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 identify early clinical predictors associated with dysphagia and delayed swallowing recovery in patients with traumatic brain injury (TBI). Methods: In this retrospective study, we enrolled adult TBI patients admitted to the rehabilitation unit of a tertiary medical center between June 2019 and June 2023. Data on baseline characteristics, neurological status, imaging findings, and rehabilitation-related variables were collected. Swallowing function was assessed using two indicators: (1) nasogastric (NG) tube retention and (2) the Functional Oral Intake Scale (FOIS) scores at 1, 4, and 12 weeks post-injury. Regression analyses were conducted to identify predictors associated with dysphagia and swallowing recovery. Results: A total of 160 patients were included. At 1 week post-injury, longer intensive care unit (ICU) stay, poor initial sitting balance and use of sedative medication in ICU were associated with NG tube retention. At 4 weeks, lower initial Rancho Los Amigos Scale (RLAS) scores, immobility-related complications, longer hospitalization, and temporal lobe hematomas were associated with persistent NG tube dependence. By 12 weeks, older age, delayed ability to follow commands, and poor initial sitting balance remained associated with NG tube retention. FOIS outcomes were also associated with older age, delayed time to follow commands, impaired initial sitting balance, prolonged ICU stay, temporal lobe hematomas, lower initial RLAS scores, immobility-related complications, prolonged endotracheal tube placement and extended hospital stays. Conclusion Impaired cognitive status, poor physical function, immobility-related complications, and temporal lobe hematomas were key factors associated with dysphagia and delayed oral intake in individuals with TBI.

Aim To study the mechanism of salidro-side combined with rosavin in rats with ischemic stroke.Methods The MCAO rats was established by using thread-embolic method.The rats were divided into the sham group,MCAO group,salidroside com-bined with rosavin group,and positive control group;the drug was given continuously for seven days.Western blot was used to detect apoptosis indicators.Proteomics was used to analyse differential proteins(DEPs).STEP receptor inhibitor was injected into the lateral ventricles,the rats were administered for seven days,then the apoptosis indicators were detected.Re-sults Salidroside combined with rosavin could reduce neurological function scores in MCAO rats and inhibit cell apoptosis.Quantitative proteomics identified 496 DEPs in brain tissue and discovered core proteins STEP,p38,and CRTC1.Salidroside combined with rosavin could promote the STEP and CRTC1 while in-hibiting p38 protein.After treatment with STEP inhibi-tor,those effects were reversed.Conclusion Salidro-side combined with rosavin can inhibit cell apoptosis in MCAO rats,which is closely related to the regulation of the STEP/p38/CRTC1 signaling pathway.

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.

Objective To observe the clinical efficacy and safety of dexmedetomidine injection combined with esketamine injection in the treatment of patients with oral squamous cell carcinoma radical resection.Methods Patients with oral squamous cell carcinoma radical resection were randomly divided into treatment and control groups.The treatment group will receive intravenous administration of 0.6 μg·kg-1 dexmedetomidine 10 minutes before anesthesia induction.Subsequently,anesthesia induction will be performed with intravenous administration of 0.5 mg·kg-1 esketamine.Anesthesia maintenance will be achieved with intravenous infusion of 0.25 mg·kg-1·h-1 esketamine and 0.3 μg·kg-1·h-1 dexmedetomidine used an infusion pump.The control group will receive intravenous administration of an equivalent volume of 0.9％NaCl 10 minutes before anesthesia induction.Anesthesia induction will then be performed with intravenous administration of 2.5-5.0 μg·kg-1 fentanyl.Anesthesia maintenance will involve intravenous infusion of 0.10-0.25 μg·kg-1·min-1 remifentanil used an infusion pump.The anesthesia effectiveness,analgesic effectiveness,hemodynamics and safety were compared between the two groups.Results Treatment group were enrolled 62 cases,1 case dropped out,and 61 cases were finally included in the statistical analysis.Control group were enrolled 61 cases,1 case dropped out,and 60 cases were finally included in the statistical analysis.The recovery room stay time of treatment and control groups was(25.97±4.52)and(18.39±3.64)min,the extubation time was(16.75±4.84)and(10.16±3.18)min,and the differences were statistically significant(all P＜0.05).After operation 24 h,visual analogue scores of treatment and control groups were(0.85±0.17)and(1.39±0.25)points,adrenocorticotropin levels were(60.07±7.13)and(72.64±9.81)pg·mL-1,cortisol levels were(481.20±49.15)and(539.94±57.77)nmol·L-1,and the differences were statistically significant(all P＜0.05).The mean arterial pressure at 30 min after anesthesia induction(T1)and at the end of surgery(T2)in treatment group were(82.34±4.98)and(86.57±4.18)mmHg,while those in control group were(77.25±7.16)and(76.02±6.29)mmHg;the heart rates of T1 and T2 in treatment groups were(64.08±4.19)and(66.45±4.83)time·min-1,while those in control group were(68.44±6.02)and(72.08±7.27)time·min-1;and the differences were statistically significant(all P＜0.05).The adverse drug reactions in two groups were nausea,vomiting,bradycardia and dizziness.The total incidences of adverse drug reactions in treatment and control groups were 8.20％and 15.00％,without significant difference(P＞0.05).Conclusion Dexmedetomidine injection combined with esketamine injection has a definitive analgesia efficacy in the treatment of patients with oral squamous cell carcinoma radical resection,which can significantly reduce stress responses,maintain hemodynamic stability,without increasing the incidence of adverse drug reactions.

Objective:Using ecological niche width and situation model,the operation and management of public Traditional Chinese Medicine(TCM)hospitals in 16 cities in Shandong Province were evaluated to put forward countermeasures.Methods:The relevant index data of public TCM hospitals in 16 cities in Shandong Province from 2019 to 2022 were collected to analyze the development status.The ecological niche width model and niche situation model were applied to evaluate the operation and management of public TCM hospitals in 16 cities in Shandong.Results:The top three public TCM hospitals in terms of average ecological niche width were City G,City P and City F.The ecological niche situation value of public TCM hospitals in City K,City A and City J ranked the top three.Conclusion:The dimensions of operation management structure of public TCM hospitals in Shandong need to be enhanced and the development of operation management space needs to be balanced.It is feasible to evaluate operation management by using ecological niche width and situation model.It is suggested to improve the allocation efficiency of health personnel,facilities,funds and other resources;promote the spatial balanced development of operation management;promote the application of ecological niche width and situation model in the field of medicine and health management.