This paper explores the well-known Atkins Diet, as it also places a strong regulation on macromolecule consumption, specifically carbohydrates, in order to assist with the weight loss process. A review of available literature will be used to investigate: the history of the diet, necessity of macromolecule consumption, the impact this has on the individual biochemical pathways (glycolysis/gluconeogenesis) and the microbiome as a whole, as well as overall success rates and long-term health complications/benefits. Additionally personal statements from various individuals who have experience with the diet, myself included, will be incorporated into a holistic analysis of the effectiveness and longevity of the Atkins weight-loss strategy.

The field of biomedical research relies on the knowledge of binding interactions between various proteins of interest to create novel molecular targets for therapeutic purposes. While many of these interactions remain a mystery, knowledge of these properties and interactions could have significant medical applications in terms of understanding cell signaling and immunological defenses. Furthermore, there is evidence that machine learning and peptide microarrays can be used to make reliable predictions of where proteins could interact with each other without the definitive knowledge of the interactions. In this case, a neural network was used to predict the unknown binding interactions of TNFR2 onto LT-ɑ and TRAF2, and PD-L1 onto CD80, based off of the binding data from a sampling of protein-peptide interactions on a microarray. The accuracy and reliability of these predictions would rely on future research to confirm the interactions of these proteins, but the knowledge from these methods and predictions could have a future impact with regards to rational and structure-based drug design.