Machado-Joseph disease, or spinocerebellar ataxia type 3 (SCA3), represents the most common autosomal dominant cerebellar ataxia worldwide. Despite its progressive and debilitating nature, disease-modifying therapies remain elusive. Repetitive transcranial magnetic stimulation (rTMS) has emerged as a promising non-invasive intervention; however, its clinical application has been hindered by inconsistent protocols and a lack of mechanistic understanding. A recent landmark study published in Brain Stimulation by Chen et al. addressed these challenges by combining a high-dose intermittent theta-burst stimulation (iTBS) protocol with concurrent transcranial magnetic stimulation-electroencephalography (TMS-EEG). This commentary provides an in-depth analysis of their findings, highlighting the restoration of cerebello-cortical inhibition (CBI) as a key therapeutic mechanism. Furthermore, we discuss the broader implications of this work, proposing that future translational research should integrate accelerated iTBS (aiTBS) paradigms, cortical response measurements (CRM), and individualized neuro-navigation to establish a new era of precision neuromodulation for ataxia.

Machado-Joseph disease, or spinocerebellar ataxia type 3 (SCA3), represents the most common autosomal dominant cerebellar ataxia worldwide. Despite its progressive and debilitating nature, disease-modifying therapies remain elusive. Repetitive transcranial magnetic stimulation (rTMS) has emerged as a promising non-invasive intervention; however, its clinical application has been hindered by inconsistent protocols and a lack of mechanistic understanding. A recent landmark study published in Brain Stimulation by Chen et al. addressed these challenges by combining a high-dose intermittent theta-burst stimulation (iTBS) protocol with concurrent transcranial magnetic stimulation-electroencephalography (TMS-EEG). This commentary provides an in-depth analysis of their findings, highlighting the restoration of cerebello-cortical inhibition (CBI) as a key therapeutic mechanism. Furthermore, we discuss the broader implications of this work, proposing that future translational research should integrate accelerated iTBS (aiTBS) paradigms, cortical response measurements (CRM), and individualized neuro-navigation to establish a new era of precision neuromodulation for ataxia.

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.

OBJECTIVE: To understand clinical and laboratory characteristics of acute myeloid leukemia, myelodysplasia-related (AML-MR). METHODS: Blood sample of one patient with AML-MR admitted to our hospital in September 2021 was collected and synthetically analyzed by using techniques including complete blood cell count, peripheral blood and bone marrow cell morphology, bone marrow pathology and immunohistochemistry, hematology examination, flow cytometry (FCM), chromosome karyotype analysis and molecular pathology. The clinical and laboratory characteristics of AML-MR were analyzed and summarized according to the World Health Organization (WHO) standards. RESULTS: The patient showed pancytopenia and increased proportion of blasts in smear of peripheral blood cells. Bone marrow cytology and pathological examination showed significant proliferation of hematopoietic cells. Pathological immunohistochemistry showed increased expression of CD61, CD34, and CD117, while MPO, CD13, and CD33 were positive. FCM showed that abnormal myeloid progenitor cells accounted for approximately 18.61% of the total number of nuclear cells, with expression of CD34, CD13, CD117, HLA-DR, and CD33 (small amount). Additionally, 36.34% of the cells were primitive/immature red blood cells which expressed CD36, CD71, and CD117 (small amount). Chromosome karyotype analysis and molecular pathology detected three kinds of abnormalities including -5 and two kinds of TP53 related gene mutation, respectively. CONCLUSION AML-MR patient shows pancytopenia and increased proportion of blasts in smear of peripheral blood cells. Bone marrow cytology and pathological examination show significant proliferation of hematopoietic cells. FCM can detect myeloid progenitor cells and primitive/immature red blood cells, while chromosome karyotype analysis can detect three abnormal karyotypes.
Humans ; Leukemia, Myeloid, Acute/diagnosis* ; Myelodysplastic Syndromes ; Flow Cytometry ; Karyotyping ; Male ; Middle Aged ; Mutation

BACKGROUND: Our understanding of the correlation between postdischarge cancer and mortality in patients with coronary artery disease (CAD) remains incomplete. The aim of this study was to investigate the relationships between postdischarge cancers and all-cause mortality and cardiovascular mortality in CAD patients. METHODS: In this retrospective cohort study, 25% of CAD patients without prior cancer history who underwent coronary artery angiography between January 1, 2011 and December 31, 2015, were randomly enrolled using SPSS 26.0. Patients were monitored for the incidence of postdischarge cancer, which was defined as cancer diagnosed after the index hospitalization, survival status and cause of death. Cox regression analysis was used to explore the association between postdischarge cancer and all-cause mortality and cardiovascular mortality in CAD patients. RESULTS: A total of 4085 patients were included in the final analysis. During a median follow-up period of 8 years, 174 patients (4.3%) developed postdischarge cancer, and 343 patients (8.4%) died. A total of 173 patients died from cardiovascular diseases. Postdischarge cancer was associated with increased all-cause mortality risk (HR = 2.653, 95% CI: 1.727-4.076, P < 0.001) and cardiovascular mortality risk (HR = 2.756, 95% CI: 1.470-5.167, P = 0.002). Postdischarge lung cancer (HR = 5.497, 95% CI: 2.922-10.343, P < 0.001) and gastrointestinal cancer (HR = 1.984, 95% CI: 1.049-3.750, P = 0.035) were associated with all-cause mortality in CAD patients. Postdischarge lung cancer was significantly associated with cardiovascular death in CAD patients (HR = 4.979, 95% CI: 2.114-11.728, P < 0.001), and cardiovascular death was not significantly correlated with gastrointestinal cancer or other types of cancer. CONCLUSIONS Postdischarge cancer was associated with all-cause mortality and cardiovascular mortality in CAD patients. Compared with other cancers, postdischarge lung cancer had a more significant effect on all-cause mortality and cardiovascular mortality in CAD patients.

Objective To investigate the status of population dynamics and distribution changes of Aedes aegypti in Guangdong Province.Methods Continuous monitoring was conducted from May 2018 to July 2024 in Wushi Town and Qishui Town,Leizhou City,Zhanjiang City,Guangdong Province.Additionally,a survey of the distribution of Ae.aegypti along the Leizhou Peninsula coast was carried out.Results The density of Ae.aegypti in Zhanjiang showed a gradual decline from 2018 to 2024.The last detection of adult Ae.aegypti in Wushi Town was in September 2021,and the last larva was found in October 2023.No Ae.aegypti was detected in Qishui Town during surveys from 2021 to 2024.A survey of 18 coastal villages in the Leizhou Peninsula revealed no detections of Ae.aegypti.Conclusions This study provides a basis for understanding the distribution and population density fluctuations of Ae.aegypti,assessing its invasion risk,and scientifically conducting relevant prevention and control efforts.