Increased Infarct Size and Lack of Hyperphagic Response after Focal Cerebral Ischemia in Peroxisome Proliferator-Activated Receptor β-Deficient Mice
Bibliographic record
Abstract
Peroxisome proliferator-activated receptors (PPARs) are involved in energy expenditure, regulation of inflammatory processes, and cellular protection in peripheral tissues. Among the different types of PPARs, PPARbeta is the only one to be widely expressed in cortical neurons. Using PPARbeta knockout (KO) mice, we report here a detailed investigation of the role of PPARbeta in cerebral ischemic damage, associated inflammatory and antioxidant processes as well as food intake regulation after middle cerebral artery occlusion (MCAO). The PPARbeta KO mice had a two-fold increase in infarct size compared with wild-type (WT) mice. Brain oxidative stress was dramatically enhanced in these KO mice, as documented by an increased content of malondialdehyde, decreased levels of glutathione and manganese superoxide dismutase, and no induction of uncoupling protein 2 (UCP2) mRNA. Unlike WT mice, PPARbeta KO mice showed a marked increase of prooxidant interferon-gamma but no induction of nerve growth factor and tumor necrosis factor alpha after MCAO. In WT mice, MCAO resulted in inflammation-specific transient hyperphagia from day 3 to day 5 after ischemia, which was associated with an increase in neuropeptide Y (NPY) mRNA. This hyperphagic phase and NPY mRNA induction were not observed in PPARbeta KO mice. Furthermore, our study also suggests for the first time that UCP2 is involved in MCAO food intake response. These data indicate that PPARbeta plays an important role in integrating and regulating central inflammation, antioxidant mechanisms, and food intake after MCAO, and suggest that the use of PPARbeta agonists may be of interest for the prevention of central ischemic damage.
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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".