Comparison of99mTc-sestamibi and doxorubicin to monitor inhibition of P-glycoprotein function
Bibliographic record
Abstract
P-glycoprotein (Pgp) overexpression is a well-recognized factor in resistance to chemotherapy. Doxorubicin flow cytometry is used to monitor Pgp function in haematological specimens and biopsies from other cancers, and radionuclide imaging with sestamibi has recently shown promise for non-invasive monitoring. In the present study the two methods were directly compared in single-cell suspensions of three variants of the human breast carcinoma cell line MCF7: sensitive MCF7/WT, doxorubicin-selected MCF7/AdrR, and MDR1-gene-transfected MCF7/BC19 cells with doxorubicin resistance factors of 1, 192, and 14, respectively. Accumulation of sestamibi and mean fluorescence of doxorubicin (5.5 microM) were assessed over 60 min in the presence and absence of Pgp modulators GG918 (0.01 to 0.2 microM) and PSC833 (0.05 to 2.0 microM). Accumulation curves for sestamibi and doxorubicin differed among the cell variants under control conditions, with sestamibi showing a significantly greater difference between WT and resistant cells than doxorubicin. Both GG918 and PSC833 reversed uptake deficits to WT levels for sestamibi in MCF7/BC19 cells and doxorubicin in MCF7/BC19 and MCF7/AdrR cells, but failed to show the same effect for sestamibi in MCF7/AdrR cells (approximately 30% of MCF7/WT level). Thus, both methods clearly distinguished sensitive from resistant MCF7 variants, with the radionuclide method showing greater sensitivity.
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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) | 0.001 | 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 | 0.001 | 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".