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.

ObjectiveThe widespread adoption of portable fundus cameras for primary care and community screening is hindered by limitations in current autofocus(AF) technologies. Image-based methods relying on sharpness evaluation require iterative searches, resulting in slow convergence, while projection-based techniques are susceptible to optical artifacts and calibration errors. To address these challenges, this study introduces a novel AF system based on direct wavefront sensing, designed to deliver simultaneous high speed, high precision, and operational robustness within the compact form factor essential for portable ophthalmic devices. MethodsOur approach fundamentally reimagines the AF process by directly measuring the ocular wavefront aberration. We developed a custom portable fundus camera integrating a miniaturized Shack-Hartmann wavefront sensor (SHWS) into the optical path. An 850 nm laser diode projects a point source onto the retina via oblique illumination to minimize corneal reflections. Light scattered from this spot carries the eye’s refractive error through the imaging optics and is directed to the SHWS, positioned at a plane optically conjugate to the primary color CMOS imaging sensor. A microlens array within the SHWS samples the incident wavefront, generating a pattern of focal spots on a CCD. Real-time centroid analysis of these spots provides a map of local wavefront slopes. These measurements are processed through a singular value decomposition (SVD) algorithm to fit a Zernike polynomial basis set, enabling real-time reconstruction of the wavefront phase. The defocus component (S) is extracted from the second-order Zernike coefficients, providing a direct, quantitative measure of the refractive error in diopters. This value serves as a precise error signal in a closed-loop control system, which commands a voice-coil actuated focusing lens to its null position in a single, deterministic step, eliminating the need for iterative search algorithms. ResultsComprehensive evaluation demonstrated the system’s high performance. Testing on a calibrated model eye (OEMI-7) established a highly linear relationship between the computed defocus S and the focusing lens position across a ±20 Diopter (D) compensation range, achievable within a 5 mm mechanical travel. The system achieved a focusing precision of 0.08 D, corresponding to an 18-fold improvement over a conventional projection spot-size method tested under identical conditions. The total focus acquisition time, encompassing wavefront measurement, computation, and lens actuation, averaged under 0.5 s. Clinical validation with 25 human volunteers (50 eyes, refractive range -15 D to +10 D) confirmed practical efficacy. The wavefront-sensing AF succeeded in 92% of attempts with a mean time of 0.5 s, substantially outperforming a projection-based benchmark which achieved only a 32% success rate with an average time of 4.25 s. The system provided instantaneous directional guidance and maintained stability during minor ocular movements. Objective assessment of image quality, via amplitude contrast of retinal vasculature, showed consistent and significant enhancement following AF correction across the entire tested diopter range. ConclusionThis work successfully implements and validates a direct wavefront-sensing autofocus paradigm for portable fundus cameras. By directly quantifying and compensating for the optical defocus aberration, this method bypasses the fundamental limitations of image-processing and projection-based techniques, enabling rapid, precise, and deterministic diopter compensation. The developed system delivers an exceptional combination of a wide operational range (±20 D), high accuracy (0.08 D), fast convergence (0.5 s), and a compact physical footprint. This technology provides a practical and high-performance focusing solution capable of enhancing the reliability, throughput, and diagnostic utility of portable retinal imaging in large-scale screening applications. Future efforts will be directed towards system cost optimization and performance adaptation for diverse ocular conditions.

ObjectiveTo investigate the expression characteristics of serum HBV RNA in patients with low-level viremia （LLV） and its value in the diagnosis of LLV. MethodsA total of 402 chronic hepatitis B （CHB） patients who attended Affiliated Hospital of Zunyi Medical University from December 2023 to May 2025 were enrolled， and according to their viral load， they were divided into complete virologic response （CVR） group （190 patients with HBV DNA <20 IU/mL） and LLV group （212 patients with an HBV DNA level of ≥20 IU/mL and <2 000 IU/mL）. The two groups were analyzed in terms of age， sex， disease type， serum HBV RNA， HBeAg， alanine aminotransferase （ALT）， aspartate aminotransferase （AST）， and total bilirubin （TBil）， and according to HBeAg status， the patients in the LLV group were further divided into HBeAg-negative group with 140 patients and HBeAg-positive group with 72 patients. The chi-square test was used for comparison of categorical data between two groups， and the Mann-Whitney U test was used for comparison of continuous data between two groups. A multivariate Logistic regression analysis was used to investigate the influencing factors for LLV in CHB patients， and a Spearman’s rank correlation analysis was used to analyze the correlation of HBV RNA with HBV DNA， ALT， AST， and TBil in the LLV group. The receiver operating characteristic （ROC） curve was plotted to evaluate the efficacy of HBV RNA in the diagnosis of LLV. ResultsCompared with the CVR group， the LLV group had a significantly higher serum level of HBV RNA ［3 （1　—　5） log₁₀ copies/mL vs 2 （1　—　3） log₁₀ copies/mL， Z=-2.346， P=0.019］ and a significantly higher proportion of patients with hepatitis B cirrhosis （31.13% vs 22.11%， χ²=4.155， P=0.042） or hepatocellular carcinoma （9.91% vs 4.74%， χ²=3.876， P=0.049）. The multivariate Logistic regression analysis showed that HBV RNA （odds ratio=1.163， 95% confidence interval： 1.058　—　1.278， P=0.002） was an independent risk factor for the onset of LLV in CHB patients. Among the patients with LLV， HBeAg-positive patients had a significantly higher level of HBV RNA than HBeAg-negative patients ［6 （4　—　7） log₁₀ copies/mL vs 2 （1　—　3） log₁₀ copies/mL， Z=-9.962， P<0.001］. The correlation analysis showed that HBV RNA level had no significant correlation with HBV DNA， ALT， AST， or TBil in the patients with LLV （all P>0.05）. The ROC curve analysis showed that HBV RNA had an AUC of 0.567 for the diagnosis of LLV （P=0.021）， with an optimal cut-off value of 4.5 log₁₀ copies/mL， a sensitivity of 30.7%， and a specificity of 85.8%. ConclusionSerum HBV RNA level is an independent risk factor for the development of LLV in CHB patients， and there is a significant increase in the expression of HBV RNA in HBeAg-positive patients. Therefore， it may serve as a potential biomarker for clinical risk assessment.

ObjectiveTo investigate the mechanism of action of Jiedu Huayu granules in improving liver injury in mice with acute liver failure （ALF） by observing its effect on a mouse model of ALF after prophylactic administration， and to provide a basis for clinical medication. MethodsA total of 60 specific pathogen-free male C57BL/6J mice were divided into normal group， model group， Jiedu Huayu granules group （JDHY group）， and farnesoid X receptor （FXR） agonist （GW4064） group using a random number table， with 15 mice in each group. The model of ALF was induced by a single intraperitoneal injection of D-galactosamine combined with lipopolysaccharide. The mice in the JDHY group were given prophylactic administration of 0.3 g/mL drug solution of Jiedu Huayu granules by gavage for 3 days before modeling， those in the normal group and the model group were given 0.9% NaCl solution by gavage， and those in the GW4064 group were given intraperitoneal injection of GW4064 for 3 consecutive days before modeling. The mice were sacrificed after modeling， and serum and liver tissue samples were collected. A veterinary automatic biochemical analyzer was used to measure the serum levels of total bilirubin （TBil）， total bile acids （TBA）， gamma-glutamyl transferase （GGT）， alanine aminotransferase （ALT）， and aspartate aminotransferase （AST） in mice from each group； HE staining was used to observe liver pathological changes； RT-PCR was used to measure the mRNA expression levels of FXR， fibroblast growth factor 15 （FGF15）， fibroblast growth factor receptor 4 （FGFR4）， small heterodimer partner （SHP）， and bile salt export pump （BSEP） in mice， and Western blot was used to measure the protein expression levels of FXR， FGF15， FGFR4， SHP， and BSEP. A one-way analysis of variance was used for comparison between groups， and the Dunett method was used for further comparison between two groups. ResultsCompared with the normal group， the model group had significant increases in the serum levels of TBil， ALT， AST， TBA， and GGT （all P<0.01）， and compared with the model group， the JDHY group and the GW4064 group had significant reductions in the serum levels of TBil， ALT， AST， TBA， and GGT （all P <0.01）. HE staining showed that compared with the model group， the JDHY group and the GW4064 group had milder pathological injury， a reduction in the area of hepatocyte necrosis， and alleviation of cellular swelling and edema. Compared with the normal group， the model group had significant reductions in the mRNA and protein expression levels of FXR， FGF15， FGFR4， SHP， and BSEP in liver tissue （all P <0.01）， and compared with the model group， the JDHY group and the GW4064 group had significant increases in the mRNA and protein expression levels of FXR， FGF15， FGFR4， SHP， and BSEP in liver tissue （all P <0.05）. ConclusionJiedu Huayu granules may alleviate liver injury in mice with ALF through the FXR/SHP axis.

Objective: To analyze changes in tuberculosis infection among junior high school students before and after tuberculosis exposure, so as to provide a reference for improving school tuberculosis prevention and control measures and policy formulation. Methods: Retrospectively collect data on a tuberculosis outbreak that occurred in a grade of a junior high school in Chongqing in 2025, including tuberculosis screening records of students in this grade upon their enrollment in 2022 (1 156 students) and after two tuberculosis outbreaks in 2023 (206 students) and 2025 (171 students). The Wilcoxon signed rank test for paired design was used to compare the induration diameters of the subjects, and the Chi square test was adopted to analyze the rate of tuberculosis infection among students. Results: In the tuberculosis outbreak in 2023, the rate of tuberculosis infection among close contacts ( 11.84 %) and the rate of tuberculosis infection among freshrman at school enrollment (12.89%) showed no statistically significant difference ( χ 2=0.25, P >0.05). The rate of tuberculosis infection of close contacts in the 2025 tuberculosis outbreak (55.56%) was higher than that in the 2023 outbreak (11.84%) ( χ 2=30.42, P <0.01). Among the 106 students included in the cohort analysis, the median induration diameter was 3.50 (1.50, 7.50) mm in 2023 and 8.75 (4.25, 11.50) mm in 2025, with a statistically significant difference ( Z=-5.76, P <0.01). There was no statistically significant difference between the infection rate in 2022 (16.98%) and that in 2023 (10.38%) ( χ 2=1.96, P =0.16). The infection rate in 2025 (43.40%) was higher than those in 2022 and 2023 ( χ 2=17.55, 29.39, both P <0.017). The seroconversion rate of students in the same class in 2025 ( 58.00 %) was higher than that of students in different classes (16.07%), with a statistically significant difference ( χ 2=20.19, P <0.01). All 72 individuals with latent tuberculosis infections identified during the pandemic in 2023 and 2025 refused to undergo prophylactic treatment. Conclusions The lack of preventive treatment may be the underlying cause of the successive outbreaks during the epidemic. Early detection of infection sources and standardized outbreak management are crucial to controlling the spread of the epidemic.

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.

China has entered the ranks of “moderately aging” countries. The aging of the population is accompanied by the phenomenon of a low birth rate， with insufficient newborns and an increasing proportion of the elderly population， resulting in aging issues persisting. Conversely， the low birth rate has further exacerbated the degree of aging， promoting China’s fertility policy to focus on constructing an effective fertility support policy system to comprehensively enhance the fertility willingness of the reproductive-age groups. At the current stage， China’s fertility support policy system seeks to reduce the costs of childbearing， alleviate family child-rearing burdens， ease work-family conflicts， and meet childcare needs for reproductive-age individuals， infants， and caregivers through providing economic， time， and service support. However， numerous issues persist throughout the process from design to application and implementation， such as policy fragmentation， unclear responsibilities， and mismatched supply and demand. The current policy objectives tend to emphasize population regulation， lacking systematic support for the entire lifecycle of families. Furthermore， China’s fertility support policies have partially drawn on international experience and undergone localized adjustments， receiving varying degrees of feedback. This validates that research should focus on the heterogeneous effects of fertility support policies on the fertility willingness of different reproductive-age groups and identify their specific fertility needs. This approach would facilitate more precise policy implementation and more effectively construct the entire lifecycle fertility support policy system， thereby achieving high-quality development of the population.

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.