Combined effects of EFA deficiency and tumor necrosis factor‐α on circulating lipoproteins in rats
Bibliographic record
Abstract
Both tumor necrosis factor-alpha (TNF-alpha) and EFA deficiency (EFAD) have been established as causes of marked perturbations in lipid and lipoprotein metabolism. Excessive levels of circulating TNF-alpha can coexist with EFAD in various clinical disorders such as cystic fibrosis and type I diabetes. The present study therefore aimed to investigate their combined effects on lipid profile and lipoprotein composition by administering TNF-alpha to EFAD rats. Lipoprotein lipase (LPL), the rate-limiting enzyme in TG catabolism, was also measured in epididymal adipose tissue. EFAD, after a 4-wk period, induced significant increases in plasma TG (80%, P < 0.001), total cholesterol (TC, 27%, P < 0.025), and HDL-cholesterol (HDL-C, 62%). Two hours after the administration of TNF-alpha, a further rise in TG (43%, P < 0.05) was noted in controls, but not EFAD animals. TC and HDL-C were unaffected by TNF-alpha treatment. In addition, TNF-alpha modified lipoprotein-lipid composition. VLDL and HDL2 derived from EFAD rats were depleted in apolipoprotein (apo) E and apo A-II, and enriched in apo A-I 2 h after TNF-alpha administration. Finally, TNF-alpha decreased adipose tissue LPL activity in both control and EFAD animals. The TNF-alpha-induced inhibition was more marked in EFAD rats. The present results demonstrated that TNF-alpha can amplify or antagonize the effects of EFAD on lipid profile, lipoprotein composition, and LPL activity. These data also suggest that the host's nutritional status is a determining factor for the modulating effect of TNF-alpha on lipid metabolism.
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 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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".