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), ß-cyclodextrin/chlorhexidine clathrate (ß-CD-CHX), and strontium-nanohydroxyapatite nanoparticles (Sr-nHA). The ß-CD-CHX and Sr-nHA components were synthesized using a saturated aqueous solution and a coprecipitation method, respectively. Subsequently, these elements were encapsulated within the CSH matrix. Comprehensive characterization of the CMCS/SA/ß-CD-CHX/Sr-nHA composite hydrogel scaffold via scanning electron microscopy, X-ray photoelectron spectroscopy, and Fourier-transform infrared spectroscopy validated the successful synthesis. The swelling and in vitro degradation behaviors proved that the composite hydrogel had good physical properties, while in vitro evaluations demonstrated favorable biocompatibility and osteoinductive properties. Additionally, antibacterial assessments revealed its effectiveness against common pathogens, Staphylococcus aureus and Escherichia coli. Overall, our results indicate that the CMCS/SA/ß-CD-CHX/Sr-nHA composite hydrogel scaffolds exhibit significant potential for effectively treating infection-prone jaw defects.

Preparation and properties of a 3D printed nHA/PLA bone tissue engineering scaffold loaded with a ß-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. This study engineered a novel temperature-sensitive, sustained-release hydrogel by amalgamating ß-cyclodextrin (ß-CD) with chlorhexidine (CHX) and a decellularized extracellular matrix (dECM). This hydrogel was further integrated with a polylactic acid (PLA)-nano hydroxyapatite (nHA) scaffold, fabricated through 3D printing, to form a multifaceted composite scaffold (nHA/PLA/dECM/ß-CD-CHX). Drug release assays revealed that this composite scaffold ensures prolonged and sustained release. Bacteriological studies confirmed that the ß-CD-CHX loaded scaffold exhibits persistent antibacterial efficacy, thus effectively inhibiting bacterial growth. Moreover, the scaffold demonstrated robust mechanical strength. Cellular assays validated its superior biocompatibility, attributed to dECM and nHA components, significantly enhancing the proliferation, adhesion, and osteogenic differentiation of osteogenic precursor cells (MC3T3-E1). Consequently, the nHA/PLA/dECM/ß-CD-CHX composite scaffold, synthesized via 3D printing technology, shows promise in inducing bone regeneration, preventing infection, and facilitating the repair of jaw defects, positioning itself as a potential breakthrough in bone tissue engineering.

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. METHODS: Chest CT were reconstructed using volume rendering (VR) and maximum intensity projection (MIP) separately. Retrospective data of 2500 patients aged 20.00-69.99 years were obtained. The cohort was split into training (80%) and validation (20%) sets. Additional independent data from 200 patients were used as the test set and external validation set. Different modality DL models were developed accordingly. Comparisons were hierarchically performed by VR versus MIP, single-modality versus multi-modality, and DL versus manual method. Mean absolute error (MAE) was the primary parameter of comparison. RESULTS: A total of 2700 patients (mean age = 45.24 years ± 14.03 [SD]) were evaluated. Of single-modality models, MAEs yielded by VR were lower than MIP. Multi-modality models generally yielded lower MAEs than the optimal single-modality model. The best-performing multi-modality model obtained the lowest MAEs of 3.78 in males and 3.40 in females. On the test set, DL achieved MAEs of 3.78 in males and 3.92 in females, which were far better than the MAEs of 8.90 and 6.42 respectively, for the manual method. For the external validation, MAEs were 6.05 in males and 6.68 in females for DL, and 6.93 and 8.28 for the manual method. CONCLUSIONS: DL demonstrated better performance than the manual method in AAE based on CT reconstruction of the costal cartilage. CLINICAL RELEVANCE STATEMENT: Aging leads to diseases, functional performance deterioration, and both physical and physiological damage over time. Accurate AAE may aid in diagnosing the personalization of aging processes. KEY POINTS: • VR-based DL models outperformed MIP-based models with lower MAEs and higher R2 values. • All multi-modality DL models showed better performance than single-modality models in adult age estimation. • DL models achieved a better performance than expert assessments.