Systemic inhibition of extracellular signal-regulated kinase (ERK) has a protective effect in neonatal cerebral white matter injury
Bibliographic record
Abstract
The extracellular signal-regulated kinase (ERK) cascade, a key component of mitogen-activated protein kinase signalling, is important in synaptic plasticity, in mediating mitogenic and trophic effects, as well as for cell proliferation in normal and transformed non-neuronal cells. Here, we explored phosphorylated ERK immunoreactivity (pERK-IR) following hypoxic-ischaemic (HI) insult in postnatal day 7–8 mice (equivalent to approx 31–32 week human gestation) by unilateral carotid artery occlusion, followed by hypoxia (8% O2/N2). Exposure to 30 min HI resulted in massive increase in forebrain pERK-IR followed by strong white matter (WM) damage, but only mild involvement of the overlying cortical grey matter. Mapping for activated pERK revealed a time-clock sequence of cellular events, beginning with periventricular WM axons (15–45 min post HI onset), followed by white and grey matter glia and cortical neurons (1–4 h post HI onset), returning to normal by 8 h. Systemic inhibition of MEK1/2 with SL327 resulted in significant decrease in WM damage. This could point to activated MEK1/2 and ERK as promising targets for therapeutic intervention in neonatal brain damage, and in prevention of periventricular leukomalacia.
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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".