Effect of Exercise Training on Interleukin-6, Tumour Necrosis Factor Alpha and Functional Capacity in Heart Failure
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Background. We pooled data from four studies, to establish whether exercise training programs were able to modulate systemic cytokine levels of tumour necrosis factor-alpha (TNF-alpha) and interleukin-6 (IL-6). A second aim was to establish if differences in ExT regimens are related to degree of change in cytokines and peak VO(2). Methods. Data from four centres relating to training protocol, exercise capacity, and cytokine measures (TNF-alpha and IL-6) were pooled for analysis. Results. Data for 106 CHF patients were collated (98 men, age 62 ± 10 yrs, wt 79 ± 14 Kg). Patients were moderately impaired (peak VO(2) 16.9 ± 4.4 mls/kg/min), with moderate LV systolic dysfunction (EF 30 ± 6.9%), 78% (83) had ischaemic cardiomyopathy. After ExT, peak VO(2) increased 1.4 ± 3.4 ml/kg/min (P < .001), serum TNF-alpha decreased 1.9 ± 8.6 pg/ml (P = .02) and IL-6 was not significantly changed (0.5 ± 5.4 pg/ml, P = .32) for the whole group. Baseline and post-training peak VO(2) changes were not correlated with change in cytokine levels. Conclusions. Exercise training reduces levels TNF-alpha but not IL-6 in CHF. However, across a heterogenic patient group, change in peak VO(2) was not correlated with alterations in cytokine levels. While greater exercise volume (hours) was superior in improving peak VO(2), no particular characteristic of ExT regimes appeared superior in effecting change in serum cytokines.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 it