Evaluation of Dental Plaque Area with Artificial Intelligence Model.

Objectives: This study aims to assess the diagnostic accuracy of an artificial intelligence (AI) system employing deep learning for identifying dental plaque, utilizing a dataset comprising photographs of permanent teeth.

Materials And Methods: In this study, photographs of 168 teeth belonging to 20 patients aged between 10 and 15 years, who met our criteria, were included. Intraoral photographs were taken of the patients in two stages, before and after the application of the plaque staining agent.

Delta synchronization of Poincaré chaos in gas discharge-semiconductor systems.

We introduce a new type of chaos synchronization, specifically the delta synchronization of Poincaré chaos. The method is demonstrated for the irregular dynamics in coupled gas discharge-semiconductor systems (GDSSs). It is remarkable that the processes are not generally synchronized.

Evaluation of maxillary sinusitis from panoramic radiographs and cone-beam computed tomographic images using a convolutional neural network.

Purpose: This study developed a convolutional neural network (CNN) model to diagnose maxillary sinusitis on panoramic radiographs (PRs) and cone-beam computed tomographic (CBCT) images and evaluated its performance.

Materials And Methods: A CNN model, which is an artificial intelligence method, was utilized. The model was trained and tested by applying 5-fold cross-validation to a dataset of 148 healthy and 148 inflamed sinus images.

Use of colistin with rifampicin, trimethoprim-sulfamethoxazole and teicoplanin in mouse infection model.

Infections with multidrug-resistant Gram-negative bacteria such as are major cause of morbidity and mortality. Colistin is used commonly to treat these infections. In this study, we evaluated the efficacy of different colistin combinations in a infection mouse model.

Is BECLIN-1 Immunoreactivity More Effective than HBME-1 in Diagnosis of Papillary Thyroid Cancer?

Background: Even though morphologic findings are generally important in the diagnosis of thyroid tumors, in some cases, morphologic similarities between benign and malignant lesions lead to noticeable differences in evaluation and different diagnoses in the same cases. In our study, we researched whether the autophagy-related protein Beclin-1 (BECN1) is a diagnostic marker in thyroid tumors, as well as its correlation with HBME-1, which has high sensitivity and specificity in distinguishing malignant thyroid lesions.

Methods: Samples from 136 patients that received a thyroidectomy were fixed in 10% formalin.