Benzalkonium chloride (BAK), a common preservative in multi-dose ophthalmic products, is often studied under acute high-dose exposure, which highlights overt damage to corneal epithelial cells. These studies tend to exaggerate the maximum cytotoxic effects and do not accurately reflect the gradual, cumulative damage seen during long-term use. By reviewing clinical, animal, and cellular data, we argue that repeated low-dose BAK exposure leads to barrier dysfunction, energy depletion, priming of inflammation, and impaired regeneration before significant cell death occurs. We suggest a repair-exhaustion model: minor injuries from repeated exposure are initially managed by membrane resealing and epithelial renewal, but over time, repair capacity becomes exhausted. This has two main implications: (i) long-term toxicity should be evaluated using functional measures such as barrier integrity, mitochondrial health, inflammation, and repair ability, and not cell viability alone; (ii) there may be a window during which reducing preservative levels or adding barrier-supporting and mitochondrial-protective agents can minimize cumulative damage. Instead of simply using or avoiding the preservative, this approach encourages formulation and clinical strategies that maintain antimicrobial effects while strengthening tissue resilience.

Purpose:High glucose (HG), hypoxia (Hyp), and their combination are major risk factors for proliferative diabetic retinopathy (PDR). Although these conditions induce features of the Warburg-like metabolic reprogramming in human retinal endothelial cells (HRECs), it remains unclear whether they produce distinct metabolic and angiogenic subtypes. This study aimed to characterize the Warburg-like-associated metabolic heterogeneity induced by these PDR-related risk factors and evaluate the ability of supervised machine-learning models to distinguish these subtypes. Methods:HRECs were cultured under normoglycemic, HG, Hyp (2% O2), and combined HG-Hyp conditions. Untargeted LC-MS/MS metabolomics quantified metabolites spanning carbohydrates, amino acids, nucleotides, and lipids. Principal component analysis (PCA) assessed overall metabolic variation, and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis identified metabolic pathways associated with angiogenesis. In vitro angiogenesis assays measured endothelial tube formation and branching. Nine supervised classifiers (decision tree, logistic regression, naïve Bayes, random forest, K-Nearest Neighbors, neural network, gradient boosting, AdaBoost, and Support Vector Machine) were trained on the highest-ranked metabolites selected by the Information Gain Ratio feature-ranking approach. Model performance was evaluated using 10-fold cross-validation, leave-one-out cross-validation (LOOCV), permutation testing, and a classifier stability analysis under biologically meaningful distributional shift using an independent chemically induced hypoxia model (CoCl2). Results:PCA revealed partial separation of metabolic profiles across conditions, indicating different Warburg-like metabolic subtypes. The combined HG-Hyp condition exhibited enhanced angiogenic potential relative to either HG or Hyp alone. KEGG pathway enrichment analysis identified fatty acid biosynthesis and elongation among the most significantly enriched pathways in HRECs under combined HG-Hyp conditions, alongside amino sugar and nucleotide sugar metabolism, glycerophospholipid metabolism, the pentose phosphate pathway, and glycolysis/gluconeogenesis. Supervised machine-learning classifiers distinguished these metabolic subtypes, with AdaBoost and gradient Boosting showing the most balanced, reproducible performance across 10-fold cross-validation, LOOCV, and permutation testing, and remaining the most reliable classifiers under domain-shift testing (area under the curve = 0.88, P = 0.0061). Conclusions:In this exploratory analysis, HG, Hyp, and their combination drive metabolically and functionally distinct subtypes of Warburg-like metabolic reprogramming in HRECs, with HG-Hyp in combination producing a highly angiogenic phenotype. Boosting-based ensemble classifiers provide a promising framework for detecting these subtypes even under domain-shift conditions, warranting validation in larger independent datasets. Translational Relevance:Integrating metabolomics with machine-learning classification offers a strategy to identify Warburg-like metabolic subtypes in retinal endothelial cells, providing insights into angiogenic mechanisms and guiding the development of targeted diagnostics or therapeutics for PDR.

The human cornea is a transparent avascular structure that is crucial for transmitting light to the retina. The innermost layer of the cornea is the corneal endothelium, which is located distal to Descemet’s membrane, that connects it to the corneal stroma and is responsible for maintaining corneal clarity via barrier and pump functions. Loss of endothelial transparency significantly impairs vision, and the conditions responsible for disrupting transparency include endothelial dysfunction. This review focuses on the treatment of corneal endothelial pathophysiology. The shape of CECs is maintained by the actin filaments, which are located apically as a thick band. Cadherin forms the junctional complex at the apical cell junctions, which further extends up to the basolateral borders of the cells. The ability of the cornea to transmit light depends on the deturgescence of the connective tissue stroma, which is regulated by an active fluid transport system connected to the corneal endothelium. The disruption of the ability of CECs to regulate visual clarity and corneal hydration either indirectly affects the contraction of actomyosin or directly threatens the integrity of barrier function because cell loss leads to corneal edema, epispodic ocular pain, blurred vision or blistering of the corneal endothelium. Once damaged, the corneal endothelium does not regenerate in humans. With the advancement of modern surgical techniques, endothelial corneal dystrophies have become treatable. While pharmacological treatments and emerging therapies are important, surgical approaches remain the cornerstone of treatment. Research into gene therapy, stem cells, and tissue engineering may further transform the management of endothelial corneal dystrophies in the future.

Purpose:To assess the influence of varying concentrations of benzalkonium chloride (BAK), the predominant preservative utilized in ophthalmic formulations, on the barrier integrity and mitochondrial function of primary cultured human corneal epithelial cells (HCECs). Methods:Primary HCEC monolayers were exposed to BAK at concentrations ranging from 0.02% to 0.00002%. The barrier function was monitored using electric cell-substrate impedance sensing (ECIS), where a decrease in electrical resistance signified a loss of barrier function. Mitochondrial function was evaluated after 24 hours of BAK exposure with the Seahorse XFe96 Flux Analyzer, which measured basal respiration, adenosine triphosphate (ATP) production, and maximal respiration. Results:High BAK concentrations (≥0.02%) caused a rapid, dose-dependent decrease in resistance, exceeding 40% within 1 hour. In contrast, lower concentrations (0.00025%-0.002%) led to a delayed, gradual reduction. Specifically, 0.00025% BAK resulted in a 37% decrease in resistance by 72 hours, whereas 0.0001% caused a 26% reduction; concentrations ≤ 0.00005% had no significant effect. Increased capacitance accompanied the resistance loss, indicating membrane disturbance. Seahorse analysis revealed that BAK concentrations ≥ 0.00005% significantly reduced basal respiration and ATP production. Maximal respiration decreased at higher doses (≥0.0001%). Conclusions:BAK induces concentration-dependent, cumulative toxicity in HCECs, causing rapid membrane disruption and irreversible barrier failure at or above its critical micelle concentration (CMC), along with ongoing sub-CMC toxicity through mitochondrial suppression at lower doses. These findings highlight the need for preservative strategies that reduce both acute and chronic epithelial damage in ophthalmic applications. Translational Relevance:Real-time impedance and mitochondrial assessments determine thresholds for BAK toxicity, guiding the development of safer ophthalmic formulations to protect the ocular surface.

Purpose: To develop silk fibroin nanoparticles (SFNs) for prolonged drug delivery to the ocular surface. Methods: SFNs were prepared using nanoprecipitation, coated with chitosan (CS; positively charged), and stabilized with polyethylene glycol 400. Their morphology, particle size distribution, and surface charge were analyzed using dynamic light scattering. Fourier transform infrared (FTIR) spectroscopy assessed the intermolecular interactions between CS and silk fibroin. The entrapment efficiencies (EE) for sodium fluorescein (NaF) and Nile Red (NR), which served as hydrophilic and hydrophobic drug surrogates, respectively, were determined. The mucoadhesiveness of SFNs was examined ex vivo with freshly isolated porcine eyes. Cellular uptake and cytotoxicity were evaluated in a human corneal epithelium cell line (HCEC). Results: SFNs were spherical, measuring 198.47 ± 35.54 nm in diameter, and had a surface charge of 38.33 ± 0.67 mV. The coating of CS on SFNs resulted in a peak shift in the amide group in the FTIR spectrum. The maximum EEs for NaF and NR in SFNs were approximately 95% and 67%, respectively. SFNs exhibited prolonged mucoadhesion on corneas for over 240 min and were rapidly endocytosed by HCEC in less than 30 min without inducing cytotoxicity. Conclusion: The properties of SFNs are suitable for delivering drugs to the ocular surface.

Background: Fungal keratitis is a serious ophthalmic problem due to low antifungal medication penetration and bioavailability at the ocular surface, necessitating novel delivery strategies for successful therapeutic outcomes. This study created amphotericin B-loaded silk fibroin nanoparticles (AmB-SFNs) as a targeted drug delivery platform for long-term ocular antifungal therapy. Methods: Silk fibroin-chitosan nanoparticles were produced using a precipitation technique, with chitosan coating for mucoadhesion and polyethylene glycol-400 surface stability. Clinical fungal isolates from keratitis patients were identified as species by morphological and molecular analysis, followed by in vitro antifungal susceptibility testing. Results: The optimized formulation produced spherical AmB-SFNs with an average diameter of 220 nm, a positive zeta potential of +34 mV, and a maximum amphotericin B entrapment effectiveness of 76%. Molecular identification confirmed that all five clinical isolates were Fusarium solani. AmB-SFNs showed strong antifungal activity against all tested isolates, with a minimum inhibitory dose of 50 μg/mL (0.25% w/v). Conclusions: The developed nanoparticulate system has optimal characteristics for enhanced corneal drug delivery, such as appropriate particle size for tissue penetration and mucoadhesive properties for prolonged ocular residence time, suggesting that this nanoparticulate system warrants further investigation in vivo to evaluate its potential for clinical translation in treating Fusarium keratitis and as a platform for topical ophthalmic therapies.

The cornea is a critical component of the human eye, essential for forming clear images on the retina. It comprises multiple layers, each contributing to the transparency and health of the tissue. The corneal endothelium, a key structure within the cornea, plays an important role to maintain clarity through its tight junctions, primarily constituted by Zonula Occludens-1 (ZO-1). It is a cytoplasmic protein located in the outer layers of endothelial cells and is crucial for the integrity of these tight junctions. This work aims to detect damage to ZO-1 in endothelial cells by applying Gabor filters to ZO-1 images, followed by feature extraction and the application of various machine learning techniques. The machine learning algorithms used include Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Decision Tree, Naïve Bayes, and Stochastic Gradient Descent (SGD). Our findings show that the KNN algorithm outperforms the others, achieving the highest accuracy in identifying ZO-1 damage, while SGD exhibited the lowest performance. Consequently, KNN appears to be the most effective classifier for detecting ZO-1 damage, offering valuable support for doctors and medical professionals in making quick, accurate decisions regarding corneal endothelial health.

Anatomically inflammation in the anterior chamber of the eye, specifically in the iris and choroid is named as anterior uveitis. For the effective management of the disease it is essential for regular monitoring. Quantifying aqueous flare as a continuous measure of the intensity of light scatter (ILS) assists in accurately evaluating inflammation. Nevertheless, there is a potential for the subject’s blinking to disrupt the ILS data. This leads to increased and misleading levels of aqueous flare when assessing the extent of inflammation. Thus, our objective was to use an EOG-based spot fluorometer to examine the influence of eyeblink artifacts on ILS outcomes. This approach was founded on empirical data collected by quantifying the blink-induced and blink-artifact-free ILS in individuals with good health. A dataset of synthetic uveitis was generated using the LSTM deep learning technique. In addition, unsupervised machine learning techniques including k-means clustering, agglomerative hierarchical clustering, and Gaussian mixture clustering were used to identify blink artifacts in both the healthy and synthetic uveitis data. The optimal choice for minimizing artifacts was found to be the model that demonstrated superior performance. Our study findings indicate that the Gaussian mixture model outperformed other models in predicting blink-induced ILS, resulting in the most substantial decrease in blink artifacts. Furthermore, we successfully resolved the ILS by using our artifact removal technique, resulting in an impressive accuracy rate of 89%. The experiment verifies that our methodology successfully mitigates the occurrence of blinking errors in ILS measurements, thereby allowing a spot fluorometer to precisely grade uveitis.

Biomedical applications of nanomaterials, especially in diagnosing, management, and treatment of diseases are evolving. However, nanotoxicity remains a major challenge in availing the full biomedical potential of engineered nanomaterials. Nevertheless, recent advancements in the field have suggested that smart engineering of targeting ligands and presence of biomolecules on the surface of nanomaterials can reduce nanotoxicity through differential affinity, enhanced biocompatibility, and efficient internalization. Further, certain ligand-functionalized nanomaterials permit their tracking in cells and tissues over a prolonged period of time, making them suitable for nanomedicine applications. In this seminal review, a range of strategies, which have been employed for surface functionalization of nanomaterials using various biomolecules that confer amide / hydrazone bonds, thiol binding, and surface silanization have been evaluated. The challenges, and impact of surface functionalization of nanomaterials on cellular uptake, drug targeting, molecular imaging, and biocompatibility are also discussed. Finally, nanotoxicity aspects and recommendations of ligand-based surface engineered nanomaterials are detailed for future biomedical applications.

Diabetes is a chronic endocrine disorder affecting millions of people worldwide. The classical methods for diagnosing diabetes is the tracking of fasting blood glucose, yet this method has several limitations. Alternatively, recently developed nanotechnology-based methods present advantages such as detection at individual cell and molecular level with possible incorporation in diagnostic biochips. In particular, nanozymes, which are nanomaterials with enzymatic properties, have been developed for optical sensing of glucose due to the unique properties. Metal-, metal oxide-, carbon-, and metal sulfide-based nanozymes are now widely used for glucose detection due to enhanced sensitivity and specificity. This chapter reviews nanozymes for glucose sensing in the context of diabetes management. Challenges to implement the developed sensors in real-life scenarios are also discussed.

Assessing anterior chamber inflammation is highly subjective and challenging. Although various grading systems attempt to offer objectivity and standardization, the clinical assessment has high interobserver variability. Traditional techniques, such as laser flare meter and fluorophotometry, are not widely used since they are time-consuming. With the development of optical coherence tomography with high sensitivity, direct imaging offers an excellent alternative to assess objectively inflammation with the potential for automated analysis. We describe various anterior chamber inflammation grading methods and discuss their utility, advantages, and disadvantages.

Purpose: In many epithelia, including the corneal endothelium, intracellular/extracellular stresses break down the perijunctional actomyosin ring (PAMR) and zonula occludens-1 (ZO-1) at the apical junctions. This study aims to grade the severity of damage to PAMR and ZO-1 through machine learning.Methods: Immunocytochemical images of PAMR and ZO-1 were drawn from recent studies on the corneal endothelium subjected to hypothermia and oxidative stress. The images were analyzed for their morphological (e.g., Hu moments) and textural features (based on gray-level co-occurrence matrix [GLCM] and Gabor filters). The extracted features were ranked by SHapley analysis and analysis of variance. Then top features were used to grade the severity of damage using a suite of ensemble classifiers, including random forest, bagging classifier (BC), AdaBoost, extreme gradient boosting, and stacking classifier.Results: A partial set of features from GLCM, along with Hu moments and the number of hexagons, enabled the classification of damage to PAMR into Control, Mild, Moderate, and Severe with the area under the receiver operating characteristics curve (AUC) = 0.92 and F1 score = 0.77 with BC. In contrast, a bank of Gabor filters provided a partial set of features that could be combined with Hu moments, branch length, and sharpness for the classification of ZO-1 images into four levels with AUC = 0.95 and F1 score of 0.8 with BC.Conclusions: We have developed a workflow that enables the stratification of damage to PAMR and ZO-1. The approach can be applied to similar data during drug discovery or pathophysiological studies of epithelia.