Microplastic studies are crucial due to their impact on human health and the environment. However, the lack of a standardized method for microplastic identification and quantification hinders comprehensive analysis. Our research addresses these challenges by employing the advanced imaging technique Multimodal Multiphoton Tomography (MPT) including two-photon autofluorescence (AF), fluorescence lifetime imaging (FLIM) with phasor analysis, second harmonics generation (SHG), and reflectance confocal microscopy (RCM) with 50/80 MHz femtosecond laser pulses. The combined imaging approach allows also tracking of cosmetics with microplastics in-vivo in the human skin.
This project proposes an alternate method to DIN/ISO parameters for analyzing the effects of intrinsic and extrinsic aging on skin morphology. This method improves the efficacy of assessing aging by evaluating the surface and subsurface of the skin. The volumetric data obtained using OCT was processed using a machine learning approach to obtain DEJ segmentation, which was further segmented to obtain the morphological information of the micro-plateaus. The results showed that the flattening of DEJ due to aging is accompanied by changes in the size and shape of micro-plateaus in the DEJ.
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