In vivo TNF-α inhibition ameliorates cardiac mitochondrial dysfunction, oxidative stress, and apoptosis in experimental heart failure
Bibliographic record
Abstract
Heart failure is associated with increased myocardial expression of TNF-alpha. However, the role of TNF-alpha in the development of heart failure is not fully understood. In the present study, we investigated the contribution of TNF-alpha to myocardial mitochondrial dysfunction, oxidative stress, and apoptosis in a unique dog model of heart failure characterized by an activation of all of these pathological processes. Male mongrel dogs were randomly assigned (n = 10 each) to 1) normal controls; 2) chronic pacing (250 beats/min for 4 wk) with concomitant administration of etanercept, a soluble p75 TNF receptor fusion protein, 0.5 mg/kg subcutaneously twice weekly; 3) chronic pacing with administration of saline vehicle. Mitochondrial function was assessed by left ventricular (LV) tissue mitochondrial respiratory enzyme activities. Oxidative stress was assessed with aldehyde levels, and apoptosis was quantified by photometric enzyme immunoassay for cytoplasmic histone-associated DNA fragments and terminal deoxynucleotide transferase-mediated nick-end labeling (TUNEL) assays. LV activity levels of mitochondrial respiratory chain enzyme complex III and V were reduced in the saline-treated dogs and restored either partially (complex III) or completely (complex V) in the etanercept-treated dogs. Aldehyde levels, DNA fragments, and TUNEL-positive cells were increased in the saline-treated dogs and normalized in etanercept-treated dogs. These changes were accompanied by an attenuation of LV dilatation and partial restoration of ejection fraction. Our data demonstrate that TNF-alpha contributes to progressive LV dysfunction in pacing-induced heart failure, mediated in part by a local impairment in mitochondrial function and increase in oxidative stress and myocyte apoptosis.
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) | 0.001 | 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) | 0.001 | 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".