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.

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.

Objective:To evaluate the application value of mini-clinical evaluation exercise-direct observation of procedural skills (Mini-CEX-DOPS) in the assessment of the Simodont digital virtual simulation training system in general dentistry residency training.Methods:A total of 172 general dentistry residents at West China Hospital of Stomatology, Sichuan University, from January 2023 to December 2024 were enrolled and randomly assigned to two groups using a random number table. The control group received traditional teaching, and the training group received Simodont digital virtual simulation training and traditional teaching. Through clinical treatment of real patients, differences between groups were compared in Mini-CEX+DOPS assessment scores, exit examination scores, and teaching satisfaction. A statistical analysis was performed using SPSS 22.0 software. Categorical data were analyzed using the chi-square test, and inter-group comparisons were performed using the independent-samples t test with a significance level of α=0.05. Results:There were no significant differences in Mini-CEX scores between the two groups at the beginning of residency training at the Department of General Dentistry ( P>0.05). At the end of residency training, the Mini-CEX scores of the training group in clinical consultation, physical examination, and humanistic care were higher than those of the control group ( P<0.05); the comprehensive competency score of the training group was (7.65±0.50), while that of the control group was (6.84±0.43). The comprehensive competency scores of both groups were higher at the end of residency training compared to that at the beginning of residency training ( P<0.05). At the beginning of residency training, there were no significant differences in DOPS assessment scores between the two groups ( P>0.05). At the end of residency training, the DOPS evaluation scores were higher in the training group than in the control group ( P<0.05). The overall operation skill scores for the training and control groups were (7.61±0.45) and (6.90±0.31), respectively. The DOPS scores of both groups were higher at the end of residency training compared to those at the beginning of residency training ( P<0.05). The theoretical evaluation, professional skill, and medical record writing quality scores of the training group were higher than those of the control group ( P<0.05); the professional skill scores of the training and control groups were (86.32±4.12) and (77.39±4.58), respectively. The overall satisfaction of the training group was higher than that of the control group [95.35％(82/86) vs. 70.93％(61/86), P<0.05]. Conclusions:Based on the formative evaluation system of Mini-CEX+DOPS, the Simodont digital virtual simulation training system can improve the training effectiveness, physician satisfaction, teaching quality, and patient satisfaction in general dentistry residency training. This approach has significant application value and promising prospects for broader promotion.

Objective:To establish a TaqMan-MGB probe-based method for detecting the polymorphic loci C677T and A1298C of the MTHFR gene.Methods:Specific primers and TaqMan-MGB probes targeting the C677T and A1298C polymorphic loci of the MTHFR gene were designed and optimized based on the gene sequence information.A real-time quantitative PCR detection system was established.Gradient dilution experiments were conducted to determine the limit of detection,and reproducibility experiments were performed to evaluate detection consistency.Specificity was validated using wild-type and mutant plasmid templates.The method was applied to detect 56 clinical samples,and its accuracy and practicality were assessed through comparison with traditional Sanger sequencing.Results:The TaqMan-MGB probe method demonstrated high specificity for detecting the C677T and A1298C loci,with no cross-reactivity between wild-type and mutant probes,enabling accurate genotype differentiation.Sensitivity experiments revealed detection limits of 1.13 × 103 copies/μL for C677T and 8.39 × 101 copies/μL for A1298C.Reproducibility experiments showed coefficients of variation below 1％,indicating stable and reliable results.Among the 56 clinical samples,the overall detection rate for the C677T locus was 86.99％,and for the A1298C locus,it was 97.92％.The TaqMan-MGB method exhibited good concordance with Sanger sequencing results.Conclusion:The TaqMan-MGB method exhibits high specificity,sensitivity,and excellent reproducibility in detecting the polymorphic loci C677T and A1298C of the MTHFR gene,making it suitable for rapid detection in large-scale clinical samples.This method provides an effective molecular diagnostic tool for the early diagnosis and prevention of folate-related diseases.

Objective To delineate the current research landscape,emerging hotspots,and frontiers re-garding the relationship between the oral microbiome and digestive system diseases.Methods We retrieved publications from the Web of Science Core Collection database using topic-specific queries on"oral microbi-ome"and"digestive diseases."Bibliometric analysis was performed using VOSviewer,CiteSpace,and the"bibliometrix"package in R for data mining and visualization.Results A total of 1228 eligible articles were included.Analysis revealed that research on the correlation between oral microbiota and digestive system diseases will remain a global hotspot.Academic institutions dominated the publications,with centralized institutional distribution and team-based collaboration,though overall collaboration networks remained fragmented.Geo-graphically,the United States emerged as the leading contributor,followed by China and the United Kingdom.While China-U.S.collaborations were prominent,China's engagement with other regions remained limited.Current research hotspots focus on the interplay between oral microbiota and gut microbiota,inflam-matory bowel disease(IBD),and digestive system tumors.Conclusions Research in this field demonstrates high activity and diversity.Studies on the associations of oral microbiota with gut microbiota,IBD,and diges-tive system tumors(particularly esophageal,gastric,pancreatic,and colorectal cancers)remain prominent.Future studies should prioritize elucidating underlying mechanisms and innovating in biomarker discovery and application.However,insufficient collaboration and resource-sharing among institutions currently hinder pro-gress in this field.

AIM To investigate the effects of Changpu Yujin Decoction(CPYJD)on striatal mitophagy and PINK1/Parkin signaling pathway in a rat model of Tourette syndrome(TS).METHODS Thirty-six SPF male SD rats were randomly assigned to the control group(n=9)and the TS modeling group(n=27).Rats in the modeling group received daily intraperitoneal injections of 3,3'-iminodipropionitrile(IDPN)(300 mg/kg)for 7 consecutive days to establish the TS model.Post-modeling,successfully induced TS rats were re-randomized into model group(no treatment),tiapride group(47.91 mg/kg)and CPYJD group(77.28 g/kg).All groups received their respective interventions via intragastric administration daily for 28 days.Following drug administration,behavioral scores were assessed in each group.Pathological alterations in the striatum were examined using HE staining,while ultrastructural changes were evaluated by transmission electron microscopy(TEM).Neuronal apoptosis was quantified via TUNEL staining,and ROS levels in striatum were measured by ELISA.Co-localization of PINK1 and LC3B was assessed using immunofluorescence(IF).Finally,mRNA and protein expressions of PINK1,Parkin,Beclin-1,P62 and LC3B(LC3B-Ⅱ/Ⅰ ratio)were analyzed by RT-qPCR and Western blot.RESULTS Compared to the control group,the model group demonstrated significantly increased behavioral scores(P＜0.01),elevated neuronal apoptosis rate and higher ROS levels in the striatum(P＜0.01);severe neuronal and mitochondrial damage in the striatum;significantly reduced mRNA and protein expressions of PINK1,Parkin,Beclin-1 and LC3B(LC3B-Ⅱ/Ⅰ ratio)in the striatum(P＜0.01);markedly upregulated P62 mRNA and protein expressions(P＜0.01).Compared to the model group,both the tiapride and CPYJD intervention groups exhibited significantly reduced behavioral scores(P＜0.01);decreased neuronal apoptosis rate and lower ROS levels(P＜0.01);improved pathological alterations in the striatal neurons and mitochondria;increased mRNA and protein expressions of PINK1,Parkin and Beclin-1 in the striatum(P＜0.05,P＜0.01);and decreased P62 mRNA and protein expressions(P＜0.01).Furthermore,the rats in the CPYJD group specifically showed elevated LC3B mRNA level and LC3B-Ⅱ/Ⅰ protein ratio in striatum(P＜0.05,P＜0.01).CONCLUSION The effect of CPYJD intervention in TS rats may involve activation of mitophagy through regulation of the PINK1/Parkin signaling pathway,improving mitochondrial function,reducing ROS levels,and thereby protecting neurons.

Objective:To investigate the anti-heart failure mechanism of N-acetylcysteine(NAC)in diabetic cardiomyop-athy independent from coronary artery factors.Methods:A total of 40 diabetic mice after heart failure model construction were randomly divided into two groups,NAC group(n=20,NAC 100mg·kg-1·d-1)and control group(n=20,Saline 100 mg·kg-1·d-1).Echocardiography was performed to detect left ventricular end-diastolic volume(LVEDV),left ventricular end-systolic volume(LVESV),left ventricular ejection fraction(LVEF),mitral left ventricular early-dias-tolic peak flow velocity/left ventricular late-diastolic peak flow velocity(E/A),isovolumic relaxation time(IVRT)and cardiac output(CO)after 4 weeks.Terminal uridine nick-end labeling(TUNEL)was performed to detect apoptosis in-dex,and Western Blot was performed to detect the expression of extracellular regulated protein kinases(ERK)1/2 after 6 weeks in two groups.Results:Compared to those in control group,mice in NAC group had significant higher LVEF[(40.5±3.4)％vs.(36.9±3.2)％],E/A[(1.5±0.1)vs.(1.4±0.1)]and CO[(10.3±0.6)ml/min vs.(9.9±0.5)ml/min](P＜0.05 or＜0.01);and significant lower LVESV[(23.1±1.3)μl vs.(24.7±1.5)μl],apoptosis index[(31.2±0.5)％vs.(45.1±0.9)％]and the expression of ERK1/2[(2.2±0.2)vs.(3.9±0.1)](P＜0.001 all).Conclusion:NAC exerts anti-heart failure effect by attenuating apoptosis of cardiomyocytes via regulating ERK1/2 pathway.

Objective:To investigate the anti-heart failure mechanism of N-acetylcysteine(NAC)in diabetic cardiomyop-athy independent from coronary artery factors.Methods:A total of 40 diabetic mice after heart failure model construction were randomly divided into two groups,NAC group(n=20,NAC 100mg·kg-1·d-1)and control group(n=20,Saline 100 mg·kg-1·d-1).Echocardiography was performed to detect left ventricular end-diastolic volume(LVEDV),left ventricular end-systolic volume(LVESV),left ventricular ejection fraction(LVEF),mitral left ventricular early-dias-tolic peak flow velocity/left ventricular late-diastolic peak flow velocity(E/A),isovolumic relaxation time(IVRT)and cardiac output(CO)after 4 weeks.Terminal uridine nick-end labeling(TUNEL)was performed to detect apoptosis in-dex,and Western Blot was performed to detect the expression of extracellular regulated protein kinases(ERK)1/2 after 6 weeks in two groups.Results:Compared to those in control group,mice in NAC group had significant higher LVEF[(40.5±3.4)％vs.(36.9±3.2)％],E/A[(1.5±0.1)vs.(1.4±0.1)]and CO[(10.3±0.6)ml/min vs.(9.9±0.5)ml/min](P＜0.05 or＜0.01);and significant lower LVESV[(23.1±1.3)μl vs.(24.7±1.5)μl],apoptosis index[(31.2±0.5)％vs.(45.1±0.9)％]and the expression of ERK1/2[(2.2±0.2)vs.(3.9±0.1)](P＜0.001 all).Conclusion:NAC exerts anti-heart failure effect by attenuating apoptosis of cardiomyocytes via regulating ERK1/2 pathway.