Effects of Blood Transportation on Human Peripheral Mononuclear Cell Yield, Phenotype and Function: Implications for Immune Cell Biobanking
Bibliographic record
Abstract
Human biospecimen collection, processing and preservation are rapidly emerging subjects providing essential support to clinical as well as basic researchers. Unlike collection of other biospecimens (e.g. DNA and serum), biobanking of viable immune cells, such as peripheral blood mononuclear cells (PBMC) and/or isolated immune cell subsets is still in its infancy. While certain aspects of processing and freezing conditions have been studied in the past years, little is known about the effect of blood transportation on immune cell survival, phenotype and specific functions. However, especially for multicentric and cooperative projects it is vital to precisely know those effects. In this study we investigated the effect of blood shipping and pre-processing delay on immune cell phenotype and function both on cellular and subcellular levels. Peripheral blood was collected from healthy volunteers (n = 9): at a distal location (shipped overnight) and in the central laboratory (processed immediately). PBMC were processed in the central laboratory and analyzed post-cryopreservation. We analyzed yield, major immune subset distribution, proliferative capacity of T cells, cytokine pattern and T-cell receptor signal transduction. Results show that overnight transportation of blood samples does not globally compromise T- cell subsets as they largely retain their phenotype and proliferative capacity. However, NK and B cell frequencies, the production of certain PBMC-derived cytokines and IL-6 mediated cytokine signaling pathway are altered due to transportation. Various control experiments have been carried out to compare issues related to shipping versus pre-processing delay on site. Our results suggest the implementation of appropriate controls when using multicenter logistics for blood transportation aiming at subsequent isolation of viable immune cells, e.g. in multicenter clinical trials or studies analyzing immune cells/subsets. One important conclusion might be that despite changes due to overnight shipment, highly standardized central processing (and analysis) could be superior to multicentric de-central processing with more difficult standardization.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".