Platelet Desialylation Is a Novel Mechanism and Therapeutic Target in Daboia siamensis and Agkistrodon halys Envenomation-Induced Thrombocytopenia
Bibliographic record
Abstract
Venom-induced thrombocytopenia (VIT) is one of the most important hemotoxic effects of a snakebite, which is often associated with venom-induced consumptive coagulopathy (VICC). Refractory thrombocytopenia without significant coagulation abnormalities has also been reported after envenomation by some viperid snakes; however, the mechanisms are not well understood and therapeutic strategies are lacking. Here, we found that patients injured by Daboia siamensis or Agkistrodon halys snakes, who were resistant to standard antivenom treatment, had developed coagulopathy-independent thrombocytopenia. Venoms from these viperid snakes, rather than from the elapid snake (Bungarus multicinctus), induced platelet surface expression of neuraminidase-1 (NEU-1), and significantly increased the desialylation of the glycoproteins on human platelets. The desialylated platelets caused by viperid snake venoms were further internalized by macrophages, which resulted in reduced platelet numbers in peripheral blood. Importantly, neuraminidase inhibitor significantly decreased viper venom-induced platelet desialylation, therefore inhibiting platelet phagocytosis by macrophages, and alleviating venom-induced thrombocytopenia. Collectively, these findings support an important role for desialylated platelet clearance in the progression of viper envenomation-induced, coagulopathy-independent thrombocytopenia. Our study demonstrates that the neuraminidase inhibitor may be a potential therapy or adjuvant therapy to treat snakebite-induced thrombocytopenia.
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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) | 0.000 | 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".