Signs and Symptoms of Dry Eye in Keratoconus Patients: A Pilot Study

Purpose: To compare signs and symptoms of dry eye in keratoconus (KC) patients versus healthy subjects. Methods: A total of 15 KC patients (KC group, n = 15 eyes) and 16 healthy subjects (control group, 16 eyes) were enrolled in this study. The Schirmer I test with no anesthetic, tear break-up time (TBUT), corneal staining characteristics, and ocular surface disease index (OSDI) scores were evaluated for both groups. Impression cytology, combined with/scanning laser confocal microscopy (LCM), was performed to evaluate goblet cell density, mucin cloud height (MCH), and goblet cell layer thickness (CLT). Finally, tear concentrations of di-adenosine tetraphosphate (Ap4A) were assessed. Results were statistically analyzed using Shapiro–Wilk and non-parametric Wilcoxon rank sum tests. Statistical significance was set at p < 0.05. Results: KC patients had lower tear volumes and greater corneal staining than did healthy subjects (p < 0.05). OSDI scores were 44.96 ± 8.65 and 17.78 ± 6.50 for the KC and control groups, respectively (p < 0.05). We found no statistically significant differences in TBUT between groups. Impression cytology revealed lower goblet cell densities in KC group patients versus control group subjects (84.88 ± 32.98 and 128.88 ± 50.60 cells/mm,2 respectively, p < 0.05). There was a statistically significant reduction in MCH and CLT in KC group patients compared with control group subjects. Ap4A tear concentrations were higher in KC group patients than in control group subjects (2.56 ± 1.10 and 0.15 ± 0.12 µM, respectively, p < 0.05). Conclusions: The parameters evaluated in this study indicate that KC patients suffer greater symptoms of dry eye and greater tear instability, primarily due to the decreased mucin production in their tears, than do healthy patients with no KC.

Effect of overnight orthokeratology on conjunctival goblet cells

Objective: To evaluate the differences between goblet cell density (GCD) and symptomatology after one month of orthokeratology lens wear. Methods: A pilot, short-term study was conducted. Twenty-two subjects (29.7. ±. 7.0 years old) participated voluntarily in the study. Subjects were divided into two groups: habitual silicone hydrogel contact lens wearers (SiHCLW) and new contact lens wearers (NCLW). Schirmer test, tear break up time (TBUT), Ocular Surface Disease Index (OSDI) questionnaire and conjunctival impression cytology. GCD, mucin cloud height (MCH) and cell layer thickness (CLT) were measured. All measurements were performed before orthokeratology fitting and one month after fitting to assess the evolution of the changes throughout this time. Results: No differences in tear volume and TBUT between groups were found (p>0.05). However, the OSDI score was statistically better after one month of orthokeratology lens wear than the baseline for the SiHCLW group (p=0.03). Regarding the goblet cell analysis, no differences were found in CLT and MCH from the baseline visit to the one month visit for the SiHCLW compared with NCLW groups (p>0.05). At baseline, the GCD in the SiHCLW group were statistically lower than NCLW group (p<0.001). There was a significant increase in GCD after orthokeratology fitting from 121±140cell/mm2 to 254±130cell/mm2 (p<0.001) in the SiHCLW group. Conclusion: Orthokeratology improves the dry eye subject symptoms and GCD after one month of wearing in SiHCLW. These results suggest that orthokeratology could be considered a good alternative for silicone hydrogel contact lens discomfort and dryness. © 2016 British Contact Lens Association.

Automatic Counting of Microglial Cells in Healthy and Glaucomatous Mouse Retinas

Proliferation of microglial cells has been considered a sign of glial activation and a hallmark of ongoing neurodegenerative diseases. Microglia activation is analyzed in animal models of different eye diseases. Numerous retinal samples are required for each of these studies to obtain relevant data of statistical significance. Because manual quantification of microglial cells is time consuming, the aim of this study was develop an algorithm for automatic identification of retinal microglia. Two groups of adult male Swiss mice were used: age-matched controls (naïve, n = 6) and mice subjected to unilateral laser-induced ocular hypertension (lasered; n = 9). In the latter group, both hypertensive eyes and contralateral untreated retinas were analyzed. Retinal whole mounts were immunostained with anti Iba-1 for detecting microglial cell populations. A new algorithm was developed in MATLAB for microglial quantification; it enabled the quantification of microglial cells in the inner and outer plexiform layers and evaluates the area of the retina occupied by Iba-1+ microglia in the nerve fiber-ganglion cell layer. The automatic method was applied to a set of 6,000 images. To validate the algorithm, mouse retinas were evaluated both manually and computationally; the program correctly assessed the number of cells (Pearson correlation R = 0.94 and R = 0.98 for the inner and outer plexiform layers respectively). Statistically significant differences in glial cell number were found between naïve, lasered eyes and contralateral eyes (P<0.05, naïve versus contralateral eyes; P<0.001, naïve versus lasered eyes and contralateral versus lasered eyes). The algorithm developed is a reliable and fast tool that can evaluate the number of microglial cells in naïve mouse retinas and in retinas exhibiting proliferation. The implementation of this new automatic method can enable faster quantification of microglial cells in retinal pathologies.

A Diagnostic Calculator for Detecting Glaucoma on the Basis of Retinal Nerve Fiber Layer, Optic Disc, and Retinal Ganglion Cell Analysis by Optical Coherence Tomography

Purpose: The purpose of this study was to develop and validate a multivariate predictive model to detect glaucoma by using a combination of retinal nerve fiber layer (RNFL), retinal ganglion cell-inner plexiform (GCIPL), and optic disc parameters measured using spectral-domain optical coherence tomography (OCT). Methods: Five hundred eyes from 500 participants and 187 eyes of another 187 participants were included in the study and validation groups, respectively. Patients with glaucoma were classified in five groups based on visual field damage. Sensitivity and specificity of all glaucoma OCT parameters were analyzed. Receiver operating characteristic curves (ROC) and areas under the ROC (AUC) were compared. Three predictive multivariate models (quantitative, qualitative, and combined) that used a combination of the best OCT parameters were constructed. A diagnostic calculator was created using the combined multivariate model. Results: The best AUC parameters were: inferior RNFL, average RNFL, vertical cup/disc ratio, minimal GCIPL, and inferior-temporal GCIPL. Comparisons among the parameters did not show that the GCIPL parameters were better than those of the RNFL in early and advanced glaucoma. The highest AUC was in the combined predictive model (0.937; 95% confidence interval, 0.911–0.957) and was significantly (P = 0.0001) higher than the other isolated parameters considered in early and advanced glaucoma. The validation group displayed similar results to those of the study group. Conclusions: Best GCIPL, RNFL, and optic disc parameters showed a similar ability to detect glaucoma. The combined predictive formula improved the glaucoma detection compared to the best isolated parameters evaluated. The diagnostic calculator obtained good classification from participants in both the study and validation groups.