Induction of serpinb1a by<scp>PACAP</scp>or<scp>NGF</scp>is required for<scp>PC</scp>12 cells survival after serum withdrawal
Bibliographic record
Abstract
Abstract PC12 cells are used to study the signaling mechanisms underlying the neurotrophic and neuroprotective activities of pituitary adenylate cyclase‐activating polypeptide (PACAP) and nerve growth factor (NGF). Previous microarray experiments indicated that serpinb1a was the most induced gene after 6 h of treatment withPACAPorNGF. This study confirmed that serpinb1a is strongly activated byPACAPandNGFin a time‐dependent manner with a maximum induction (~ 50‐fold over control) observed after 6 h of treatment. Co‐incubation withPACAPandNGFresulted in a synergistic up‐regulation of serpinb1a expression (200‐fold over control), suggesting thatPACAPandNGFact through complementary mechanisms. Consistently,PACAP‐induced serpinb1a expression was not blocked by TrkA receptor inhibition. Nevertheless, the stimulation of serpinb1a expression byPACAPandNGFwas significantly reduced in the presence of extracellular signal‐regulated kinase, calcineurin, protein kinase A, p38, andPI3K inhibitors, indicating that the two trophic factors share some common pathways in the regulation of serpinb1a. Finally, functional investigations conducted with siRNArevealed that serpinb1a is not involved in the effects ofPACAPandNGFonPC12 cell neuritogenesis, proliferation or body cell volume but mediates their ability to block caspases 3/7 activity and to promotePC12 cell survival. image Pituitary adenylate cyclase‐activating polypeptide (PACAP) and nerve growth factor (NGF) induce a strong increase in serpinb1 a expression in PC12 cells. Functional investigations revealed that this increase in serpinb1a does not affect cell proliferation or differentiation but inhibits caspase 3 activity and promotes cell survival.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".