Adverse Drug Reactions to Anti-TB Drugs: Pharmacogenomics Perspective for Identification of Host Genetic Markers.

Adverse drug reactions (ADRs) are associated with clinical morbidity and, in severe cases, even mortality. Globally billions of dollars are spent on managing these ADRs for common and uncommon diseases. The developing world suffers from a high burden of tuberculosis, which requires 6-8 months of multi-drug treatment. In spite of most cases being treatable the problem persists mainly due to a high attrition rate associated with ADR mediated complications. Due to these reasons drug resistant strains have emerged and are now a serious challenge to TB eradication. To effectively deliver the available treatment regimen and ensure patient compliance it is important to manage ADRs more efficiently. Recent studies have demonstrated that drug outcomes are patient-specific and can, therefore be predicted. A few of these drugs, including a few administered for TB, have shown excellent correlation with response rates and development of ADRs. In this review, we profile information available in public domain for existing anti-TB drugs to understand the genesis of ADRs and patient response. Additionally, human genome variation databases have been used to correlate the frequency of these markers and their genomic variants in different populations.

A motor learning strategy reflects neural circuitry for limb control.

During motor skill acquisition, the brain learns a mapping between intended limb motion and requisite muscular forces. We propose that regions where sensory and motor representations overlap are crucial for motor learning. In primary motor cortex, for example, cells that modulate their activity for motor actions at a joint tend to receive input from that same portion of the periphery. We predict that this correspondence reflects a default strategy--a Bayesian prior--in which subjects tend to associate loads at a joint with motion at that joint (local sensorimotor association) when there is ambiguity regarding the nature of the load. As predicted, we found that in the presence of uncertainty, humans inappropriately generalized elbow loads as though they were based on elbow velocity. Generalization improved when we reduced uncertainty by decreasing coupling between elbow velocity and load during training. These results illustrate a key link between motor learning and the underlying neural circuitry.

Morphometry of Macaca mulatta forelimb. II. Fiber-type composition in shoulder and elbow muscles.

The present study examined the fiber-type proportions of 22 muscles spanning the shoulder and/or elbow joints of three Macaca mulatta. Fibers were classified as one of three types: fast-glycolytic (FG), fast-oxidative-glycolytic (FOG), or slow-oxidative (SO). In most muscles, the FG fibers predominated, but proportions ranged from 25-67% in different muscles. SO fibers were less abundant except in a few deep, small muscles where they comprised as much as 56% of the fibers. Cross-sectional area (CSA) of the three fiber types was measured in six different muscles. FG fibers tended to be the largest, whereas SO fibers were the smallest. While fiber-type size was not always consistent between muscles, the relative size of FG fibers was generally larger than FOG and SO fibers within the same muscle. When fiber CSA was taken into consideration, FG fibers were found to comprise over 50% of the muscle's CSA in almost all muscles.