Showing papers by "Prashant Jain published in 2023"

Preparation of 3D printed calcium sulfate filled PLA scaffolds with improved mechanical and degradation properties

Abstract: Abstract Scaffold is one of the key components for tissue engineering application. Three-dimensional (3D) printing has given a new avenue to the scaffolds design to closely mimic the real tissue. However, material selection has always been a challenge in adopting 3D printing for scaffolds fabrication, especially for hard tissue. The fused filament fabrication technique is one of the economical 3D printing technology available today, which can efficiently fabricate scaffolds with its key features. In the present study, a hybrid polymer-ceramic scaffold has been prepared by combining the benefit of synthetic biodegradable poly (lactic acid) (PLA) and osteoconductive calcium sulphate (CaS), to harness the advantage of both materials. Composite PLA filament with maximum ceramic loading of 40 wt% was investigated for its printability and subsequently scaffolds were 3D printed. The composite filament was extruded at a temperature of 160 °C at a constant speed with an average diameter of 1.66 ± 0.34 mm. PLA-CaS scaffold with ceramic content of 10%, 20%, and 40% was 3D printed with square pore geometry. The developed scaffolds were characterized for their thermal stability, mechanical, morphological, and geometrical accuracy. The mechanical strength was improved by 29% at 20 wt% of CaS. The porosity was found to be 50–60% with an average pore size of 550 µm with well-interconnected pores. The effect of CaS particles on the degradation behaviour of scaffolds was also assessed over an incubation period of 28 days. The CaS particles acted as porogen and improved the surface chemistry for future cellular activity, while accelerating the degradation rate.

The implication of oversampling on the effectiveness of force signals in the fault detection of endodontic instruments during RCT.

Abstract: This work provides an innovative endodontic instrument fault detection methodology during root canal treatment (RCT). Sometimes, an endodontic instrument is prone to fracture from the tip, for causes uncertain the dentist's control. A comprehensive assessment and decision support system for an endodontist may avoid several breakages. This research proposes a machine learning and artificial intelligence-based approach that can help to diagnose instrument health. During the RCT, force signals are recorded using a dynamometer. From the acquired signals, statistical features are extracted. Because there are fewer instances of the minority class (i.e. faulty/moderate class), oversampling of datasets is required to avoid bias and overfitting. Therefore, the synthetic minority oversampling technique (SMOTE) is employed to increase the minority class. Further, evaluating the performance using the machine learning techniques, namely Gaussian Naïve Bayes (GNB), quadratic support vector machine (QSVM), fine k-nearest neighbor (FKNN), and ensemble bagged tree (EBT). The EBT model provides excellent performance relative to the GNB, QSVM, and FKNN. Machine learning (ML) algorithms can accurately detect endodontic instruments' faults by monitoring the force signals. The EBT and FKNN classifier is trained exceptionally well with an area under curve values of 1.0 and 0.99 and prediction accuracy of 98.95 and 97.56%, respectively. ML can potentially enhance clinical outcomes, boost learning, decrease process malfunctions, increase treatment efficacy, and enhance instrument performance, contributing to superior RCT processes. This work uses ML methodologies for fault detection of endodontic instruments, providing practitioners with an adequate decision support system.