Impact of sample processing delays on plasma markers of inflammation, chemotaxis, cell death, and blood coagulation
Bibliographic record
Abstract
BACKGROUND: Biosampling studies in critically ill patients traditionally involve bedside collection of samples followed by local processing (ie. centrifugation, aliquotting, and freezing) and storage. However, community hospitals, which care for the majority of Canadian patients, often lack the infrastructure for local processing and storage of specimens. A potential solution is a "simplified" biosampling protocol whereby blood samples are collected at the bedside and then shipped to a central site for processing and storage. One potential limitation of this approach is that delayed processing may alter sample characteristics. OBJECTIVE: To determine whether delays in blood sample processing affect the stability of cytokines (IL-6, TNF, IL-10, IFN-γ), chemokines (IL-8, IP-10, MCP-1, MCP-4, MIP-1α, MIP-1β), cell-free DNA (cfDNA) (released by dying cells), and blood clotting potential in human blood samples. METHODS: Venous blood was collected into EDTA and citrate sample tubes and stored at room temperature (RT) or 4°C for progressive intervals up to 72 hours, prior to processing. Plasma cytokines and chemokines were quantified using single or multiplex immunoassays. cfDNA was measured using Picogreen DNA Quantification. Blood clotting potential was measured using a thrombin generation assay. RESULTS: Blood samples were collected from 9 intensive care unit (ICU) patients and 7 healthy volunteers. Admission diagnoses for the ICU patients included sepsis, trauma, ruptured abdominal aortic aneurysm, intracranial hemorrhage, gastrointestinal bleed, and hyperkalemia. After pre-processing delays of up to 72 hours at RT or 4°C, no significant changes were observed in plasma cytokines, chemokines, cfDNA, or thrombin formation. CONCLUSIONS: Delayed sample processing for up to 72 hours at either RT or 4°C did not significantly affect cytokines, chemokines, cfDNA, or blood clotting potential in plasma samples from healthy volunteers and ICU patients. A "simplified" biosampling protocol is a feasible solution for conducting biosampling research at hospitals without local processing capacity.
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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 | 0.001 |
| 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".