Test-retest variability of mesopic microperimetry-associated parameters in patients with retinitis pigmentosa: REPEAT Study Report No. 2.

Purpose: Understanding test-retest variability (TRV) of mesopic microperimetry is critical for defining meaningful treatment effects in retinitis pigmentosa (RP) trials. This study uniquely evaluates intra- and intervisit TRV and coefficients of repeatability (CoRs) for microperimetry parameters in RP patients with varying best-corrected visual acuity (BCVA) levels.

Methods: In this single-centre prospective cohort study, RP patients were assessed on two visits, 14.

Differences in Gut Microbes Across Age and Sex Linked to Metabolism and Microbial Stability in a Hibernating Mammal.

The gut microbiome has a well-documented relationship with host fitness, physiology, and behavior. However, most of what is known comes from captive animals where diets and environments are more homogeneous or controlled. Studies in wild populations that experience dynamic environments and have natural life history variation are less common but are key to understanding the drivers of variation in the gut microbiome.

Age-related macular degeneration: natural history revisited in geographic atrophy.

Progression of geographic atrophy varies significantly based on individual and lesion characteristics. Much research has strived to understand prognostic indicators of lesion progression over time, yet integrating findings to date may pose a challenge to clinicians. This review strives to synthesize current knowledge on genetic, behavioral, structural, and functional factors that influence geographic atrophy across the lifetime.

Multicenter Normative Data for Mesopic Microperimetry.

Purpose: The purpose of this study was to provide a large, multi-center normative dataset for the Macular Integrity Assessment (MAIA) microperimeter and compare the goodness-of-fit and prediction interval calibration-error for a panel of hill-of-vision models.

Methods: Microperimetry examinations of healthy eyes from five independent study groups and one previously available dataset were included (1137 tests from 531 eyes of 432 participants [223 women and 209 men]). Linear mixed models (LMMs) were fitted to the data to obtain interpretable hill-of-vision models.

[Use of artificial intelligence for recognition of biomarkers in intermediate age-related macular degeneration].

Advances in imaging and artificial intelligence (AI) have revolutionized the detection, quantification and monitoring for the clinical assessment of intermediate age-related macular degeneration (iAMD). The iAMD incorporates a broad spectrum of manifestations, which range from individual small drusen, hyperpigmentation, hypopigmentation up to early stages of geographical atrophy. Current high-resolution imaging technologies enable an accurate detection and description of anatomical features, such as drusen volumes, hyperreflexive foci and photoreceptor degeneration, which are risk factors that are decisive for prediction of the course of the disease; however, the manual annotation of these features in complex optical coherence tomography (OCT) scans is impractical for the routine clinical practice and research.