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ObjectiveTo investigate the causal link between ametropia and diabetic retinopathy, as well as to offer genetic support for the association between these two conditions.MethodsThis study employed a methodology involving the utilisation of genome-wide association studies data that are publicly accessible. Specifically, single nucleotide polymorphisms (SNPs) that exhibit a strong association with ametropia were employed as instrumental variables, and a two-sample Mendelian randomization (MR) approach was employed to examine the causal relationship between different types of ametropia and diabetic retinopathy. The main findings were derived from the utilisation of inverse variance weighted (IVW), while supplementary results were obtained through the utilisation of MR Egger, weighted median, simple mode and weighted mode. Additionally, a sensitivity analysis was conducted using the ‘leave-one-out’ method. Cochran’s Q statistics were also used to quantify the heterogeneity of SNPs.Results38 SNPs were finally included. The results of the IVW analysis indicate that myopia may exert an inhibitory effect on the development of diabetic retinopathy (OR=0.596, 95% CI (0.371, 0.957), p<0.05). Conversely, hypermetropia (OR=8.882, 95% CI (0.389×10-3, 2.06×105), p>0.05) and astigmatism (OR=1.004, 95% CI (0.888, 1.135), p>0.05) do not exhibit a causal relationship with the risk of diabetic retinopathy.ConclusionThis two-sample Mendelian randomization study provides evidence that myopia may impede diabetic retinopathy occurrence, while hypermetropia and astigmatism show no significant causal effects. However, our analysis treats refractive errors as independent entities, which may not reflect their clinical interdependence. Further investigations are warranted to elucidate myopia’s protective mechanisms.

Background Current human retina studies predominantly utilize post-mortem tissue, and the sample accessibility constraints make the characterization of the living human retina at single-cell resolution a challenge. Although single-nucleus RNA-seq expands the utility of frozen samples, it provides a nuclear-centric view, potentially missing key cytoplasmic information and transient biological processes. Thus, it is important to generate resources directly from living human retinal tissue to complement existing datasets. Methods We profiled 106,829 single cells from nine unfrozen human retina samples. Living samples were collected within 10 min of therapeutic enucleation and four postmortem samples were collected within 6 h. After standardized dissociation, single-cell transcriptomes were generated using 10x Genomics 3’ RNA-seq and applied scVI to generate batch-corrected integrated atlas. Major cell types and subtypes were annotated through iterative Leiden clustering, canonical markers. Subsequent analyses included differential expression comparisons between cell states and regulon activity profiling to further characterize cellular identities and regulatory networks. Transcriptional dynamics were assessed using RNA velocity, and cell-cell signaling pathways were inferred with CellChat. Key findings were validated in independent samples from two additional donors (four samples) using the identical workflow. Results We contribute to establishing a reference for retinal cell type proportions and cellular states. Our analysis revealed ELF1-mlCone, a distinct cluster of mlCone photoreceptors identified by distinct transcriptional features. The presence and transcriptional features of this cluster were validated in independent samples. Additionally, by comparing living and post-mortem samples, our study highlights differences in transcriptional dynamics: living tissue preserved coherent RNA velocity streams, enabling clear dynamic state transitions, while post-mortem tissue exhibited disorganized patterns. These findings suggest that using living tissue can improve the capture of active cellular states and transitions. Conclusions Our atlas provides a single-cell reference contrasting living versus early postmortem human retina, integrating cell type composition, transcriptional diversity, and functional insights. It may serve as a useful resource for retinal research and for understanding aspects of human retinal biology, particularly given its inclusion of living tissue and diverse pathological states.

Blepharospasm is a focal dystonia characterized by involuntary eyelid contractions that impair vision and social function. The subtle clinical signs of blepharospasm make early and accurate diagnosis difficult, delaying timely intervention. In this study, we propose a dual cross-attention deep learning framework that integrates temporal video features and facial landmark dynamics to assess blepharospasm severity, frequency, and diagnosis from smartphone-recorded facial videos. A retrospective dataset of 847 patient videos collected from two hospitals (2016–2023) was used for model development. The model achieved high accuracy for severity (0.828) and frequency (0.82), and moderate performance for diagnosis (0.674).SHAP analysis identified case-specific video fragments contributing to predictions, enhancing interpretability. In a prospective evaluation on an independent dataset ( N  = 179), AI assistance improved junior ophthalmologist’s diagnostic accuracy by up to 18.5%. These findings demonstrate the potential of an explainable, smartphone-compatible video model to support early detection and assessment of blepharospasm.

Patients afflicted with dry eye disease (DED) experience significant discomfort. The underlying cause of DED is the excessive accumulation of ROS on the ocular surface. Here, we investigated the nitrogen doped-graphene quantum dots (NGQDs), known for their ROS-scavenging capabilities, as a treatment for DED. NGQDs were prepared by using citric acid and urea as precursors through hydrothermal method. The antioxidant abilities of NGQDs were evaluated through: scavenging the ROS both extracellular and intracellular, regulating the nuclear factor-erythroid 2-related factor (Nrf2) antioxidant pathway of human corneal epithelial cells (HCECs) and their transcription of inflammation related genes. Furthermore, NGQDs were modified by Arg-Gly-Asp-Ser (RGDS) peptides to obtain RGDS@NGQDs. , both the NGQDs and RGDS@NGQDs were suspended in 0.1% Pluronic F127 (w/v) and delivered as eye drops in the scopolamine hydrobromide-induced DED mouse model. Preclinical efficacy was compared to the healthy and DPBS treated DED mice. These NGQDs demonstrated pronounced antioxidant properties, efficiently neutralizing free radicals and activating the intracellular Nrf2 pathway. In vitro studies revealed that treatment of H O -exposed HCECs with NGQDs induced a preservation in cell viability. Additionally, there was a reduction in the transcription of inflammation-associated genes. To prolong the corneal residence time of NGQDs, they were further modified with RGDS peptides and suspended in 0.1% Pluronic F127 (w/v) to create RGDS@NGQDs F127 eye drops. RGDS@NGQDs exhibited superior intracellular antioxidant activity even at low concentrations (10 μg/mL). Subsequent in vivo studies revealed that RGDS@NGQDs F127 eye drops notably mitigated the symptoms of DED mouse model, primarily by reducing ocular ROS levels. Our findings underscore the enhanced antioxidant benefits achieved by modifying GQDs through nitrogen doping and RGDS peptide tethering. Importantly, in a mouse model, our novel eye drops formulation effectively ameliorated DED symptoms, thereby representing a novel therapeutic pathway for DED management.

Diabetic retinopathy (DR) remains a principal cause of vision impairment worldwide, involved complex retinal cellular pathophysiology that remains incompletely understood. To elucidate cell-type-specific molecular signatures underlying DR, we generated a high-resolution single-cell transcriptomic atlas of 297,121 retinal cells from 20 Chinese donors, including non-diabetic controls (26.4%), diabetic without retinopathy (23.4%) and DR (50.2%). Following rigorous quality control, batch-effect correction, and clustering and annotation, 10 major retinal cell populations were delineated. Differential expression analyses across disease states within each cell type yielded candidate gene sets, which were further refined via a multi-stage machine-learning pipeline combining L1-regularized logistic regression and recursive feature elimination with cross-validation, alongside bootstrap stability selection. Resulting cell-type-specific classifiers achieved high accuracy (79–95%) and AUCs (0.85–0.99) in distinguishing DR disease states. Enrichment analyses implicated immune activation, oxidative stress, neurodegeneration and synaptic dysfunction pathways across multiple cell types in retina. Integrating 567 unique marker genes from all cell types, a general multilayer perceptron classifier achieved 95.31% overall accuracy on held-out test data, demonstrating the translational potential of these signatures for non-diabetic controls, diabetic without retinopathy and DR classification. This high-resolution atlas and the accompanying analytic framework provide a robust computational framework for biomarker discovery, mechanistic insight and targeted intervention strategies in diabetic retinal diseases.

Single-cell RNA sequencing (scRNA-seq) has significantly advanced our understanding of retinal aging, yet the specific molecular characteristics within cell populations remain incompletely defined. We profiled 223,612 single cells from 18 unfrozen human retinas obtained from 13 Chinese donors aged 34–92 years, providing an ethnically specific atlas across the adult lifespan. A sparsity-driven machine-learning (ML) pipeline (L1-regularized logistic regression, recursive feature elimination with cross-validation) identified age-discriminatory genes within each major retinal cell type, complemented by gene set scoring for cellular senescence and metabolic pathways. Using integrated differential expression and ML feature selection, we identified eleven major retinal cell populations and observed aging-associated shifts. ML classifiers achieved high accuracy (80–96%), particularly for microglia (96%), revealing mitochondria-centric aging signatures in rods and bipolar cells, proteostasis and retinoid metabolism in cones, and structural-RNA maintenance signatures in horizontal cells. This study delivers the first ML-derived, cell-type-specific aging gene signatures for the human retina in a Chinese cohort, offering a reference for population-tailored biomarker discovery.

The human retina exhibits complex cellular heterogeneity which is critical for visual function, yet comprehensive ethnic-specific references are scarce in ophthalmic transcriptomics. The lack of single-cell RNA sequencing (scRNA-seq) data from Asian populations particularly Chinese donors imposes significant limitations in understanding population-specific retinal biology. We constructed the first comprehensive single-cell transcriptomic atlas of the human retina from Chinese donors, generated through high-throughput scRNA-seq of ∼290,000 viable cells obtained from 18 fresh retinal specimens (living donor and post-mortem specimens). Our multi-level analysis identified 10 distinct retinal cell types, encompassing all major neuronal lineages, Müller glia, astrocytes, microglia. Detailed subcluster analyses further revealed extensive heterogeneity, identifying distinct subtypes within several cell populations such as 7 amacrine cell subtypes and 14 bipolar cell subtypes. Concurrently, through systematic analysis of delineated subtype-specific molecular programs, we mapped their associated biological signaling pathways, functions, and mechanistic processes. This analysis explained the critical involvement of these subpopulations in core biological processes including synaptic organization, neurotransmission, and phototransduction cascades, potentially governing retinal homeostatic regulation and disease mechanisms. Single-cell transcriptomic atlas of the human retina from Chinese donors describes a comprehensive cellular landscape, encompassing major cell types and subtypes including neuronal, glial, and immune populations. This ethnic-specific atlas provided an important reference for understanding retinal development, cellular interaction and disease pathogenesis in Chinese populations, addressing a longstanding gap in ophthalmic transcriptomic resources.

AbstractDiabetic retinopathy (DR) is a major cause of vision loss worldwide. Here, we perform single-cell RNA sequencing of thirteen human retina samples (from living and post-mortem donors) across non-diabetic, diabetic, and DR states to create a comprehensive transcriptomic atlas. We uncover three distinct microglial states—homeostatic, stress-response, and inflammatory—along a functional continuum, rather than discrete activation states, with dynamic transitions occurring throughout disease progression. Trajectory analysis indicates bifurcating paths starting from the homeostatic state branching toward stress-response and inflammatory states. Three major functional modules: ribosomal/translation, coordinated immune cell function, and inflammatory/transcriptional regulation, showing disease-specific activation patterns were identified. Cell communication analysis further highlights sophisticated neural-immune interactions, particularly between photoreceptors and microglia. Our findings provide insights into the complex cellular dynamics of DR progression and suggest potential therapeutic targets for early intervention.Competing Interest StatementThe authors have declared no competing interest.

The human retina has a remarkable diversity of cell types, which is crucial for understanding the mechanisms underpinning visual development and function. While single-cell RNA sequencing (scRNA-seq) has advanced our understanding of retinal biology, most studies have relied on postmortem or frozen samples, potentially missing important transcriptional information. The aim of this study was to create the first comprehensive scRNA-seq atlas of fresh human retinal samples from living donors and recently deceased individuals using scRNA-seq technology. A total of 106,829 cells were analyzed, which were collected from nine retinal samples using standardized scRNA-seq workflow. Our findings identified several novel subtypes of known retinal cells, including new subgroups of cones and amacrine cells (ACs), each characterized by distinct gene expression profiles. Notably, we discovered a novel cone subtype, the ELF1-Cone, which shows a clear developmental trajectory from mlCone precursors. This subtype exhibits unique functional properties and metabolic profiles, regulated by key transcription factors ELF1 and PRKAA1. We also identified five distinct AC subtypes, including three GABAergic ACs, each with unique gene expression profiles and functional characteristics. Our study highlights the critical importance of using fresh human retinal tissue for accurate cellular mapping and trajectory inference. Significant differences were observed between fresh and postmortem retinal samples in terms of pseudo time analysis such as RNA velocity. We also performed a comparative analysis of diabetic without retinopathy, diabetic retinopathy, and non-diabetic samples, suggesting diabetes-related transcriptional variation. In conclusion, we present a comprehensive human retina atlas using fresh samples that contribute to existing knowledge of retinal cell diversity, function, and transcriptional profiles. Our study is a milestone for future studies that will improve understanding of retinal biology and disease mechanisms.

Molecularly soft organic-inorganic hybrid perovskites are susceptible to dynamic instabilities of the lattice called octahedral tilt, which directly impacts their carrier transport and exciton-phonon coupling. Although the structural phase transitions associated with octahedral tilt has been extensively studied in 3D hybrid halide perovskites, its impact in hybrid 2D perovskites is not well understood. Here, we used scanning tunneling microscopy (STM) to directly visualize surface octahedral tilt in freshly exfoliated 2D Ruddlesden-Popper perovskites (RPPs) across the homologous series, whereby the steric hindrance imposed by long organic cations is unlocked by exfoliation. The experimentally determined octahedral tilts from n  = 1 to n  = 4 RPPs from STM images are found to agree very well with out-of-plane surface octahedral tilts predicted by density functional theory calculations. The surface-enhanced octahedral tilt is correlated to excitonic redshift observed in photoluminescence (PL), and it enhances inversion asymmetry normal to the direction of quantum well and promotes Rashba spin splitting for n  > 1. The surface octahedral tilt in exfoliated 2D perovskites is directly visualized by STM and the degree of the tilt varies with the number of layers of inorganic slabs and result in different amounts of excitonic red shift in photoluminescence.