UV spectrophotometric methods for quantitative determination of masitinib; extraction of qualitative information

Artificial neural network and convolutional neural network for prediction of dental caries

Dental caries has high prevalence among kids and adults thus it has become one of the global health concerns. The current modern dentistry focused on the preventives measures to reduce the number of dental caries cases. The employment of machine learning coupled with UV spectroscopy plays a crucial role to detect the early stage of caries. Artificial neural network with hyperparameter tuning was employed to train spectral data for the classification based on the International Caries Detection and Assesment System (ICDAS). Spectra preprocessing namely mean center (MC), autoscale (AS) and Savitzky Golay smoothing (SG) were applied on the data for spectra correction. The best performance of ANN model obtained has accuracy of 0.85 with precision of 1.00. Convolutional neural network (CNN) combined with Savitzky Golay smoothing performed on the spectral data has accuracy, precision, sensitivity and specificity for validation data of 1.00 respectively. The result obtained shows that the application of ANN and CNN capable to produce robust model to be used as an early screening of dental caries.

Evaluation of the therapeutic potential of masitinib and expression of its specific targets c-Kit, PDGFR-α, PDGFR-β, and Lyn in canine prostate cancer cell lines

Canine prostate cancer is classified into adenocarcinoma, transitional cell carcinoma with prostatic involvement, and mixed forms. Early metastatic spread leads to poor prognosis and limited treatment options. Masitinib is approved for the treatment of canine mast cell tumours and inhibits tyrosine kinase c-Kit, tyrosine-protein kinase Lyn (Lyn), and platelet-derived growth factor receptors alpha and beta (PDGFR-α, PDGFR-β), which are known to be expressed in canine prostate cancer. The aim of this study was to evaluate masitinib in an in vitro model consisting of cell lines from primary prostate adenocarcinoma, the associated lymph node metastasis of the same patient, and transitional cell carcinoma. To assess the suitability of the model system, the targets of masitinib were investigated by immunocytochemistry in the cell lines and by immunohistochemistry in the respective formalin-fixed, paraffin-embedded (FFPE) original neoplastic tissue. After exposure to masitinib, cell viability, cell count, apoptosis induction, and protein expression of c-Kit, Lyn, PDGFR-α, and PDGFR-β were assessed. To hedge the efficacy, two application protocols of masitinib (single application or 12-h double-dose regimen) were compared. Immunocytochemical and immunohistochemical analysis revealed increased Lyn, PDGFR-α, and PDGFR-β expression in cell lines and FFPE original neoplastic tissue compared to healthy prostate tissue. Masitinib exposure increased apoptosis, while the cell counts and cell viability decreased in a dose- and application interval-dependent manner, with increased impact in the 12-h double-dose regimen. These in vitro effects of masitinib in canine prostate cancer and associated metastasis support further in vivo research and modifications of the clinical treatment protocol in future studies.