Neuregulin-1 regulates the expression of Akt, Bcl-2, and Bad signaling after focal cerebral ischemia in ratsThis paper is one of a selection of papers published in this special issue entitled “Second International Symposium on Recent Advances in Basic, Clinical, and Social Medicine” and has undergone the Journal's usual peer review process.
Bibliographic record
Abstract
Neuregulin-1 (NRG-1) is a member of the epidermal growth factor family. Our previous study showed that NRG-1 protected neurons from apoptosis following focal cerebral ischemia by the inhibition of caspase-3 and TNF-alpha expression. However, the molecular signaling mechanisms for this action of NRG-1 following cerebral ischemia are not fully understood. Presently, activation of the PI3K/Akt pathway has been implicated as a major contributor to neuronal survival after an ischemic insult. In the present study, we investigated whether NRG-1 modulates the activation of Akt and its downstream targets Bad and Bcl-2 expression after transient focal cerebral ischemia by intraluminal blockade of the middle cerebral artery. Western blot was employed to analyze the change of phosphorylated Akt (p-Akt) expression; reverse transcription and polymerization chain reaction (RT-PCR) were used to measure changes of Bcl-2 mRNA. The level of phosphorylation of Bad (p-Bad) was determined using an enzyme-linked immunosorbent assay (ELISA). Our results showed that recombinant human NRG-1(3.0 ng.kg-1) significantly increased the expression of p-Akt protein, Bcl-2 mRNA, and the level of p-Bad, respectively, whereas administration of LY294002, a specific inhibitor of PI3K, significantly decreased the expression of p-Akt, p-Bad, and Bcl-2 induced by NRG-1 after a 60 min ischemic insult, followed by 24 h of reperfusion. These results indicate that NRG-1 may be involved in regulating the expression of Bcl-2 and p-Bad through the PI3K/Akt pathway after transient focal cerebral ischemia.
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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 | 0.001 | 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".