Expression of translation initiation factor eIF‐2α is increased in benign and malignant melanocytic and colonic epithelial neoplasms
Bibliographic record
Abstract
BACKGROUND: Stimulation of resting cells by growth factors leads to an increase in the rate of protein synthesis, which is necessary for cell growth and division. Translation initiation factor eIF-2alpha is one of the key translation factors mediating the effects of growth factors on protein synthesis. In normal cells, expression of eIF-2alpha is increased transiently, but its levels are elevated constitutively in oncogene-transformed cells. Overexpression of constitutively active eIF-2alpha in rodent cells makes them tumorigenic. In this article, the authors report their findings on the increased expression of eIF-2alpha in human benign and malignant neoplasms originating from melanocytes and colonic epithelium. METHODS: Immunohistochemistry was used to analyze the expression of eIF-2alpha, eIF-4E, and cyclin D1 in melanocytic nevi and melanomas and the expression of eIF-2alpha in colonic adenomas and carcinomas. RESULTS: The authors found that the expression of eIF-2alpha was increased markedly in both benign and malignant neoplasms of melanocytes and colonic epithelium. CONCLUSIONS: Increased expression of eIF-2alpha took place in both benign and malignant neoplasms of melanocytes and colonic epithelium. These findings suggest that elevated expression of this translation initiation factor may contribute to tumor initiation and progression but that it is not sufficient for establishing a malignant phenotype in the tumors analyzed in this study.
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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 | 0.001 | 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".