The genetic link between «qkl» and «p53» in the central nervous system
Bibliographic record
Abstract
A definitive role for the QKI RNA-binding protein has been demonstrated in multiple biological processes, including myelinogenesis, vascular remodeling and cell fate determination.qkI has also been implicated as a candidate tumor suppressor gene as the qkI locus maps to a region of genetic instability in Glioblastoma Multiforme (GBM), an aggressive brain tumor of astrocytic lineage.Mice homozygous for the qk v mutation have not been reported to develop GBM, suggesting that additional genetic mutations may be required to induce tumorigenesis.In order to increase the potential tumorigenetic effect of the qk v mutation as well as to investigate whether p53 and qkI are involved genetically in myelin formation, we bred qk v /qk v mice onto a p53-/background.qk v /qk v ; p53-/mice demonstrated a reduced survival rate compared to p53-/and qk v /qk v littermate controls and developed the cerebellar tumor medulloblastoma at a low frequency.qk v /qk v ; p53-/mice also displayed neurological defects including hydrocephaly and Purkinje neuron degeneration, however myelination was not further impaired compared to qk v /qk v mice.In support of qkI as a potential tumor suppressor, we show that QKI negatively regulates of oncogenic signaling factors including gli1 and ERK1/2.These findings indicate a new role for qkI in tumor development. WHOWorld health organization WNT Wingless
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".