P2‐102: A Role of PTPN21 in Neuron Survival and Degeneration
Bibliographic record
Abstract
Alzheimer's disease (AD) chronically leads to dramatic neuronal loss, as they undergo apoptotic cell death, a direct consequence of the β-amyloid deposition or due to damage to their axon. The nervous system has an extremely poor regenerative capabilities and the adult brain's potential at replacing neurons and regrowing axons is very limited. Therefore, recovering from AD faces many challenges. Moreover, the secondary response at the site establishes a toxic environment, quashing any tentative repair. Previously, our published results revealed that PTPN21 promotes neuron migration and survival via Elk-1 transcription factor, one of the transcription factors of Presenilin 1 (PS1). Henceforth, in this study we explore the potential role of PTPN21 in pathological changes in AD. We used our established model of overexpressing PTPN21 or functional lost mutant for PTPN21 and expose the cells to high levels of amyloid precursor protein (APP), and quantify the levels of β-amyloid (1-42 & 1-40), PS1 and other AD’s markers using immunoblotting and ELISA assays. PTPN21 reduces the secretion of β-amyloid and importantly, our preliminary data showing significant PTPN21-dependent reduction of both PS1 levels and human β-Amyloid (1-42). Furthermore, PTPN21 promotes neuron survival through NRG3 pathway. To this end, our current study uncovered a link between PTPN21 and a specific mechanism of via PS1 to reduce β-amplyoid production, of which, might help to slow the progression of AD.
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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 | 0.001 |
| 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".