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Updated: Aug 8, 2026

Production of Autologous Platelet-Rich Plasma for Boosting In Vitro Human Fibroblast Expansion
Published on: February 24, 2021
A protocol for autologous platelet-rich plasma collection using an automated blood cell separator: a retrospective
Lei Shi1, Lin Peng1, Lisheng Chen1
1Department of Transfusion Medicine, West China Hospital, West China Xiamen Hospital, Sichuan University, Xiamen Fujian, China.
Introduction:
Automated blood cell separators are commonly used for autologous platelet-rich plasma (PRP) preparation. Although these devices ensure stable platelet concentrations, low contamination, and high reproducibility, unified protocols remain lacking. This study explored a collection protocol that uses real-time visual feedback and dynamic parameter adjustment.
Methods:
In this single-center retrospective study, we analyzed 35 autologous PRP collections. Visual criteria (pale lemon-yellow appearance, uniform turbidity, absence of macroscopically visible particles) and adjustment rules were developed. Platelet concentration, enrichment factor, and residual red blood cell (RBC) and white blood cell levels were assessed.
Results:
Median platelet concentration was 1500 × 109/L (IQR, 1031-1618 × 109/L), and the mean (SD) enrichment factor was 6.33 (2.76); 71.43% (25/35) of products fell within a range of 1000 to 1800 × 109/L, consistent with reported 3-fold to 8-fold enrichment. Red blood cell counts ranged from 0 to 0.20 × 1012/L. No macroscopic RBCs were detected, but 37.1% (13/35) of RBCs exceeded a threshold of 0.05 × 1012/L. White blood cell counts ranged from 0 to 1.69 × 109/L, below the leukocyte-poor threshold proposed by Kikuchi et al.
Discussion:
This study presented a protocol capable of producing PRP within the therapeutic range and achieving effective leukocyte control. Our findings provide a practical strategy for translating subjective experience into objective, trainable procedures. Given the limitations of small sample size and subjective visual criteria, further prospective machine vision validation is warranted.
