ObjectiveIn traditional Chinese medicine (TCM), the foundational doctrine that the eyes reflect the essence of the internal viscera establishes ocular observation as a cornerstone of diagnostic practice. Specifically, the morphological characteristics and coloration variations of the scleral microvasculature serve as critical clinical indicators for assessing the dynamic balance of Qi and Blood, as well as the pathological status of internal organs. Historically, however, TCM eye diagnosis has relied predominantly on the subjective clinical experience and visual acuity of individual practitioners, leading to inherent challenges in standardization and reproducibility. While automated computer-aided diagnostic systems offer a promising solution, existing vessel segmentation algorithms encounter significant domain-specific bottlenecks when applied to scleral imagery. These challenges primarily stem from the highly reflective and moist nature of the ocular surface, which generates severe reflective interference. Furthermore, the inherent low contrast of fine capillary networks against complex background textures, compounded by non-uniform illumination, frequently results in high false-positive rates, misdetections, and severe vessel fragmentation. To address these critical limitations and advance the objective quantification of TCM diagnostics, this paper proposes a novel, highly robust sclera vessel segmentation framework that innovatively integrates Frangi-Sato dual-filter adaptive enhancement with pixel-level reflection detection. MethodsThe proposed methodology systematically addresses the segmentation pipeline through three synergistic stages. First, to overcome the structural limitations of single-filter approaches, a multi-scale weighted fusion strategy is meticulously designed to harness the complementary extraction capabilities of both Frangi and Sato filters. This adaptive enhancement optimally balances the preservation of main vessel trunk continuity with the heightened sensitivity required for delineating delicate, low-contrast peripheral capillaries. Second, to tackle the persistent issue of reflective highlights, a sophisticated multi-feature synergistic reflection detection module is introduced. By jointly analyzing local information entropy, gradient field variations, and intensity statistical distributions, this module achieves precise, pixel-level identification and elimination of reflective artifacts without compromising the underlying vascular structures. Finally, a dual-level adaptive thresholding strategy, featuring an innovative “core protection” mechanism, is implemented. This critical step effectively suppresses complex background noise while rigorously preserving the structural and topological integrity of the intricate vessel network, preventing the structural breaks often seen in conventional binarization methods. ResultsThe efficacy of the proposed framework was rigorously evaluated using both self-constructed clinical datasets specifically acquired for TCM research and standardized public datasets. Extensive experimental results demonstrate that the proposed method consistently outperforms state-of-the-art traditional approaches and contemporary deep learning models. Specifically, the proposed method achieves a Dice similarity coefficient of approximately 0.71 on the private clinical dataset, and secures the best performance across the majority of quantitative metrics on both datasets. Notably, the framework exhibits exceptional robustness and generalization capabilities in highly challenging scenarios characterized by intense reflective interference, low signal-to-noise ratios, and cross-domain image variations. ConclusionThis study successfully realizes the high-integrity, automated segmentation of scleral vessel networks under complex clinical imaging conditions. By overcoming the fundamental algorithmic challenges of reflection interference and micro-vessel loss, the proposed methodology provides potential support for the digitization, objective standardization, and intelligent advancement of modern TCM eye diagnosis systems.

ObjectiveIn traditional Chinese medicine (TCM), the foundational doctrine that the eyes reflect the essence of the internal viscera establishes ocular observation as a cornerstone of diagnostic practice. Specifically, the morphological characteristics and coloration variations of the scleral microvasculature serve as critical clinical indicators for assessing the dynamic balance of Qi and Blood, as well as the pathological status of internal organs. Historically, however, TCM eye diagnosis has relied predominantly on the subjective clinical experience and visual acuity of individual practitioners, leading to inherent challenges in standardization and reproducibility. While automated computer-aided diagnostic systems offer a promising solution, existing vessel segmentation algorithms encounter significant domain-specific bottlenecks when applied to scleral imagery. These challenges primarily stem from the highly reflective and moist nature of the ocular surface, which generates severe reflective interference. Furthermore, the inherent low contrast of fine capillary networks against complex background textures, compounded by non-uniform illumination, frequently results in high false-positive rates, misdetections, and severe vessel fragmentation. To address these critical limitations and advance the objective quantification of TCM diagnostics, this paper proposes a novel, highly robust sclera vessel segmentation framework that innovatively integrates Frangi-Sato dual-filter adaptive enhancement with pixel-level reflection detection. MethodsThe proposed methodology systematically addresses the segmentation pipeline through three synergistic stages. First, to overcome the structural limitations of single-filter approaches, a multi-scale weighted fusion strategy is meticulously designed to harness the complementary extraction capabilities of both Frangi and Sato filters. This adaptive enhancement optimally balances the preservation of main vessel trunk continuity with the heightened sensitivity required for delineating delicate, low-contrast peripheral capillaries. Second, to tackle the persistent issue of reflective highlights, a sophisticated multi-feature synergistic reflection detection module is introduced. By jointly analyzing local information entropy, gradient field variations, and intensity statistical distributions, this module achieves precise, pixel-level identification and elimination of reflective artifacts without compromising the underlying vascular structures. Finally, a dual-level adaptive thresholding strategy, featuring an innovative “core protection” mechanism, is implemented. This critical step effectively suppresses complex background noise while rigorously preserving the structural and topological integrity of the intricate vessel network, preventing the structural breaks often seen in conventional binarization methods. ResultsThe efficacy of the proposed framework was rigorously evaluated using both self-constructed clinical datasets specifically acquired for TCM research and standardized public datasets. Extensive experimental results demonstrate that the proposed method consistently outperforms state-of-the-art traditional approaches and contemporary deep learning models. Specifically, the proposed method achieves a Dice similarity coefficient of approximately 0.71 on the private clinical dataset, and secures the best performance across the majority of quantitative metrics on both datasets. Notably, the framework exhibits exceptional robustness and generalization capabilities in highly challenging scenarios characterized by intense reflective interference, low signal-to-noise ratios, and cross-domain image variations. ConclusionThis study successfully realizes the high-integrity, automated segmentation of scleral vessel networks under complex clinical imaging conditions. By overcoming the fundamental algorithmic challenges of reflection interference and micro-vessel loss, the proposed methodology provides potential support for the digitization, objective standardization, and intelligent advancement of modern TCM eye diagnosis systems.

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.

Background/Aims: Liver transplantation (LT) following total hepatectomy is a life-saving treatment for hepatocellular carcinoma (HCC). The HCC recurrence after LT hinders the effectiveness of the procedure. The objective of this study is to develop a pre-operative risk stratification model based on a liquid biopsy. Methods: We conducted a comprehensive multi-omics study of 260 HCC patients from three centers, including clinical data, low-coverage whole-genome sequencing of cell-free DNA (cfDNA) from plasma, as well as whole-exome, single-nucleus RNA, and spatial transcriptomics from matched tumor and non-tumor tissues. Results: We identified cfDNA-derived copy number alteration (CNA) signatures associated with post-transplant recurrence. By integrating cfDNA-derived CNA profiles with single-cell transcriptomic data, we traced recurrence-associated cfDNA to a distinct subpopulation of malignant cells within the primary tumor. These cells were embedded in a pro-metastatic microenvironment of specialized endothelial subtypes and cancer-associated fibroblasts. Notably, most recurrence-associated lesions were detectable in cfDNA prior to liver transplantation (LT). Building on these insights, we developed the ZJU Criteria based on CNA fragments and tumor markers, a pre-LT risk prediction tool that integrates conventional clinical factors with cfDNA-derived CNA signatures, and validated it using internal and independent external cohorts. Conclusion Our findings suggest that post-transplant recurrence commonly originates from advanced subclones that emerge late during tumor evolution. The ZJU Criteria provides an accurate, non-invasive strategy that significantly improves pre-LT risk stratification and clinical decision-making for patients with HCC.

Cilia,as cellular sensory organelles,actively partici-pate in and regulate cellular processes such as autophagy and metabolic breakdown during their generation and transportation.Autophagy,on the other hand,is a cell self-protection mecha-nism that maintains cellular homeostasis by clearing aggregates and damaged organelles.Combining recent research findings,this review comprehensively elucidates the bidirectional crosstalk between primary cilia and autophagy.Specifically,it highlights the crucial role of cilia-dependent signaling pathways in activa-ting cellular autophagy and how autophagy regulates cilia genera-tion and length by degrading specific ciliary proteins.Moreover,the dysregulation of primary cilia and autophagy is closely asso-ciated with the clinical manifestations and pathogenesis of vari-ous ciliopathy-related diseases such as polycystic kidney disease and tuberous sclerosis.In terms of pharmacotherapy,this review provides a comprehensive and in-depth overview of small mole-cule inhibitors targeting ciliogenesis,including cytoskeletal drugs and Hedgehog signaling pathway inhibitors.Despite the current limitations in clinical use,these drugs lay the groundw-ork for developing highly specific targeted small molecule inhibi-tors of ciliogenesis and for the treatment of ciliopathies and canc-ers.By systematically discussing ciliogenesis,autophagy,disea-ses and drugs,this review offers new insights for further elucida-ting the crosstalk between ciliogenesis and autophagy,exploring their pathological mechanisms in disease development,and de-veloping therapeutic strategies in the future.

Family with sequence similarity 3 member A(FAM3A),a novel mitochondrial protein,plays a pivotal role in hepatic glucose and lipid metabolism by enhancing ATP synthesis and secretion and mod-ulating the ATP-P2 receptor-Akt signaling pathway.Dysregulation of FAM3A is closely associated with the pathogenesis of non-alcoholic fatty liver disease(NAFLD)and type 2 diabetes mellitus(T2DM).In this study,targeting FAM3A as a therapeutic candidate,we conducted virtual screening to identify 47 small-molecule compounds with potential binding activity.Surface plasmon resonance(SPR)analysis re-vealed three compounds exhibiting high binding affinity to FAM3 A.Further structural characterization of the FAM3A-compound complexes,combined with intermolecular interaction analysis,elucidated the binding mode of the lead compound Index 2(taxifolin)to FAM3A at atomic resolution.These findings provide critical insights into the molecular mechanisms underlying ligand-FAM3A interactions and deliver valuable chemical scaffolds for the development of therapeutics targeting NAFLD and T2DM.This work establishes a foundation for advancing drug discovery efforts focused on FAM3A-mediated metabolic disor-ders.

Objective To explore the clinical value of energy-spectrum CT single-energy imaging in enhancing the image quality and stent display of stent placement CT angiography(CTA)in lower extremity atherosclerotic occlusive disease.Methods Twenty patients[mean(65.61±9.65)years;male/female,16/4]who underwent stenting for chronic occlusive disease of the lower extremity arteries by lower extremity arterial energetic spectral CTA were retrospectively recruited at our hospital.The original images were reconstructed into seven sets of single energy(40-100 keV),120 kVp,virtual unenhanced images(VUE)and metal artifact reduction(MAR)technique images.Images were debossed and then scaffolded for display with volumetric reconstruction(VR),maximum density projection(MIP)and curve planar reconstruction(CPR),and were objectively and subjectively assessed and compared using one-way analysis of variance(ANOVA).Results The 80 keV and MAR images had the highest scores compared to the other reconstruction group images(P＜0.01).Conclusion 80 keV single-energy imaging and de-metallization artifacts MAR improve the image quality of lower extremity arterial stent lumen and structure display;therefore,they have higher diagnostic value for clinicians.

Global procurement of health products is an important way for multilateral organizations in the health field to perform their functions.As one of the most important multilateral organizations in the global health field today,the Global Fund to Fight AIDS,Tuberculosis and Malaria occupies a huge share of the global procurement market for health products.This article introduces in detail the procurement methods of the Global Fund and the quality standards of its procured products;also it describes and analyzes the total amount of funds procured by the Global Fund,the types of procured health products,the main supply countries and their changing trends over time.Corresponding policy suggestions therefore are proposed for China to better participate in the governance of global health and for Chinese related companies to enter the international procurement market.

The implementation of the Good Pharmacovigilance Practice(GVP)in 2021 has laid a solid foundation for improving China's pharmacovigilance system,a matter of paramount importance currently emphasized by Marketing Authorization Holders(MAHs).Given the unique organizational structures of corporate groups,establishing a centralized pharmacovigilance system can optimize resource utilization.This paper analyzes the challenges faced by corporate groups in developing such central-ized systems and proposes an organizational framework with defined operational divisions.The findings aim to provide actionable insights for vaccine manufacturers in establishing and refining life cycle pharmacovigilance systems.

The implementation of the Good Pharmacovigilance Practice(GVP)in 2021 has laid a solid foundation for improving China's pharmacovigilance system,a matter of paramount importance currently emphasized by Marketing Authorization Holders(MAHs).Given the unique organizational structures of corporate groups,establishing a centralized pharmacovigilance system can optimize resource utilization.This paper analyzes the challenges faced by corporate groups in developing such central-ized systems and proposes an organizational framework with defined operational divisions.The findings aim to provide actionable insights for vaccine manufacturers in establishing and refining life cycle pharmacovigilance systems.