Factors affecting stability of plasma brain-derived neurotrophic factor
Bibliographic record
Abstract
Circulating concentrations of brain-derived neurotrophic factor (BDNF) have been linked to cancer, neuropsychiatric, diabetes, and gynecological disorders. However, factors influencing plasma storage and subsequent BDNF quantification are incompletely understood. Therefore, the anticoagulant used in plasma separator tubes, storage-time, storage-temperature, and repeated freeze-thaw cycles on circulating BDNF concentrations was evaluated. Peripheral blood samples were collected from healthy women (n = 14) and men (n = 10) recruited prospectively from McMaster University (August 2014). Blood was collected from the cubital vein into plasma separator tubes containing five different anticoagulant systems [K2EDTA, Li-Hep, Li-Hep (gel), Na-Hep, Na-Hep (glass)], and placed on ice for transport to the lab for centrifugation. Plasma samples (n = 16) collected in K2EDTA tubes from women recruited to a previous study (April 2011 to December 2012) were used to determine the effect of multiple freeze-thaw cycles. Plasma BDNF was quantified using a commercially available ELISA kit. Plasma concentrations of BDNF were significantly affected by the type of plasma separator tube, storage-time, and number of freeze-thaw cycles. Storage temperature (- 20 vs. - 80 °C) did not significantly affect the quantity of BDNF measured as mean BDNF concentrations generally fell within our calculated acceptable change limit up to 6 months in the freezer. Our results suggest that for quantification of circulating BDNF blood collected in K2EDTA tubes and plasma stored up to 6 months at either - 20 or - 80 °C produces reproducible results that fall within an acceptable range. However, plasma samples stored beyond 6 months and repeated freeze-thaw cycles should be avoided.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 | 0.000 |
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".