Intracellular signalling targets downstream of GT 1061, a novel nitric oxide mimetic
Bibliographic record
Abstract
Nitric oxide (NO) is a gaseous intracellular messenger that mediates a wide range of physiological, behavioural, and cognitive events. A novel nitric oxide mimetic, GT 1061 was developed as an Alzheimer's Disease therapy, and has previously been shown to improve performance in rodent learning paradigms. The aim of the present studies was to determine if GT 1061 confers protection against neurotoxicity, and activates via phosphorylation, survival and learning-related intracellular targets. Cell viability assays showed that GT 1061 (30-300 muM) was not able to protect neurons against hydrogen peroxide toxicity or serum deprivation. Primary rat hippocampal and cortical cultures treated with GT 1061 showed some elevation in phosphorylated CREB (cyclic-AMP response element binding protein) and MAPK (mitogen-activated protein kinase) between concentrations of 10-300 muM, though without reaching statistical significance. No change in phosphorylated Akt (protein kinase B) or PKC (protein kinase C) was observed. Intraperioneal injection of GT 1061 (1 and 5 mg/kg) had no effect on the levels of phosphorylated CREB, MAPK, or PKC in the CNS, but phosphorylated Akt showed a tendency to increase with GT 1061. Taken together, these data suggest that these intracellular signalling pathways are not strongly stimulated by GT 1061 in our experimental models.
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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 | 0.000 |
| 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".