Desialylated Platelet Clearance in the Liver is a Novel Mechanism of Systemic Immunosuppression
Bibliographic record
Abstract
Platelets are small, versatile blood cells that are critical for hemostasis/thrombosis. Local platelet accumulation is a known contributor to proinflammation in various disease states. However, the anti-inflammatory/immunosuppressive potential of platelets has been poorly explored. Here, we uncovered, unexpectedly, desialylated platelets (dPLTs) down-regulated immune responses against both platelet-associated and -independent antigen challenges. Utilizing multispectral photoacoustic tomography, we tracked dPLT trafficking to gut vasculature and an exclusive Kupffer cell-mediated dPLT clearance in the liver, a process that we identified to be synergistically dependent on platelet glycoprotein Ibα and hepatic Ashwell–Morell receptor. Mechanistically, Kupffer cell clearance of dPLT potentiated a systemic immunosuppressive state with increased anti-inflammatory cytokines and circulating CD4+regulatory T cells, abolishable by Kupffer cell depletion. Last, in a clinically relevant model of hemophilia A, presensitization with dPLT attenuated anti-factor VIII antibody production after factor VIII ( infusion. As platelet desialylation commonly occurs in daily-aged and activated platelets, these findings open new avenues toward understanding immune homeostasis and potentiate the therapeutic potential of dPLT and engineered dPLT transfusions in controlling autoimmune and alloimmune diseases.
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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".