Divided Attention: Definition & Performance Factors – Making Sense of the Environment | Lecturio





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– Task similarity
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Dr. Tarry Ahuja is currently a senior medical real-world evidence scientist for a major pharmaceutical company and a lecturer at Carleton University, Canada. He has worked in the hospital setting and National Research Council of Canada for over 10 years.

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► Watch the complete course “Making Sense of the Environment” on http://lectur.io/dividedattention and prepare for your next medical exam with video lectures, in-depth, high-yield content and thousands of interactive quiz questions! The lecture “Divided Attention” is part of our online “Making Sense of the Environment” course. It will give you a brief overview of divided attention, or multitasking, which is the ability to successfully execute more than one action at a time.

Learn about:
– Definition of Divided Attention
– Factors Associated With Performance

Dr. Tarry Ahuja is currently a senior medical real-world evidence scientist for a major pharmaceutical company and a lecturer at Carleton University, Canada. He has worked in the hospital setting and National Research Council of Canada for over 10 years.

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Medical Videos – Divided Attention: Definition & Performance Factors – Making Sense of the Environment | Lecturio #Medical #Videos

Atherosclerosis: Risk Factors – Cardiovascular Pathology | Medical Education Videos





Watch this medical education video about the risk factors that encourage atherosclerosis and understand the difference between modifiable and nonmodifiable risk factors.

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The video “Atherosclerosis: Risk Factors” is part of the lecture “Cardiovascular Pathology” of the online medical course “Pathology”.

Learn more about:
• Modifiable risk factors
– Smoking
– Hypertension
– Hyperlipidemia
– Diabetes
• Nonmodifiable risk factors
– Age
– Gender
– Family history
– Diabetes

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Medical News – Study provides further support for genetic factors underlying addictions


Impairment of a particular gene raises increases susceptibility to opioid addiction liability as well as vulnerability to binge eating according to a new study.

Dysfunction of the gene, casein kinase1-epsilon (CSNK1E), increases opioid’s euphoric response and produces a marked increase in sensitivity to binge eating in a female experimental model but not in the male.

Similar to opioid addiction, very little is known regarding the genetic basis of binge eating. These combined findings provide further support indicating that shared genetic factors may underlie behavioral traits associated with the addictions and eating disorders. Furthermore, they also provide an important clue that the genetic basis of binge eating and eating disorders in women versus men is likely to differ. The findings appear online in the journal Genes, Brain and Behavior.

Addiction is a multi-stage process that begins with drug exposure and the initial pleasurable experience and progresses toward tolerance, dependence, physiological and emotional withdrawal upon cessation of use, protracted withdrawal that can last years, and finally, relapse to drug taking. The genes associated with risk for opioid addiction could potentially affect one or more of these stages.

“Because increasing evidence points toward an association between CSNK1E and opioid addiction in humans, our findings indicate that genetic variation in CSNK1E could function as a potential risk factor that influences the initial pleasurable/euphoric response to opioids and thus, could ultimately have implications for personalized medicine with regard to drug choice for therapeutic treatment (e.g., non-opioid pain relief) and therapeutic dosing of opioids,” explained corresponding author Camron Bryant, PhD, assistant professor of pharmacology and experimental therapeutics & psychiatry at BUSM.

The researchers also believe the female-specific binge eating property associated with Csnk1e dysfunction suggests that different genetic loci (position on the chromosome) are likely to be uncovered for binge eating and eating disorders in women versus men and may lead to sex-specific treatments ultimately being developed for treating eating disorders.

According to the researchers CSNK1E may also play a role in a subset of patients with alcohol use disorders. Additionally, CSNK1E is known to be a crucial player in regulating circadian rhythms. “The potential interaction of CSNK1E with circadian biology in affecting addiction is an unexplored area of investigation that could be a crucial piece to the puzzle in fully understanding its role in the addictions,” said Bryant.

Funding for this study was provided by the National Institutes of Health/National Drug Abuse (NIH/NIDA) Grant Nos. R00DA029635 (CDB), R21DA038738 (CDB), F31DA40324 (NY); NIH/National Institute of General Medical Sciences (NIGMS) for Grant No. T32GM008541 (LRG, NY); the Burroughs Wellcome Fund Transformative Training Program in Addiction Science Grant No. 1011479 (NY); and Boston University’s Undergraduate Research Opportunities Program.

Article: Casein kinase 1-epsilon deletion increases mu opioid receptor-dependent behaviors and binge eating, Camron D. Bryant Ph.D, Genes, Brain and Behavior, doi: 10.1111/gbb.12397, published online 8 June 2017.



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Muscular Fitness, adherence to the Southern European Atlantic Diet and cardiometabolic risk factors in adolescents


Nutrition, Metabolism & Cardiovascular Diseases

AgostinisSobrinho C, et al. –

The objective of the study described in this paper is to evaluate the independent and combined relationship of muscular fitness and adherence to the SEADiet on cardiometabolic risk in adolescents. These outcomes appear to recommend that improving muscular fitness and also an adherence to the SEADiet could be a vital strategy to decrease clustered cardiometabolic risk in youth.

Methods

  • In this research, they designed a cross–sectional study.
  • A sum of 467 Portuguese adolescents (275 girls) participated in this study.
  • Sum of the Z–Scores of Curl–Up and Push–Up tests was utilized to create a muscular fitness score.
  • Adherence to SEADiet was acquired utilizing a food frequency questionnaire.
  • A cardiometabolic risk score was computed from sum of Z–score of triglycerides, systolic blood pressure, total cholesterol/HDL ratio, HOMA–IR and waist circumference.

Results

  • Adolescents with low muscular fitness and low adherence to the SEADiet had the poorest cardiovascular profile F(5, 452)=5.074 p



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