Bibliographic record
Abstract
The myocardial stress response to exercise is dependent on exercise intensity and thus understanding the molecular responses between various exercise intensity levels might aid in exercise prescription. Nuclear factor kappa B (NF-κB) is a ubiquitous transcription factor that mediates a variety of cellular processes including inflammation, immune responses, apoptosis and cell growth/development. NF-κB can be comprised of homo- and/or heterodimers formed from five distinct proteins: p50 (NF-κB1), p52 (NF-κB2), RelA (p65), c-Rel, and RelB. NF-κB is located in the cytoplasm and kept inactive by inhibitory proteins but following the exposure to a myriad of stimuli, an activated NF-κB dimer translocates to the nucleus and exerts transcriptional effects on upwards of 150 genes. To examine the activation of NF-κB in the myocardium following exercise, male Sprague-Dawley rats (n = 24) were exercised by treadmill running at 20 m/min for 30 min or 30 m/min for 20 min. At 0, 2, or 24 h following exercise, animals were anesthetized, hearts excised and immediately frozen in liquid nitrogen. Portions of hearts were homogenized, protein concentrations determined and extracts assayed for NF-κB activation (DNA binding activity) using electrophoretic mobility shift assays (EMSA). Visual examination of EMSA autoradiographs revealed an enhanced NF-κB activation in the hearts from exercised animals when compared with non-running controls. Subsequent supershift analyses using antibodies specific for NF-κB subunits showed the higher intensity exercise was associated with p65 (RelA) in the activated NF-κB complex while the NF-κB complex in hearts from animals exercised at the lower intensity was comprised primarily of p50. These data suggest exercise is capable of activating myocardial NF-κB and that a threshold for the activation of specific NF-κB subunits may exist.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".