Innate defense regulator peptide 1018 protects against perinatal brain injury
Bibliographic record
Abstract
OBJECTIVE: There is currently no pharmacological treatment that provides protection against brain injury in neonates. It is known that activation of an innate immune response is a key, contributing factor in perinatal brain injury; therefore, the neuroprotective therapeutic potential of innate defense regulator peptides (IDRs) was investigated. METHODS: The anti-inflammatory effects of 3 IDRs was measured in lipopolysaccharide (LPS)-activated murine microglia. IDRs were then assessed for their ability to confer neuroprotection in vivo when given 3 hours after neonatal brain injury in a clinically relevant model that combines an inflammatory challenge (LPS) with hypoxia-ischemia (HI). To gain insight into peptide-mediated effects on LPS-induced inflammation and neuroprotective mechanisms, global cerebral gene expression patterns were analyzed in pups that were treated with IDR-1018 either 4 hours before LPS or 3 hours after LPS+HI. RESULTS: IDR-1018 reduced inflammatory mediators produced by LPS-stimulated microglia cells in vitro and modulated LPS-induced neuroinflammation in vivo. When administered 3 hours after LPS+HI, IDR-1018 exerted effects on regulatory molecules of apoptotic (for, eg, Fadd and Tnfsf9) and inflammatory (for, eg, interleukin 1, tumor necrosis factor α, chemokines, and cell adhesion molecules) pathways and showed marked protection of both white and gray brain matter. INTERPRETATION: IDR-1018 suppresses proinflammatory mediators and cell injurious mechanisms in the developing brain, and postinsult treatment is efficacious in reducing LPS-induced hypoxic-ischemic brain damage. IDR-1018 is effective in the brain when given systemically, confers neuroprotection of both gray and white matter, and lacks significant effects on the brain under normal conditions. Thus, this peptide provides the features of a promising neuroprotective agent in newborns with brain injury.
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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) | 0.002 | 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".