Aurora Kinases and Potential Medical Applications of Aurora Kinase Inhibitors: A Review
Bibliographic record
Abstract
Aurora kinases (AKs) represent a novel group of serine/threonine kinases. They were originally described in 1995 by David Glover in the course of studies of mutant alleles characterized with unusual spindle pole configuration in Drosophila melanogaster. Thus far, three AKs A, B, and C have been discovered in human healthy and neoplastic cells. Each one locates in different subcellular locations and they are all nuclear proteins. AKs are playing an essential role in mitotic events such as monitoring of the mitotic checkpoint, creation of bipolar mitotic spindle and alignment of centrosomes on it, also regulating centrosome separation, bio-orientation of chromosomes and cytokinesis. Any inactivation of them can have catastrophic consequences on mitotic events of spindle formation, alignment of centrosomes and cytokinesis, resulting in apoptosis. Overexpression of AKs has been detected in diverse solid and hematological cancers and has been linked with a dismal prognosis. After discovery and identification of the first aurora kinase inhibitor (AKI) ZM447439 as a potential drug for targeted therapy in cancer treatment, approximately 30 AKIs have been introduced in cancer treatment.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".