Publications by authors named "N N Massamba"

This cross-sectional study compared optical coherence tomography angiography (OCTA) parameters between older Black and White adults with systemic comorbidities in an effort to further understand racial differences in the retinal microvasculature. We analyzed vessel density at the superficial (SCP), intermediate (ICP), and deep capillary plexuses (DCP), foveal avascular zone (FAZ) parameters, and blood flow area (BFA) at the choriocapillaris. We used a mixed-effects linear regression model, controlling for hypertension and two eyes from the same subject, to compare OCTA parameters.

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Computerized texture analysis uses higher-order mathematics to identify patterns beyond what the naked eye can recognize. We tested its feasibility in optical coherence tomography angiography imaging of choriocapillaris. Our objective was to determine sets of parameters that provide coherent and consistent output when applied to a homogeneous, healthy group of patients.

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This study compared macular capillary parameters between healthy black and white subjects using optical coherence tomography angiography (OCTA). We measured vessel density (VD) of superficial (SCP), intermediate (ICP), and deep (DCP) capillary plexuses and choriocapillaris blood flow area (BFA) of the fovea, parafovea and total 3 mm-diameter circular area centered on the fovea, as well as the foveal avascular zone (FAZ) parameters, controlling for axial length. Black subjects had lower foveal and parafoveal VD in the SCP (p = 0.

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This is a cross-sectional, prospective study of a population of black diabetic participants without diabetic retinopathy aimed to investigate optical coherence tomography angiography (OCTA) characteristics and correlations with systemic diseases in this population. These parameters could serve as novel biomarkers for microvascular complications; especially in black populations which are more vulnerable to diabetic microvascular complications. Linear mixed models were used to obtain OCTA mean values ± standard deviation and analyze statistical correlations to systemic diseases.

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