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

Purpose: The objective of this study is to evaluate the in vivo corneal biomechanical response to three laser refractive surgeries. Methods: Two hundred and twenty-seven patients who submitted to transepithelial photorefractive keratectomy (tPRK), femtosecond laser-assisted in-situ 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. The three groups were matched in age, gender ratio, corneal thickness, refractive error corrections, optical zone diameter, and intraocular pressure. They were also matched in the preoperative biomechanical metrics provided by the Corvis ST including stiffness parameter at first applanation (SP-A1), integrated inverse radius (IIR), deformation amplitude (DA), and deformation amplitude 2 mm away from apex and the apical deformation (DARatio2mm). Results: The results demonstrated a significant decrease post-operation in SP-A1 and significant increases in IIR, DA, and DARatio2mm (p < 0.05), all of which indicated reductions in overall corneal stiffness. Inter-procedure comparisons provided evidence that the smallest overall stiffness reduction was in the tPRK group, followed by the SMILE, and then the FS-LASIK group (p < 0.05). These results remained valid after correction for the change in CCT between pre and 6 months post-operation and for the percentage tissue altered. In all three surgery groups, higher degrees of refractive correction resulted in larger overall stiffness losses based on most of the biomechanical metrics. Conclusion: The corneal biomechanical response to the three surgery procedures varied significantly. With similar corneal thickness loss, the reductions in overall corneal stiffness were the highest in FS-LASIK and the lowest in tPRK.

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). The tangent modulus decreased after PGF2α treatment, while the stromal interfibrillar spacing increased. PGF2α was shown to also affect the lateral eye with lower effect, which did not undergo direct eyedrop treatment. Significant decreases in the numerical predictions of GAT-IOP were predicted in all treated groups relative to control groups. Different PGF2α's (travoprost, latanoprost and bimatoprost) were associated with different extents of reduction in tissue stiffness and changes in corneal microstructure. PGF2α-induced changes in corneal mechanical properties could reduce the accuracy of IOP measurement and may cause an overestimation of the effect of PGF2α in lowering IOP, possibly leading to uncertainties in glaucoma management.

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). We curated protein-gene interactions (PGIs) and GDAs from databases and assembled PGI networks and disease-gene heterogeneous networks. A screening of algorithms resulted in seven representative algorithms to be benchmarked. Performance of algorithms was assessed using both leave-one-out cross-validation (LOOCV) and external validation with MEGASTROKE genome-wide association study (GWAS). We found that random walk with restart on the heterogeneous network (RWRH) showed best LOOCV performance, with median LOOCV rediscovery rank of 185.5 (out of 19 463 genes). The GenePanda algorithm had most GWAS-confirmable genes in top 200 predictions, while RWRH had best ranks for small vessel stroke-associated genes confirmed in GWAS. In conclusion, RWRH has overall better performance for application in cSVD despite its susceptibility to bias caused by degree centrality. Choice of algorithms should be determined before applying to specific disease. Current pure network-based gene prioritization algorithms are unlikely to find novel disease-associated genes that are not associated with known ones. The tools for implementing and benchmarking algorithms have been made available and can be generalized for other diseases.

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

BACKGROUND: Stroke in UK Biobank (UKB) is ascertained via 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. Among 408,210 UKB participants, we identified all with a relevant code, creating 12 stroke definitions based on the code type and source. We performed genome-wide association studies (GWASs) for each definition, comparing summary results against the largest published stroke GWAS (MEGASTROKE), assessing genetic correlations, and replicating 32 stroke-associated loci. RESULTS: The stroke case numbers identified varied widely from 3,976 (primary care stroke-specific codes) to 19,449 (all codes, all sources). All 12 UKB stroke definitions were significantly correlated with the MEGASTROKE summary GWAS results (rg.81-1) and each other (rg.4-1). However, Bonferroni-corrected confidence intervals were wide, suggesting limited precision of some results. Six previously reported stroke-associated loci were replicated using ≥1 UKB stroke definition. CONCLUSIONS: Stroke case numbers in UKB depend on the code source and type used, with a 5-fold difference in the maximum case-sample size. All stroke definitions are significantly genetically correlated with the largest stroke GWAS to date.