Tissue-Specificity of Antibodies Raised Against TrkB and p75NTR Receptors; Implications for Platelets as Models of Neurodegenerative Diseases
Bibliographic record
Abstract
Platelets and neurons share many similarities including comparable secretory granule types with homologous calcium-dependent secretory mechanisms as well as internalization, sequestration and secretion of many neurotransmitters. Thus, platelets present a high potential to be used as peripheral biomarkers to reflect neuronal pathologies. The brain-derived neurotrophic factor (BDNF) acts as a neuronal growth factor involved in learning and memory through the binding of two receptors, the tropomyosin receptor kinase B (TrkB) and the 75 kDa pan-neurotrophic receptor (p75NTR). In addition to its expression in the central nervous system, BDNF is found in much greater quantities in blood circulation, where it is largely stored within platelets. Levels 100- to 1,000-fold those of neurons make platelets the most important peripheral reservoir of BDNF. This led us to hypothesize that platelets would express canonical BDNF receptors, i.e., TrkB and p75NTR, and that the receptors on platelets would bear significant resemblance to the ones found in the brain. However, herein we report discrepancies regarding detection of these receptors using antibody-based assays, with antibodies displaying important tissue-specificity. The currently available antibodies raised against TrkB and p75NTR should therefore be used with caution to study platelets as models for neurological disorders. Rigorous characterization of antibodies and bioassays appears critical to understand the interplay between platelet and neuronal biology of BDNF.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".