ICAT as a potential enhancer of monocytic differentiation: implications from the comparative proteome analysis of the HL60 cell line stimulated by all‐trans retinoic acid and NSC67657
Bibliographic record
Abstract
A novel sterol mesylate compound (NSC67657) was recently identified and reported by National Cancer Institute that could efficiently induce the differentiation of HL60 cells into monocytes in vitro and in vivo. The expression of many proteins would have been changed during the differentiation process, and some proteins may have played key roles in the differentiation of HL60 cell line induced by this drug. Therefore, we treated HL60 cells with NSC67657 and all-trans retinoic acid (ATRA) to identify the differentially expressed proteins and determine their functions in cellular differentiation. Of the 45 differentially expressed protein spots investigated, 24 were either elevated or decreased in both the monocytic and granulocytic differentiating HL60 cells, 8 showed significant changes only when induced by NSC67657, and 13 showed significant changes only when induced by ATRA. After verification by RT-PCR, Western blotting, and immunocytochemistry, only the protein ICAT was found to be elevated by NSC67657 treatment alone. Although the over-expression of ICAT is not sufficient to induce the differentiation of HL60 cells into monocytes, it did increase the proportion of CD14+ cells in cells pretreated with NSC67657. Successful application of multiple techniques including two-dimensional gel electrophoresis, matrix-assisted laser desorption ionization time-of-flight mass spectrometry, Western blotting, and eukaryotic electroporation revealed that proteomic and molecular biological analyses provide valuable tools in drug development research.
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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 | 0.000 |
| 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".