Women Have Higher Protein Content of β-Oxidation Enzymes in Skeletal Muscle than Men

It is well recognized that compared with men, women have better ultra-endurance capacity, oxidize more fat during endurance exercise, and are more resistant to fat oxidation defects i.e. diet-induced insulin resistance. Several groups have shown that the mRNA and protein transcribed and translated from genes related to transport of fatty acids into the muscle are greater in women than men; however, the mechanism(s) for the observed sex differences in fat oxidation remains to be determined. Muscle biopsies from the vastus lateralis were obtained from moderately active men (N = 12) and women (N = 11) at rest to examine mRNA and protein content of genes involved in lipid oxidation. Our results show that women have significantly higher protein content for tri-functional protein alpha (TFPα), very long chain acyl-CoA dehydrogenase (VLCAD), and medium chain acyl-CoA dehydrogenase (MCAD) (P<0.05). There was no significant sex difference in the expression of short-chain hydroxyacyl-CoA dehydrogenase (SCHAD), or peroxisome proliferator activated receptor alpha (PPARα), or PPARγ, genes potentially involved in the transcriptional regulation of lipid metabolism. In conclusion, women have more protein content of the major enzymes involved in long and medium chain fatty acid oxidation which could account for the observed differences in fat oxidation during exercise

Large-scale integration of cancer microarray data identifies a robust common cancer signature

<p>Abstract</p> <p>Background</p> <p>There is a continuing need to develop molecular diagnostic tools which complement histopathologic examination to increase the accuracy of cancer diagnosis. DNA microarrays provide a means for measuring gene expression signatures which can then be used as components of genomic-based diagnostic tests to determine the presence of cancer.</p> <p>Results</p> <p>In this study, we collect and integrate ~ 1500 microarray gene expression profiles from 26 published cancer data sets across 21 major human cancer types. We then apply a statistical method, referred to as the <it>T</it>op-<it>S</it>coring <it>P</it>air of <it>G</it>roups (TSPG) classifier, and a repeated random sampling strategy to the integrated training data sets and identify a common cancer signature consisting of 46 genes. These 46 genes are naturally divided into two distinct groups; those in one group are typically expressed less than those in the other group for cancer tissues. Given a new expression profile, the classifier discriminates cancer from normal tissues by ranking the expression values of the 46 genes in the cancer signature and comparing the average ranks of the two groups. This signature is then validated by applying this decision rule to independent test data.</p> <p>Conclusion</p> <p>By combining the TSPG method and repeated random sampling, a robust common cancer signature has been identified from large-scale microarray data integration. Upon further validation, this signature may be useful as a robust and objective diagnostic test for cancer.</p

Changes in agonist neural drive, hypertrophy and pre-training strength all contribute to the individual strength gains after resistance training.

PURPOSE: Whilst neural and morphological adaptations following resistance training (RT) have been investigated extensively at a group level, relatively little is known about the contribution of specific physiological mechanisms, or pre-training strength, to the individual changes in strength following training. This study investigated the contribution of multiple underpinning neural [agonist EMG (QEMGMVT), antagonist EMG (HEMGANTAG)] and morphological variables [total quadriceps volume (QUADSVOL), and muscle fascicle pennation angle (QUADSθ p)], as well as pre-training strength, to the individual changes in strength after 12 weeks of knee extensor RT. METHODS: Twenty-eight healthy young men completed 12 weeks of isometric knee extensor RT (3/week). Isometric maximum voluntary torque (MVT) was assessed pre- and post-RT, as were simultaneous neural drive to the agonist (QEMGMVT) and antagonist (HEMGANTAG). In addition QUADSVOL was determined with MRI and QUADSθ p with B-mode ultrasound. RESULTS: Percentage changes (∆) in MVT were correlated to ∆QEMGMVT (r = 0.576, P = 0.001), ∆QUADSVOL (r = 0.461, P = 0.014), and pre-training MVT (r = -0.429, P = 0.023), but not ∆HEMGANTAG (r = 0.298, P = 0.123) or ∆QUADSθ p (r = -0.207, P = 0.291). Multiple regression analysis revealed 59.9% of the total variance in ∆MVT after RT to be explained by ∆QEMGMVT (30.6%), ∆QUADSVOL (18.7%), and pre-training MVT (10.6%). CONCLUSIONS: Changes in agonist neural drive, quadriceps muscle volume and pre-training strength combined to explain the majority of the variance in strength changes after knee extensor RT (~60%) and adaptations in agonist neural drive were the most important single predictor during this short-term intervention

IPCC reasons for concern regarding climate change risks

The reasons for concern framework communicates scientific understanding about risks in relation to varying levels of climate change. The framework, now a cornerstone of the IPCC assessments, aggregates global risks into five categories as a function of global mean temperature change. We review the framework's conceptual basis and the risk judgments made in the most recent IPCC report, confirming those judgments in most cases in the light of more recent literature and identifying their limitations. We point to extensions of the framework that offer complementary climate change metrics to global mean temperature change and better account for possible changes in social and ecological system vulnerability. Further research should systematically evaluate risks under alternative scenarios of future climatic and societal conditions

The Policy Dystopia Model:an interpretive analysis of tobacco industry political activity

BACKGROUND: Tobacco industry interference has been identified as the greatest obstacle to the implementation of evidence-based measures to reduce tobacco use. Understanding and addressing industry interference in public health policy-making is therefore crucial. Existing conceptualisations of corporate political activity (CPA) are embedded in a business perspective and do not attend to CPA's social and public health costs; most have not drawn on the unique resource represented by internal tobacco industry documents. Building on this literature, including systematic reviews, we develop a critically informed conceptual model of tobacco industry political activity. METHODS AND FINDINGS: We thematically analysed published papers included in two systematic reviews examining tobacco industry influence on taxation and marketing of tobacco; we included 45 of 46 papers in the former category and 20 of 48 papers in the latter (n = 65). We used a grounded theory approach to build taxonomies of "discursive" (argument-based) and "instrumental" (action-based) industry strategies and from these devised the Policy Dystopia Model, which shows that the industry, working through different constituencies, constructs a metanarrative to argue that proposed policies will lead to a dysfunctional future of policy failure and widely dispersed adverse social and economic consequences. Simultaneously, it uses diverse, interlocking insider and outsider instrumental strategies to disseminate this narrative and enhance its persuasiveness in order to secure its preferred policy outcomes. Limitations are that many papers were historical (some dating back to the 1970s) and focused on high-income regions. CONCLUSIONS: The model provides an evidence-based, accessible way of understanding diverse corporate political strategies. It should enable public health actors and officials to preempt these strategies and develop realistic assessments of the industry's claims

Sarcopenia: etiology, clinical consequences, intervention, and assessment

The aging process is associated with loss of muscle mass and strength and decline in physical functioning. The term sarcopenia is primarily defined as low level of muscle mass resulting from age-related muscle loss, but its definition is often broadened to include the underlying cellular processes involved in skeletal muscle loss as well as their clinical manifestations. The underlying cellular changes involve weakening of factors promoting muscle anabolism and increased expression of inflammatory factors and other agents which contribute to skeletal muscle catabolism. At the cellular level, these molecular processes are manifested in a loss of muscle fiber cross-sectional area, loss of innervation, and adaptive changes in the proportions of slow and fast motor units in muscle tissue. Ultimately, these alterations translate to bulk changes in muscle mass, strength, and function which lead to reduced physical performance, disability, increased risk of fall-related injury, and, often, frailty. In this review, we summarize current understanding of the mechanisms underlying sarcopenia and age-related changes in muscle tissue morphology and function. We also discuss the resulting long-term outcomes in terms of loss of function, which causes increased risk of musculoskeletal injuries and other morbidities, leading to frailty and loss of independence

Investigating large-scale brain dynamics using field potential recordings: Analysis and interpretation

New technologies to record electrical activity from the brain on a massive scale offer tremendous opportunities for discovery. Electrical measurements of large-scale brain dynamics, termed field potentials, are especially important to understanding and treating the human brain. Here, our goal is to provide best practices on how field potential recordings (EEG, MEG, ECoG and LFP) can be analyzed to identify large-scale brain dynamics, and to highlight critical issues and limitations of interpretation in current work. We focus our discussion of analyses around the broad themes of activation, correlation, communication and coding. We provide best-practice recommendations for the analyses and interpretations using a forward model and an inverse model. The forward model describes how field potentials are generated by the activity of populations of neurons. The inverse model describes how to infer the activity of populations of neurons from field potential recordings. A recurring theme is the challenge of understanding how field potentials reflect neuronal population activity given the complexity of the underlying brain systems