Adapting clinical chemistry plasma as a source for liquid biopsies
Bibliographic record
Abstract
Circulating cell-free DNA (cfDNA) is valuable for molecular testing, but typically requires specialized collection tubes or immediate processing. We investigated whether residual plasma from heparin separators, routinely used in clinical chemistry, could serve as an accessible and underused source for cfDNA. We analyzed matched plasma samples from healthy volunteers in two experiments: an immediate-processing comparison across EDTA, Streck, and heparin separator tubes (n=5), and a clinical-handling simulation comparing EDTA and heparin separator tubes under delayed processing at room temperature or 4°C (n=6). We also analyzed matched plasma samples from viral PCR-positive patients in a hospital cohort (n=38). Whole-genome sequencing and enriched methylation sequencing were performed to assess concordance across metagenomics, copy number, methylation, and fragmentomic features. Under immediate processing, heparin separator plasma showed high concordance with EDTA and Streck plasma for methylation patterns (Spearman's ρ=0.65-0.70) and fragmentation features. In the Hospital Cohort, heparin separator plasma showed strong concordance with matched EDTA plasma for viral detection (Spearman's ρ=0.95), copy number alteration profiling (Spearman's ρ=0.72-0.96), and methylation patterns (Spearman's ρ=0.50-0.83). These findings support the feasibility of using refrigerated, promptly processed residual plasma from routine clinical chemistry as a supplementary source for cfDNA biobanking and molecular analyses.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".