Effect of tumor necrosis factor‐α converting enzyme (TACE) and metalloprotease inhibitor on amyloid precursor protein metabolism in human neurons
Bibliographic record
Abstract
Tumor necrosis factor-alpha (TNF-alpha) is implicated in inflammatory processes and much effort is being directed at inhibiting the release of TNF-alpha for treatment of inflammatory conditions. In this context, the drug CP-661,631 has been developed to inhibit the TNF-alpha converting enzyme (TACE). However, TACE is also implicated in amyloid precursor protein secretion. Amyloid precursor protein (APP) undergoes constitutive and regulated secretion by alpha-secretase endoproteolytic cleavage within the amyloid beta peptide (Abeta) domain. Alternative cleavage at the N- and C-terminus of the Abeta domain by beta- and gamma-secretases results in the production of Abeta. In many cellular and in vivo animal models, increased secretion of APP results in a concomitant decrease in the production of Abeta suggesting that the two pathways are intricately linked. However, in human primary neuron cultures, increased APP secretion is not associated with a decrease in total Abeta production. To determine if the use of CP-661,631 may enhance amyloidogenic processing in human brain, we have assessed the effect of CP-661,631 on APP metabolism in primary cultures of human neurons. Our results show that CP-661,631 effectively prevents regulated APP secretion but does not increase total Abeta levels in human primary neuron cultures.
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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".