Evaluation and Comparison of Stress in Divergent and Convergent Collar Designs of Implants With Different Bone Densities: A Finite Element Study.

Background: The failure and the success rate of an implant depends on biomechanical factors, esthetics and painless sterile implant surgery conditions, out of which stresses applied to the bone and its surrounding, bone-implant interface, material characteristics of the implant used and the strength of the bone and its surrounding are the important factors. This study aimed to evaluate the stress distribution of divergent collar design (DCD) and convergent collar design (CCD) implants placing them in four different densities of the bone (D1, D2, D3 and D4). The evaluation of the stress distribution of DCD and CCD was performed using the 3D finite element method (FEM), by placing them in four different bone densities.

Evaluation and Comparison of the Geometrical Relationship of Tooth and Lip Arcs and Their Correlation to Smile Arcs Between Males and Females in a North Indian Population.

Background With the increasing influence of social media, millennials and the generations that follow have increasingly pressing aesthetic concerns. Following this, there has been a sea change in treatment plans and procedures as well as the choice of material. Dentistry nowadays is dependent on digital data to compute and design prostheses; these technologies are often not readily available all over the world.

BRMCF: Binary Relevance and MLSMOTE Based Computational Framework to Predict Drug Functions From Chemical and Biological Properties of Drugs.

In silico machine learning based prediction of drug functions considering the drug properties would substantially enhance the speed and reduce the cost of identifying promising drug leads. The drug function prediction capability of different drug properties happens to be different. So assessing these is advantageous in drug discovery.

Clustering-Based Hybrid Approach for Identifying Quantitative Multidimensional Associations Between Patient Attributes, Drugs and Adverse Drug Reactions.

The activity of post-marketing surveillance results in a collection of large amount of data. The analysis of data is very useful for raising early warnings on possible adverse reactions of drugs. Association rule mining techniques have been heavily explored by the research community for identifying binary association between drugs and their adverse effects.