Bibliographic record
Abstract
Mice overexpressing Grg1 and Grg5 were engineered using a novel Cre-conditional transgenic system. Grg1 overexpression contributes to transformation in both in vitro assays and in transgenic mice, which develop mucinous lung adenocarcinomas. Molecular changes induced by Grg1 include alterations in levels of the ErbB1 and ErbB2 receptor tyrosine kinases, deregulation of the Mdm2/p53 pathway and lowered levels of overexpression. Lung tumors in Grg transgenic mice are sensitive to subtle alterations in Wnt/beta-catenin signaling. Grg1 overexpressing mice carrying the APCmin allele had a substantially reduced lung tumor burden. Conversely, Grg1 reduced the growth of APCmin/+-associated intestinal polyps. Thus, Grg1 overexpression and aberrations of the Wnt signaling pathway contribute to malignancy in a tissue specific and opposing manner. The data suggest a novel function for Grg proteins in the regulation of tumor-associated pathways. (Abstract shortened by UMI.)Groucho-related proteins are corepressors that are recruited to gene regulatory elements by numerous DNA-binding factors. They have no intrinsic DNA-binding activity of their own but are components of multiprotein complexes that deacetylate histone molecules and mediate long-range transcriptional repression. Differential splicing produces multiple Groucho protein isoforms. Short isoforms lack the ability to directly bind HDAC molecules and may represent dominant negative forms of Groucho.
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 | 0.000 | 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".