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.

Background/Aims: Liver transplantation (LT) following total hepatectomy is a life-saving treatment for hepatocellular carcinoma (HCC). The HCC recurrence after LT hinders the effectiveness of the procedure. The objective of this study is to develop a pre-operative risk stratification model based on a liquid biopsy. Methods: We conducted a comprehensive multi-omics study of 260 HCC patients from three centers, including clinical data, low-coverage whole-genome sequencing of cell-free DNA (cfDNA) from plasma, as well as whole-exome, single-nucleus RNA, and spatial transcriptomics from matched tumor and non-tumor tissues. Results: We identified cfDNA-derived copy number alteration (CNA) signatures associated with post-transplant recurrence. By integrating cfDNA-derived CNA profiles with single-cell transcriptomic data, we traced recurrence-associated cfDNA to a distinct subpopulation of malignant cells within the primary tumor. These cells were embedded in a pro-metastatic microenvironment of specialized endothelial subtypes and cancer-associated fibroblasts. Notably, most recurrence-associated lesions were detectable in cfDNA prior to liver transplantation (LT). Building on these insights, we developed the ZJU Criteria based on CNA fragments and tumor markers, a pre-LT risk prediction tool that integrates conventional clinical factors with cfDNA-derived CNA signatures, and validated it using internal and independent external cohorts. Conclusion Our findings suggest that post-transplant recurrence commonly originates from advanced subclones that emerge late during tumor evolution. The ZJU Criteria provides an accurate, non-invasive strategy that significantly improves pre-LT risk stratification and clinical decision-making for patients with HCC.

In this paper, near-infrared spectroscopy(NIRS) was employed to analyze 129 batches of commercial products of Reduning Injection. The batch reporting rate was estimated according to the report of Reduning Injection in the direct adverse drug reaction(ADR) reporting system of the drug marketing authorization holder of the Center for Drug Reevaluation of the National Medical Products Administration(National Center for ADR Monitoring) from August 2021 to August 2022. According to the batch reporting rate, the samples of Reduning Injection were classified into those with potential risks and those being safe. No processing, random oversampling(ROS), random undersampling(RUS), and synthetic minority over-sampling technique(SMOTE) were then employed to balance the unbalanced data. After the samples were classified according to appropriate sampling methods, competitive adaptive reweighted sampling(CARS), successive projections algorithm(SPA), uninformative variables elimination(UVE), and genetic algorithm(GA) were respectively adopted to screen the features of spectral data. Then, support vector machine(SVM), logistic regression(LR), k-nearest neighbors(KNN), naive bayes(NB), random forest(RF), and artificial neural network(ANN) were adopted to establish the risk prediction models. The effects of the four feature extraction methods on the accuracy of the models were compared. The optimal method was selected, and bayesian optimization was performned to optimize the model parameters to improve the accuracy and robustness of model prediction. To explore the correlations between potential risks of clinical use and quality test data, TreeNet was employed to identify potential quality parameters affecting the clinical safety of Reduning Injection. The results showed that the models established with the SVM, LR, KNN, NB, RF, and ANN algorithms had the F1 scores of 0.85, 0.85, 0.86, 0.80, 0.88, and 0.85 and the accuracy of 88%, 88%, 88%, 85%, 91%, and 88%, respectively, and the prediction time was less than 5 s. The results indicated that the established models were accurate and efficient. Therefore, near infrared spectroscopy combined with machine learning algorithms can quickly predict the potential risks of clinical use of Reduning Injection in batches. Three key quality parameters that may affect clinical safety were identified by TreeNet, which provided a scientific basis for improving the safety standards of Reduning Injection.
Spectroscopy, Near-Infrared/methods* ; Drugs, Chinese Herbal/administration & dosage* ; Machine Learning ; Algorithms ; Humans ; Quality Control

Objective To propose a high-precision deep learning-based image assessment method of osteosarcoma chemotherapy efficacy for clinical treatment,as existing methos have low accuracy of osteosarcoma assessment.Methods The low incidence of osteosarcoma led to the small scale of its imaging data and the problem of imbalance in data categories.This study combined deep learning with clinical medical information,combined the bone sarcoma generation module of BoneGAN and the scale lesion information capture module,and proposed OMLA-Net,a deep learning assessment network for chemotherapy effect of bone sarcoma based on multi-scale lesion attention network,which achieved computer-aided bone tumor assessment with integrated data augmentation and focused lesion information through pre-training and generalized loss training.Results In this study,40 cases of osteosarcoma MRI data were used as the basis for the comparison test on the generated dataset,and the OMLA-Net assessment outperformed the SOTA method Conv-LSTM-GAN in terms of the assessment effects such as accuracy and F1 scores,and the difference was statistically significant(Bootstrap statistical method P＜0.05);the subsequent K-fold cross-validation ablation experiments further demonstrated the effectiveness of each module proposed by OMLA-Net.Conclusion OMLA-Net can effectively perform the impact assessment of chemotherapy effect on osteosarcoma,which provides a new idea for subsequent clinical application.

Objective To investigate the anatomical and microscopic structures of interstitial fluid flow channels in the skin tissue of hand dorsum in human cadavers.Methods Totally 7 fresh cadavers within 12 hours post-mortem were included.MRI was used to observe the distribution of interstitial fluid flow from the first phalanx of the fingers to the wrist,precisely locating the flow channels.Based on imaging results,histological analyses were conducted to determine the histological characteristics of the flow channels.Furthermore,multi-immunofluorescence and microcomputed tomography(Micro-CT)techniques were employed to analyze the channels,and image post-processing was used to elucidate their anatomical structures at the microscopic level.Results After injecting a contrast agent into the first phalanx of ten finger specimens and applying repeated pressure,MRI image revealed centripetal long-range interstitial fluid flow along channels distinct from blood vessels and lymphatic vessels.Histological analysis and Micro-CT further confirmed that the flow primarily occurred within the fibrous connective tissue and adventitia of the skin.Conclusion The orderly fibrous connective tissue and adventitia in the skin form the interstitial fluid flow channels in the human hand dorsum skin,named as"stromal membrane channels"in the skin.

The rapid development of artificial intelligence (AI), especially generative AI (GenAI), has already brought, and will continue to bring, revolutionary changes to our daily production and life, as well as create new opportunities and challenges for diagnostic and therapeutic practices in the medical field. Haihe Hospital of Tianjin University collaborates with the National Supercomputer Center in Tianjin, Tianjin University, and other institutions to carry out research in areas such as smart healthcare, smart services, and smart management. We have conducted research and development of a full-process disease diagnosis and treatment assistance system based on GenAI in the field of smart healthcare. The development of this project is of great significance. The first goal is to upgrade and transform the hospital's information center, organically integrate it with existing information systems, and provide the necessary computing power storage support for intelligent services within the hospital. We have implemented the localized deployment of three models: Tianhe "Tianyuan", WiNGPT, and DeepSeek. The second is to create a digital avatar of the chief physician/chief physician's voice and image by integrating multimodal intelligent interaction technology. With generative intelligence as the core, this solution provides patients with a visual medical interaction solution. The third is to achieve deep adaptation between generative intelligence and the entire process of patient medical treatment. In this project, we have developed assistant tools such as intelligent inquiry, intelligent diagnosis and recognition, intelligent treatment plan generation, and intelligent assisted medical record generation to improve the safety, quality, and efficiency of the diagnosis and treatment process. This study introduces the content of a full-process disease diagnosis and treatment assistance system, aiming to provide references and insights for the digital transformation of the healthcare industry.
Artificial Intelligence ; Humans ; Delivery of Health Care ; Generative Artificial Intelligence

OBJECTIVE: Observational studies have shown inconsistent associations of loneliness or social isolation (SI) with ischemic heart disease (IHD), with unknown mediators. METHODS: Using data from genome-wide association studies of predominantly European ancestry, we performed a bidirectional two-sample Mendelian Randomization (MR) study to estimate causal effects of loneliness ( N = 487,647) and SI traits on IHD ( N = 184,305). SI traits included whether individuals lived alone, participated in various types of social activities, and how often they had contact with friends or family ( N = 459,830 to 461,369). A network MR study was conducted to evaluate the mediating roles of 20 candidate mediators, including metabolic, behavioral and psychological factors. RESULTS: Loneliness increased IHD risk ( OR= 2.129; 95% confidence interval [ CI]: 1.380 to 3.285), mediated by body fat percentage, waist-hip ratio, total cholesterol, and low-density lipoprotein cholesterol. For SI traits, only fewer social activities increased IHD risk ( OR= 1.815; 95% CI: 1.189 to 2.772), mediated by hypertension, high-density lipoprotein cholesterol, triglycerides, fasting insulin, and smoking cessation. No reverse causality of IHD with loneliness and SI was found. CONCLUSION These findings suggested more attention should be paid to individuals who feel lonely and have fewer social activities to prevent IHD, with several mediators as prioritized targets for intervention.
Loneliness/psychology* ; Humans ; Mendelian Randomization Analysis ; Social Isolation ; Myocardial Ischemia/etiology* ; Male ; Female ; Middle Aged ; Genome-Wide Association Study ; Risk Factors ; Aged

OBJECTIVES: Magnetic resonance imaging (MRI) was used to study the efficacy of modified anterior splint (MAS) combined with anterior repositioning splint (ARS) in maintaining the normal disk-condyle relationship. METHODS: We obtained clinical and imaging data of 10 patients with acute anterior disk displacement without reduction (ADDwoR) of the temporomandibular joint undergoing successful mandibular manipulation in the Affiliated Stomatological Hospital, Nanchang University from January to June 2024. The MAS and ARS were used for subsequent treatment. Maximum active mouth opening (MMO) and visual analogue score (VAS) were recorded before treatment and 2 weeks, 1 month, and 3 months after treatment. The changes in the disk-condyle relationship, disk-condyle angle, joint effusion, and double contour sign before and after treatment were analyzed through MRI. RESULTS: Three months after treatment, the MMO increased from 28.90 mm to 46.85 mm, VAS decreased from 4.68 to 0.61, and the disk-condyle angle decreased from 58.84° to 25.99°. MRI analysis found that 6 patients showed normal disk-condyle relationship, 2 patients exhibited displacement with reduction, and 2 patients exhibited ADDwoR. The disk-condyle angle was 58.84° before treatment and 25.99° after treatment. CONCLUSIONS The MAS combined with ARS could further stabilize the coordinated disk-condyle relationship and reduce the recurrence rate.
Humans ; Male ; Magnetic Resonance Imaging ; Female ; Adult ; Temporomandibular Joint Disorders/diagnostic imaging* ; Splints ; Middle Aged ; Mandible ; Temporomandibular Joint Disc ; Occlusal Splints ; Temporomandibular Joint ; Young Adult ; Joint Dislocations/therapy*