Human platelets contain a pool of free zinc in dense granules
Bibliographic record
Abstract
Background Activated platelets release procoagulant factors that include Ca 2+ and Zn 2+ . Releasable Ca 2+ stores have been identified in platelet dense granules and the dense tubular system, but similar stores of free Zn 2+ have not been identified. Objectives Guided by studies of platelet Ca 2+ , we employed minimally disruptive methods to identify and localize concentrated free Zn 2+ in human platelets. Methods Resting platelets from normal donors (NDs), patients with gray platelet syndrome (GPS) lacking α-granules, and patients with Hermansky-Pudlak syndrome (HPS) deficient in dense granules were loaded with cell-permeant fluorescent probes specific to free Ca 2+ or Zn 2+ . Ion concentrations were detected in fixed cells as bright puncta via high-resolution confocal microscopy. Ions were also directly detected via transmission electron microscopy and energy dispersive X-ray analysis. Levels of total platelet Ca, Zn, and Mg were measured via inductively coupled plasma optical emission spectroscopy. Results Fluorescent Zn 2+ puncta counts were similar in ND and GPS platelets and markedly lower in HPS platelets, pointing to dense granules as likely reservoirs of free Zn 2+ . This localization was supported by direct detection of Ca 2+ , Zn 2+ , and Na + in platelet dense granules via transmission electron microscopy and energy dispersive X-ray analysis. Measurements of total platelet Ca, Zn, and Mg via inductively coupled plasma optical emission spectroscopy indicated that free Zn 2+ represents a small proportion of total platelet zinc, consistent with the strong affinity of Zn 2+ for binding proteins, including several abundant in platelet α-granules. Conclusion We conclude that normal human platelets contain a pool of free Zn 2+ concentrated in dense granules that is available for secretion upon platelet activation and potentially contributes to hemostasis.
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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 | 0.000 |
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".