Erbb Receptors and Epithelial-Cadherin–Catenin Complex In Human Carcinomas
Bibliographic record
Abstract
The ErbB family of receptor tyrosine kinases have important roles in maintaining normal epithelial cell function. The ErbBs are involved in the interaction between cells and cell-matrix adhesion molecules and have proven critical in maintaining the integrity of the epithelial cell environment. Deregulation of these tyrosine receptors has been associated with several human diseases. In particular, the expression or activation of epidermal growth factor receptor (EGFR) and ErbB2 is altered in many epithelial tumors. Epithelial (E)-cadherin is another major molecule expressed by epithelial cells. To create efficient cell-cell adhesion, E-cadherin couples its cytoplasmic domain to catenins and the actin cytoskeleton. The loss of intercellular adhesion appears to be a fundamental aspect of the neoplastic phenomena. In addition, EGFR and ErbB2 signaling associated with the E-cadherin-catenin complex has been demonstrated in normal and cancer cells. This signaling is involved in regulating cell adhesion and the invasive growth of cancers. This article provides an overview of the interaction between the ErbB tyrosine receptors and the E-cadherin-catenin complex in human carcinomas.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".