markers were risk factors for patients with vestibular neuritis. The OR values of neutrophil value and blood neutrophil/lymphocyte ratio were 1.81（1.38-2.37, P<0.001） and 2.11（1.41-3.16, P<0.001）, respectively. Apolipoprotein A1 was a protective factor for patients with vestibular neuritis, and the OR value was 0.004（0.001-0.042, P<0.001）. Conclusion:Electrocochleogram could be used in vestibular neuritis patients with normal pure tone threshold to test whether there is hidden hearing loss in these patients. Neutrophil value, lymphocyte value, neutrophil/lymphocyte ratio and apolipoprotein A1 were correlated with vestibular neuritis. The Neutrophil value and neutrophil/lymphocyte ratio were risk factors for morbidity.
Humans ; Vestibular Neuronitis/physiopathology* ; Retrospective Studies ; Female ; Male ; Audiometry, Pure-Tone ; Hearing Loss, Sensorineural/physiopathology* ; Middle Aged ; Adult ; Vestibular Function Tests ; Vestibular Evoked Myogenic Potentials ; Aged

Pediatric vertigo presents with complex etiologies, and its clinical diagnosis and management can be challenging due to children's limited ability to describe their symptoms. Recurrent vertigo episodes in children, who are at a critical stage of development, can readily lead to emotional disorders in both the children themselves and their guardians. This article reports a case of benign paroxysmal vertigo of childhood（BPVC） in a child who sought care from multiple providers. The child was found to have a patent foramen ovale（PFO） and mild emotional disorder. By analyzing challenges encountered during the diagnostic and management process in this case of recurrent pediatric vertigo, this study provides practical guidance for rational selection of diagnostic techniques and the comprehensive assessment of disease status in pediatric vertigo/dizziness.
Child ; Humans ; Affective Symptoms/complications* ; Foramen Ovale, Patent/complications* ; Vertigo/complications*

Intestinal fibrosis is a significant clinical challenge in inflammatory bowel diseases, but no effective anti-fibrotic therapy is currently available. Glucagon receptor (GCGR) and glucagon-like peptide 1 receptor (GLP1R) are both peptide hormone receptors involved in energy metabolism of epithelial cells. However, their role in intestinal fibrosis and the underlying mechanisms remain largely unexplored. Herein GCGR and GLP1R were found to be reduced in the stenotic ileum of patients with Crohn's disease as well as in the fibrotic colon of mice with chronic colitis. The downregulation of GCGR and GLP1R led to the accumulation of the metabolic byproduct lactate, resulting in histone H3K9 lactylation and exacerbated intestinal fibrosis through epithelial-to-mesenchymal transition (EMT). Dual activating GCGR and GLP1R by peptide 1907B reduced the H3K9 lactylation in epithelial cells and ameliorated intestinal fibrosis in vivo. We uncovered the role of GCGR/GLP1R in regulating EMT involved in intestinal fibrosis via histone lactylation. Simultaneously activating GCGR/GLP1R with the novel dual agonist peptide 1907B holds promise as a treatment strategy for alleviating intestinal fibrosis.

Coronavirus-related diseases pose a significant challenge to the global health system. Given the diversity of coronaviruses and the unpredictable nature of disease outbreaks, the traditional "one bug, one drug" paradigm struggles to address the growing number of emerging crises. Therefore, there is an urgent need for therapeutic agents with broad-spectrum anti-coronavirus activity. Here, we provide evidence that ATV006, an anti-SARS-CoV-2 nucleoside analog targeting RNA-dependent RNA polymerase (RdRp), has broad antiviral activity against human and animal coronaviruses. Using mouse hepatitis virus (MHV) and human coronavirus NL63 (HCoV-NL63) as a model, we show that ATV006 has potent prophylactic and therapeutic activity against murine coronavirus infection in vivo. Remarkably, ATV006 successfully inhibits viral replication in mice even when administered 96 h after infection. Due to its oral bioavailability and potency against multiple coronaviruses, ATV006 has the potential to become a useful antiviral agent against SARS-CoV-2 and other circulating and emerging coronaviruses in humans and animals.

Photodynamic immunotherapy is a promising strategy for cancer treatment. However, the dysfunctional tumor vasculature results in tumor hypoxia and the low efficiency of drug delivery, which in turn restricts the anticancer effect of photodynamic immunotherapy. In this study, we designed photosensitive lipid nanoparticles. The synthesized PFBT@Rox Lip nanoparticles could produce type I/II reactive oxygen species (ROS) by electron or energy transfer through PFBT under light irradiation. Moreover, this nanosystem could alleviate tumor hypoxia and promote vascular normalization through Roxadustat. Upon irradiation with white light, the ROS produced by PFBT@Rox Lip nanoparticles in situ dysregulated calcium homeostasis and triggered endoplasmic reticulum stress, which further promoted the release of damage-associated molecular patterns, enhanced antigen presentation, and stimulated an effective adaptive immune response, ultimately priming the tumor microenvironment (TME) together with the hypoxia alleviation and vessel normalization by Roxadustat. Indeed, in vivo results indicated that PFBT@Rox Lip nanoparticles promoted M1 polarization of tumor-associated macrophages, recruited more natural killer cells, and augmented infiltration of T cells, thereby leading to efficient photodynamic immunotherapy and potentiating the anti-primary and metastatic tumor efficacy of PD-1 antibody. Collectively, photodynamic immunotherapy with PFBT@Rox Lip nanoparticles efficiently program TME through the induction of immunogenicity and oxygenation, and effectively suppress tumor growth through immunogenic cell death and enhanced anti-tumor immunity.

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.

Early correction of childhood malocclusion is timely managing morphological, structural, and functional abnormalities at different dentomaxillofacial developmental stages. The selection of appropriate imaging examination and comprehensive radiological diagnosis and analysis play an important role in early correction of childhood malocclusion. This expert consensus is a collaborative effort by multidisciplinary experts in dentistry across the nation based on the current clinical evidence, aiming to provide general guidance on appropriate imaging examination selection, comprehensive and accurate imaging assessment for early orthodontic treatment patients.
Humans ; Malocclusion/diagnostic imaging* ; Child ; Consensus