Photorefractive Keratectomy (PRK) is a widely used laser-assisted refractive surgical technique. While generally safe, in some cases it leads to subepithelial inflammation or fibrosis. We here present a robust, machine learning based algorithm for the detection of fibrosis based on Spectral Domain Optical Coherence Tomography (SD-OCT) images recorded in vivo on standard clinical devices. The images first undergo a treatment by a previously developed algorithm for standardisation. The analysis of the pre-treated images allow the extraction of quantitative parameters characterizing the transparency of human corneas. We here propose an extension of this work. Our model is based on 9 morphological quantifiers of the corneal epithelium and in particular of Bowman's layer. In a first step it is trained on SD-OCT images of corneas presenting Fuchs dystrophy, which causes similar symptoms of fibrosis. We trained a Random Forest model for the classification of corneas into "healthy" and "pathological" classes resulting in a classification accuracy (or success rate) of 97%. The transfer of this same model to images from patients who have undergone Photorefractive Keratectomy (PRK) surgery shows that the model output for probability of healthy classification provides a quantified indicator of corneal healing in the post-operative follow-up. The sensitivity of this probability was studied using repeatability data. We could therefore demonstrate the ability of artificial intelligence to detect sub-epithelial scars identified by clinicians as the origin of post-operative visual haze.
Solar ultraviolet longwave UVA1 exposure of human skin has short-term consequences at cellular and molecular level, leading at long-term to photoaging. Following exposure, reactive oxygen species (ROS) are generated, inducing oxidative stress that might impair cellular metabolic activity. However, the dynamic of UVA1 impact on cellular metabolism remains unknown because of lacking adequate live imaging techniques. Here we assess overtime the UVA1- induced metabolic stress response in reconstructed human skin with multicolor two-photon fluorescence lifetime microscopy (FLIM). Simultaneous imaging of the two endogenous biomarkers nicotinamide adenine dinucleotide (NAD(P)H) and flavin adenine dinucleotide (FAD) by wavelength mixing allows quantifying cellular metabolism in function of NAD(P)+/NAD(P)H and FAD/FADH2 redox ratios We measure NAD(P)H and FAD fluorescence lifetime and fraction of bound coenzymes both in keratinocytes in the epidermis basal layer and in fibroblasts in the dermis superficial layer. After UVA1 exposure, we observe an increase of fraction of bound NAD(P)H and decrease of fraction of bound FAD indicating a metabolic switch from glycolysis to OXPHOS or oxidative stress possibly correlated to ROS generation. NAD(P)H and FAD biomarkers have unique temporal dynamics and sensitivities to skin cell types and UVA1 dose. While FAD biomarker is UVA1 dose-dependent in keratinocytes, NAD(P)H biomarker shows earlier time points modulation in fibroblasts, thus reflecting different skin cells sensitivities to oxidative stress. Finally, we show that a sunscreen including a UVA1 filter MCE prevents UVA1 metabolic stress response from occurring.
Large-scale microscopy approaches are transforming brain imaging, but currently lack efficient multicolor contrast modalities. We address this issue by introducing chromatic multiphoton serial (ChroMS) microscopy, a method combining multicolor multiphoton excitation through wavelength mixing and microtome-assisted serial block-face image acquisition. This approach delivers large-scale micrometric imaging of spectrally distinct fluorescent proteins with constant micrometer-scale resolution and sub-micron channel registration over the entire imaged volume. We achieve multicolor 3D imaging over several cubic millimeters and brain-wide serial 2D multicolor imaging. We illustrate the potential of this method for several novel types of measurements interesting for region-scale or whole brain studies: (i) color-based analysis of astrocyte morphology and spatial interactions in the mouse cerebral cortex, (ii) tracing of densely labeled neurons, and (iii) brain-wide mapping of axonal projections labeled with distinct tracers.
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