Effect of fetal hemoglobin-stimulating medicines on the interaction of DNA and protein of important erythroid regulatory elements
Bibliographic record
Abstract
Beta-Thalassemia is the most common single gene disorder in the world, which is caused by the imbalance between alpha-globin chain and beta-globin chain synthesis. Several medicines, such as 5-azacytidine, hydroxyurea, cytarabine, vinblatine, butyrate, and myleran, have been shown to be able to reactivate gamma-globin chain synthesis during the adult stage, and some of them (5-azacytidine, hydroxyurea, myleran, and butyrate) have been used clinically to treat thalassemia and sickle cell disease. Much research efforts are focusing on the determination of the underlying mechanisms of medicine action. In this experiment, as an effort to probe the underlying mechanism of medicine action, we used ligation-mediated polymerase chain reaction and in vivo footprinting methods to study the DNA-protein interaction at critical erythroid regulatory elements after hydroxyurea or myleran administration to mice. Our results showed that the patterns of in vivo footprints at both the hypersensitive site 2 of the locus control region and the beta-globin gene promoter were changed after medicine treatment. We proposed based on these results that the medicines' administration might result in a change in the interaction between trans-acting factors and cis-acting elements at these regions. These changes might influence the assembly of the transcription complex and, lastly, influence the expression of the beta-globin gene.
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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".