Modulatory Effects of Photobiomodulation on Oxidative and Inflammatory Responses in a Murine Model of Periodontitis.

Periodontitis, an oral disease initiated by a dysbiotic dental biofilm, has an unclear response to photobiomodulation (PBM) as an adjunctive treatment. This study investigates the effects of PBM on reactive oxygen species (ROS), apoptosis, oxidative stress, and inflammatory markers in a periodontitis model using C57BL/6 mice, divided into four groups: control (C), control + PBM (C + PBM), periodontitis (P), and periodontitis + PBM (P + PBM). An infrared diode laser (808 nm, 133.

Fluorescence Lifetime Imaging for Quantification of Targeted Drug Delivery in Varying Tumor Microenvironments.

Trastuzumab (TZM) is a monoclonal antibody that targets the human epidermal growth factor receptor 2 (HER2) and is clinically used for the treatment of HER2-positive breast tumors. However, the tumor microenvironment can limit the access of TZM to the HER2 targets across the whole tumor and thereby compromising TZM's therapeutic efficacy. An imaging methodology that can non-invasively quantify the binding of TZM-HER2, which is required for therapeutic action, and distribution within tumors with varying tumor microenvironments is much needed.

Deep learning-based temporal deconvolution for photon time-of-flight distribution retrieval.

The acquisition of the time of flight (ToF) of photons has found numerous applications in the biomedical field. Over the last decades, a few strategies have been proposed to deconvolve the temporal instrument response function (IRF) that distorts the experimental time-resolved data. However, these methods require burdensome computational strategies and regularization terms to mitigate noise contributions.

A comprehensive comparison of deep learning-based compound-target interaction prediction models to unveil guiding design principles.

The evaluation of compound-target interactions (CTIs) is at the heart of drug discovery efforts. Given the substantial time and monetary costs of classical experimental screening, significant efforts have been dedicated to develop deep learning-based models that can accurately predict CTIs. A comprehensive comparison of these models on a large, curated CTI dataset is, however, still lacking.