[Diagnostics and treatment of infectious keratitis]. / Fertozéses keratitisek diagnosztikája és kezelése.

We summarize up-to-date diagnostic and treatment of infectious keratitis using literature data and some clinical examples. In the clinical practice, most commonly bacterial, herpetic, mycotic and acanthamoeba keratitis occur. Beside slitlamp examination, for diagnostic purpose, we analyse corneal sensitivity, perform in vivo confocal microscopy, polymerase-chain-reaction (PCR), in vitro culture and histological examination of the corneal sample. As conservative treatment we use primarily topical moxifloxacin or cephasolin with fortified tobramycin or gentamycin in bacterial, topical antiviral gel (in some cases in combination with systemic antiviral treatment) in part in combination with topical corticosteroids in herpetic, voriconasole or amphotericin-B in mycotic, and topical-triple-therapy (diamidine, biguanid and antibiotics) in acanthamoeba keratitis. In case of early diagnosis and initiation of topical therapy, most cases of infectious keratitis recover successfully. However, beside conservative treatment, penetrating keratoplasty, amniotic membrane transplantation and crosslinking therapy may be necessary. Crosslinking is solely contraindicated in herpetic keratitis. Orv Hetil. 2017; 158(31): 1203-1212.

Fractal-based analysis of optical coherence tomography data to quantify retinal tissue damage.

BACKGROUND: The sensitivity of Optical Coherence Tomography (OCT) images to identify retinal tissue morphology characterized by early neural loss from normal healthy eyes is tested by calculating structural information and fractal dimension. OCT data from 74 healthy eyes and 43 eyes with type 1 diabetes mellitus with mild diabetic retinopathy (MDR) on biomicroscopy was analyzed using a custom-built algorithm (OCTRIMA) to measure locally the intraretinal layer thickness. A power spectrum method was used to calculate the fractal dimension in intraretinal regions of interest identified in the images. ANOVA followed by Newman-Keuls post-hoc analyses were used to test for differences between pathological and normal groups. A modified p value of <0.001 was considered statistically significant. Receiver operating characteristic (ROC) curves were constructed to describe the ability of each parameter to discriminate between eyes of pathological patients and normal healthy eyes. RESULTS: Fractal dimension was higher for all the layers (except the GCL + IPL and INL) in MDR eyes compared to normal healthy eyes. When comparing MDR with normal healthy eyes, the highest AUROC values estimated for the fractal dimension were observed for GCL + IPL and INL. The maximum discrimination value for fractal dimension of 0.96 (standard error =0.025) for the GCL + IPL complex was obtained at a FD ≤ 1.66 (cut off point, asymptotic 95% Confidence Interval: lower-upper bound = 0.905-1.002). Moreover, the highest AUROC values estimated for the thickness measurements were observed for the OPL, GCL + IPL and OS. Particularly, when comparing MDR eyes with control healthy eyes, we found that the fractal dimension of the GCL + IPL complex was significantly better at diagnosing early DR, compared to the standard thickness measurement. CONCLUSIONS: Our results suggest that the GCL + IPL complex, OPL and OS are more susceptible to initial damage when comparing MDR with control healthy eyes. Fractal analysis provided a better sensitivity, offering a potential diagnostic predictor for detecting early neurodegeneration in the retina.

Automated classifiers for early detection and diagnosis of retinopathy in diabetic eyes.

BACKGROUND: Artificial neural networks (ANNs) have been used to classify eye diseases, such as diabetic retinopathy (DR) and glaucoma. DR is the leading cause of blindness in working-age adults in the developed world. The implementation of DR diagnostic routines could be feasibly improved by the integration of structural and optical property test measurements of the retinal structure that provide important and complementary information for reaching a diagnosis. In this study, we evaluate the capability of several structural and optical features (thickness, total reflectance and fractal dimension) of various intraretinal layers extracted from optical coherence tomography images to train a Bayesian ANN to discriminate between healthy and diabetic eyes with and with no mild retinopathy. RESULTS: When exploring the probability as to whether the subject's eye was healthy (diagnostic condition, Test 1), we found that the structural and optical property features of the outer plexiform layer (OPL) and the complex formed by the ganglion cell and inner plexiform layers (GCL + IPL) provided the highest probability (positive predictive value (PPV) of 91% and 89%, respectively) for the proportion of patients with positive test results (healthy condition) who were correctly diagnosed (Test 1). The true negative, TP and PPV values remained stable despite the different sizes of training data sets (Test 2). The sensitivity, specificity and PPV were greater or close to 0.70 for the retinal nerve fiber layer's features, photoreceptor outer segments and retinal pigment epithelium when 23 diabetic eyes with mild retinopathy were mixed with 38 diabetic eyes with no retinopathy (Test 3). CONCLUSIONS: A Bayesian ANN trained on structural and optical features from optical coherence tomography data can successfully discriminate between healthy and diabetic eyes with and with no retinopathy. The fractal dimension of the OPL and the GCL + IPL complex predicted by the Bayesian radial basis function network provides better diagnostic utility to classify diabetic eyes with mild retinopathy. Moreover, the thickness and fractal dimension parameters of the retinal nerve fiber layer, photoreceptor outer segments and retinal pigment epithelium show promise for the diagnostic classification between diabetic eyes with and with no mild retinopathy.

Detailed Evaluation of Possible Ganglion Cell Loss in the Retina of Zucker Diabetic Fatty (ZDF) Rats.

A thinning of the inner retina is one of the earliest potential markers of neuroretinal damage in diabetic subjects. The histological background is uncertain; retinal ganglion cell (RGC) loss and changes in the structure or thickness of the inner plexiform layer (IPL) have been suspected. Studies conducted on animal models on RGC pathology gave contradictory results. Hereby we present RGC numbers, distribution patterns and IPL thickness from Zucker Diabetic Fatty (ZDF) rats. After labelling RGCs on retinal whole mounts, isodensity maps were constructed, RGC numbers and distribution patterns analysed using a custom-built algorithm, enabling point-by-point comparison. There was no change in staining characteristics of the antibodies and no significant difference in average RGC densities was found compared to controls. The distribution patterns were also comparable and no significant difference was found in IPL thickness and stratification or in the number of apoptotic cells in the ganglion cell layer (GCL). Our results provide a detailed evaluation of the inner retina and exclude major RGC loss in ZDF rats and suggest that other factors could serve as a potential explanation for inner retinal thinning in clinical studies. Our custom-built method could be adopted for the assessment of other animal or human retinas.

Histological Evaluation of Diabetic Neurodegeneration in the Retina of Zucker Diabetic Fatty (ZDF) Rats.

In diabetes, retinal dysfunctions exist prior to clinically detectable vasculopathy, however the pathology behind these functional deficits is still not fully established. Previously, our group published a detailed study on the retinal histopathology of type 1 diabetic (T1D) rat model, where specific alterations were detected. Although the majority of human diabetic patients have type 2 diabetes (T2D), similar studies on T2D models are practically absent. To fill this gap, we examined Zucker Diabetic Fatty (ZDF) rats - a model for T2D - by immunohistochemistry at the age of 32 weeks. Glial reactivity was observed in all diabetic specimens, accompanied by an increase in the number of microglia cells. Prominent outer segment degeneration was detectable with changes in cone opsin expression pattern, without a decrease in the number of labelled elements. The immunoreactivity of AII amacrine cells was markedly decreased and changes were detectable in the number and staining of some other amacrine cell subtypes, while most other cells examined did not show any major alterations. Overall, the retinal histology of ZDF rats shows a surprising similarity to T1D rats indicating that despite the different evolution of the disease, the neuroretinal cells affected are the same in both subtypes of diabetes.