Rate and Predictors of Misclassification of Active Diabetic Macular Edema as Detected by an Automated Retinal Image Analysis System.

INTRODUCTION: The aim of this work is to estimate the sensitivity, specificity, and misclassification rate of an automated retinal image analysis system (ARIAS) in diagnosing active diabetic macular edema (DME) and to identify factors associated with true and false positives. METHODS: We conducted a cross-sectional study of prospectively enrolled patients with diabetes mellitus (DM) referred to a tertiary medical retina center for screening or management of DME. All patients underwent two-field fundus photography (macula- and disc-centered) with a true-color confocal camera; images were processed by EyeArt V.2.1.0 (Woodland Hills, CA, USA). Active DME was defined as the presence of intraretinal or subretinal fluid on spectral-domain optical coherence tomography (SD-OCT). Sensitivity and specificity and their 95% confidence intervals (CIs) were calculated. Variables associated with true (i.e., DME labeled as present by ARIAS + fluid on SD-OCT) and false positives (i.e., DME labeled as present by ARIAS + no fluid on SD-OCT) of active DME were explored. RESULTS: A total of 298 eyes were included; 92 eyes (31%) had active DME. ARIAS sensitivity and specificity were 82.61% (95% CI 72.37-89.60) and 84.47% (95% CI 78.34-89.10). The misclassification rate was 16%. Factors associated with true positives included younger age (p = 0.01), shorter DM duration (p = 0.006), presence of hard exudates (p = 0.005), and microaneurysms (p = 0.002). Factors associated with false positives included longer DM duration (p = 0.01), worse diabetic retinopathy severity (p = 0.008), history of inactivated DME (p < 0.001), and presence of hard exudates (p < 0.001), microaneurysms (p < 0.001), or epiretinal membrane (p = 0.06). CONCLUSIONS: The sensitivity of ARIAS was diminished in older patients and those without DME-related fundus lesions, while the specificity was reduced in cases with a history of inactivated DME. ARIAS performed well in screening for naïve DME but is not effective in surveillance inactivated DME.

Assessing Diabetic Retinopathy Staging With AI: A Comparative Analysis Between Pseudocolor and LED Imaging.

Purpose: To compare the diagnostic performance of artificial intelligence (AI)-based diabetic retinopathy (DR) staging system across pseudocolor, simulated white light (SWL), and light-emitting diode (LED) camera imaging modalities. Methods: A cross-sectional investigation involved patients with diabetes undergoing imaging with an iCare DRSplus confocal LED camera and an Optos confocal, ultra-widefield pseudocolor camera, with and without SWL. Macula-centered and optic nerve-centered 45 × 45-degree photographs were processed using EyeArt v2.1. Human graders established the ground truth (GT) for DR severity on dilated fundus exams. Sensitivity and weighted Cohen's weighted kappa (wκ) were calculated. An ordinal generalized linear mixed model identified factors influencing accurate DR staging. Results: The study included 362 eyes from 189 patients. The LED camera excelled in identifying sight-threatening DR stages (sensitivity = 0.83, specificity = 0.95 for proliferative DR) and had the highest agreement with the GT (wκ = 0.71). The addition of SWL to pseudocolor imaging resulted in decreased performance (sensitivity = 0.33, specificity = 0.98 for proliferative DR; wκ = 0.55). Peripheral lesions reduced the likelihood of being staged in the same or higher DR category by 80% (P < 0.001). Conclusions: Pseudocolor and LED cameras, although proficient, demonstrated non-interchangeable performance, with the LED camera exhibiting superior accuracy in identifying advanced DR stages. These findings underscore the importance of implementing AI systems trained for ultra-widefield imaging, considering the impact of peripheral lesions on correct DR staging. Translational Relevance: This study underscores the need for artificial intelligence-based systems specifically trained for ultra-widefield imaging in diabetic retinopathy assessment.

Nicotinamide Prevents Retinal Vascular Dropout in a Rat Model of Ocular Hypertension and Supports Ocular Blood Supply in Glaucoma Patients.

Purpose: To investigate whether nicotinamide (NAM) modulates retinal vasculature in glaucoma. Methods: This was a prospective controlled clinical trial investigating animal and human histopathology. Participants included normotensive and ocular hypertensive rats, postmortem human ocular tissue, glaucoma patients (n = 90), and healthy controls (n = 30). The study utilized histopathology, computer-assisted retinal vasculature analysis, optical coherence tomography angiography (OCTA), and NAM treatment. The main outcome measures included retinal vascular parameters in rats as assessed by AngioTool; retinal vasculature integrity in rats and humans as assessed by histopathology, antibody-staining, and ImageJ-based measurements; and retinal perfusion density (PD) and flux index in humans as assessed by OCTA. Results: A number of vessel parameters were altered in ocular hypertension/glaucoma compared to healthy controls. NAM treatment improved the retinal vasculature in ocular hypertensive rats, with an increase in mean vessel area, percentage area covered by vessels, total vessel length, total junctions, and junction density as assessed by AngioTool (all P < 0.05); vessel wall integrity as assessed by VE-cadherin antibody staining was also improved (P < 0.01). In humans, as assessed by OCTA, increases in PD in the optic nerve head and macula complete image (0.7%, P = 0.04 and 1.0%, P = 0.002, respectively) in healthy controls, and an increase in the temporal quadrant of the macula (0.7%, P = 0.02) in glaucoma patients was seen after NAM treatment. Conclusions: NAM can prevent retinal vascular damage in an animal model of glaucoma. After NAM treatment, glaucoma patients and healthy controls demonstrated a small increase in retinal vessel parameters as assessed by OCTA.

Tumor immunogenicity dictates reliance on TCF1 in CD8+ T cells for response to immunotherapy.

Stem-like CD8+ T cells are regulated by T cell factor 1 (TCF1) and are considered requisite for immune checkpoint blockade (ICB) response. However, recent findings indicate that reliance on TCF1+CD8+ T cells for ICB efficacy may differ across tumor contexts. We find that TCF1 is essential for optimal priming of tumor antigen-specific CD8+ T cells and ICB response in poorly immunogenic tumors that accumulate TOX+ dysfunctional T cells, but is dispensable for T cell priming and therapy response in highly immunogenic tumors that efficiently expand transitory effectors. Importantly, improving T cell priming by vaccination or by enhancing antigen presentation on tumors rescues the defective responses of TCF1-deficient CD8+ T cells upon ICB in poorly immunogenic tumors. Our study highlights TCF1's role during the early stages of anti-tumor CD8+ T cell responses with important implications for guiding optimal therapeutic interventions in cancers with low TCF1+CD8+ T cells and low-neo-antigen expression.

The upcoming role of Artificial Intelligence (AI) for retinal and glaucomatous diseases.

In recent years, the role of artificial intelligence (AI) and deep learning (DL) models is attracting increasing global interest in the field of ophthalmology. DL models are considered the current state-of-art among the AI technologies. In fact, DL systems have the capability to recognize, quantify and describe pathological clinical features. Their role is currently being investigated for the early diagnosis and management of several retinal diseases and glaucoma. The application of DL models to fundus photographs, visual fields and optical coherence tomography (OCT) imaging has provided promising results in the early detection of diabetic retinopathy (DR), wet age-related macular degeneration (w-AMD), retinopathy of prematurity (ROP) and glaucoma. In this review we analyze the current evidence of AI applied to these ocular diseases, as well as discuss the possible future developments and potential clinical implications, without neglecting the present limitations and challenges in order to adopt AI and DL models as powerful tools in the everyday routine clinical practice.

Widespread retina and optic nerve neuroinflammation in enucleated eyes from glaucoma patients.

Neuroinflammation is recognized as a key component of neurodegenerative disease. In glaucoma, a common neurodegenerative disease and the leading cause of irreversible blindness, the evidence for neuroinflammation in patients is lacking. Animal models have demonstrated significant pro-inflammatory activation of resident glia in the retina, as well as influx of blood-derived monocytes and pro-inflammatory factors. Confirmation of this in human donor tissue has been challenging due to a lack of well-preserved and well-characterized post-mortem tissue. To address this we utilize archived, wax embedded eyes fixed immediately following enucleation from living glaucoma patients. We compared glaucoma to control eyes (enucleated for uveal melanoma where the tumor did not impact the central retina or optic nerve). We performed immunolabelling for neurodegenerative and glial markers (CD45, CD163, IBA1, GFAP, Vimentin) which were quantified by high-resolution light microscopy and image analysis in FIJI. Glaucoma eyes demonstrated significant neural loss consistent with advanced neurodegeneration. IBA1 and GFAP were significantly increased in the retina and optic nerve head of the glaucomatous eyes indicating that significant neuroinflammation had occurred which support findings in animal models. Inflammation is a treatable symptom of many diseases and as such, identification of earlier inflammatory processes in glaucoma could be important for potential future treatment options.

Valproic Acid Reduces Neuroinflammation to Provide Retinal Ganglion Cell Neuroprotection in the Retina Axotomy Model.

Neuroinflammation is a critical and targetable pathogenic component of neurodegenerative diseases, including glaucoma, the leading cause of irreversible blindness. Valproic acid has previously been demonstrated to reduce neuroinflammation and is neuroprotective in a number of experimental settings. To determine whether valproic acid can limit retinal neuroinflammation and protect retinal neurons we used an ex vivo retina explant (axotomy) model to isolate resident glial responses from blood-derived monocytes. Neuroinflammatory status was defined using high resolution confocal imaging with 3D morphological reconstruction and cytokine protein arrays. Valproic acid significantly reduced microglia and astrocyte morphological changes, consistent with a reduction in pro-inflammatory phenotypes. Cytokine profiling demonstrated that valproic acid significantly attenuated or prevented expression of pro-inflammatory cytokines in injured retina. This identifies that the retinal explant model as a useful tool to explore resident neuroinflammation in a rapid timescale whilst maintaining a complex system of cell interactions and valproic acid as a useful drug to further explore anti-neuroinflammatory strategies in retinal disease.

Combination of In Situ Lcn2 pRNA-RNAi Nanotherapeutics and iNSC Transplantation Ameliorates Experimental SCI in Mice.

Spinal cord injury (SCI) is a debilitating neurological condition characterized by different cellular and molecular mechanisms that interplay in exacerbating the progression of the pathology. No fully restorative therapies are yet available, and it is thus becoming recognized that combinatorial approaches aimed at addressing different aspects of SCI will likely results in greater functional outcomes. Here we employed packaging RNA-mediated RNA interference (pRNA-RNAi) nanotherapeutics to downregulate in situ the expression of lipocalin 2 (Lcn2), a known mediator of neuroinflammation and autocrine mediator of reactive astrogliosis, and to create a more amenable niche for the subsequent transplantation of induced neural stem cells (iNSCs). To our knowledge, this is the first approach that takes advantage of the modular and multifunctional pRNA three-way junction platform in the SCI niche, while also exploiting the therapeutic potential of immune-compatible and feasible iNSC transplants. We show the combination of such treatments in a mouse model of contusion thoracic SCI leads to significant improvement of locomotor function, albeit not better than single pRNA-RNAi treatment, and results in synergistic histopathological effects, such as reduction of glial scar volume, diminished pro-inflammatory response, and promotion of neuronal survival. Our results provide evidence for a novel combinatorial approach for treating SCI.