Abstract LB-388: Macrophage migration inhibitory factor (MIF) plays a role in proliferation, differentiation, and survival of Ewing tumor cells through the activation of several kinases
Bibliographic record
Abstract
Abstract Cytokines and chemokines are involved in various mechanisms of cell signaling networks, which control cell growth, proliferation, differentiation, and survival. In normal cells, expression of cytokines and their downstream cell signaling pathways are tightly and accurately regulated. However, it has been shown that cytokine signaling pathways are deregulated in many tumors. Thus, identification and understanding of deregulated cytokines and their related pathways may provide unique and promising opportunities for better-targeted therapies of cancer. In the previous experiments we showed that Macrophage migration Inhibitory Factor (MIF) is highly expressed in Ewing tumor cells and it is involved in the proliferation and survival of Ewing tumors. Our present data implies that MIF might functionally contribute to Ewing tumor cell growth, differentiation and survival through the activation of several kinases such as AMPK (AMP activated protein kinase), Paxillin and PYK2 (Protein Tyrosine Kinase2). In conclusion, coupled with earlier and present studies about MIF-dependent effects on Ewing tumors our data suggested that MIF might be involved in various pathways of Ewing tumor biology and might be a promising target of Ewing tumor cells therapy. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 101st Annual Meeting of the American Association for Cancer Research; 2010 Apr 17-21; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2010;70(8 Suppl):Abstract nr LB-388.
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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) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.021 | 0.006 |
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".