Behavioural, metabolic, and endothelial effects of the TNF-α suppressor thalidomide on rats subjected to chronic mild stress and fed an atherogenic diet
Bibliographic record
Abstract
There is accumulating evidence suggesting that depression is a risk factor for cardiovascular diseases. This study aimed to examine the hypothesis that the proinflammatory cytokine TNF-α would partially explain the link between depression and atherosclerotic endothelial changes. Rats were distributed among 6 groups: (i) control group; (ii) group subjected to chronic mild stress (CMS); (iii) group fed a cholesterol-cholic acid-thiouracil (CCT diet); and (iv) CMS group fed the CCT diet and treated with the vehicle for 8 weeks. The last 2 groups were subjected to CMS-CCT and received thalidomide (THAL) or imipramine (IMIP). Rats were assessed behaviorally (sucrose preference, open field, and forced-swimming tests). TNF-α protein was assessed from the serum, aorta, and liver. Aortic TNF-α gene expression (assessed using RT-PCR), serum lipid profile, and insulin levels were measured. Endothelial function was assessed in isolated aortic rings. The THAL and IMIP groups showed ameliorated CMS-CCT-related behavioral changes. CMS-CCT-induced metabolic and endothelial dysfunctions were improved in the THAL group but were worsened in the IMIP group. RT-PCR showed a significant reduction of aortic TNF-α mRNA expression in the THAL and IMIP treatment groups. These data paralleled the findings for aortic immunohistochemistry. The THAL group, but not the IMIP group, showed improved CMS-CCT-induced changes in the vascular reactivity of the aortic rings. Thus, TNF-α provides a target link between depression, metabolic syndrome, and endothelial dysfunction. This could open a new therapeutic approach to address the comorbidities of depression.
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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 | 0.000 | 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".