Non-invasive in vivo measurement of ocular rigidity: Clinical validation, repeatability and method improvement.

Ocular rigidity (OR) is thought to play a role in the pathogenesis of glaucoma, but the lack of reliable non-invasive measurements has been a major technical challenge. We recently developed a clinical method using optical coherence tomography time-lapse imaging and automated choroidal segmentation to measure the pulsatile choroidal volume change (ΔV) and calculate OR using Friedenwald's equation. Here we assess the validity and repeatability of this non-invasive technique. We also propose an improved mathematical model of choroidal thickness to extrapolate ΔV from the pulsatile submacular choroidal thickness change more accurately. The new mathematical model uses anatomical data accounting for the choroid thickness near the equator. The validity of the technique was tested by comparing OR coefficients obtained using our non-invasive method (OROCT) and those obtained with an invasive procedure involving intravitreal injections of Bevacizumab (ORIVI) in 12 eyes. Intrasession and intersession repeatability was assessed for 72 and 8 eyes respectively with two consecutive measurements of OR. Using the new mathematical model, we obtained OR values which are closer to those obtained using the invasive procedure and previously reported techniques. A regression line was calculated to predict the ORIVI based on OROCT, such that ORIVI = 0.655 × OROCT. A strong correlation between OROCT and ORIVI was found, with a Spearman coefficient of 0.853 (p < 0.001). The intraclass correlation coefficient for intrasession and intersession repeatability was 0.925, 95% CI [0.881, 0.953] and 0.950, 95% CI [0.763, 0.990] respectively. This confirms the validity and good repeatability of OR measurements using our non-invasive clinical method.

An open-source algorithm for rapid unbiased determination of DNA fiber length.

DNA fiber fluorography is widely employed to study the kinetics of DNA replication, but the usefulness of this approach has been limited by the lack of freely-available automated analysis tools. Quantification of DNA fibers usually relies on manual examination of immunofluorescence microscopy images, which is laborious and prone to inter- and intra-operator variability. To address this, we developed an unbiased, fully automated algorithm that quantifies length and color of DNA fibers from fluorescence microscopy images. Our fiber quantification method, termed FiberQ, is an open-source image processing tool based on edge detection and a novel segment splicing approach. Here, we describe the algorithm in detail, validate our results experimentally, and benchmark the analysis against manual assessments. Our implementation is offered free of charge to the scientific community under the General Public License.