Bovine Autologous Platelet Concentrate: Production, Hematologic Classification and In Vitro Biologic Characterization
Bibliographic record
Abstract
Introduction: Processing method and in vitro characterization of autologous platelet concentrates (APC), used for tissue healing, have not been validated for bovine whole blood (WB). The objective of the study was to compare hematologic findings of processing methods for APC production, and to compare cytokines and growth factors (GF) concentrations. Materials and Methods: APC were prepared from WB of four cows (Group 1) with single-step centrifugation using 16 processing methods. The two protocols that yielded the highest platelet to lowest WBC concentrate were APC-1 (2200 rpm, 5 minute) and APC-2 (2500 rpm, 3 minute). They were subsequently reproduced and compared using WB from eight cows (Group 2). Hematologic findings were quantified, cytokines (IL-1β) and GF (PDGF, TGF-β, bFGF) measured, and enrichment factors compared between samples and processing methods. Results: Hematologic characteristics and platelet enrichment varied among tested protocols. APC-2 had a significantly ( p = 0.001) greater degree of platelet enrichment (mean 156%) than APC-1 (125%). Both protocols diluted WBC and had similar mean GF enrichment (124–125% PDGF, 95–100% TGF-β, 102–104% bFGF and 56–74% IL-1β) without significant differences between APC ( p = 0.08 and p = 0.32–0.96). Discussion/Conclusion: Platelet enrichment and cellular reproducibility of APC-2 was confirmed and could be used as a successful processing method. GF measurement showed that APC may have healing modulation properties, but further studies are needed to determine their influence in vivo and impact on clinical outcomes. Acknowledgement: The commercial kits used in this study were donated by the manufacturer (Arthrex). All other costs were covered by institutional funding. None of the authors received any financial support.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".