Optimal Feature Selection-Based Dental Caries Prediction Model Using Machine Learning for Decision Support System.

The high frequency of dental caries is a major public health concern worldwide. The condition is common, particularly in developing countries. Because there are no evident early-stage signs, dental caries frequently goes untreated. Meanwhile, early detection and timely clinical intervention are required to slow disease development. Machine learning (ML) models can benefit clinicians in the early detection of dental cavities through efficient and cost-effective computer-aided diagnoses. This study proposed a more effective method for diagnosing dental caries by integrating the GINI and mRMR algorithms with the GBDT classifier. Because just a few clinical test features are required for the diagnosis, this strategy could save time and money when screening for dental caries. The proposed method was compared to recently proposed dental procedures. Among these classifiers, the suggested GBDT trained with a reduced feature set achieved the best classification performance, with accuracy, F1-score, precision, and recall values of 95%, 93%, 99%, and 88%, respectively. Furthermore, the experimental results suggest that feature selection improved the performance of the various classifiers. The suggested method yielded a good predictive model for dental caries diagnosis, which might be used in more imbalanced medical datasets to identify disease more effectively.

Isomer-specific LC/MS and LC/MS/MS profiling of the mouse serum N-glycome revealing a number of novel sialylated N-glycans.

Mice are the premier mammalian models for studies of human physiology and disease, bearing extensive biological similarity to humans with far fewer ethical, economic, or logistic complications. To facilitate glycomic studies based on the mouse model, we comprehensively profiled the mouse serum N-glycome using isomer-specific nano-LC/MS and -LC/MS/MS. N-Glycans were identified by accurate mass MS and structurally elucidated by MS/MS. Porous graphitized carbon nano-LC was able to separate out nearly 300 N-linked glycan compounds (including isomers) from just over 100 distinct N-linked glycan compositions. Additional MS/MS structural analysis was performed on a number of novel N-glycans, revealing the structural characteristics of modifications such as dehydration, O-acetylation, and lactylation. Experimental findings were combined with known glycobiology to generate a theoretical library of all biologically possible mouse serum N-glycan compositions. The library may be used for automated identification of complex mixtures of mouse N-glycans, with possible applications to a wide range of mouse-related research endeavors, including pharmaceutical drug development and biomarker discovery.