Protective effects of telmisartan and tempol on lipopolysaccharide-induced cognitive impairment, neuroinflammation, and amyloidogenesis: possible role of brain-derived neurotrophic factor
Bibliographic record
Abstract
Angiotensin II has pro-inflammatory and pro-oxidant potentials. We investigated the possible protective effects of the Angiotensin II receptor blocker telmisartan, compared with the superoxide scavenger tempol, on lipopolysaccharide (LPS)-induced cognitive decline and amyloidogenesis. Briefly, mice were allocated into a normal control group, an LPS control group, a tempol treatment group, and 2 telmisartan treatment groups. A behavioral study was conducted followed by a biochemical study via assessment of brain levels of beta amyloid (Aβ) and brain-derived neurotropic factor (BDNF) as amyloidogenesis and neuroplasticity markers, tumor necrosis factor alpha (TNF-α), nitric oxide end products (NOx), neuronal and inducible nitric oxide synthase (nNOS and iNOS) as inflammatory markers, and superoxide dismutase (SOD), malondialdehyde (MDA), glutathione reduced (GSH), and nitrotyrosine (NT) as oxido-nitrosative stress markers. Finally, histopathological examination of cerebral cortex, hippocampus, and cerebellum sections was performed using routine and special Congo red stains. Tempol and telmisartan improved cognition, decreased brain Aβ deposition and BDNF depletion, decreased TNF-α, NOx, nNOS, iNOS, MDA, and NT brain levels, and increased brain SOD and GSH contents, parallel to confirmatory histopathological evidences. In conclusion, tempol and telmisartan are promising drugs in managing cognitive impairment and amyloidogenesis, at least via upregulation of BDNF with inhibition of neuroinflammation and oxido-nitrosative stress.
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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) | 0.000 | 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 | 0.001 |
| 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".