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.

Objective:To analyze the genetic characteristics of the VWF gene c.7332G＞A nonsense mutation and explore its molecular pathogenesis.Methods:Phenotypic diagnosis of the proband was performed using VWF:Ag,VWF:RCo,FⅧ:C and multimeric analysis.The probands were genotyped by NGS whole-exome sequencing,and the sequencing results were validated by sanger sequencing.The family members were genotyped by Sanger sequencing.The VWF gene c.7332G＞A nonsense mutant plasmid was constructed.After transfection,the function of VWF gene c.7332G＞A mutant plasmid was verified at cell level in vitro.The mRNA level was detected by qRT-PCR,and the expression level of protein was detected by Western blot,the function of multimerization was verified by the multimeric analysis.Results:VWF:Ag and VWF:RCo were all less than 3％in the proband,and the multimeric analysis showed multimer deficiency.The proband was diagnosed as type 3 VWD.The homozygous nonsense mutation of VWF gene c.7332G＞A was detected by gene sequencing.The VWF mRNA level of the mutant plasmid was decreased,and the VWF protein expression in the cell supernatant was decreased,the mutant protein was truncated and the function of VWF multimerization was impaired.Conclusion:A homozygous mutation in exon 43 of VWF gene,c.7332G＞A,was responsible for the probands type 3 VWD in the proband.The mutation caused a decrease in the relative level of VWF mRNA and protein,and impaired the function of VWF multimerization.

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.

Aim To investigate the effects of Agrimonia pilosa(AP)on the proliferation and apoptosis of non-small cell lung cancer(NSCLC)H1299 cells using non-targeted metabolomics and other methods,and to explore the underlying molecular mechanisms.Meth-ods Taking H1299 cells as the research object,the effect of AP on cell proliferation and apoptosis was de-tected through CCK-8 method,colony formation,LDH,Hoechst 33258 staining,AO/EB staining,flow cytometry detection,RT qPCR and other experiments.The main differential metabolites were detected by the metabolomics method of ultra-high phase liquid chro-matography and mass spectrometry(UHPLC-Q-Orbi-trap MS),and related metabolic pathways were ana-lyzed.Results Compared with the control group,AP treatment was able to significantly inhibit the prolifera-tion and colony formation of H1299 cells,while the re-lease of LDH increased in a dose-dependent manner.Fluorescence microscopy and flow cytometry and RT-qPCR analysis revealed that H1299 cells underwent crumpling and increased nuclear fragmentation after AP administration,blocked in G0/G1 phase,up-regulated apoptotic genes caspase-3 and Bax,and down-regulated apoptosis-inducing effects of Bcl-2.Metabolomics anal-ysis screened 35 differential metabolites,which were PC(O-30∶1),D-Glutamic acid,PE(18∶0/15∶0),etc.The main metabolic pathways involved includ-ed amino acid metabolism,glycerophospholipid metabo-lism and purine metabolism so on.Conclusions AP may exert its pharmacological effects by interfering with multiple metabolic pathways in H1299 cells,inhibiting cell proliferation and promoting apoptosis.

A mounting body of research suggests that circRNAs significantly contribute to the develop-ment of myocardial fibrosis.The microarray results of human circular RNA expression profile indicated that circHERC4_041 expression increased in the myocardium of patients with heart failure,RT-qPCR a-nalysis confirmed that the myocardial expression level of circHERC4_041 in individuals with heart failure were considerably elevated compared to that in healthy organ donors.Fluorescence in situ hybridization(FISH)confirmed that circHERC4_041 was abundant in the cytoplasm of human cardiomyocyte AC16.Overexpression of circHERC4_041 in mouse myocardial fibroblasts(mCFs)mediated by adenovirus in-hibited the expression of fibrosis-related proteins in mCFs.Experiments involving cell proliferation,wound healing,and Transwell assays demonstrated that overexpression of circHERC4_041 suppressed the growth and mobility of mCFs(P＜0.001).Sequence analysis results suggested that circHERC4_041 con-tains potential ribosome entry sequence(IRES)and open reading frame(ORF).Western blot confirmed that circHERC4_041 could translate the 516 amino acid HERC4-516aa protein,which was mainly located in the cytoplasm of the cell.Cell functional experiments confirmed that circHERC4_041 inhibited the fi-brotic phenotype of mCFs by specifically translating HERC4-516aa(P＜0.05).The specific interaction between HERC4-516aa and transglutaminase 2(TGM2)was confirmed by IP-MS screening and Co-IP i-dentification.Further results found that the degradation of TGM2 was promoted through proteasome path-way.The overexpression of TGM2 in mCFs facilitated by adenoviral vectors could counteract the suppres-sive effects of HERC4-516aa on the fibrotic phenotype of mCFs.Therefore,this study confirmed that the HERC4-516aa protein translated by circHERC4_041 can specifically bind to TGM2 to inhibit the fibrotic phenotype of myocardial fibroblasts.

With the rapid development of generative artificial intelligence(GAI)technologies,their widespread application in academic research and writing is continuously expanding the boundaries of sci-entific inquiry.However,this trend has also raised a series of ethical and regulatory challenges,inclu-ding issues related to authorship,content authenticity,citation accuracy,and accountability.In light of the growing involvement of AI in generating academic content,establishing an open,controllable,and trustworthy ethical governance framework has become a key task for safeguarding research integrity and maintaining trust within the academic community.This expert consensus outlines ethical requirements across key stages of AI-assisted academic writing-including topic selection,data management,citation practices,and authorship attribution.It aims to clarify the boundaries and ethical obligations surrounding AI use in academic writing,ensuring that technological tools enhance efficiency without compromising in-tegrity.The goal is to provide guidance and institutional support for building a responsible and sustainable research ecosystem.

Objective To investigate the stability and reproducibility of the treatment fractions during the intensity-modulated radiotherapy(IMRT)for breast cancer and the effect of respiratory motion on the dose irradiation of breast cancer radiotherapy by comparing the results of breast cancer dosimetric verification based on fractionated images by an electronic portal imaging device(EPID).Methods A total of 28 IMRT patients admitted to some hospital from January to June 2023 were grouped according to the pathological results and effects of respiratory motion on the accuracy of radiotherapy during clinical treatment,including 14 cases in a breast group and 14 cases in a non-breast group with 8 ones of head and neck tumors,5 ones of esophageal cancer and 1 case of cervical cancer.All the patients underwent a scan with cone beam computed tomography(CBCT)before the first radiotherapy,and image registration was carried out with a positioning CT.An EPID was used to acquire transmission dose images of 10 fractions of radiotherapy,and γ analysis was performed using the RIT 113 QA software to compare the images of the subsequent 9 fractions with those of the first fraction,with the images of the first fraction of radiotherapy as the baseline values.Absolute maximum dose normalization was implemented under the condition of 10％dose assessment threshold,and the γ-pass rates under the 3 criteria of 2％/2 mm,3％/2 mm and 3％/3 mm were counted separately.The fraction dose verification results of the 28 patients were divided into 3 treatment phases of 2-4 times(T1),5-7 times(T2)and 8-10 times(T3)to analyze the stability of dose irradiation during the radiotherapy.SPSS 22.0 software was used for statistical analysis.Results Under the condition of 10％dose assessment threshold,the breast and non-breast groups had the γ-pass rates being(95.80±2.65)％and(94.60±6.59)％under the 2％/2 mm criterion and(98.46±1.31)％and(97.50±3.30)％under the 3％/2 mm criterion respectively,and the differences were statistically significant(all P<0.05).Under the assessment criteria of 2％/2 mm,3％/2 mm and 3％/3 mm,the breast group had the γ-pass rates of fractions of treatment significantly lower than those of the non-breast group(all P<0.05),while the γ-pass rates showed no significant differences at T1,T2 and T3 treatment phases(all P>0.05).Conclusion EPID fraction images contribute to evaluating IMRT accuracy effectively.IMRT has high stability and reproducibility during the treatment cycle,while respiration may result in dose deviation during the fraction radiotherapy for breast cancer,and optical surface tracking technology or active breathing control technology is suggested to be involved in to relieve dose deviation.[Chinese Medical Equipment Journal,2025,46(6):54-58]

Objective:To investigate the diagnostic value and influencing factors of endoscopic ultrasonography (EUS) for detecting rectal neuroendocrine neoplasms (R-NENs).Methods:A retrospective case-control study was performed on data of patients with suspected R-NENs by white light endoscopy who underwent endoscopic diagnosis and treatment or surgical operation and obtained pathological diagnosis at the Affiliated Hospital of Qingdao University from March 2016 to June 2023. Clinical data, EUS characteristics and pathological results were statistically analyzed, and the diagnostic accuracy of EUS for R-NENs were obtained by comparing the EUS results with the pathological results. Influencing factors affecting accuracy were analyzed by using the binary logistic regression model.Results:A total of 317 patients were included. The sensitivity, the specificity, the positive predictive value and the negative predictive value of EUS in diagnosing R-NENs were 98.03% (249/254), 34.92% (22/63), 85.86% (249/290) and 81.48% (22/27) respectively. The accuracy was 85.49% (271/317) and the Jorden index was 0.33. Tumor size ≤5 mm ( P=0.002, OR=2.892, 95% CI: 1.464-5.713), absence of surface vascular dilation ( P=0.019, OR=2.613, 95% CI: 1.170-5.837), normal tumor coloration ( P=0.001, OR=3.460, 95% CI: 1.645-7.279) and erythematous surface appearance ( P=0.048, OR=7.242, 95% CI: 1.015-51.680) were independent risk factors affecting the accuracy of R-NENs diagnosis by EUS. Depth assessment accuracy of EUS was 76.77% (195/254), with echo heterogeneity ( P<0.001, OR=4.008, 95% CI: 1.980-8.113) and surface depression ( P=0.035, OR=2.664, 95% CI: 1.073-6.615) emerging as significant factors affecting invasion depth evaluation. Conclusion:EUS demonstrates substantial clinical utility for R-NENs assessment, with diagnostic performance being significantly associated with tumor morphology and sonographic features. Macroscopic characteristics including tumor size, vascular patterns, and chromatic features influence diagnostic accuracy, while echo-textural heterogeneity and surface depression affect invasion depth precision. These findings underscore the clinical relevance of comprehensive EUS evaluation in R-NENs management.