April 2016 at a Glance. Focus on Cardiac Remodeling, Biomarkers and Treatment
Bibliographic record
Abstract
Cardiac remodelingMohamed et al. show the long-term effects of volume overload induced by aortacaval shunt in mice followed by serial echocardiography until the development of reduced left ventricular ejection fraction (LVEF) and heart failure (HF).Transition to HF was characterized by reduced sarcomeric titin phosphorylation, a cause of increased sarcomeric stiffness, activation of calcium/calmodulin-dependent protein kinase II, decreased protein kinase B (Akt) phosphorylation, high oxidative stress, and increased apoptosis.These changes were worsened in Akt-deficient mice showing the critical role of this kinase.1Tenascin-C is a large glycoprotein which appears in the extracellular matrix following tissue injury or tumor formation.Yokokawa et al. have measured it in 123 patients with dilated cardiomyopathy undergoing endomyocardial biopsy.Patients with high myocardial tenascin had worse cardiac remodeling and poorer outcomes.Diabetes and HF severity were independent determinants of its content. 2 Suematsu et al. have compared valsartan and LCZ696 with a control group in streptozotocin induced diabetic mice.Compared with control and with valsartan groups, administration of LCZ696 improved LVEF and reduced LV ANP mRNA, the LV fibrotic area and TGF-𝛽 levels.3 Atrial flutter-related tachycardiomyopathy is a specific form of cardiac remodeling.Brembilla-Perot et al. report the 6 months follow-up of 1269 patients who underwent radiofrequency ablation for atrial flutter.A marked improvement in LVEF was shown in 56% of the patients who had a reduced LVEF before ablation.An ischemic etiology and prescription of antiarrhythmic drugs were associated with a lower likelihood of LVEF improvement.Patients who improved their LVEF had similar outcomes as those with a normal LVEF at baseline whereas those with a persistent low LVEF post-ablation had an almost 3-fold higher mortality rate.4
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.216 | 0.159 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".