Macrophage galactose lectin is critical for Kupffer cells to clear aged platelets
Bibliographic record
Abstract
Every day, megakaryocytes produce billions of platelets that circulate for several days and eventually are cleared by the liver. The exact removal mechanism, however, remains unclear. Loss of sialic acid residues is thought to feature in the aging and clearance of platelets. Using state-of-the-art spinning disk intravital microscopy to delineate the different compartments and cells of the mouse liver, we observed rapid accumulation of desialylated platelets predominantly on Kupffer cells, with only a few on endothelial cells and none on hepatocytes. Kupffer cell depletion prevented the removal of aged platelets from circulation. Ashwell-Morell receptor (AMR) deficiency alone had little effect on platelet uptake. Macrophage galactose lectin (MGL) together with AMR mediated clearance of desialylated or cold-stored platelets by Kupffer cells. Effective clearance is critical, as mice with an aged platelet population displayed a bleeding phenotype. Our data provide evidence that the MGL of Kupffer cells plays a significant role in the removal of desialylated platelets through a collaboration with the AMR, thereby maintaining a healthy and functional platelet compartment.
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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 | 0.001 |
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".