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.

ObjectiveTo investigate the mechanism of action of Jiedu Huayu granules in improving liver injury in mice with acute liver failure （ALF） by observing its effect on a mouse model of ALF after prophylactic administration， and to provide a basis for clinical medication. MethodsA total of 60 specific pathogen-free male C57BL/6J mice were divided into normal group， model group， Jiedu Huayu granules group （JDHY group）， and farnesoid X receptor （FXR） agonist （GW4064） group using a random number table， with 15 mice in each group. The model of ALF was induced by a single intraperitoneal injection of D-galactosamine combined with lipopolysaccharide. The mice in the JDHY group were given prophylactic administration of 0.3 g/mL drug solution of Jiedu Huayu granules by gavage for 3 days before modeling， those in the normal group and the model group were given 0.9% NaCl solution by gavage， and those in the GW4064 group were given intraperitoneal injection of GW4064 for 3 consecutive days before modeling. The mice were sacrificed after modeling， and serum and liver tissue samples were collected. A veterinary automatic biochemical analyzer was used to measure the serum levels of total bilirubin （TBil）， total bile acids （TBA）， gamma-glutamyl transferase （GGT）， alanine aminotransferase （ALT）， and aspartate aminotransferase （AST） in mice from each group； HE staining was used to observe liver pathological changes； RT-PCR was used to measure the mRNA expression levels of FXR， fibroblast growth factor 15 （FGF15）， fibroblast growth factor receptor 4 （FGFR4）， small heterodimer partner （SHP）， and bile salt export pump （BSEP） in mice， and Western blot was used to measure the protein expression levels of FXR， FGF15， FGFR4， SHP， and BSEP. A one-way analysis of variance was used for comparison between groups， and the Dunett method was used for further comparison between two groups. ResultsCompared with the normal group， the model group had significant increases in the serum levels of TBil， ALT， AST， TBA， and GGT （all P<0.01）， and compared with the model group， the JDHY group and the GW4064 group had significant reductions in the serum levels of TBil， ALT， AST， TBA， and GGT （all P <0.01）. HE staining showed that compared with the model group， the JDHY group and the GW4064 group had milder pathological injury， a reduction in the area of hepatocyte necrosis， and alleviation of cellular swelling and edema. Compared with the normal group， the model group had significant reductions in the mRNA and protein expression levels of FXR， FGF15， FGFR4， SHP， and BSEP in liver tissue （all P <0.01）， and compared with the model group， the JDHY group and the GW4064 group had significant increases in the mRNA and protein expression levels of FXR， FGF15， FGFR4， SHP， and BSEP in liver tissue （all P <0.05）. ConclusionJiedu Huayu granules may alleviate liver injury in mice with ALF through the FXR/SHP axis.

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.

China has entered the ranks of “moderately aging” countries. The aging of the population is accompanied by the phenomenon of a low birth rate， with insufficient newborns and an increasing proportion of the elderly population， resulting in aging issues persisting. Conversely， the low birth rate has further exacerbated the degree of aging， promoting China’s fertility policy to focus on constructing an effective fertility support policy system to comprehensively enhance the fertility willingness of the reproductive-age groups. At the current stage， China’s fertility support policy system seeks to reduce the costs of childbearing， alleviate family child-rearing burdens， ease work-family conflicts， and meet childcare needs for reproductive-age individuals， infants， and caregivers through providing economic， time， and service support. However， numerous issues persist throughout the process from design to application and implementation， such as policy fragmentation， unclear responsibilities， and mismatched supply and demand. The current policy objectives tend to emphasize population regulation， lacking systematic support for the entire lifecycle of families. Furthermore， China’s fertility support policies have partially drawn on international experience and undergone localized adjustments， receiving varying degrees of feedback. This validates that research should focus on the heterogeneous effects of fertility support policies on the fertility willingness of different reproductive-age groups and identify their specific fertility needs. This approach would facilitate more precise policy implementation and more effectively construct the entire lifecycle fertility support policy system， thereby achieving high-quality development of the population.

Objective To investigate the epidemiological characteristics of knee osteoarthritis(KOA)among military personnel in plateau regions and to explore its risk factors.Methods From July 2023 to July 2024,a multi-stage stratified cluster random sampling method was employed to survey the prevalence of KOA and related risk factors among military personnel in the northwest plateau regions of China,covering different altitudes(1500-4500 m)and geographical areas(Gansu,Qinghai,Tibet,and Xinjiang).All study subjects were divided into KOA and non-KOA groups based on the presence or absence of KOA.Variables including age,gender,body mass index(BMI),education level,smoking status,military rank,military branch,service duration,regional altitude,annual average temperature,training duration,perceived training intensity,and history of knee injury were selected for univariate analyses between groups.Variables with P<0.05 in the univariate analyses were included in the binary multifactor logistic regression to identify risk factors for KOA.Results A total of 3000 questionnaires were distributed,and 2854 valid questionnaires were collected,with a response rate of 95.13%.The sample included 2584 males and 270 females,with 510 cases of KOA,resulting in a prevalence rate of 17.9%.Univariate analysis showed that there were statistically significant differences between KOA and non-KOA groups in terms of age,BMI,smoking status,military rank,military branch,service duration,regional altitude,annual average temperature,training duration,perceived training intensity,and history of knee injury(P<0.05).However,no significant differences were found in gender and education level(P>0.05).Binary multivariate logistic regression analysis revealed that older age(OR=1.382,P=0.017),higher BMI(P<0.01),smoking(OR=1.929,P<0.01),higher military rank(OR=1.485,P=0.007),being a member of the Armed Police(P<0.01),longer service duration(P<0.01),higher regional altitude(OR=1.459,P<0.01),lower annual average temperature(OR=1.188,P=0.001),longer training duration(P<0.01),higher perceived training intensity(OR=2.450,P<0.01),and history of knee injury(OR=2.768,P=0.002)were independent risk factors for KOA.Conclusions Older age,overweight/obesity,smoking,higher military rank,being a member of the Armed Police,longer service duration,higher altitude,cold climate,longer training duration,higher training intensity,and history of knee injury are independent risk factors for KOA among military personnel in the northwest plateau regions of China.

A method was developed for determination of dilauryl thiodipropionate(DLTDP)in fried foods by coupling solid-phase extraction(SPE)pretreatment with reverse-phase liquid chromatography-tandem mass spectrometry(RPLC-MS/MS)detection.Samples were extracted with n-hexane as the solvent,purified using a neutral alumina SPE cartridge,and finally analyzed by RPLC-MS/MS.Quantitative analysis was performed using matrix-matched calibration curves combined with an external standard method under optimal experimental conditions.The results showed that DLTDP exhibited good linearity in the range of 2.0-50.0 μg/L,with a correlation coefficient(R2)≥0.999.The limit of detection(LOD)and the limit of quantification(LOQ)of the method were 0.15 mg/kg and 0.5 mg/kg,respectively.The mean recoveries at three fortification levels(0.5,1.0,and 200 mg/kg)in different samples ranged from 84.8%to 96.8%,with the relative standard deviations(RSDs)all less than 8.0%.The developed method was highly sensitive,accurate and reliable,and easy to operate,making it well suited for the routine quantitative analysis of DLTDP in fried foods.

Objective To understand the current situation of healthcare-associated infection(HAI)in China,pro-vide data support and decision-making basis for formulating scientific and effective strategies for HAI prevention and control.Methods A nationwide cross-sectional survey on HAI was conducted among various types and levels of medical institutions in China according to a unified protocol of bedside surveys and case investigations.Results In 2024,a total of 5 736 medical institutions and 2 751 765 patients were surveyed.Among them,34 889 HAI cases were identified,with a prevalence rate of 1.27％.The number of HAI episodes was 38 032,and case prevalence rate was 1.38％.The prevalence rate of HAI in medical institutions in different regions of China ranged from 0.66％to 2.35％.Among medical institutions of different scales,those with a bed capacity of ≥900 had the high-est incidence of HAI,reaching 1.65％.The most common infection site was the lower respiratory tract(44.66％),followed by the urinary tract(12.94％),surgical site(9.32％),upper respiratory tract(7.02％),and bloodstream infection(5.78％).The top 3 departments with the highest HAI rates were the general intensive care unit(10.02％),department of neurosurgery(5.51％),and department(group)of hematology(5.34％).A total of 23 238 strains of HAI pathogens were detected,with 10 714 strains(46.10％)from lower respiratory tract speci-mens.The top 5 detected strains were Klebsiella pneumoniae(14.76％),Pseudomonas aeruginosa(13.33％),Escherichia coli(12.79％),Acinetobacter baumannii(9.23％),and Staphylococcus aureus(7.88％).231 944 pa-tients underwent class Ⅰ incision surgery were monitored,with 1 647 cases experienced surgical site infection,and the prevalence rate of surgical site infection was 0.71％.The number of patients who should undergo pathogen de-tection(patients receiving therapeutic and therapeutic combined prophylactic antimicrobial agents)was 715 179,while the actual number was 480 492,with a pathogen detection rate of 67.18％.425 225 patients received patho-genic detection before treatment,with a detection rate of 59.46％.Conclusion The overall HAI prevalence in Chi-na is lower,showing disparities among medical institutions of different regions and scales.Therefore,precise imple-mentation of measures is necessary for HAI prevention and control,with a focus on high-risk institutions and high-risk departments,key areas,and critical procedures.All levels of medical institutions should continuously reduce the incidence of HAI by strengthening monitoring,standardizing the use of antimicrobial agents,and reinforcing basic HAI prevention and control measures.

Objective To observe the effect of a Jianpi Huayu Jiedu formula on the NLRP3-mediated pyroptosis pathway in gastric precancerous lesion(GPL)associated with Helicobacter pylori(Hp)infection.Methods A GPL mouse model was prepared using Hp suspension gavage combined with Atp4a gene-deficient mice.The Jianpi Huayu Jiedu formula was administered as an intervention.Gastric mucosal tissue pathological damage was observed using hematoxylin and eosin(HE)staining.The presence of intestinal metaplasia(IM)was assessed using Alcian Blue-Periodic Acid Schiff(AB-PAS)staining.Ultrastructural changes in cell organelles were observed using transmission electron microscopy.Enzyme-linked immunosorbent assay(ELISA)was used to measure levels of gastrin-17(G-17),pepsinogen I(PGI),and proinflammatory cytokines IL-1β and IL-18.The expression of molecules related to the pyroptosis pathway was detected using Western blot and real-time quantitative PCR(RT-qPCR).Results Compared to the control group,Hp-related GPL mice exhibited gastric mucosal atrophy accompanied by IM and dysplasia.Damage to mitochondria and endoplasmic reticulum in parietal cells was observed.Levels of G-17,PGI,and proinflammatory cytokines IL-1β and IL-18 were elevated.The expression of molecules related to the pyroptosis pathway was increased.The Jianpi Huayu Jiedu formula significantly reduced gastric mucosal tissue pathological damage in GPL mice,decreased G-17 and PGI levels,mitigated inflammatory responses,and downregulated the expression of molecules related to the pyroptosis pathway.Conclusion The Jianpi Huayu Jiedu formula may exert its effects by inhibiting the NLRP3-mediated pyroptosis signaling pathway,thereby alleviating or even reversing the pathological damage of gastric mucosa in Hp-related GPL.

Objective To explore the impact factors of misjudge of pneumoconiosis stage based on GBZ70-2015 criteria.Methods Sixty-five case of pneumoconiosis were retrospectively enrolled.Chest high-resolution CT were independently analyzed by one associate chief physician,one attending physician and one resident physician,respectively.The stage of pneumoconiosis was judged,and the inter-observer consistency was observed.Taken the results of 5 experts using Delphi method as gold standard,the impact factors of misjudge of pneumoconiosis stage were analyzed.Results Among 65 cases,there were 42 cases of stage 1,16 cases of stage 2 and 7 cases of stage 3 pneumoconiosis.The inter-observer consistency of pneumoconiosis stage was relatively high(Kappa=0.653),of stage 3 pneumoconiosis was the best(Kappa=0.803),followed by stage 1(Kappa=0.661),while of stage 2 was the lowest(Kappa=0.518).Radiologist lack experience(OR=4.872),the lesions mainly located in the posterior upper area of lungs(OR=2.317),the present of r-type small shadows(OR=3.105)and high CT pulmonary emphysema index(OR=1.214)were all independent impact factors of misjudge of pneumoconiosis stage(all P＜0.05).Conclusion Both the experience of radiologist and lesions'heterogeneity were main impact factors of misjudge of pneumoconiosis stage based on GBZ70-2015 criteria.