Preparation and Characterization of Carboxymethyl Chitosan/Sodium Alginate Composite Hydrogel Scaffolds Carrying Chlorhexidine and Strontium-Doped Hydroxyapatite.

Herein, we introduce a novel composite hydrogel scaffold designed for addressing infectious jaw defects, a significant challenge in clinical settings caused by the inherent limited self-regenerative capacity of bone tissues. The scaffold was engineered from a blend of carboxymethyl chitosan (CMCS)/sodium alginate (SA) hydrogel (CSH), β-cyclodextrin/chlorhexidine clathrate (β-CD-CHX), and strontium-nanohydroxyapatite nanoparticles (Sr-nHA). The β-CD-CHX and Sr-nHA components were synthesized using a saturated aqueous solution and a coprecipitation method, respectively.

Preparation and properties of a 3D printed nHA/PLA bone tissue engineering scaffold loaded with a β-CD-CHX combined dECM hydrogel.

Jaw defects, which can result from a multitude of causes, significantly affect the physical well-being and psychological health of patients. The repair of these infected defects presents a formidable challenge in the clinical and research fields, owing to their intricate and diverse nature. This study aims to develop a personalized bone tissue engineering scaffold that synergistically offers antibacterial and osteogenic properties for treating infected maxillary defects.

Deep learning enables automatic adult age estimation based on CT reconstruction images of the costal cartilage.

Objective: Adult age estimation (AAE) is a challenging task. Deep learning (DL) could be a supportive tool. This study aimed to develop DL models for AAE based on CT images and compare their performance to the manual visual scoring method.