Comparison of traditional regression modeling vs. AI modeling for the prediction of dental caries: a secondary data analysis.

Introduction: There are substantial gaps in our understanding of dental caries in primary and permanent dentition and various predictors using newer modeling methods such as Machine Learning (ML) algorithms and Artificial Intelligence (AI). The objective of this study is to compare the accuracy, precision, and differences between the caries predictive capability of AI vs. traditional multivariable regression techniques. Methods: The study was conducted using secondary data stored in the Temple University Kornberg School of Dentistry electronic health records system (axiUm) of pediatric patients aged 6-16 years who were patients on record at the Pediatric Dentistry Clinic. The outcome variables considered in the study were the decayed-missing-filled teeth (DMFT) and the decayed-extracted-filled teeth (deft) scores. The predictors included age, sex, insurance, fluoride exposure, having a dental home, consumption of sugary meals, family caries experience, having special needs, visible plaque, medications reducing salivary flow, and overall assessment questions. Results: The average DMFT score was 0.85 ± 2.15, while the average deft scores were 0.81 ± 2.15. For childhood dental caries, XGBoost was the best performing ML algorithm with accuracy, sensitivity. and Kappa as 81%, 84%, and 61%, respectively, followed by Support Vector Machine and Lasso Regression algorithms, both with 84% specificity. The most important variables for prediction found were age and visible plaque. Conclusions: The machine learning model outperformed the traditional statistical model in the prediction of childhood dental caries. Data from a more diverse population will help improve the quality of caries prediction for permanent dentition where the traditional statistical method outperformed the machine learning model.

Effect of caffeine on the aggregation of amyloid-ß-A 3D RISM study.

Alzheimer's disease is a detrimental neurological disorder caused by the formation of amyloid fibrils due to the aggregation of amyloid-ß peptide. The primary therapeutic approaches for treating Alzheimer's disease are targeted to prevent this amyloid fibril formation using potential inhibitor molecules. The discovery of such inhibitor molecules poses a formidable challenge to the design of anti-amyloid drugs. This study investigates the effect of caffeine on dimer formation of the full-length amyloid-ß using a combined approach of all-atom, explicit water molecular dynamics simulations and the three-dimensional reference interaction site model theory. The change in the hydration free energy of amyloid-ß dimer, with and without the inhibitor molecules, is calculated with respect to the monomeric amyloid-ß, where the hydration free energy is decomposed into energetic and entropic components, respectively. Dimerization is accompanied by a positive change in the partial molar volume. Dimer formation is spontaneous, which implies a decrease in the hydration free energy. However, a reverse trend is observed for the dimer with inhibitor molecules. It is observed that the negatively charged residues primarily contribute for the formation of the amyloid-ß dimer. A residue-wise decomposition reveals that hydration/dehydration of the side-chain atoms of the charged amino acid residues primarily contribute to dimerization.

Exploring the aggregation of amyloid-ß 42 through Monte Carlo simulations.

Coarse-grained Monte Carlo simulations are performed for a disordered protein, amyloid-ß 42 to identify the interactions and understand the mechanism of its aggregation. A statistical potential is developed from a selected dataset of intrinsically disordered proteins, which accounts for the respective contributions of the bonded and non-bonded potentials. While, the bonded potential comprises the bond, bend, and dihedral constraints, the nonbonded interactions include van der Waals interactions, hydrogen bonds, and the two-body potential. The two-body potential captures the features of both hydrophobic and electrostatic interactions that brings the chains at a contact distance, while the repulsive van der Waals interactions prevent them from a collapse. Increased two-body hydrophobic interactions facilitate the formation of amorphous aggregates rather than the fibrillar ones. The formation of aggregates is validated from the interchain distances, and the total energy of the system. The aggregate is structurally characterized by the root-mean-square deviation, root-mean-square fluctuation and the radius of gyration. The aggregates are characterized by a decrease in SASA, an increase in the non-local interactions and a distinct free energy minimum relative to that of the monomeric state of amyloid-ß 42. The hydrophobic residues help in nucleation, while the charged residues help in oligomerization and aggregation.

Medicinal and therapeutic properties of garlic, garlic essential oil, and garlic-based snack food: An updated review.

Garlic (Allium sativum) is an edible tuber belonging to the family Liliaceae. It has been used since ancient times as a spice to enhance the sensory characteristics of food and as a household remedy for the treatment of a variety of ailments. Garlic has been studied for its medicinal and therapeutic effects in the treatment of various human diseases for a long time. Health benefits associated with the consumption of garlic are attributed to the various sulfur compounds present in it such as allicin, ajoene, vinyl-dithiin, and other volatile organosulfur compounds which are all metabolized from alliin. Several researches in the literature have shown evidence that garlic exhibits antioxidant, antiviral, anti-microbial, anti-fungal, antihypertensive, anti-anemic, anti-hyperlipidemic, anticarcinogenic, antiaggregant, and immunomodulatory properties. The present review identifies and discusses the various health benefits associated with the consumption of garlic, its essential oil, and bioactive constituents, along with exploring the various snack-food products developed by incorporating garlic.

Relaxation dynamics measure the aggregation propensity of amyloid-ß and its mutants.

Atomistic molecular dynamics simulations are employed to investigate the global and segmental relaxation dynamics of the amyloid-ß protein and its causative and protective mutants. Amyloid-ß exhibits significant global/local dynamics that span a broad range of length and time scales due to its intrinsically disordered nature. The relaxation dynamics of the amyloid-ß protein and its mutants is quantitatively correlated with its experimentally measured aggregation propensity. The protective mutant has slower relaxation dynamics, whereas the causative mutants exhibit faster global dynamics compared with that of the wild-type amyloid-ß. The local dynamics of the amyloid-ß protein or its mutants is governed by a complex interplay of the charge, hydrophobicity, and change in the molecular mass of the mutated residue.

Aggregation propensities of proteins with varying degrees of disorder.

The hydration thermodynamics of a globular protein (AcP), three intrinsically disordered protein regions (1CD3, 1MVF, 1F0R) and a fully disordered protein (α-synuclein) is studied by an approach that combines an all-atom explicit water molecular dynamics simulations and three-dimensional reference interaction site model (3D-RISM) theory. The variation in hydration free energy with percentage disorder of the selected proteins is investigated through its nonelectrostatic and electrostatic components. A decrease in hydration free energy is observed with an increase in percentage disorder, indicating favorable interactions of the disordered proteins with the solvent. This confirms the role of percentage disorder in determining the aggregation propensity of proteins which is measured in terms of the hydration free energy in addition to their respective mean net charge and mean hydrophobicity. The hydration free energy is decoupled into energetic and entropic terms. A residue-wise decomposition analysis of the hydration free energy for the selected proteins is evaluated. The decomposition shows that the disordered regions contribute more than the ordered ones for the intrinsically disordered protein regions. The dominant role of electrostatic interactions is confirmed from the residue-wise decomposition of the hydration free energy. The results depict that the negatively charged residues contribute more to the total hydration free energy for the proteins with negative mean net charge, while the positively charged residues contribute more for proteins with positive mean net charge.

Sodium alginate potentiates antioxidant defense and PR proteins against early blight disease caused by Alternaria solani in Solanum lycopersicum Linn.

The use of biopolymers as elicitors in controlling plant diseases is gaining momentum world-wide due to their eco-friendly and non-toxic nature. In the present study, we have used an algal biopolymer (sodium alginate) and tested its applicability as an elicitor in inducing resistance factors against Alternaria solani, which causes early blight disease in Solanum lycopersicum (tomato plant). We have pre-treated tomato plants with different concentrations of sodium alginate (0.2%, 0.4%, and 0.6%) before A. solani infection. We found that sodium alginate has effectively controlled the growth of A. solani. In addition, a significant increase in the expression levels of SOD was observed in response to pathogen infection. The increased protease inhibitors activity further suggest that sodium alginate restrict the development of A. solani infection symptoms in tomato leaves. This corroborates well with the cell death analysis wherein increased sodium alginate pre-treatment results in decreased cell death. Also, the expression profile analyses reveal the induction of genes only in sodium alginate-pretreated tomato leaves, which are implicated in plant defense mechanism. Taken together, our results suggest that sodium alginate can be used as an elicitor to induce resistance against A. solani in tomato plants.