Ocular residual astigmatism and topographic disparity vector indexes in normal healthy eyes

Purpose: To define a range of normality for the vectorial parameters Ocular Residual Astigmatism (ORA) and topography disparity (TD) and to evaluate their relationship with visual, refractive, anterior and posterior corneal curvature, pachymetric and corneal volume data in normal healthy eyes. Methods: This study comprised a total of 101 consecutive normal healthy eyes of 101 patients ranging in age from 15 to 64 years old. In all cases, a complete corneal analysis was performed using a Scheimpflug photography-based topography system (Pentacam system Oculus Optikgeräte GmbH). Anterior corneal topographic data were imported from the Pentacam system to the iASSORT software (ASSORT Pty. Ltd.), which allowed the calculation of the ocular residual astigmatism (ORA) and topography disparity (TD). Linear regression analysis was used for obtaining a linear expression relating ORA and posterior corneal astigmatism (PCA). Results: Mean magnitude of ORA was 0.79 D (SD: 0.43), with a normality range from 0 to 1.63 D. 90 eyes (89.1%) showed against-the-rule ORA. A weak although statistically significant correlation was found between the magnitudes of posterior corneal astigmatism and ORA (r = 0.34, p < 0.01). Regression analysis showed the presence of a linear relationship between these two variables, although with a very limited predictability (R2: 0.08). Mean magnitude of TD was 0.89 D (SD: 0.50), with a normality range from 0 to 1.87 D. Conclusion: The magnitude of the vector parameters ORA and TD is lower than 1.9 D in the healthy human eye.

Hydrogen Peroxide in Biocatalysis. A Dangerous Liaison

Hydrogen peroxide is a substrate or side-product in many enzyme-catalyzed reactions. For example, it is a side-product of oxidases, resulting from the re-oxidation of FAD with molecular oxygen, and it is a substrate for peroxidases and other enzymes. However, hydrogen peroxide is able to chemically modify the peptide core of the enzymes it interacts with, and also to produce the oxidation of some cofactors and prostetic groups (e.g., the hemo group). Thus, the development of strategies that may permit to increase the stability of enzymes in the presence of this deleterious reagent is an interesting target. This enhancement in enzyme stability has been attempted following almost all available strategies: site-directed mutagenesis (eliminating the most reactive moieties), medium engineering (using stabilizers), immobilization and chemical modification (trying to generate hydrophobic environments surrounding the enzyme, to confer higher rigidity to the protein or to generate oxidation-resistant groups), or the use of systems capable of decomposing hydrogen peroxide under very mild conditions. If hydrogen peroxide is just a side-product, its immediate removal has been reported to be the best solution. In some cases, when hydrogen peroxide is the substrate and its decomposition is not a sensible solution, researchers coupled one enzyme generating hydrogen peroxide “in situ” to the target enzyme resulting in a continuous supply of this reagent at low concentrations thus preventing enzyme inactivation. This review will focus on the general role of hydrogen peroxide in biocatalysis, the main mechanisms of enzyme inactivation produced by this reactive and the different strategies used to prevent enzyme inactivation caused by this “dangerous liaison”.

Comparison of the predictability of refractive cylinder correction by laser in situ keratomileusis in eyes with low or high ocular residual astigmatism

Purpose: To compare the manifest refractive cylinder (MRC) predictability of myopic astigmatism laser in situ keratomileusis (LASIK) between eyes with low and high ocular residual astigmatism (ORA). Setting: London Vision Clinic, London, United Kingdom. Design: Retrospective case study. Methods: The ORA was considered the vector difference between the MRC and the corneal astigmatism. The index of success (IoS), difference vector ÷ MRC, was analyzed for different groups as follows: stage 1, low ORA (ORA ÷ MRC <1), high ORA (ORA ÷ MRC ≥1); stage 2, low ORA group reduced to match the high ORA group for MRC; stage 3, grouped by ORA magnitude with low ORA (<0.50 diopters [D]), mid ORA (0.50 to 1.24 D), and high ORA (≥1.25 D); stage 4, high ORA group subdivided into low (<0.75 D) and high (≥0.75 D) corneal astigmatism. Results: For stage 1, the mean preoperative MRC and mean IoS were −1.32 D ± 0.65 (SD) (range −0.55 to −3.77 D) and 0.27, respectively, for low ORA and −0.79 ± 0.20 D (range −0.56 to −2.05 D) and 0.37, respectively, for high ORA. For stage 2, the mean IoS increased to 0.32 for low ORA. For stage 3, the mean IoS was 0.28, 0.29, and 0.31 for low ORA, mid ORA, and high ORA, respectively. For stage 4, the mean IoS was 0.20 for high ORA/low corneal astigmatism and 0.35 for high ORA/high corneal astigmatism. Conclusions: The MRC predictability was slightly worse in eyes with high ORA when grouped by the ORA ÷ MRC. Matching for the MRC and grouping by ORA magnitude resulted in similar predictability; however, eyes with high ORA and high corneal astigmatism were less predictable.

Clinical utility of ocular residual astigmatism and topographic disparity vector indexes in subclinical and clinical keratoconus

Purpose. We aimed to characterize the distribution of the vector parameters ocular residual astigmatism (ORA) and topography disparity (TD) in a sample of clinical and subclinical keratoconus eyes, and to evaluate their diagnostic value to discriminate between these conditions and healthy corneas. Methods. This study comprised a total of 43 keratoconic eyes (27 patients, 17–73 years) (keratoconus group), 11 subclinical keratoconus eyes (eight patients, 11–54 years) (subclinical keratoconus group) and 101 healthy eyes (101 patients, 15–64 years) (control group). In all cases, a complete corneal analysis was performed using a Scheimpflug photography-based topography system. Anterior corneal topographic data was imported from it to the iASSORT software (ASSORT Pty. Ltd), which allowed the calculation of ORA and TD. Results. Mean magnitude of the ORA was 3.23 ± 2.38, 1.16 ± 0.50 and 0.79 ± 0.43 D in the keratoconus, subclinical keratoconus and control groups, respectively (p < 0.001). Mean magnitude of the TD was 9.04 ± 8.08, 2.69 ± 2.42 and 0.89 ± 0.50 D in the keratoconus, subclinical keratoconus and control groups, respectively (p < 0.001). Good diagnostic performance of ORA (cutoff point: 1.21 D, sensitivity 83.7 %, specificity 87.1 %) and TD (cutoff point: 1.64 D, sensitivity 93.3 %, specificity 92.1 %) was found for the detection of keratoconus. The diagnostic ability of these parameters for the detection of subclinical keratoconus was more limited (ORA: cutoff 1.17 D, sensitivity 60.0 %, specificity 84.2 %; TD: cutoff 1.29 D, sensitivity 80.0 %, specificity 80.2 %). Conclusion. The vector parameters ORA and TD are able to discriminate with good levels of precision between keratoconus and healthy corneas. For the detection of subclinical keratoconus, only TD seems to be valid.