Bibliographic record
Abstract
Stroke is a debilitating disease that affects millions each year. While in many cases cerebral ischemic injury can be limited by effective resuscitation or thrombolytic treatment, the injured neurons wither in a process known as delayed neuronal death (DND). Mounting evidence indicates that DND is not simply necrosis played out in slow motion but apoptosis is triggered. Of particular interest are two groups of signal proteins that participate in apoptosis cyclin dependent kinases (CDKs) and p53 among a myriad of signaling events after an ischemic insult. Recent investigations have shown that CDKs, a family of enzymes initially known for their role in cell cycle regulation, are activated in injured neurons in DND. As for p53, new reports suggest that its up regulation may represent a failed attempt to rescue injured neurons, although its up regulation was previously considered an indication of apoptosis. These observations thus rekindle an old quest to identify new neuroprotective targets to minimize the stroke damage. In this review, the author will examine the evidence that indicates the participation of CDKs and p53 in DND and then introduce pre clinical data to explore CDK inhibition as a potential neuroprotective target. Finally, using CDK inhibition as an example, this paper will discuss the pertinent criteria for a viable neuroprotective strategy for ischemic injury.
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) | 0.000 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".