Previous studies for melanin visualization in the retinal pigment epithelium (RPE) have exploited either its absorption properties (using photoacoustic tomography or photothermal optical coherence tomography [OCT]) or its depolarization properties (using polarization sensitive OCT). However, these methods are only suitable when the melanin concentration is sufficiently high. In this work, we present the concept of hyperspectral OCT for melanin visualization in the RPE when the concentration is low. Based on white light OCT, a hyperspectral stack of 27 wavelengths (440‐700 nm) was created in post‐processing for each depth‐resolved image. Owing to the size and shape of the melanin granules in the RPE, the variations in backscattering coefficient as a function of wavelength could be identified—a result which is to be expected from Mie theory. This effect was successfully identified both in eumelanin‐containing phantoms and in vivo in the low‐concentration Brown Norway rat RPE.

We present a system for automatic determination of the intradermal volume of hydrogels based on optical coherence tomography (OCT) and deep learning. Volumetric image data was acquired using a custom-built OCT prototype that employs an akinetic swept laser at ∼1310 nm with a bandwidth of 87 nm, providing an axial resolution of ∼6.5 µm in tissue. Three-dimensional data sets of a 10 mm × 10 mm skin patch comprising the intradermal filler and the surrounding tissue were acquired. A convolutional neural network using a u-net-like architecture was trained from slices of 100 OCT volume data sets where the dermal filler volume was manually annotated. Using six-fold cross-validation, a mean accuracy of 0.9938 and a Jaccard similarity coefficient of 0.879 were achieved.

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We trained a deep learning algorithm to use skin optical coherence tomography (OCT) angiograms to differentiate between healthy and type 2 diabetic mice. OCT angiograms were acquired with a custom-built OCT system based on an akinetic swept laser at 1322 nm with a lateral resolution of ∼13 μm and using split-spectrum amplitude decorrelation. Our data set consisted of 24 stitched angiograms of the full ear, with a size of approximately 8.2 × 8.2 mm, evenly distributed between healthy and diabetic mice. The deep learning classification algorithm uses the ResNet v2 convolutional neural network architecture and was trained on small patches extracted from the full ear angiograms. For individual patches, we obtained a cross-validated accuracy of 0.925 and an area under the receiver operating characteristic curve (ROC AUC) of 0.974. Averaging over multiple patches extracted from each ear resulted in the correct classification of all 24 ears.

During wound healing, the rapid re-establishment of a functional microcirculation in the wounded tissue is of utmost importance. We applied optical coherence tomography (OCT) angiography to evaluate vascular remodeling in an excisional wound model in the pinnae of C57BL/6 and db/db mice receiving different proangiogenic topical treatments. Analysis of the high-resolution OCT angiograms, including the four quantitative parameters vessel density, vessel length, number of bifurcations, and vessel tortuosity, revealed changes of the microvasculature and allowed identification of the overlapping wound healing phases hemostasis, inflammation, proliferation, and remodeling. Angiograms acquired in the inflammatory phase in the first days showed a dilation of vessels and recruitment of pre-existing capillaries. In the proliferative phase, angiogenesis with the sprouting of new capillaries into the wound tissue led to an increase of the OCT angiography parameters vessel density, normalized vessel length, number of bifurcations, and vessel tortuosity by 28–47, 39–52, 33–48, and 3–8 versus baseline, respectively. After the peak observed on study days four to seven, the parameters slowly decreased but remained still elevated 18 days after wounding, indicating a continuing remodeling phase. Our study suggests that OCT angiography has the potential to serve as a valuable preclinical research tool in studies investigating impaired vascular remodeling during wound healing and potential new treatment strategies.

Purpose Cyclodextrin nanoparticles form water‐soluble complexes with lipophilic and poorly water‐soluble drugs and thus can greatly improve drug transport from the ocular surface to the posterior eye segment. The aim of the current study was to evaluate the ocular pharmacokinetics and biodistribution of two newly developed □‐cyclodextrin based angiotensin receptor antagonist formulations for topical administration: cyclodextrin‐Irbesartan 1.5% and cyclodextrin‐Candesartan 0.15%. Methods 59 rabbits were included in the study to receive one of the two study drugs. For each drug group, single and multiple dose pharmacokinetics were performed in a randomized fashion: Single dose rabbits were euthanized 0.5, 1.5, 3 or 6 hours post eye drop administration, whereas multiple dose animals were dosed for 5 days twice daily. Pharmacokinetic parameters including maximal drug concentration (Cmax) and time of maximal drug concentrations (Tmax) for single dosing and mean concentrations for multiple dosing were calculated for aqueous humor (AH) and retina/choroid (RT). Analysis was done using LC‐MS/MS. Results Topical administration of the study drugs was well tolerated. Single dose Irbesartan eye drop administration led to a RT Cmax of 251 ng/g ± 143 ng/g at 0.5 hours whereas in the AH a Cmax of 121 ng/g ± 69 ng/g was reached at 3 hours post instillation. For single dose Candesartan eye drops, RT Cmax was 63 ng/g ± 39 ng/g at 0.5 hours after application. AH reached a Cmax of 30 ng/g ± 14 ng/g at the 3 hours time point. For multiple dosing mean RT concentration reached 338 ng/g ± 124 ng/g for Irbesartan and 36 ng/g ± 10 ng/g for Candesartan, whereas mean AH concentrations were 231 ng/g ± 68 ng/g and 70 ng/g ± 22 ng/g, respectively. Conclusions The present data confirm that cyclodextrin based Candesartan and Irbesartan eye drops deliver drugs to the posterior pole of the eye in biologically relevant concentrations.

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