![]() For amacrine and ganglion cell density, only a single article was identified, which presented cell density with respect to the fovea. We determined these background density functions, in addition to ganglion cell and amacrine cell density, by review of the published literature 18 – 21 searched using PubMed and Google Scholar. ![]() For example, if choroideremia starts in cone cells one might see central loss initially with peripheral preservation, versus a different a pattern if the initial site of injury for choroideremia is in either rods, RPE, choriocapillaris, or other neural cellular elements of the retina. 18 We then hypothesized that the pattern that was seen in RPE cell loss might be affected by the target cell of injury in choroideremia. As an initial modification of this process, we repeated the analysis using an RPE density function from the literature, because it is known that the RPE density is greatest centrally and decreases in the periphery. Initially, we used a uniform background function where each array element had the same chance of atrophy. Then, an algorithm was implemented to simulate rules that may govern cell death, namely, either a background effect, neighbor effect, or a combination of the two over a course of time steps. When a cell dies, the dark grey cluster turns to light grey ( Fig. In the initial condition, each cell cluster was assumed to contain only live cells, which gives the array a homogeneous dark grey color ( Fig. This assignment was represented by either dark or light clusters, respectively, on a diagram to visualize this effect (see Fig. 18 Each array element was assigned one of two states either a “1” indicating the cluster of cells was healthy, or “0” indicating the cell cluster was dead. 8 As a result, each array element or “cell cluster” represents approximately 205 to 260 cells. The size of this array was modeled as being equivalent to 55° of retinal eccentricity, centered on the fovea, as this was the field limit used in the comparative meta-analysis. The RPE was modeled as a 300 × 300 dimensional array, wherein each array element represents a cluster of cells, and the array was made circular by excluding array elements farther than 150 units from the center (see Fig. 15 – 17 All simulations were performed on a Dell Inspiwith 8 gb of RAM and using an Intel i7-10510u processor at 1.80 gHz processing speed. The following model was conducted in a Python 3.7 environment, importing NumPy and Scikit-learn libraries for computational functions, and the MatPlotLib library for visualization. Further studies are needed to validate these findings. ![]() The RPE preservation pattern typically seen in choroideremia may be related to the underlying pattern of rod density. The pattern of RPE loss in choroideremia can be predicted by applying simple rules. Also, our model predicts a residual island of RPE that resembles the topographic distribution of residual RPE seen in choroideremia. When the neighbor effect multiplies the background effect using the rod density function, our model follows an exponential decay, similar to previous observations. An additive neighbor effect alone does not simulate exponential decay. The main outcome measures: included topography of atrophy over time and fit of simulated residual RPE area to exponential decay.Ī background effect alone can simulate exponential decay, but does not simulate the central island preservation seen in choroideremia. Atrophy growth rates were measured over arbitrary time units and fit to the known exponential decay model. The known anatomic distribution of rods, RPE, choriocapillaris density, amacrine, ganglion, and cone cells were derived from the literature and applied to this model. Two rules were applied to this model: the background effect gave each cell a chance of dying defined by a background function, and the neighbor effect increased the chance of RPE cell death if a neighbor were dead. To use empirical data to develop a model of cell loss in choroideremia that predicts the known exponential rate of RPE loss and central, scalloped preservation pattern seen in this disease.Ī computational model of RPE loss was created in Python 3.7, which constructed an array of RPE cells clusters, binarized as either live or atrophic. ![]()
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