Accumulation of Sestamibi in Lymphoma Cell Lines<i>In Vitro</i>
Bibliographic record
Abstract
Although some authors have suggested that sestamibi imaging is useful in evaluation of patients with lymphoma, others have obtained equivocal results. This discrepancy has been further investigated in vitro using two patient-derived non-Hodgkin's lymphoma cell lines, OCI-Ly3 and OCI-Ly18. Sestamibi (0.2 MBq/ml) was added to a suspension of OCI-Ly3 or OCI-Ly18 cells and aliquots were removed over 1 h and centrifuged to determine cell-associated radioactivity. Further experiments studied the effect of addition of a P-glycoprotein (Pgp) modulator or alteration in plasma and/or mitochondrial membrane potentials. Accumulation of sestamibi reached plateau values within 30 min, but these values were 6-fold higher in OCI-Ly3 than in OCI-Ly18. Inhibition of Pgp function with GG918 or PSC833 did not affect OCI-Ly3 cells but increased accumulation in OCI-Ly18 cells 3-fold, indicating a moderate level of Pgp. However, both cell lines responded similarly to membrane potential alterations: hyperpolarization of the mitochondrial membrane with nigericin had little effect on accumulation: in contrast, depolarization of the plasma membrane with an isotonic high potassium buffer reduced accumulation of sestamibi to 52% of control and additional depolarization of the mitochondrial membrane with valinomycin further reduced accumulation to 12% of control levels. These studies suggest that there can be wide differences in accumulation between cell lines, in part due to Pgp-mediated efflux, but that both of these cell lines have highly polarized mitochondria with little further capacity for hyperpolarization.
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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 | 0.000 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".