Platelets and osteoblasts: secretome connections
Bibliographic record
Abstract
Megakaryocyte hyperplasia associated with myeloproliferative neoplasms commonly leads to abnormal bone tissue deposition in the bone marrow, known as osteosclerosis. In this study, we aimed to synthesize the known proteomics literature describing factors released by megakaryocytes and platelets and to examine if any of the secreted factors have a known ability to stimulate the bone-forming cells, osteoblasts. Using a systematic search of Medline, we identified 77 articles reporting on factors secreted by platelets and megakaryocytes. After a full-text screening and analysis of the studies, we selected seven papers that reported proteomics data for factors secreted by platelets from healthy individuals. From 60 proteins reported in at least two studies, we focused on 23 that contained a putative signal peptide, which we searched for a potential osteoblast-stimulatory function. From nine proteins with a positive effect on osteoblast formation and function, two extracellular matrix (ECM) proteins, secreted protein acidic and rich in cysteine (SPARC) and tissue inhibitor of metalloproteinase-1 (TIMP1), and three cellular proteins with known extracellular function, the 70-kDa heat shock protein (HSP70), thymosin-β4 (TB4), and super dismutase (SOD), were identified as hypothetical candidate molecules to be examined as potential mediators in mouse models of osteomyelofibrosis. Thus, careful analysis of prior literature can be beneficial in assisting the planning of future experimental studies.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".