Analysis of Na<sup>+</sup>,K<sup>+</sup>-ATPase Motion and Incorporation into the Plasma Membrane in Response to G Protein–coupled Receptor Signals in Living Cells
Bibliographic record
Abstract
Dopamine (DA) increases Na(+),K(+)-ATPase activity in lung alveolar epithelial cells. This effect is associated with an increase in Na(+),K(+)-ATPase molecules within the plasma membrane (). Analysis of Na(+),K(+)-ATPase motion was performed in real-time in alveolar cells stably expressing Na(+),K(+)-ATPase molecules carrying a fluorescent tag (green fluorescent protein) in the alpha-subunit. The data demonstrate a distinct (random walk) pattern of basal movement of Na(+),K(+)-ATPase-containing vesicles in nontreated cells. DA increased the directional movement (by 3.5 fold) of the vesicles and an increase in their velocity (by 25%) that consequently promoted the incorporation of vesicles into the plasma membrane. The movement of Na(+),K(+)-ATPase-containing vesicles and incorporation into the plasma membrane were microtubule dependent, and disruption of this network perturbed vesicle motion toward the plasma membrane and prevented the increase in the Na(+),K(+)-ATPase activity induced by DA. Thus, recruitment of new Na(+),K(+)-ATPase molecules into the plasma membrane appears to be a major mechanism by which dopamine increases total cell Na(+),K(+)-ATPase activity.
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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 | 0.000 |
| 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".