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: High-mobility group box protein 1 (HMGB1) is a chromatin-binding protein involved in arthritis, ischemia, sepsis, atherosclerosis, neurodegenerative disorders, meningitis, and cancer. HMGB1 exhibits dual roles in cancer, acting as either a tumor suppressor or oncoprotein depending on context. Objective: This research aimed to elucidate HMGB1’s functional significance in cutaneous squamous cell carcinoma (cSCC). Methods: We overexpressed HMGB1 in cSCC cell lines using recombinant adenovirus and examined its effects on cell proliferation, colony formation, and cell migration. Results: Immunohistochemical analysis revealed elevated HMGB1 expression levels in cSCC tissue relative to normal epidermis. To assess the influence of HMGB1, we employed recombinant adenoviruses expressing HMGB1 to transduce SCC cell lines (SCC12 and SCC13). Enhanced HMGB1 expression significantly promoted cellular proliferation and colony formation capacity.Notably, HMGB1 overexpression elevated the levels of proliferation regulators, including P63, SOX2, CDK4 and CDK6. Furthermore, HMGB1 overexpression substantially enhanced tumor invasiveness, accompanied by upregulation of epithelial-mesenchymal transition (EMT) biomarkers. Mechanistically, overexpression of HMGB1 enhanced transforming growth factor-β signaling by increasing phosphorylation of SMAD2/3, the key mediators of EMT. Conclusion These data imply that HMGB1 acts as a tumor-promoting factor in cSCC.

Objective This study reports on the obligatory parasitism of water mites Arrenurus sp. on Anopheles messeae at the Manzhouli Port, Inner Mongolia, China. Methods Duing July 2024, a survey on the mosquito diversity was conducted at the Manzhouli Port. Captured mosquitoes and their ectoparasites were identified to species level. Results A total of 1840 adult mosquitoes were collected, representing species from three genera: Culex(Cx. modestus, Cx. pipiens pallens), Aedes(Ae. dorsalis, Ae. flavidorsalis, Ae. flavescens), and Anopheles (An. messeae). Among all the mosqutioes specimens,3 out of 150 captured An. messeae were found to carry ectoparasitic mites, with number of 2,4,27 mites separately. Morphological and molecular identification reached the same result as water mites(Hydrachnidiae, Hydracrina). COI gene sequence showed 94% similarity with the closest species Arrenurus truncatellus. Conlusions Literature review suggests water mites are host-specific parasitism of mosquito species and herein with the first record of Arrenurus sp. parasiting on An. Messeae in the most high-latitude region globally.

Parkinson’s disease (PD) is a progressive neurodegenerative disorder characterized by the loss of dopaminergic neurons in the substantia nigra pars compacta and abnormal accumulation of α‑synuclein. Its pathogenesis involves multiple interconnected processes, including oxidative stress, mitochondrial dysfunction, endoplasmic reticulum stress, neuroinflammation, and impaired protein homeostasis. Extracellular signal-regulated kinase 1/2 (ERK1/2), a major component of the mitogen-activated protein kinase signaling network, plays an important role in neuronal survival, synaptic plasticity, stress responses, and cell death. However, accumulating evidence indicates that ERK signaling in PD is not unidirectional, as both insufficient activity and sustained overactivation have been associated with neuronal dysfunction and disease progression. In this review, we systematically summarize the evidence for the pathological and protective roles of ERK signaling in PD. Insufficient ERK activity may impair neurotrophic support, synaptic plasticity, mitochondrial homeostasis, and neuronal adaptation to stress, thereby increasing the vulnerability of dopaminergic neurons. In contrast, persistent and excessive ERK activation may promote neuroinflammation, oxidative stress, mitochondrial dysfunction, apoptosis, autophagy-lysosomal impairment, and abnormal α-synuclein accumulation. Conversely, moderate and transient ERK activation induced by neurotrophic factors, bioactive compounds, or exercise may enhance neuronal survival, antioxidant defense, synaptic plasticity, and functional recovery. These apparently contradictory findings suggest that ERK activation itself cannot be simply classified as either neuroprotective or neurotoxic. Based on the available evidence, we propose the concept of an “ERK activity window” to explain the context-dependent effects of ERK signaling in PD. This hypothesis suggests that ERK activity needs to remain within an appropriate dynamic range to maintain neuronal homeostasis and adaptive responses. ERK activity below this range may weaken neurotrophic support and stress resistance, whereas prolonged and high-intensity activation may exceed the adaptive capacity of neurons and glial cells, thereby promoting inflammatory and degenerative processes. Importantly, this functional window should not be regarded as a fixed threshold. Its biological effects are likely determined by the intensity and duration of activation, cell type, subcellular localization, upstream stimuli, interaction with other signaling pathways, and disease stage. This framework may therefore help reconcile the apparently inconsistent findings among different PD models and experimental conditions. From a therapeutic perspective, these findings indicate that ERK should not necessarily be considered a target for simple activation or inhibition. Instead, restoring ERK signaling toward an appropriate functional state may represent a more rational strategy. Exercise is of particular interest because it can simultaneously influence neurotrophic signaling, oxidative stress, inflammation, synaptic plasticity, and ERK activity, potentially promoting adaptive remodeling of ERK signaling according to pathological conditions. Nevertheless, current evidence is predominantly derived from cellular and animal studies, and the dynamic, cell-specific, and spatial characteristics of ERK signaling remain insufficiently defined. Blood-brain barrier penetration, long-term safety, preservation of physiological ERK functions, and the lack of reliable biomarkers for monitoring brain ERK activity also limit clinical translation. Importantly, the “ERK activity window” remains a theoretical framework, and direct clinical evidence that ERK modulation can alter the progression of human PD is currently lacking. Future studies should therefore integrate longitudinal analysis, cell-specific and spatial approaches, and clinical biomarker validation to determine whether precise regulation of ERK signaling can provide a viable strategy for disease-modifying intervention in PD.

Laparoscopic subtotal cholecystectomy (LSC) has been a safe and viable alternative to conversion to laparotomy in cases of severe cholecystitis. The objective of this study is to determine the utility of intraoperative choledochoscopy in LSC for the exploration of the gallbladder, cyst duct, and subsequent stone clearance of the cystic duct in cases of severe cholecystitis. A total of 72 patients diagnosed with severe cholecystitis received choledochoscopy-assisted laparoscopic subtotal cholecystectomy (CALSC). A choledochoscopy was performed to explore the gallbladder cavity and/or cystic duct, and to extract stones using a range of techniques. The clinical records, including the operative records and outcomes, were subjected to analysis. No LSC was converted to open surgery, and no bile duct or vascular injuries were sustained. All stones within the cystic duct were removed by a combination of techniques, including high-frequency needle knife electrotomy, basket, and electrohydraulic lithotripsy. A follow-up examination revealed the absence of residual bile duct stones, with the exception of one common bile duct stone, which was extracted via endoscopic retrograde cholangiopancreatography. In certain special cases, CALSC may prove to be an efficacious treatment for the management of severe cholecystitis. This technique allows for optimal comprehension of the situation within the gallbladder cavity and cystic duct, facilitating the removal of stones from the cystic duct and reducing the residue of the non-functional gallbladder remnant.

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.

This study investigated the mechanism by which Jianpi Bushen Yiqi Decoction promotes acetylcholine receptor(AChR) clustering in myasthenia gravis through the Agrin/low-density lipoprotein receptor-related protein 4(LRP4)/muscle-specific receptor tyrosine kinases(MuSK) signaling pathway. A total of 114 female C57BL/6J mice were divided into the normal group, modeling group, and solvent control group. The normal group and the solvent control group were immunized with phosphate-buffered saline(PBS), while the modeling group was established as an experimental autoimmune myasthenia gravis(EAMG) model using the murine-derived AChR-α subunit R97-116 peptide fragment. After successful modeling, the mice were randomly assigned to the model group, the low-, medium-, and high-dose Jianpi Bushen Yiqi Decoction groups, and the prednisone group. After four weeks of continuous treatment, muscle strength was assessed using Lennon scores and grip strength tests. Immunofluorescence staining was conducted on differentiated C2C12 myotubes incubated with a drug-containing serum to observe the number of AChR clusters. The integrity of AChR on myofilaments in mouse gastrocnemius muscles was further assessed by immunofluorescence staining. Hematoxylin-Eosin(HE)staining was applied to examine pathological changes in the gastrocnemius muscles of EAMG mice treated with Jianpi Bushen Yiqi Decoction. Western blot was utilized to detect the expression of key proteins in the Agrin/LRP4/MuSK signaling pathway in both C2C12 myotubes and mouse gastrocnemius muscles. The results demonstrated that compared to the model group, the prednisone group exhibited a significant decrease in the body weights of mice, whereas no significant differences in the body weights of mice were observed among the low-, medium-, and high-dose Jianpi Bushen Yiqi Decoction groups. All treatment groups showed significantly improved grip strength and Lennon scores. Additionally, the formula promoted AChR clustering on myotubes and enhanced AChR integrity in gastrocnemius myofilaments and reduced inflammatory infiltration between muscle tissue and fibrous hyperplasia. Furthermore, Jianpi Bushen Yiqi Decoction upregulated the protein expression of AChRα1, Agrin, and p-MuSK in C2C12 myotubes and increased the protein expression of AChRα1, Agrin, MuSK, p-MuSK, LRP4, and docking protein 7(Dok-7)in gastrocnemius tissue. In conclusion, Jianpi Bushen Yiqi Decoction may promote AChR clustering by targeting key proteins in the Agrin/LRP4/MuSK signaling pathway, thereby improving neuromuscular junction function and enhancing muscle strength.
Animals ; Agrin/genetics* ; Mice ; Drugs, Chinese Herbal/administration & dosage* ; Signal Transduction/drug effects* ; Receptors, Cholinergic/genetics* ; Female ; Mice, Inbred C57BL ; Receptor Protein-Tyrosine Kinases/genetics* ; Neuromuscular Junction/metabolism* ; Myasthenia Gravis, Autoimmune, Experimental/physiopathology* ; Humans ; LDL-Receptor Related Proteins

This article reports a child with cardioaciocutaneous syndrome (CFCS) caused by a rare microdeletion of chromosome 19p13.3, and a literature review is conducted. The child had unusual facies, short stature, delayed mental and motor development, macrocephaly, and cardiac abnormalities. Whole-exome sequencing identified a 1 040 kb heterozygous deletion in the 19p13.3 region of the child, which was rated as a "pathogenic variant". This is the first case of CFCS caused by a loss-of-function mutation reported in China, which enriches the genotype characteristics of CFCS. It is imperative to enhance the understanding of CFCS in children. Early identification based on its clinical manifestations should be pursued, and genetic testing should be performed to facilitate diagnosis.
Humans ; Chromosome Deletion ; Chromosomes, Human, Pair 19/genetics* ; Ectodermal Dysplasia/genetics* ; Facies ; Failure to Thrive/genetics* ; Heart Defects, Congenital/genetics*