Evaluation of Molecular Response to Imatinib in Iraqi Chronic Myeloid Leukemia Patients Using Real Time – Reveres Transcriptase-Polymerase Chain Reaction (RT-RT-PCR) – Taqman Assay
Bibliographic record
Abstract
Real-time quantitative (RQ-PCR) assays were developed to monitor the kinetics of residual BCR-ABL transcripts over time and to follow up chronic myeloid leukemia patients (CML) treated with imatinb mesylate (IM) for assessment of response at different IM treatment durations. This is a prospective study enrolled 135 patients at The National Center of Hematology (NCH) /Al-Mustensseria University from February 2006 to August 2008, in addition to 25 healthy individuals consider as health negative control. Only 42 patients were could be followed up regularly. From them, only 40 venous blood (VB) samples related to 20 CML patients (collected at different intervals from starting IM treatment) were give sufficient extracted RNA that could be use in PCR reaction. Quantitative analysis using Real time-Reverse Transcriptase-PCR (RT-RT-PCR) was done for each patient at two different time points of IM treatment duration. The results of RT-RT-PCR reaction were recorded as ratio between transcripts of bcr-abl /abl ×100 at these two time points of analysis. The mean values of ratio at first and second time points of analysis were (0.879) and (0.471), respectively. There was a statically significant difference (p=0.05, LSD=0.0324). Also, RT-RT-PCR results were recorded as log reduction of bcr-abl transcripts. There is no significant differences in the percentage of patients who achieved complete molecular response (CMR) (?4 log reduction) and major molecular response (MMR) (3 log reduction), but there is a significant differences in the percentage of patients who achieved only Minor-MR (2 log reduction) (p=0.05, LSD=13.789) and patients who not achieved MR(< 2log reduction) (p=0.05, LSD=5.779). Quantification molecular analysis using RT–RT-PCR is essential tool for assessment of molecular responsiveness to imatinib because it is most specific in discriminating between response subgroups of patients than cytogenetic analysis.
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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 | 0.001 | 0.001 |
| 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".