A physiologically based model to capture species-dependent differences in oxygen distribution in the posterior eye

Ocular barriers to drug transport make delivery of effective doses to posterior targets exceptionally difficult. Animal models have commonly been used to evaluate drug distribution and penetrability, but translational tools to determine human dosing are lacking. Here we present a framework for modeling interspecies variation by simulating oxygen distribution in the posterior eye, from outer vitreous to the sclera. Posterior eye models of mouse, rabbit, and human are presented with modifications based solely on species-dependent anatomical and physiological differences. The model includes tissue and vascular contributions to transport. In addition to oxygen, nitric oxide and its impact on oxygen metabolism is simulated. Depth-dependent retinal oxygen partial pressure profiles are in good agreement with experimental data for all three species. The model can be further extended to evaluate the variations of retinal oxygenation in response to various drugs, formulations, administration protocols, and treatment plans. Further, this framework of ocular physiologically based pharmacokinetic/pharmacodynamic models could support animal to human translation, a critical step in the drug development process.

Comparison of inner macular thickness and superficial macular capillary vessel density acquired using classic and high-definition optical coherence tomography angiography scans

Background: In ophthalmology, thickness and vessel density (VD) measurements for the 6 x 6 mm inner macular retinal area have received increasing attention in glaucomatous progression research. For this area, the Angiovue optical coherence tomography system introduced a 304 x 304 A/B scans function (classic Angio Retina scan) in 2014, and a 400 x 400 A/B scans function (high-definition [HD] Angio Retina scan) in 2017. These scan types cannot be used in combination for the software provided for progression analysis.Purpose: Since losing data for 3 years may negatively influence progression analysis, we investigated whether clinically significant differences exist between consecutive measurements acquired with these scan types on the same eyes.Methods: As a part of our noninterventional prospective glaucoma imaging study, primary-open-angle glaucoma patients (POAG group), and ocular hypertensive and healthy control participants (structurally undamaged group) were imagedusing both the classic and the HD Angio Retina scans, respectively, without changing the patients’ position. High-quality images were obtained on 12 POAG eyes of 12 consecutive POAG patients, and 10 healthy and ocular hypertensive eyes of 10 consecutive participants before the data collection had to be suspended due to the new coronavirus epidemic.Results: For Early Treatment Diabetic Retinopathy Study image area, the mean difference (classic minus HD value) was 0.02 ± 0.37 μm for inner retinal thickness (P = 0.869) and 0.33 ± 1.33 % (P = 0.452) for superficial capillary VD in the structurally normal group (between-methods difference: ≤ 0.8% of the respective normal value). In the POAG group, the corresponding figures were -0.07 ± 1.22 μm for inner retinal thickness (P = 0.854; between-methods difference: 0.6% of the normal value) and 1.12 ± 2.58 % for superficial capillary VD (P = 0.158; classic scan value minus HD scan value: 1.12 ± 2.58 %; 2.3% of the normal value).Conclusion: Our results suggest that combined use of thickness and VD values for structurally normal eyes and thickness values for POAG eyes derived from classic and HD scans, respectively, for progression analysis can be reasonable since the differences between the corresponding values are small. However, combining the corresponding VD parameters for POAG eyes is useful only when the follow-up time before the scan type change is long enough to counterbalance the effect of the change on the result.

Automatic detection of the cornea location in video captures of fluorescence

Purpose: Fluorescence imaging is a valuable tool for studying tear film dynamics andcorneal staining. Automating the quantification of fluorescence images is a challenging necessary step for making connections to mathematical models. A significant partof the challenge is identifying the region of interest, specifically the cornea, for collected data with widely varying characteristics. Methods: The gradient of pixel intensity at the cornea–sclera limbus is used as the objective of standard optimization to find a circle that best represents the cornea. Results of the optimization in one image are used as initial conditions in the next imageof a sequence. Additional initial conditions are chosen heuristically. The algorithm iscoded in open-source software. Results: The algorithm was first applied to 514 videos of 26 normal subjects, for a total of over 87,000 images. Only in 12 of the videos does the standard deviation in thedetected corneal radius exceed 1% of the image height, and only 3 exceeded 2%. The algorithm was applied to a sample of images from a second study with 142 dry-eye subjects. Significant staining was present in a substantial number of these images. Visual inspection and statistical analysis show good resuls for both normal and dry-eye images. Conclusion: The new algorithm is highly effective over a wide range of tear film andcorneal staining images collected at different times and locations.

Cloud-based genomics pipelines for ophthalmology: reviewed from research to clinical practice

Aim: To familiarize clinicians with clinical genomics, and to describe the potential of cloud computing for enabling the future routine use of genomics in eye hospital settings.Design: Review article exploring the potential for cloud-based genomic pipelines in eye hospitals.Methods: Narrative review of the literature relevant to clinical genomics and cloud computing, using PubMed and Google Scholar. A broad overview of these fields is provided, followed by key examples of their integration.Results: Cloud computing could benefit clinical genomics due to scalability of resources, potentially lower costs, and ease of data sharing between multiple institutions. Challenges include complex pricing of services, costs from mistakes or experimentation, data security, and privacy concerns.Conclusions and future perspectives: Clinical genomics is likely to become more routinely used in clinical practice. Currently this is delivered in highly specialist centers. In the future, cloud computing could enable delivery of clinical genomics services in non-specialist hospital settings, in a fast, cost-effective way, whilst enhancing collaboration between clinical and research teams.