Photodynamic therapy (PDT) is a modern treatment technique employed as an antitumor, antibacterial, or rejuvenation aid in superficial tissues. The use of fluorescent photosensitizers (PSs) implements the principles of theranostics when the applied medical agents serve both for diagnostic and treatment purposes. For efficient PDT performance it is important to evaluate the in-depth distribution of PSs in tissues prior to irradiation. Fluorescence imaging is a common technique to monitor the distribution of PSs in tissue. However, in-depth resolution is challenging. Chlorin-based PSs reveal two narrow fluorescence excitation peaks at 405 and 660 nm providing additional diagnostic opportunities. We demonstrate that the ratio of the fluorescence signals upon excitation at these wavelengths provides the evaluation of the PS penetration depth after topical application. The study is based on Monte Carlo simulations that are in agreement with phantom experiments. The effect of medium optical properties on the depth-dependent fluorescence signal ratio is analyzed.

The goal of this study is a comparative analysis of the efficiency of the PDT protocols for CT26 tumor model treatment in Balb/c mice employing red and blue light with both topical and intravenous administration of chlorin-based photosensitizers (PSs). The considered protocols include the doses of 250 J/cm2 delivered at 660 nm, 200 J/cm2 delivered at 405 nm, and 250 J/cm2 delivered at both wavelengths with equal energy density contribution. Dual-wavelength fluorescence imaging was employed to estimate both photobleaching efficiency, typical photobleaching rates and the procedure impact depth, while optical coherence tomography with angiography modality (OCT-A) was employed to monitor the tumor vasculature response for up to 7 days after the procedure with subsequent histology inspection. Red light or dual-wavelength PDT regimes with intravenous PS injection were demonstrated to provide the most pronounced tumor response among all the considered cases. On the contrary, blue light regimes were demonstrated to be most efficient among topical application and irradiation only regimes. Tumor size dynamics for different groups is in good agreement with the tumor response predictions based on OCT-A taken in 24h after exposure and the results of histology analysis performed in 7 days after the exposure.

Modern trends in optical bioimaging require novel nanoproducts combining high image contrast with efficient treatment capabilities. Silicon nanoparticles are a wide class of nanoobjects with tunable optical properties, which has potential as contrasting agents for fluorescence imaging and optical coherence tomography. In this paper we report on developing a novel technique for fabricating silicon nanoparticles by means of picosecond laser ablation of porous silicon films and silicon nanowire arrays in water and ethanol. Structural and optical properties of these particles were studied using scanning electron and atomic force microscopy, Raman scattering, spectrophotometry, fluorescence, and optical coherence tomography measurements. The essential features of the fabricated silicon nanoparticles are sizes smaller than 100 nm and crystalline phase presence. Effective fluorescence and light scattering of the laser-ablated silicon nanoparticles in the visible and near infrared ranges opens new prospects of their employment as contrasting agents in biophotonics, which was confirmed by pilot experiments on optical imaging.

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Chronic rhinitis (CR) is among the most frequent inflammatory diseases of ear-nose-throat (ENT) covering up to 30% of the population. Different forms of CR require different treatment tactics, which indicates the need for an efficient tool for differential diagnostics of CR. Optical coherence tomography (OCT) is a promising tool for fast non-invasive evaluation of nasal mucosa, which, however, requires further interpretation of the obtained diagnostic image. In this paper, we provide a comparative analysis of several machine learning approaches that aim at automated differential diagnostics of CR based on diagnostic OCT images of 78 patients aged between 28 and 74 ages. Gradient boosting decision trees (GBT) approach reveals the best classification accuracy (98% and 94% for binary and diagnostic classification, respectively). It shows that proposed approaches have potential for automated classification of CR OCT images.