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.

As the only effective treatment for end-stage diseases, organ transplantation has been facing core challenges such as shortage of donor organs, technical bottlenecks, and inadequate system construction. Based on the practical experience of the organ donation and transplantation team of the First Affiliated Hospital of Xi’an Jiaotong University, this article systematically reviews the pathways for the specialization of organ donation and the disciplinary development of Organ Procurement Organization (OPO). It thoroughly analyzes the innovative achievements in kidney transplantation, including key breakthroughs in the establishment of technical standards for donor and organ evaluation, preservation and repair, the research and development of mechanical perfusion repair systems, the creation of an independent human leukocyte antigen (HLA) detection platform, and the improvement of the rejection prevention and control system. Meanwhile, combined with the latest research progress at home and abroad, this paper discusses current issues in the field of organ donation and transplantation such as the utilization of marginal donors, multidisciplinary collaboration, and ethical norms. It proposes a future direction to promote the systematic disciplinary development with "Chinese Technology, Chinese Standards, and Chinese Solutions", providing a reference for the high-quality development of organ transplantation in China.

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.

ObjectiveTo explore the molecular mechanism of aerobic exercise to improve hippocampal neuronal degeneration by regulating tryptophan metabolic pathway. Methods60 SPF-grade C57BL/6J male mice were divided into a young group (2 months old, n=30) and a senile group (12 months old, n=30), and each group was further divided into a control group (C/A group, n=15) and an exercise group (CE/AE group, n=15). An aerobic exercise program was used for 8 weeks. Learning memory ability was assessed by Y-maze, and anxiety-depression-like behavior was detected by absent field experiment. Hippocampal Trp levels were measured by GC-MS. Nissl staining was used to observe the number and morphology of hippocampal neurons, and electron microscopy was used to detect synaptic ultrastructure. ELISA was used to detect the levels of hippocampal Trp,5-HT, Kyn, KATs, KYNA, KMO, and QUIN; Western blot was used to analyze the activities of TPH2, IDO1, and TDO enzymes. ResultsGroup A mice showed significant decrease in learning and memory ability (P<0.05) and increase in anxiety and depressive behaviors (P<0.05); all of AE group showed significant improvement (P<0.05). Hippocampal Trp levels decreased in group A (P<0.05) and increased in AE group (P<0.05). Nidus vesicles were reduced and synaptic structures were degraded in group A (P<0.05), and both were significantly improved in group AE (P<0.05). The levels of Trp, 5-HT, KATs, and KYNA were decreased (P<0.05) and the levels of Kyn, KMO, and QUIN were increased (P<0.05) in group A. The activity of TPH2 was decreased (P<0.05), and the activities of IDO1 and TDO were increased (P<0.05). The AE group showed the opposite trend. ConclusionThe aging process significantly reduces the learning memory ability and increases the anxiety-depression-like behavior of mice, and leads to the reduction of the number of nidus vesicles and degenerative changes of synaptic structure in the hippocampus, whereas aerobic exercise not only effectively enhances the spatial learning memory ability and alleviates the anxiety-depression-like behavior of aging mice, but also improves the morphology and structure of neurons in hippocampal area, which may be achieved by the mechanism of regulating the tryptophan metabolic pathway.

OBJECTIVE To evaluate the quality of Euscaphis japonica from different habitats. METHODS The relative correction factors of gallic acid， protocatechuic acid， ellagic acid， isoquercitrin， astragalin and apigenin were calculated with quercetin as the internal reference； the relative correction factors of euscaphic acid， oleanolic acid， stigmasterol and β-sitosterol were also calculated with ursolic acid as the internal reference. The contents of 12 components in 18 batches of samples were calculated by QAMS method and were compared with external standard method. At the same time， the contents of water-soluble extract， alcohol-soluble extract， total ash and acid-insoluble ash were detected. The quality of E. japonica was evaluated by principal component analysis （PCA）， orthogonal partial least squares-discriminant analysis （OPLS-DA）， and weighted technique for order preference by similarity to ideal solution （TOPSIS） method. RESULTS There was no significant difference between the results of QAMS method and external standard method for the 12 components in the 18 batches of samples. However， notable content variations were observed among different batches of samples. The results of PCA and OPLS-DA showed that S1-S7， S8- S12， and S13-S18 were clustered into one category respectively. Seven key characteristic components variable importance in projection values ＞1， euscaphic acid， ursolic acid， protocatechuic acid， apigenin， β-sitosterol， isoquercitrin， and oleanolic acid， respectively. The analysis results of the weighted TOPSIS method revealed that the relative closeness for evaluating the quality of 18 batches of samples ranged from 0.283 5 to 0.644 1， with the samples of E. japonica from Fengjie， Chongqing， demonstrating the highest quality. CONCLUSIONS The established method is accurate and feasible， which can be used for the quality evaluation of E. japonica combined with chemometrics and weighted TOPSIS model.

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.

ObjectiveTo construct a comprehensive evaluation indicator system for clinical practice guidelines of traditional Chinese medicine （TCM） that is scientific， systematic， and reflects the characteristics of TCM. MethodsA systematic search was conducted in Chinese and English databases， including CNKI， Wanfang， VIP， SinoMed， PubMed， Embase， and Cochrane Library， to include literature on domestic and international guideline evaluation tools and TCM-related research. Document analysis and CiteSpace were utilized for keyword co-occurrence and clustering analysis. ResultsA total of 65 relevant studies were included， from which seven core thematic domains were identified. Based on the research objectives， a two-step construction strategy was adopted： first， an external evaluation framework was established by referencing international tools to cover methodological rigor and procedural standardization； second， an internal evaluation framework was developed to reflect the distinctive features of TCM clinical practice， including syndrome differentiation and efficacy feedback. Through expert consensus， the indicator system was refined， resulting in a dual-layered structure comprising 8 primary indicators， 22 secondary indicators， and 62 evaluation criteria. ConclusionThe comprehensive evaluation system for TCM clinical practice guidelines， based on bibliometric analysis and core element extraction， integrates both theoretical integrity and practical applicability. This study provides a preliminary research foundation for further optimization， validation， and development of a refined comprehensive evaluation system.

Objective:To investigate anxiety symptoms and associated factors in relocated elderly residents of new townships,and to provide evidence for prevention interventions.Methods:A cross-sectional study was conduc-ted in relocated elderly residents in new townships of three urban areas in Shandong Province from 2021 to 2023.The study instruments included Ascertain Dementia-8,Generalized Anxiety Disorder-7,self-administered de-mographic characteristics information questionnaire.Multivariate analysis of factors associated with anxiety symp-toms in elderly residents was performed using multivariate logistic regression.Results:The prevalence rate of mild anxiety symptoms was 5.8％,and the rate of moderate-to-severe anxiety symptoms was 1.3％in 3 313 resi-dents.Multivariate analysis found that self-assessed general psychological condition(OR=0.52),good family envi-ronment(OR=0.34),no self-perceived cognitive impairment(OR=0.31),no chronic diseases(OR=0.42),and only one chronic disease(OR=0.61)were protective factors for mild anxiety symptoms,and very good dietary structure(OR=2.15)and fair dietary structure(OR=2.39)were risk factors for those.Very good family environ-ment(OR=0.11)and average family environment(OR=0.16),and no self-perceived cognitive impairment(OR=0.14)were protective factors for moderate-to-severe anxiety symptoms,and 0-3 years(OR=3.24)and 4-6 years(OR=3.28)of relocation were risk factors for those.Conclusion:Family environment,dietary structure,and duration since relocation are key factors associated with anxiety symptoms among relocated elderly residents in new townships.Targeted interventions should be implemented to address their mental health needs.

Objective To evaluate the prognostic nutritional index(PNI)in patients with newly diagnosed type 2 diabetes mellites(T2DM)complicated with diabetic peripheral neuropathy(DPN).Methods A total of 300 patients with newly diagnosed T2DM from the Wenzhou People's Hospital during January 2017 to March 2023 were enrolled in this study.The patients were divided into uncomplicated DPN(n=214)and complicated DPN(n=86).The general data,biochemical indicators,PNI and other clinical indicators of the two groups were compared.According to PNI thirds,patients were divided into three groups:low,medium and high,comparing the proportion of DPN among the three groups;Logistic regression calculated the risk of DPN in different groups;Drawing receiver operating characteristic curve to analyze PNI and other indicators to predict the value of DPN.Results Compared with the non-DPN group,patients had lower PNI in the DPN group(P＜0.05);lower PNI was associated with higher risk of DPN(P＜0.001).Area under the curve of PNI was 0.882(95％CI:0.841-0.923,P＜0.001),and better predictive value of PNI for DPN than the systemic immune inflammation index,the neutrophil/lymphocyte ratio.Conclusion PNI is closely associated with the occurrence of DPN in newly diagnosed T2DM complicated,and PNI may be used as an important indicator for screening patients with T2DM complicated with DPN.