Assessment of <i><scp>P</scp>lasmodium falciparum</i> Pf<scp>MDR</scp>1 transport rates using Fluo‐4
Bibliographic record
Abstract
Mutations in the multidrug resistance transporter of Plasmodium falciparum PfMDR1 have been implicated to play a significant role in the emergence of worldwide drug resistance, yet the molecular and biochemical mechanisms of this transporter are not well understood. Although it is generally accepted that drug resistance in P. falciparum is partly associated with PfMDR1 transport activity situated in the membrane of the digestive vacuole, direct estimates of the pump rate of this transport process in the natural environment of the intact host-parasite system have never been analysed. The fluorochrome Fluo-4 is a well-documented surrogate substrate of PfMDR1 and has been found to accumulate by actively being transported into the digestive vacuole of several parasitic strains. In the present study, we designed an approach to use Fluo-4 fluorescence uptake as a measure of compartmental Fluo-4 concentration accumulation in the different compartments of the host-parasite system. We performed a 'reverse Fluo-4 imaging' approach to relate fluorescence intensity to changes in dye concentration rather than Ca(2+) fluctuations and were able to calculate the overall rate of transport for PfMDR1 in Dd2 parasites. With this assay, we provide a powerful method to selectively measure the effect of PfMDR1 mutations on substrate transport kinetics. This will be of high significance for future compound screening to test for new drugs in resistant P. falciparum strains.
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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 | 0.001 |
| 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".