GraphCompass: spatial metrics for differential analyses of cell organization across conditions.

Summary: Spatial omics technologies are increasingly leveraged to characterize how disease disrupts tissue organization and cellular niches. While multiple methods to analyze spatial variation within a sample have been published, statistical and computational approaches to compare cell spatial organization across samples or conditions are mostly lacking. We present GraphCompass, a comprehensive set of omics-adapted graph analysis methods to quantitatively evaluate and compare the spatial arrangement of cells in samples representing diverse biological conditions.

High-throughput anaerobic screening for identifying compounds acting against gut bacteria in monocultures or communities.

The human gut microbiome is a key contributor to health, and its perturbations are linked to many diseases. Small-molecule xenobiotics such as drugs, chemical pollutants and food additives can alter the microbiota composition and are now recognized as one of the main factors underlying microbiome diversity. Mapping the effects of such compounds on the gut microbiome is challenging because of the complexity of the community, anaerobic growth requirements of individual species and the large number of interactions that need to be quantitatively assessed.

Progression, reliability, predicting parameters and sample size calculations for quantitative fundus autofluorescence measures in -related retinopathy.

Background/aims: To investigate the progression of quantitative autofluorescence (qAF) measures and the potential as clinical trial endpoint in -related retinopathy.

Methods: In this longitudinal monocentre study, 64 patients with -related retinopathy (age (mean±SD), 34.84±16.

Foveal structure and visual function in nanophthalmos and posterior microphthalmos.

Background/aims: The reason for visual impairment in patients with nanophthalmos and posterior microphthalmos is not completely understood. Therefore, this study aims to investigate foveal structure, and the impact of demographic, clinical and imaging parameters on best-corrected visual acuity (BCVA) in these conditions.

Methods: Sixty-two eyes of 33 patients with nanophthalmos (n=40) or posterior microphthalmos (n=22), and 114 eyes of healthy controls with high-resolution retinal imaging including spectral-domain or swept-source optical coherence tomography images were included in this cross-sectional case-control study.

Reliability of Retinal Pathology Quantification in Age-Related Macular Degeneration: Implications for Clinical Trials and Machine Learning Applications.

Purpose: To investigate the interreader agreement for grading of retinal alterations in age-related macular degeneration (AMD) using a reading center setting.

Methods: In this cross-sectional case series, spectral-domain optical coherence tomography (OCT; Topcon 3D OCT, Tokyo, Japan) scans of 112 eyes of 112 patients with neovascular AMD (56 treatment naive, 56 after three anti-vascular endothelial growth factor injections) were analyzed by four independent readers. Imaging features specific for AMD were annotated using a novel custom-built annotation platform.

Inferred retinal sensitivity in recessive Stargardt disease using machine learning.

Spatially-resolved retinal function can be measured by psychophysical testing like fundus-controlled perimetry (FCP or 'microperimetry'). It may serve as a performance outcome measure in emerging interventional clinical trials for macular diseases as requested by regulatory agencies. As FCP constitute laborious examinations, we have evaluated a machine-learning-based approach to predict spatially-resolved retinal function ('inferred sensitivity') based on microstructural imaging (obtained by spectral domain optical coherence tomography) and patient data in recessive Stargardt disease.

PROGRESSION OF ABCA4-RELATED RETINOPATHY: Prognostic value of demographic, functional, genetic, and imaging parameters.

Purpose: To investigate the prognostic value of demographic, functional, genetic, and imaging parameters on retinal pigment epithelium atrophy progression secondary to ABCA4-related retinopathy.

Methods: Patients with retinal pigment epithelium atrophy secondary to ABCA4-related retinopathy were examined longitudinally with fundus autofluorescence imaging. Lesion area, perimeter, circularity, caliper diameters, and focality of areas with definitely decreased autofluorescence were determined.

Prediction of Function in ABCA4-Related Retinopathy Using Ensemble Machine Learning.

Full-field electroretinogram (ERG) and best corrected visual acuity (BCVA) measures have been shown to have prognostic value for recessive Stargardt disease (also called "-related retinopathy"). These functional tests may serve as a performance-outcome-measure (PerfO) in emerging interventional clinical trials, but utility is limited by variability and patient burden. To address these limitations, an ensemble machine-learning-based approach was evaluated to differentiate patients from controls, and predict disease categories depending on ERG ('inferred ERG') and visual impairment ('inferred visual impairment') as well as BCVA values ('inferred BCVA') based on microstructural imaging (utilizing spectral-domain optical coherence tomography) and patient data.