Transforming Growth Factor-β1-Mediated Neuroprotection against Excitotoxic Injury <i>in Vivo</i>
Bibliographic record
Abstract
Ischemic preconditioning is a phenomenon that describes how a sublethal ischemic insult can induce tolerance to subsequent ischemia. This phenomenon has been observed after focal or global ischemia in different animal models. However, the hypothesis that bacterial infection might lead to neuronal tolerance to injury has not been investigated. To mimic cerebral bacterial infection, we injected bacterial lipopolysaccharide (LPS) in the right dorsal hippocampus, followed 24 hours later by an excitotoxic lesion using kainic acid in the mouse model. Quantification of lesion size after cresyl violet counterstaining revealed that LPS pretreatment afforded neuroprotection to CA3 neurons against KA challenge. To investigate the events underlying this protection, we studied the cytokine profile induced after LPS injection. Interleukin (IL)-1 beta and transforming growth factor beta 1 (TGF-beta 1) were the main cytokines expressed at 24 hours after LPS injection. Because IL-1 beta has been described as deleterious in acute injury, we decided to investigate the function of TGF-beta 1. An adenovirus expressing a constitutively active form of TGF-beta 1 was injected intracerebrally 1 week before the induction of excitotoxic lesion, and neuronal protection was observed. To confirm the neuroprotective role of TGF-beta 1, the TGF-beta 1 adenovirus was replaced by recombinant human TGF-beta 1 protein and total neuroprotection was observed. Furthermore, the antibody-mediated blocking of TGF-beta 1 action prevented the protective effect of pretreatment with LPS. We have demonstrated in vivo that the cerebral tolerance phenomenon induced by LPS pretreatment is mediated by TGF-beta 1 cytokine.
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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) | 0.000 | 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".