mTOR inhibition as a possible pharmacological target in the management of systemic inflammatory response and associated neuroinflammation by lipopolysaccharide challenge in rats
Bibliographic record
Abstract
Neuroinflammation plays a critical role during sepsis triggered by microglial activation. Mammalian target of rapamycin (mTOR) has gained attraction in neuroinflammation, however, the mechanism remains unclear. Our goal was to assess the effects of mTOR inhibition by rapamycin on inflammation, microglial activation, oxidative stress, and apoptosis associated with the changes in the inhibitor-κB (IκB)-α/nuclear factor-κB (NF-κB)/hypoxia-inducible factor-1α (HIF-1α) pathway activity following a systemic challenge with lipopolysaccharide (LPS). Rats received saline (10 mL/kg), LPS (10 mg/kg), and (or) rapamycin (1 mg/kg) intraperitoneally. Inhibition of mTOR by rapamycin blocked phosphorylated form of ribosomal protein S6, NF-κB p65 activity by increasing degradation of IκB-α in parallel with HIF-1α expression increased by LPS in the kidney, heart, lung, and brain tissues. Rapamycin attenuated the increment in the expression of tumor necrosis factor-α and interleukin-1β, the inducible nitric oxide synthase, gp91phox, and p47phox in addition to nitrite levels elicited by LPS in tissues or sera. Concomitantly, rapamycin treatment reduced microglial activation, brain expression of caspase-3, and Bcl-2-associated X protein while it increased expression of B cell lymphoma 2 induced by LPS. Overall, this study supports the hypothesis that mTOR contributes to the detrimental effect of LPS-induced systemic inflammatory response associated with neuroinflammation via IκB-α/NF-κB/HIF-1α signaling pathway.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".