Advanced integrative molecular platform for high-throughput screening of drug-resistant tuberculosis.

A real time-polymerase chain reaction-based test in lyophilized form, was developed to simultaneously identify Mycobacterium tuberculosis complex (MTC) by targeting IS6110, rrs as dual markers, as well as mutations causing rifampicin and isoniazid resistance. The test was evaluated for pulmonary and non-pulmonary specimens from sample isolation to PCR analysis. The test demonstrated limit of detection of 25 CFU/mL for MTB, 200 CFU/mL for rpoB and inhA/katG targets with >95 % CI. The specificity for MTC was supported by a comprehensive clinical validation (n = 100). This load-and-go molecular platform, with features of high throughput, long shelf-life, room temperature storage provides simultaneous detection of MTC and its drug-resistant mutations in minimal time. The test named "PathoDetect TM MTB-RIF and INH resistance detection kit" has been approved by Central Drugs Standard Control Organisation, Indian Council of Medical Research and would have implications for tuberculosis elimination programs.

Prediction of wear of dental composite materials using machine learning algorithms.

Since dental materials are worn down over time and eventually need to be replaced. Resin composites are frequently employed as dental restorative materials. By employing the in-vitro test findings of the pin-on-disc tribometer [ASTM G99-04], the goal of this study is to evaluate the capability of three different machine learning (ML) models in analyzing the wear of dental composite materials when immersed in chewable tobacco solution. Four distinct dental composite material samples are used in this investigation, and after being dipped in a chewing tobacco solution for a few days, the samples are taken out and subjected to a wear test. Three different ML models (MLP, KNN, XGBoost) have been chosen for predicting the wear of dental composite specimens. XGBoost ML model yields an R2 value of 0.9996 and it performs noticeably better than the other approaches.

Prediction of abrasive wears behavior of dental composites using an artificial neural network.

Resin composites are widely used as dental restorative materials since dental parts are subjected to prolonged wear and ultimately need to be replaced. The objective of this study is to analyze the potential of the feed-forward back propagation artificial neural network (ANN) in assessing the wear of dental composite materials when immersed in chewable tobacco solution, by utilizing the in-vitro test results of the pin-on-disc tribometer [ASTM G99-04]. In this study, four different dental composite material specimens are dipped in a chewable tobacco solution for a few days, and the specimens are removed from the solution for conducting the wear test. Three different training procedures are used to simulate ANN models for predicting the wear of dental composite specimens. The Bayesian regularization training algorithm outperforms the other algorithms significantly. The findings of the ANN modeling were prominently matching with the results of the experiments; therefore, parametric analysis was used based on the model's predicted values.

Tribological behavior of dental restorative composites in chewable tobacco environment.

This study investigates the effect of smokeless tobacco on the tribological properties of two commercially used dental composite materials: Tetric N-Ceram and Z350 Dentin shade. It is to evaluate the effect of smokeless tobacco on the wear properties of two dental composite materials after some stipulated period. The wear test was conducted on pin-on-disk tribometer in the presence of artificial saliva under different loading conditions of 10, 15 and 20 N. The pins of the dental composite material were immersed in tobacco solution. The tribological behavior was studied after 2 days, 3.5 days, 6 days, 15 days and 1 month which represented the real conditions for the contact of 1 week, 2 weeks, 1 month, 2 months and 5 months, respectively, between the dental material and the tobacco solution. Under different loading conditions, Z350 Dentin material exhibited much less wear than the Tetric N-Ceram material in the presence of synthetic saliva for the specimen with or without tobacco immersion. The microstructure of the pin surface was inspected using scanning electron microscopy.