P-glycoprotein targeting: a unique strategy to selectively eliminate immunoreactive T cells
Bibliographic record
Abstract
T lymphocytes have been found to harbor P-glycoprotein (Pgp) and to demonstrate modulation of its ion channel transporter function according to the state of activation of T lymphocytes. We hypothesized that cytotoxic chemicals that are extruded by Pgp could be used to specifically eliminate immunoreactive T-cell populations. In this study, we evaluated the capacity of 4,5-dibromorhodamine methyl ester (TH9402), a photosensitizer structurally similar to rhodamine, a dye transported by Pgp, and which becomes highly cytotoxic on activation with visible light to selectively deplete alloreactive T lymphocytes. Stimulation of T cells with mitogens or allogeneic major histocompatibility complex-mismatched cells resulted in the preferential retention of the TH9402 rhodamine-derivative in activated T cells, both CD4+ and CD8+. Photodynamic cell therapy of TH9402-exposed T cells led to the selective elimination of immunoreactive T-cell populations. In addition, this treatment preserved resting T cells and their capacity to respond to third-party cells. Inhibition of Pgp enhanced cellular trapping of the dye in nonactivated T cells and resulted in their depletion after exposure to light. Targeting of Pgp-deficient cells may therefore represent an appealing strategy for the prevention and treatment of graft-versus-host disease and other alloimmune or autoimmune disorders.
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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 | 0.001 | 0.001 |
| 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".