Biomechanical Effects of tPRK, FS-LASIK, and SMILE on the Cornea.

The objective of this study is to evaluate the corneal biomechanical response to three laser refractive surgeries. Two hundred and twenty-seven patients who submitted to transepithelial photorefractive keratectomy (tPRK), femtosecond laser-assisted keratomileusis (FS-LASIK), or small-incision lenticule extraction (SMILE) were included in this study. All cases were examined with the Corvis ST preoperatively (up to 3 months) and postoperatively at 1, 3, and 6 months, and the differences in the main device parameters were assessed.

Physician-Confirmed and Administrative Definitions of Stroke in UK Biobank Reflect the Same Underlying Genetic Trait.

Background: Stroke in UK Biobank (UKB) is ascertained linkages to coded administrative datasets and self-report. We studied the accuracy of these codes using genetic validation.

Methods: We compiled stroke-specific and broad cerebrovascular disease (CVD) code lists (Read V2/V3, ICD-9/-10) for medical settings (hospital, death record, primary care) and self-report.

Effect of travoprost, latanoprost and bimatoprost PGF2α treatments on the biomechanical properties of in-vivo rabbit cornea.

Prostaglandin F2α analogues (PGF2α), one of the most commonly prescribed classes of hypotensive agents, could decrease collagen fibril density and remodel the extracellular matrix in cornea. We hypothesized that PGF2α's would induce corneal softening, reduce the accuracy of intraocular pressure (IOP) measurement and lead to uncertainty in the effectiveness of the therapy. We determined the stress-strain behavior of rabbit cornea after PGF2α usage and evaluated the effect of biomechanical changes associated with PGF2α treatment on IOP measurements by Goldmann Applanation Tonometry (GAT).

Benchmarking network-based gene prioritization methods for cerebral small vessel disease.

Network-based gene prioritization algorithms are designed to prioritize disease-associated genes based on known ones using biological networks of protein interactions, gene-disease associations (GDAs) and other relationships between biological entities. Various algorithms have been developed based on different mechanisms, but it is not obvious which algorithm is optimal for a specific disease. To address this issue, we benchmarked multiple algorithms for their application in cerebral small vessel disease (cSVD).