Predicting the risk of dental implant loss using deep learning.

Aim: To investigate the feasibility of predicting dental implant loss risk with deep learning (DL) based on preoperative cone-beam computed tomography.

Materials And Methods: Six hundred and three patients who underwent implant surgery (279 high-risk patients who did and 324 low-risk patients who did not experience implant loss within 5 years) between January 2012 and January 2020 were enrolled. Three models, a logistic regression clinical model (CM) based on clinical features, a DL model based on radiography features, and an integrated model (IM) developed by combining CM with DL, were developed to predict the 5-year implant loss risk.

pH and lipase-responsive nanocarrier-mediated dual drug delivery system to treat periodontitis in diabetic rats.

Precise and controlled drug delivery to treat periodontitis in patients with diabetes remains a significant clinical challenge. Nanoparticle-based drug delivery systems offer a potential therapeutic strategy; however, the low loading efficiency, non-responsiveness, and single effect of conventional nanoparticles hinder their clinical application. In this study, we designed a novel self-assembled, dual responsive, and dual drug-loading nanocarrier system, which comprised two parts: the hydrophobic lipid core formed by 1, 2-Distearoyl-sn-glycero-3-phosphoethanolamine-Poly (ethylene glycol) (DSPE-PEG) loaded with alpha-lipoic acid (ALA); and a hydrophilic shell comprising a poly (amidoamine) dendrimer (PAMAM) that electrostatically adsorbed minocycline hydrochloride (Mino).

Morphology-Based Deep Learning Approach for Predicting Osteogenic Differentiation.

Early, high-throughput, and accurate recognition of osteogenic differentiation of stem cells is urgently required in stem cell therapy, tissue engineering, and regenerative medicine. In this study, we established an automatic deep learning algorithm, i.e.