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.

ObjectiveObesity, a global chronic metabolic disease, is closely associated with disruptions in lipid metabolism and gut microbiota. Current intervention strategies still have limitations in terms of safety and microecological regulation, necessitating the exploration of novel natural regulatory approaches. Based on the early pathological characteristics of obesity, this study innovatively employs a rectal delivery method alongside a high-fat diet (HFD)-induced obesity model to systematically evaluate the inhibitory effects, safety, and gut microbiota regulation mechanisms of leek-derived and konjac-derived extracellular vesicles on obesity development. By simulating early clinical intervention scenarios, this study aims to explore the preventive potential of plant-derived extracellular vesicles during the initial stages of obesity onset. MethodsExtracellular vesicles from leek and konjac were isolated using ultracentrifugation combined with density gradient centrifugation. Their nanoscale properties were characterized by dynamic light scattering (DLS), transmission electron microscopy (TEM), and nanoparticle tracking analysis (NTA). Male C57BL/6J mice were randomly divided into four groups: normal control (NC), high-fat diet (HFD), leek-derived extracellular vesicles (LEVs), and konjac-derived extracellular vesicles (KEVs). Beginning simultaneously with HFD feeding, mice in the intervention groups received 20 g/L vesicles rectally every 3 d for 4 weeks. Body mass and body composition were monitored throughout. At endpoint, mouse serum, adipose tissue, and colonic contents were collected. Serum biochemical indices (lipid profile, liver and kidney function, cardiac markers) were assessed to evaluate safety and metabolic efficacy, while 16S rRNA sequencing was employed to analyze gut microbial structure and diversity. ResultsDLS, NTA, and TEM confirmed that both LEVs and KEVs exhibited typical cup-shaped nanostructures with average particle sizes of approximately 284 nm and 223 nm, respectively. LEVs and KEVs treatment significantly suppressed HFD-induced weight gain and elevation of body-fat percentage (P<0.05), and reduced accumulation of abdominal white and epididymal adipose tissue. Serological analyses showed that both vesicles lowered total cholesterol, triglycerides and LDL-cholesterol, and ameliorated liver enzyme profiles (ALT, AST), demonstrating lipid-metabolic regulation and hepatoprotective effects. No hepatic, renal or cardiac dysfunction was observed, indicating favorable safety. Gut microbiota analyses revealed that vesicle intervention partially restored HFD-depleted microbial diversity and reshaped community structure. Notably, LEVs markedly increased the relative abundance of the beneficial taxon Lachnospiraceae at the family level, which is known for producing short-chain fatty acids and enhancing intestinal barrier function. Furthermore, Phylogenetic Investigation of Communities by Reconstruction of Unobserved States (PICRUSt) functional prediction suggested that LEVs and KEVs modulated gut microbial functions through distinct mechanisms: LEVs downregulated pathways related to ribosomes and DNA replication while enhancing xenobiotic degradation, whereas KEVs tended to upregulate energy metabolism and protein synthesis toward healthy levels. ConclusionRectally administered LEVs and KEVs exhibit excellent safety and pronounced metabolic benefits during the early phase of obesity, suppressing weight gain, correcting lipid dysregulation, and exerting effects via modulation of gut microbial composition and function. This study provides systematic experimental evidence supporting plant-derived exosome-like vesicles as an early intervention strategy against obesity.

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.

ObjectiveObesity, a global chronic metabolic disease, is closely associated with disruptions in lipid metabolism and gut microbiota. Current intervention strategies still have limitations in terms of safety and microecological regulation, necessitating the exploration of novel natural regulatory approaches. Based on the early pathological characteristics of obesity, this study innovatively employs a rectal delivery method alongside a high-fat diet (HFD)-induced obesity model to systematically evaluate the inhibitory effects, safety, and gut microbiota regulation mechanisms of leek-derived and konjac-derived extracellular vesicles on obesity development. By simulating early clinical intervention scenarios, this study aims to explore the preventive potential of plant-derived extracellular vesicles during the initial stages of obesity onset. MethodsExtracellular vesicles from leek and konjac were isolated using ultracentrifugation combined with density gradient centrifugation. Their nanoscale properties were characterized by dynamic light scattering (DLS), transmission electron microscopy (TEM), and nanoparticle tracking analysis (NTA). Male C57BL/6J mice were randomly divided into four groups: normal control (NC), high-fat diet (HFD), leek-derived extracellular vesicles (LEVs), and konjac-derived extracellular vesicles (KEVs). Beginning simultaneously with HFD feeding, mice in the intervention groups received 20 g/L vesicles rectally every 3 d for 4 weeks. Body mass and body composition were monitored throughout. At endpoint, mouse serum, adipose tissue, and colonic contents were collected. Serum biochemical indices (lipid profile, liver and kidney function, cardiac markers) were assessed to evaluate safety and metabolic efficacy, while 16S rRNA sequencing was employed to analyze gut microbial structure and diversity. ResultsDLS, NTA, and TEM confirmed that both LEVs and KEVs exhibited typical cup-shaped nanostructures with average particle sizes of approximately 284 nm and 223 nm, respectively. LEVs and KEVs treatment significantly suppressed HFD-induced weight gain and elevation of body-fat percentage (P<0.05), and reduced accumulation of abdominal white and epididymal adipose tissue. Serological analyses showed that both vesicles lowered total cholesterol, triglycerides and LDL-cholesterol, and ameliorated liver enzyme profiles (ALT, AST), demonstrating lipid-metabolic regulation and hepatoprotective effects. No hepatic, renal or cardiac dysfunction was observed, indicating favorable safety. Gut microbiota analyses revealed that vesicle intervention partially restored HFD-depleted microbial diversity and reshaped community structure. Notably, LEVs markedly increased the relative abundance of the beneficial taxon Lachnospiraceae at the family level, which is known for producing short-chain fatty acids and enhancing intestinal barrier function. Furthermore, Phylogenetic Investigation of Communities by Reconstruction of Unobserved States (PICRUSt) functional prediction suggested that LEVs and KEVs modulated gut microbial functions through distinct mechanisms: LEVs downregulated pathways related to ribosomes and DNA replication while enhancing xenobiotic degradation, whereas KEVs tended to upregulate energy metabolism and protein synthesis toward healthy levels. ConclusionRectally administered LEVs and KEVs exhibit excellent safety and pronounced metabolic benefits during the early phase of obesity, suppressing weight gain, correcting lipid dysregulation, and exerting effects via modulation of gut microbial composition and function. This study provides systematic experimental evidence supporting plant-derived exosome-like vesicles as an early intervention strategy against obesity.

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: As Taiwan’s population ages, falls among older adults have become a critical public health concern. However, limited data exist regarding temporal trends and injury patterns in fall-related emergency department (ED) visits. This study aimed to examine trends in fall-related ED visits and hospitalizations among older adults in Taiwan and to explore injury distributions by age group. Methods: We conducted a retrospective cohort study using data from the National Taiwan University Hospital between 2011 and 2017. Patients aged ≥65 years were compared with those aged 20–64 years. Fall-related visits were identified using chief complaints and the International Classification of Diseases 9th/10th revision (ICD-9/ICD-10) codes. Outcomes included hospitalization rates, length of stay, and 30-day mortality. Results: A total of 22,471 fall-related ED visits were analyzed. While visits among younger adults declined (annual growth rate, -1.34%), visits among older adults increased (2.37% annually), with the steepest rise in those aged ≥85 years. Hospitalization occurred in 27.1% of older adults, nearly double that of younger adults (14.4%). Older adults also had longer hospital stays and higher 30-day mortality rates, findings consistent even when restricted to lower limb fractures. Conclusion Fall-related ED visits and hospitalizations are rising disproportionately among Taiwan’s older population. Targeted prevention strategies and transitional care interventions are urgently needed to address the growing clinical and economic burden of falls in aging societies.

Objective: To develop and validate a clinically actionable prediction model for postoperative pulmonary complications (PPCs) in cardiac surgery patients, focusing on modifiable preoperative risk factors amenable to targeted optimization. Methods: In this prospective observational cohort study, 492 adults undergoing open-chest cardiac surgery between August 15, 2023 and December 31, 2023 were analyzed. Prespecified predictors included gas exchange variables, pulmonary function, inspiratory muscle strength, and physical performance. Univariable and multivariable logistic regression analyses were used to develop the prediction model. Discrimination was assessed by the area under the receiver operating characteristic curve (AUC). Results: A total of 90 patients (14.1%) developed PPCs after surgery. Five independent predictors were identified: elevated arterial PaCO2 (odds ratio [OR] 1.12, 95% confidence interval [CI] 1.00–1.26), oxygen desaturation (SpO2<93%) (OR 12.47, 95% CI 3.51–48.13), reduced gait speed (OR 0.17, 95% CI 0.04–0.71), lower FEV1/FVC ratio (OR 0.96, 95% CI 0.92–1.00), and diminished inspiratory muscle strength (MIP % predicted) (OR 0.96, 95% CI 0.92–0.99). The model demonstrated good discriminative ability with an AUC of 0.86 (95% CI 0.80–0.93) in the training cohort and 0.87 (95% CI 0.74–0.93) in the validation cohort. Conclusion This parsimonious model achieved high predictive accuracy using five modifiable physiological variables. By targeting abnormalities in gas exchange, pulmonary mechanics, muscle strength, and functional reserve, the model offers a practical tool to guide individualized prehabilitation strategies for reducing PPC risk in cardiac surgery patients.

OBJECTIVE To identify the active components of Desmodium styracifolium （Osb.） Merr. extract （DME） against cholestatic liver injury （CLI）， and to verify its target and molecular mechanism， so as to provide scientific evidence for the clinical application of D.styracifolium （Osb.） Merr. and the development of new hepatoprotective agents.METHODS High-performance liquid chromatography was applied to establish fingerprints of 19 batches of DME， followed by similarity evaluation and identification of common peaks. An in vitro CLI model was constructed by lithocholic acid （LCA）-induced injury in HepG2 cells to evaluate the in vitro protective effect of DME against CLI. Spearman correlation analysis， grey relational analysis and partial least-squares regression analysis were adopted to investigate the spectrum-effect relationship of DME against CLI and preliminarily screen the core active components. Molecular docking and surface plasmon resonance technology were used to predict and verify the binding capacity between core active components and farnesoid X receptor （FXR）. qRT-PCR， Western blot and immunofluorescence staining tests were performed to validate the influences of core active components on the expression of molecules related to FXR/bile salt export pump （BSEP） pathway in CLI model cells.RESULTS A total of 12 common peaks were calibrated in the fingerprints of 19 batches of DME， and the similarity values were all above 0.990. Four common peaks were identified， namely peak 6 （schaftoside）， peak 7 （isoorientin）， peak 11 （isoschaftoside） and peak 12 （isovitexin）. DME at 80 μg/mL exerted the optimal protective effect on CLI-model cells， and the cell viability reached （75.66±4.60）% after 24 h of treatment. Spectrum-effect analysis revealed that schaftoside possessed the strongest correlation with the anti-CLI activity of DME and served as the core active component. Target prediction and validation results showed that the binding energy between schaftoside and FXR was －8.1 kcal/mol， and the equilibrium dissociation constant was 27.3 μmol/L. Mechanistic experiments demonstrated that 100 μmol/L schaftoside significantly elevated the expression levels of FXR and BSEP， and mRNA of their encoded genes Nr1h4 ， Abcb11 in CLI-model cells （ P ＜0.05）.CONCLUSIONS Schaftoside may be the core active component of DME against CLI， and its mechanism may be related to the activation of FXR/BSEP pathway.

Objective:The aim of this study is to investigate the main active substances of Qilin pills by high performance liq-uid chromatogre-electrostatic field orbitrap mass spectrometry(HPLC-Q-Orbitrap/MS),and explore the mechanism of its action in the treatment of asthenozoospermia by combining network pharmacology and molecular docking.Methods:(1)Qilin pills were quantita-tively and qualitatively analyzed by HPLC-Q-Orbitrap/MS.(2)The top 100 compounds in Qilin pills were screened by content analy-sis and SwissADME,and their targets were predicted.The asthenozoospermia targets were searched through the database.And a"pro-tein-protein interaction"(PPI)network was constructed.KEGG and GO analysis was performed using the DAVID database.And a"drug-target-pathway"network was constructed.(3)SailVina was used for molecular docking.Results:(1)A total of 1 275 known components were found and ranked in Qilin pills by HPLC-Q-Orbitrap/MS analysis.(2)The top 100 compounds in Qilin pills predicted a total of 1 053 targets and 184 potential therapeutic targets for asthenozoospermia.KEGG pathway analysis and GO analysis showed that the treatment of asthenozoospermia by Qilin pills may be related to the steroid hormone synthesis pathway,the response to steroid hormones,the chromosomal region of cells and the activity of steroid hydroxylase.The mechanism of Qilin pills in treating as-thenozoospermia may be related to regulating the synthesis,metabolism and reaction process of sex hormone in the body.(3)The mo-lecular docking results of its key targets(CYP19A1,ESR1,HSP90AA1,p53,HIF1α and BCL2)showed that the key active ingredi-ents M030,M039,M043,M050,M055 and M073 of Qilin pills had spontaneous binding.It had a binding energy of less than-5 kJ/mol.Conclusion:The material basis of Qilin pills has been explored by this study.And the mechanism of action of Qilin pills in the treatment of asthenozoospermia is highly bound to the expression and response process of steroid hormones,which provides a theoretical basis for the clinical application of Qilin pills.

Aim To investigate the therapeutic effects of gramine on airway inflammation and remodeling in a mouse model of asthma and to explore the potential mechanisms.Methods Female BALB/c mice sensi-tized with ovalbumin(OVA)were used to establish an asthma model,followed by gramine intervention(25,100 mg·kg-1).The improvement of lung tissue mor-phology in asthmatic mice by gramine was evaluated by HE,PAS,and Masson staining of lung tissue sections.The number of inflammatory cells in bronchoalveolar lavage fluid(BALF)was counted,and the levels ofIL-4,IL-5,IL-6,IL-13,and TNF-α in BALF were detected by ELISA.The expressions of α-smooth muscle actin(α-SMA),type Ⅰ collagen(COL-Ⅰ),and BDNF/TrkB signaling proteins in lung tissue were detected by immunohistochemistry and Western blot.Following the intervention with BDNF/TrkB agonists,the therapeutic efficacy of gramine in asthma was assessed through his-topathological analysis of lung tissue and quantification of inflammatory cell counts in BALF to determine its association with the BDNF/TrkB signaling pathway.Results Gramine significantly reduced the inflamma-tory cell infiltration and pro-inflammatory factor levels in the bronchial airway of mice induced by OVA(P＜0.01),while alleviating airway remodeling(P＜0.01)and inhibiting the expression of α-SMA and COL-Ⅰ in lung tissue(P＜0.01).Gramine could inhibit the ac-tivity of the BDNF/TrkB signaling pathway in asthma mice,while the BDNF/TrkB agonist could partially re-verse the anti-asthmatic function of gramine(P＜0.01).Conclusion Gramine exhibits significant im-provement in airway inflammation and remodeling in OVA-induced asthmatic mice,which is related to its in-hibition of the BDNF/TrkB signaling pathway.