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.

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.

In view of the characteristics of H5N6 subtype avian influenza virus(AIV)that it has both high pathogenicity and the risk of cross-species transmission,posing a serious threat to the poultry farming industry and public health security,in order to effectively prevent and control the spread of H5N6 avian influenza,a rapid,sensitive and specific detection technolo-gy was established in this study.The specific monoclonal antibodies against the neuraminidase N6 protein of avian influenza A virus subtype H5N6 were obtained through hybridoma and monoclonal antibody technology.These antibodies were coupled and labeled with carboxyl-functionalized fluorescent quantum dots,along with previously prepared specific antibodies against the hemagglutinin H5 protein.A rapid fluorescence immunochromatographic detection method for the H5N6 subtype of avian influ-enza virus was established according to the principle of double-antibody sandwich immunochromatography.This method a-chieved a detection sensitivity of 1 ng/mL for recombinant hemagglutinin H5 subtype protein and 0.1 ng/mL for recombinant neuraminidase N6 subtype protein.Moreover,the method exhibited no cross-reactivity with other influenza subtypes or patho-gens,such as Newcastle disease(ND),infectious bronchitis(IB),and infectious laryngotracheitis(ILT),thus demonstrating good specificity.The method effectively identified the highly pathogenic avian influenza virus H5 subtype and directly distin-guished the H5N6 subtype with good accuracy.The fluorescent quantum dot immunochromatographic typing detection method established herein met the sensitivity,specificity,and accuracy requirements for H5N6 subtype detection,and can be further used for rapid detection of the H5 and H5N6 subtypes of avian influenza virus.

Objective:To explore the factors associated with the efficacy of high-frequency repetitive transcranial magnetic stimulation (rTMS) in the treatment of neuropathic pain (NP) following spinal cord injury (SCI).Methods:This was a retrospective study of 89 SCI survivors with NP receiving high-frequency rTMS. Those with a ≥30% reduction in their Numeric Rating Scales (NRS) scores after 2 weeks of treatment were termed Responders ( n=36), with the others classified as non-responders ( n=53). Demographic data (gender, education level, age), SCI characteristics (injury etiology, injury severity, neurological injury level, injury duration), NP characteristics (pain type, pain intensity, analgesic use), functional assessment (Modified Ashworth Scale score, Spinal Cord Independence Measure score, Modified Barthel Index score, American Spinal Injury Association motor/sensory score) were collected. Least absolute shrinkage and selection operator (LASSO) regression was used for variable selection, followed by binary logistic regression to identify factors associated with treatment efficacy. Results:Among the 89 patients, 36 (40.4%) were Responders to high-frequency rTMS. Binary logistic regression revealed that those with a cervical spinal cord injury and/or spasticity and women were more likely to respond to high-frequency rTMS.Conclusions:Female gender, cervical spinal cord injury, and spasticity are independent factors predicting rTMS efficacy in treating SCI, with spasticity demonstrating the strongest association.

We analyzed the epidemiological features and spatial distribution characteristics of human brucellosis in Jining city from 2014 to 2023,to provide a reference for further development of targeted prevention and control strategies and measures.Descrip-tive epidemiological methods were used to analyze the epidemiological characteristics of brucellosis cases in Jining from 2014 to 2023.The spatial regional correlation of brucellosis incidence in Jining and the clustering patterns of local areas were studied through spatial autocorrelation analysis with townships as the basic unit.A total of 3 520 cases of brucellosis were reported in Jining from 2014 to 2023,and the average annual incidence rate was 4.23/100 000,thus indicating a fluctuating trend overall.Reported cases peaked from March to August,and a sex ratio of 2.71 males to 1 female was observed.The 40-59 year age group had the most reported cases(50.39％).The incidence of brucellosis in Jining showed an imbalanced spatial distribution.Brucellosis incidence showed a spatially clustered distribution(Moran's I>0,P<0.05).Hotspots were distributed primarily in Sishui,Qufu,and Zoucheng.A total of one class Ⅰ clustering area and one class Ⅱ clustering area were detected in the spatial and temporal scans,and were located in Sishui,Qufu,and Liangshan county.After pathogenic AMOS-PCR typing analysis,64 Brucella isolates collected from Jinan City from 2022 to 2024 were all of the sheep strain,and sheep biovar 3 was predominant(70.31％).In 2014-2023,although Jining City experienced a high incidence of brucellosis,a downward trend was observed.Brucellosis showed a spatial clustering pattern concentrated in the northeastern region.Therefore,awareness and education must be strengthened among brucellosis practitioners in cluster areas,to en-hance case surveillance,improve the level of protection,and achieve early detection and treatment.

We analyzed the epidemiological features and spatial distribution characteristics of human brucellosis in Jining city from 2014 to 2023,to provide a reference for further development of targeted prevention and control strategies and measures.Descrip-tive epidemiological methods were used to analyze the epidemiological characteristics of brucellosis cases in Jining from 2014 to 2023.The spatial regional correlation of brucellosis incidence in Jining and the clustering patterns of local areas were studied through spatial autocorrelation analysis with townships as the basic unit.A total of 3 520 cases of brucellosis were reported in Jining from 2014 to 2023,and the average annual incidence rate was 4.23/100 000,thus indicating a fluctuating trend overall.Reported cases peaked from March to August,and a sex ratio of 2.71 males to 1 female was observed.The 40-59 year age group had the most reported cases(50.39％).The incidence of brucellosis in Jining showed an imbalanced spatial distribution.Brucellosis incidence showed a spatially clustered distribution(Moran's I>0,P<0.05).Hotspots were distributed primarily in Sishui,Qufu,and Zoucheng.A total of one class Ⅰ clustering area and one class Ⅱ clustering area were detected in the spatial and temporal scans,and were located in Sishui,Qufu,and Liangshan county.After pathogenic AMOS-PCR typing analysis,64 Brucella isolates collected from Jinan City from 2022 to 2024 were all of the sheep strain,and sheep biovar 3 was predominant(70.31％).In 2014-2023,although Jining City experienced a high incidence of brucellosis,a downward trend was observed.Brucellosis showed a spatial clustering pattern concentrated in the northeastern region.Therefore,awareness and education must be strengthened among brucellosis practitioners in cluster areas,to en-hance case surveillance,improve the level of protection,and achieve early detection and treatment.

Objective:To explore the factors associated with the efficacy of high-frequency repetitive transcranial magnetic stimulation (rTMS) in the treatment of neuropathic pain (NP) following spinal cord injury (SCI).Methods:This was a retrospective study of 89 SCI survivors with NP receiving high-frequency rTMS. Those with a ≥30% reduction in their Numeric Rating Scales (NRS) scores after 2 weeks of treatment were termed Responders ( n=36), with the others classified as non-responders ( n=53). Demographic data (gender, education level, age), SCI characteristics (injury etiology, injury severity, neurological injury level, injury duration), NP characteristics (pain type, pain intensity, analgesic use), functional assessment (Modified Ashworth Scale score, Spinal Cord Independence Measure score, Modified Barthel Index score, American Spinal Injury Association motor/sensory score) were collected. Least absolute shrinkage and selection operator (LASSO) regression was used for variable selection, followed by binary logistic regression to identify factors associated with treatment efficacy. Results:Among the 89 patients, 36 (40.4%) were Responders to high-frequency rTMS. Binary logistic regression revealed that those with a cervical spinal cord injury and/or spasticity and women were more likely to respond to high-frequency rTMS.Conclusions:Female gender, cervical spinal cord injury, and spasticity are independent factors predicting rTMS efficacy in treating SCI, with spasticity demonstrating the strongest association.

Metabolites produced as intermediaries or end-products of microbial metabolism provide crucial signals for health and diseases, such as metabolic dysfunction-associated steatotic liver disease (MASLD). These metabolites include products of the bacterial metabolism of dietary substrates, modification of host molecules (such as bile acids [BAs], trimethylamine-N-oxide, and short-chain fatty acids), or products directly derived from bacteria. Recent studies have provided new insights into the association between MASLD and the risk of developing chronic kidney disease (CKD). Furthermore, alterations in microbiota composition and metabolite profiles, notably altered BAs, have been described in studies investigating the association between MASLD and the risk of CKD. This narrative review discusses alterations of specific classes of metabolites, BAs, fructose, vitamin D, and microbiota composition that may be implicated in the link between MASLD and CKD.

Metachromatic leukodystrophy (MLD) is an inherited disease caused by a deficiency of the enzyme arylsulfatase A (ARSA). Lentivirus-modified autologous hematopoietic stem cell gene therapy (HSCGT) has recently been approved for clinical use in pre and early symptomatic children with MLD to increase ARSA activity. Unfortunately, this advanced therapy is not available for most patients with MLD who have progressed to more advanced symptomatic stages at diagnosis. Patients with late-onset juvenile MLD typically present with a slower neurological progression of symptoms and represent a significant burden to the economy and healthcare system, whereas those with early onset infantile MLD die within a few years of symptom onset. We conducted a pilot study to determine the safety and benefit of HSCGT in patients with postsymptomatic juvenile MLD and report preliminary results. The safety profile of HSCGT was favorable in this long-term follow-up over 9 years. The most common adverse events (AEs) within 2 months of HSCGT were related to busulfan conditioning, and all AEs resolved. No HSCGT-related AEs and no evidence of distorted hematopoietic differentiation during long-term follow-up for up to 9.6 years. Importantly, to date, patients have maintained remarkably improved ARSA activity with a stable disease state, including increased Functional Independence Measure (FIM) score and decreased magnetic resonance imaging (MRI) lesion score. This long-term follow-up pilot study suggests that HSCGT is safe and provides clinical benefit to patients with postsymptomatic juvenile MLD.
Humans ; Leukodystrophy, Metachromatic/genetics* ; Pilot Projects ; Genetic Therapy/methods* ; Hematopoietic Stem Cell Transplantation ; Male ; Follow-Up Studies ; Female ; Lentivirus/genetics* ; Child ; Child, Preschool ; Hematopoietic Stem Cells/metabolism* ; Cerebroside-Sulfatase/metabolism* ; Adolescent