Nitrative stress impairs pro nerve growth factor transport in basal forebrain cholinergic neurons via c-Jun N-terminal kinase activation
Bibliographic record
Abstract
In this study, rat BFCNs were cultured in microfluidic chambers, and axonal transport of quantum dot labelled proNGF was analysed via fluorescence microscopy. BFCNs were treated with SIN-1, a peroxynitrite generator, CC401, a JNK inhibitor, or L-NAME, a nitric oxide synthase inhibitor, prior to analysis of axonal transport. JNK activity was quantified via immunocytochemistry. In vitro aging decreased retrograde transport of proNGF. Age-induced proNGF transport deficits were rescued by L-NAME and CC401. L-NAME reduced levels of activated JNK in aged BFCNs, SIN-1 increased JNK activation in young BFCNs, and SIN-1-induced proNGF transport deficits were rescued by CC401. These results indicate that nitrative stress impairs proNGF transport by activating JNK. Interestingly, SIN-1 and L-NAME increased and decreased JNK activation, respectively, in p75NTR exon III knockout BFCNs, indicating that nitrative stress-induced JNK activation occurs independently of p75NTR. Our findings identify mechanisms contributing to loss of proNGF transport in aged BFCNs, which may help to rescue BFCN function and cognition in aging.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.006 |
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".