Background The normal RPE sheet in the C57BL/6J mouse is subclassified into two major tiling patterns: a regular generally hexagonal array covering most of the surface and a “soft network” near the ciliary body made of irregularly shaped cells. Physics models predict these two patterns based on contractility and elasticity of the RPE cell, and strength of cellular adhesion between cells. Hypothesis We hypothesized and identified major changes in RPE regular hexagonal tiling pattern in rd10 compared to C57BL/6J mice. Results In rd10 mice, RPE sheet damage was extensive but occurred later than expected, after most retinal degeneration was complete. RPE sheet changes occur in zones with a bullseye pattern. In the posterior zone, around the optic nerve, RPE cells take on larger irregular and varied shapes to maintain an intact monolayer. In mid periphery, RPE cells have a compressed or convoluted morphology that progress into ingrown layers of RPE under the retina. Cells in the periphery maintain their shape and size until the late stages of the RPE reorganization. The number of neighboring cells varies widely depending on zone and progression. RPE morphology continues to deteriorate after the photoreceptors have degenerated. Conclusions The RPE cells are bystanders to photoreceptor degeneration in the rd10 model, and the collateral damage to the RPE results in changes in morphology as early as 30 days old. Quantitative measures of the tiling patterns and histopathology detected here were scripted in a pipeline written in Perl and Cell Profiler (an open source MatLab plugin) and are directly applicable to RPE sheet images from noninvasive fundus autofluorescence (FAF), adaptive optics confocal scanning laser ophthalmoscope (AO-cSLO), and spectral domain optical coherence tomography (SD-OCT) of patients with early stage AMD or RP.

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Biometric analyses of quantitative traits in eyes of mice can reveal abnormalities related to refractive or ocular development. Due to the small size of the mouse eye, highly accurate and precise measurements are needed to detect meaningful differences. We sought a non-contact measuring technique to obtain highly accurate and precise linear dimensions of the mouse eye. Laser micrometry was validated with gauge block standards. Simple procedures to measure eye dimensions on three axes were devised. Mouse eyes from C57BL/6J and rd10 on a C57BL/6J background were dissected and extraocular muscle and fat removed. External eye dimensions of axial length (anterior-posterior (A-P) axis) and equatorial diameter (superior-inferior (S-I) and nasal-temporal (N-T) axes) were obtained with a laser micrometer. Several approaches to prevent or ameliorate evaporation due to room air were employed. The resolution of the laser micrometer was less than 0.77 microns, and it provided accurate and precise non-contact measurements of eye dimensions on three axes. External dimensions of the eye strongly correlated with eye weight. The N-T and S-I dimensions of the eye correlated with each other most closely from among the 28 pair-wise combinations of the several parameters that were collected. The equatorial axis measurements correlated well from the right and left eye of each mouse. The A-P measurements did not correlate or correlated poorly in each pair of eyes. The instrument is well suited for the measurement of enucleated eyes and other structures from most commonly used species in experimental vision research and ophthalmology.

We are interested in developing quantitative tools to study RPE morphology. We want to detect changes in the RPE by strain, disease, genotype, and age. Ultimately these tools should be useful in predicting retinal disease progression. The morphometric data will also help us to understand RPE sheet formation and barrier functions. A clear disruption of the regular cell size and shape appeared in mouse mutants. Aspect ratio and cell area together gave rise to principal components that predicted age and genotype accurately and well before visually obvious damage could be seen.

A very little data are available to understand the drivers of burnout amongst health care workers in the pediatric intensive care unit. It is a survey-based, cross-sectional, point-prevalence analysis within a single children's health system with two free-standing hospitals (one academic and one private) to characterize the relationship of demographics, organizational support, organizational culture, relationship quality, conflict and work schedules with self-reported burnout. Burnout was identified in 152 (39.7%) of the 383 (38.7%) respondents. No significant relationship was identified between burnout and demographic factors or work schedule. A more constructive culture (odds ratio [OR], 0.84; 95% confidence interval [CI], 0.77–0.90; p < 0.001), more organizational support (OR, 0.94; 95% CI, 0.92–0.96; p <0 0.001), and better staff relationships (OR, 0.54, 95% CI, 0.43–0.69; p < 0.001) reduced odds of burnout. More conflict increased odds (OR, 1.25; 95% CI, 1.12–1.39; p < 0.001). Less organizational support (Z β = 0.425) was the most important factor associated with burnout overall. A work environment where staff experience defensive cultures, poor relationships, more frequent conflict, and feel unsupported by the organization is associated with significantly higher odds of burnout in pediatric critical care. The effect of targeted interventions to promote constructive cultures, collegiality, and organizational support on burnout in pediatric intensive care should be studied.