The intriguing role of platelets as custodians of brain-derived neurotrophic factor
Bibliographic record
Abstract
A State of the Art lecture titled "Platelets and neurotrophins" was presented at the International Society on Thrombosis and Haemostasis Congress in 2023. Neurotrophins, a family of neuronal growth factors known to support cognitive function, are increasingly recognized as important players in vascular health. Indeed, along with their canonical receptors, neurotrophins are expressed in peripheral tissues, particularly in the vasculature. The better-characterized neurotrophin in vascular biology is the brain-derived neurotrophic factor (BDNF). Its largest extracerebral pool resides within platelets, partly inherited from megakaryocytes and also likely internalized from circulation. Activation of platelets releases vast amounts of BDNF into their milieu and interestingly leads to platelet aggregation through binding of its receptor, the tropomyosin-related kinase B, on the platelet surface. As BDNF is readily available in plasma, a mechanism to preclude excessive platelet activation and aggregation appears critical. As such, binding of BDNF to α2-macroglobulin hinders its ability to bind its receptor and limits its platelet-activating effects to the site of vascular injury. Altogether, addition of BDNF to a forming clot facilitates not only paracrine platelet activation but also binding to fibrinogen, rendering the resulting clot more porous and plasma-permeable. Importantly, release of BDNF into circulation also appears to be protective against adverse cardiovascular and cerebrovascular outcomes, which has been reported in both animal models and epidemiologic studies. This opens an avenue for platelet-based strategies to deliver BDNF to vascular lesions and facilitate wound healing through its regenerative properties. Finally, we summarize relevant new data on this topic presented during the 2023 International Society on Thrombosis and Haemostasis Congress.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".