Translational Research in Pediatrics II: Blood Collection, Processing, Shipping, and Storage
Bibliographic record
Abstract
Translational research often involves tissue sampling and analysis. Blood is by far the most common tissue collected. Due to the many difficulties encountered with blood procurement from children, it is imperative to maximize the quality and stability of the collected samples to optimize research results. Collected blood can remain whole or be fractionated into serum, plasma, or cell concentrates such as red blood cells, leukocytes, or platelets. Serum and plasma can be used for analyte studies, including proteins, lipids, and small molecules, and as a source of cell-free nucleic acids. Cell concentrates are used in functional studies, flow cytometry, culture experiments, or as a source for cellular nucleic acids. Before initiating studies on blood, a thorough evaluation of practices that may influence analyte and/or cellular integrity is required. Thus, it is imperative that child health researchers working with human blood are aware of how experimental results can be altered by blood sampling methods, times to processing, container tubes, presence or absence of additives, shipping and storage variables, and freeze-thaw cycles. The authors of this review, in an effort to encourage and optimize translational research using blood from pediatric patients, outline best practices for blood collection, processing, shipment, and storage.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".