Liposomes as potential masking agents in sport doping. Part 2: Detection of liposome‐entrapped haemoglobin by flow cytofluorimetry
Bibliographic record
Abstract
This work presents an analytical procedure for the identification and characterization of liposome-entrapped haemoglobins, based on flow cytofluorimetry. Flow cytofluorimetric detection is carried out following labelling by two distinct fluorescent reagents, an anti-haemoglobin antibody, fluorescein isothiocyanate conjugated, and an anti-poly(ethylene glycol) antibody, streptavidin-phycoerythrin conjugated. This experimental strategy allows the detection of liposome-entrapped haemoglobins in aqueous media, including plasma; the efficacy of the proposed approach has been verified on whole blood samples added with the liposomal formulation (ex-vivo). Additionally, the proposed technique allows the characterization of several key parameters in the study of liposomal haemoglobins, including, for instance (1) the determination of the degree of haemoglobin entrapment by liposomes; (2) the poly(ethylene glycol) insertion efficiency; and (3) the evaluation of liposome-entrapped haemoglobins stability following storage at 4 °C, allowing to follow both the process of haemoglobin loss from liposomes and the liposome degradation. The procedure is proposed for the detection and characterization of liposome-entrapped haemoglobin formulations to control their misuse in sport, but is also suggested for further applications in biological and clinical laboratory investigations. Copyright © 2016 John Wiley & Sons, Ltd.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".