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.

Background: Bushen Zhuanggu Formula (BZF), derived from the classic Yougui Pills, has shown favorable clinical efficacy in treating advanced nontraumatic osteonecrosis of the femoral head (NONFH), particularly by promoting bone repair. However, its underlying mechanisms remain unclear. Objective: This study aimed to explore the mechanisms by which BZF promotes bone repair in advanced NONFH. Materials and methods: A total of 518 potential BZF targets were identified from the ETCM v2.0 database. Transcriptomic profiling of clinical cohorts revealed 485 differentially expressed genes in advanced NONFH patients compared to healthy controls. A drug target-disease gene interaction network was constructed to identify candidate BZF targets involved in NONFH pathogenesis. In vivo experiments were conducted to validate the effects of BZF in a rat model of advanced NONFH. Results: Network analysis identified key pathways associated with blood circulation obstruction, immune-inflammatory imbalance, and abnormal bone metabolism. Protein kinase C alpha (PKCA), Ras proto-oncogene (RAS), mitogen-activated protein kinase 3(ERK), ETS proto-oncogene 1 (ETS1), and receptor activator of nuclear factor-κB ligand (RANKL) formed a signaling axis implicated in NONFH pathogenesis. BZF treatment alleviated joint inflammation, preserved trabecular bone morphology, reduced bone loss, and promoted bone repair. Mechanistically, BZF significantly downregulated the expression of PKCA, RAS, ERK, ETS1, and RANKL, improved blood circulation, and inhibited osteoclast activation while promoting osteoblast activation. Conclusion: BZF may promote bone repair in advanced NONFH by enhancing blood circulation and modulating the PKC-RAS-ERK-ETS1-RANKL signaling axis, thereby reversing dysregulated bone metabolism.

Toxoplasma gondii is an intracellular protozoan pathogen with a global distribution,and dogs are considered a potential risk factor for human toxoplasmosis.This study was aimed at systematically analyzing the epidemiological characteris-tics of canine T.gondii infection in China from 1987 to 2023,to provide a scientific basis for the prevention and control of T.gondii in the country.Epidemiological data on canine T.gondii infections in China from 1987 to 2023 were retrieved from PubMed,China National Knowledge Infrastructure(CNKI),Wanfang Database,and Baidu Scholar.A database was established with Excel,and the data were visualized with ArcGIS 10.2 software.Statistical analysis was performed in SPSS 26.0 software,and group differences were analyzed with the X2 test.A P-value of＜0.05 was considered statistically significant.From 1987 to 2023,the overall seroprevalence of T.gondii antibodies in dogs in China remained stable,and the overall sero-prevalence rate was 13.97％.Yunnan Province had the highest seroprevalence,at 27.65％,whereas Shaanxi Province had the lowest seroprevalence,at 0.56％.Significant differences were observed among provinces(P＜0.05).Epidemiological data on canine T.gondii infections were not available for some regions.The seroprevalence in southwestern China was significantly higher than that in other regions(P＜0.05).A comparison of the seroprevalence between 1987-2004 and 2005-2023 revealed significant differences(P＜0.05).Canine T.gondii infection is widespread in China and shows a stable epidemic cycle.Appro-priate prevention and control measures should be implemented,along with strengthened surveillance of T.gondii outbreaks.Public education on the prevention and control of toxoplasmosis should be enhanced to decrease transmission risk and safeguard public health.

This study was aimed at understanding the prevalence and influencing factors of food-borne parasitic diseases in ethnic minority areas of Guizhou Province,to provide a scientific basis for the development of appropriate intervention measures based on the human-animal-environment One Health concept.In 2023,the infection status of the human population,reservoir hosts,intermediate hosts,food-borne parasitic diseases,and related social and environmental factors were investigated in Congjiang County in Qidongnan Miao and Dong Autonomous Prefecture;Luodian County in Qiannan Buyi and Miao Autonomous Prefecture;and Ceheng County in Qianxinan Buyi and Miao Autonomous Prefecture.At least 1 000 individuals were sampled from each county,along with at least 50 insect-protected host samples from each location.Food-borne parasite infections were detected with the modified Kato thick smear method.A questionnaire survey was administered to the population.Detection of food-borne parasitic metacercariae was performed in intermediate host fish through the flaking and digestion method,and in crabs through the pounding and sedimentation method.The chi-square test was used to compare rates,and logistic regression was applied for multivariate analysis.A total of 3 023 questionnaires and fecal samples were collected.Males accounted for 47.50%,females accounted for 52.50%,and members of ethnic minorities accounted for 96.06%.A total of 186 food-borne parasitic infections were identified,and the infection rate was 6.15%.Five insect species were detected,which showed an infection rate of 5.39%.The infection rate of Clonorchis sinensis was 0.33%,that of Taenia was 0.40%,that of Heteroceles was 0.17%,that of Acanthus was 0.17%,and that of Echinostoma was 0.03%.Human infections with Echinostomus colloides and Echinostomia transferoris had not previously been reported in China.Single-factor analysis revealed statistically significant differences in food-borne parasite infections according to various factors,including the consumption of untreated water,raw fish and shrimp,raw pig blood,raw cow gastric juice,and raw pork and beef,as well as raw pig and cow viscera(P<0.05).Multivariate analysis indicated that the risk factors for food-borne parasite infections among residents in minority areas of Guizhou Province included the consumption of raw pig blood(OR=2.841,95%CI:1.809-4.463),raw cow gastric juice(OR=2.122,95%CI:1.297-3.469),and raw fish and shrimp(OR=1.779,95%CI:1.049-3.018).A total of 173 fecal samples of the reservoir host were examined,which showed a rate of food-borne parasite infection of 5.2%.A total of 510 intermediate host fish were examined,which showed a 4.51%positivity rate of encysted metacercaria of Clonorchis sinensis.The crab,pig,and beef samples were not positive.In conclusion,food-borne parasitic infections were prevalent in ethnic minority regions of Guizhou Province,and consumption of raw food were influencing factors.A focus on populations with raw food consumption habits,including raw pig blood,cow gastric juice,fish and shrimp,is essential.Concurrently,monitoring of animal hosts must be strengthened to perform key interventions according to the One Health concept.

Objective To analyze the influence of clinical indicators and PPARD gene polymorphism on the hypoglycemic efficacy of dapagliflozin(DAPA)in patients with type 2 diabetes mellitus(T2DM).Methods A total of 102 patients with T2DM who visited the Affiliated Hospital of Jiangnan University and the Affiliated Hospital of Xuzhou Medical University from June 2021 to December 2023 were selected as the study subjects,an observational cohort of T2DM patients treated with DAPA was established,and DAPA tablets of 10 mg were administered orally once a day for 12 weeks,the venous blood and clinical data of the patients were col-lected.PPARD gene polymorphism typing was performed by using the Snapshot method.The differences in clinical indicators among patients with different genotypes were compared and the influencing factors of DA-PA in improving insulin resistance index(HOMA-IR)were analyzed.Results Eighty-two patients completed the 12-week follow-up.Before DAPA treatment,the differences in the clinical indicators of patients with dif-ferent PPARD rs3777744 genotypes were not statistically significant(P＞0.05).After 12 weeks of DAPA treatment,compared with patients with AA genotype,patients carried of G allele(GG+AG genotype)had lower levels of fasting blood glucose(FPG),HOMA-IR and its decrease,and the differences were statistically significant(P＜0.05).The results of multiple linear regression analysis showed that the genotype of PPARD rs3777744 locus and baseline HOMA-IR were correlated with the improvement of HOMA-IR after 12 weeks of DAPA treatment,and the improvement of HOMA-IR in G allele carriers was not as significant as that in AA genotype patients,and the higher the baseline HOMA-IR,the more significant the improvement of HO-MA-IR.Conclusion Different genotypes of PPARD rs3777744,baseline HOMA-IR are important influencing factors for DAPA to improve insulin resistance of T2DM patients.

Background:Bushen Zhuanggu Formula(BZF),derived from the classic Yougui Pills,has shown favorable clinical efficacy in treating advanced nontraumatic osteonecrosis of the femoral head(NONFH),particularly by promoting bone repair.However,its underlying mechanisms remain unclear.Objective:This study aimed to explore the mechanisms by which BZF promotes bone repair in advanced NONFH.Materials and methods:A total of 518 potential BZF targets were identified from the ETCM v2.0 database.Transcriptomic profiling of clinical cohorts revealed 485 differentially expressed genes in advanced NONFH patients compared to healthy controls.A drug target-disease gene interaction network was constructed to identify candidate BZF targets involved in NONFH pathogenesis.In vivo experiments were conducted to validate the effects of BZF in a rat model of advanced NONFH.Results:Network analysis identified key pathways associated with blood circulation obstruction,immune-inflammatory imbalance,and abnormal bone metabolism.Protein kinase C alpha(PKCA),Ras proto-oncogene(RAS),mitogen-activated protein kinase 3(ERK),ETS proto-oncogene 1(ETS1),and receptor activator of nuclear factor-κB ligand(RANKL)formed a signaling axis implicated in NONFH pathogenesis.BZF treatment alleviated joint inflammation,preserved trabecular bone morphology,reduced bone loss,and promoted bone repair.Mechanistically,BZF significantly downregulated the expression of PKCA,RAS,ERK,ETS1,and RANKL,improved blood circulation,and inhibited osteoclast activation while promoting osteoblast activation.Conclusion:BZF may promote bone repair in advanced NONFH by enhancing blood circulation and modulating the PKC-RAS-ERK-ETS1-RANKL signaling axis,thereby reversing dysregulated bone metabolism.

Objective:To develop an artificial intelligence (AI)-based platelet review strategy to identify abnormal platelet histograms with no significant difference between initial impedance platelet count (PLT-I) and PLT-F results.Methods:This study included 5 119 routine blood analysis in Nanfang Hospital of Southern Medical University and its Ganzhou branch from July 2023 and March 2024. Specimens exhibiting abnormal platelet histograms and an initial platelet count >40×10?/L underwent review using the fluorescent platelet count (PLT-F) channel. Consistency of the results was defined as a difference between impedance platelet count (PLT-I) and PLT-F less than ±20% of the PLT-F results. A deep learning model was developed using platelet and red blood cell histogram data from a training set of 3 807 specimens. The model′s diagnostic performance was evaluated on an independent external validation set ( n=805) using receiver operating characteristic (ROC) curve analysis. Changes in the number of reviewed samples and sample turnaround time were analyzed to assess its clinical utility. Results:The deep learning model based on platelet and red blood cell histograms achieved an area under the ROC curve (AUC) of 0.854 in the training set. At a cutoff value of 0.1, the sensitivity was 0.954 and specificity was 0.358. The model could reduce review by 16.80% (190/1 131). In the validation set, the AUC was 0.805, with a sensitivity of 0.955 and specificity of 0.307, corresponding to a reduction of 17.41% (47/270) in reviewed specimens.Conclusion:The platelet review prediction model developed based on deep learning technology can efficiently identify samples with consistent results before and after review, reducing unnecessary reviews and shortening specimen testing time, thereby improving the efficiency of platelet test.