Experimental eye research最新文献

Meibomian gland dysfunction (MGD) is the leading cause of evaporative dry eye disease and arises from failure of a coordinated molecular network that normally maintains meibomian gland homeostasis. In health, endocrine-lipidogenic signaling via peroxisome proliferator-activated receptor-γ (PPARγ) and androgen receptor (AR), trophic pathways such as epidermal growth factor and Hedgehog, a circadian-metabolic axis coupling nicotinamide adenine dinucleotide (NAD⁺)-dependent steroidogenesis to lipid output, and neuroimmune circuits involving Toll-like receptors and nuclear factor-κB (NF-κB) act together to regulate stem/progenitor maintenance, meibocyte differentiation and lipid secretion. In MGD these axes become progressively uncoupled: hormonal and metabolic stress reduce NAD⁺ and intracrine androgen production, dampening AR-PPARγ activity; loss of trophic support exhausts basal progenitor pools; and chronic NF-κB-driven inflammation with Th17 and interleukin-33 (IL-33) signaling promotes ductal hyperkeratinization, acinar loss and fibrosis. Lipidomic studies show a shift from cholesteryl esters (CEs) toward free fatty acids, along with remodeling of O-acyl-ω-hydroxy fatty acid (OAHFA) profiles, oxidized lipids and eicosanoids, which stiffen meibum, destabilize the tear film lipid layer and fuel inflammation. Recent single-cell and organoid studies further map cellular heterogeneity and disease-linked lineage changes. Biomarkers such as cholesteryl ester/wax ester ratios, OAHFA profiles, eicosanoids, cytokines and imaging metrics offer read-outs of axis-specific dysfunction. Therapeutically, emerging strategies seek to correct these defects by enhancing AR-PPARγ signaling and NAD⁺-dependent steroidogenesis, re-engaging trophic pathways, normalizing ductal keratinization with "soft" keratolytics and inhibiting inflammatory circuits. This review integrates these mechanisms, disease networks, biomarker signatures and interventions into a unified model to support mechanism-based, biomarker guided precision care in MGD.

Anterior-segment optical coherence tomography (AS-OCT) supports cataract surgery planning by revealing corneal and crystalline lens geometry, but clinical workflows require fast, user-independent delineation of key interfaces in noisy scans. We address this need with an end-to-end pipeline tailored to low signal to noise ratio (SNR), preclinical AS-OCT: a systematic architecture search selects a U-Net with an ImageNet-pretrained EfficientNet-B2 encoder; a residual-driven, mask-guided non-local means stage suppresses background clutter while preserving boundaries; and a mask-to-surface conversion fits robust third-order polynomials to produce biometry-ready corneal and lenticular interfaces. Trained on 440 B-scans of rabbit eyes from a custom system, the model achieves mean Intersection-over-Union (IoU) of 0.957 ± 0.007 (cornea) and 0.978 ± 0.007 (lens) across five validation splits, with boundary root-mean-squared-error (RMSE) of 4.03-6.91 μ m between the predicted and manual reference surfaces. On an operator/time/subject-independent test set (n = 80), performance remains high-IoU 0.939 ± 0.008 (cornea) and 0.961 ± 0.009 (lens)-and the method outperforms a classical graph-search baseline. Despite limited training data and challenging image quality, the approach delivers accurate, robust surfaces suitable for downstream biometry.

Accurate quantification of axon density is pivotal in optic nerve-related studies, as axonal degeneration is a hallmark of numerous neurological disorders. This study introduces the Point Positioning and Counting Network (PPCNet), a novel point-annotation-based deep learning framework that overcomes the limitations of manual counting and existing automated tools. PPCNet integrates a VGG16 backbone with multiscale feature extraction and lateral fusion to generate high-resolution feature maps. A dual-branch architecture regresses spatial coordinates and classifies candidate points, enabling axonal-center localization and confidence scoring. An optimized Hungarian algorithm, incorporating Euclidean distance and confidence metrics, ensures one-to-one correspondence between predicted candidates and ground-truth points. The model is trained end-to-end using a hybrid loss combining mean squared error and cross-entropy. Evaluated on a goat optic nerve semi-thin section dataset, PPCNet significantly outperformed current state-of-the-art methods (Axonet 2.0 and AxonDeepSeg). In the test set, mean ± SD axon counts were 901.6 ± 225.6 for manual counting, 890.2 ± 227.3 for PPCNet, 918.8 ± 297.9 for Axonet2.0, and 1045 ± 275 for AxonDeepSeg. Linear regression analysis demonstrated superior agreement with manual counts (R² = 0.939), surpassing Axonet 2.0 (R² = 0.8612) and AxonDeepSeg (R² = 0.9142). PPCNet also yielded a lower mean absolute error (MAE = 45.93) than those of the comparative models (97.50 and 146.33). Bland-Altman analysis confirmed narrower limits of agreement (-99.23 to 121.90) and reduced systematic bias. Confidence-interval analysis further supported PPCNet's reliability, showing substantial overlap with manual counts. In conclusion, PPCNet delivers sub-pixel-accurate automated axon quantification for optic nerve research, replacing tedious manual counting and traditional segmentation-based methods.