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.

A modified DEA model to estimate the importance of objectives with an application to agricultural economics

This paper demonstrates a connection between data envelopment analysis (DEA) and a non-interactive elicitation method to estimate the weights of objectives for decision-makers in a multiple attribute approach. This connection gives rise to a modified DEA model that allows us to estimate not only efficiency measures but also preference weights by radially projecting each unit onto a linear combination of the elements of the payoff matrix (which is obtained by standard multicriteria methods). For users of multiple attribute decision analysis the basic contribution of this paper is a new interpretation in terms of efficiency of the non-interactive methodology employed to estimate weights in a multicriteria approach. We also propose a modified procedure to calculate an efficient payoff matrix and a procedure to estimate weights through a radial projection rather than a distance minimization. For DEA users, we provide a modified DEA procedure to calculate preference weights and efficiency measures that does not depend on any observations in the dataset. This methodology has been applied to an agricultural case study in Spain.