Adapting Clinical Chemistry Plasma as a Source for Liquid Biopsies
Bibliographic record
Abstract
Background Circulating cell-free DNA (cfDNA) has become a valuable analyte for molecular testing but requires specialized collection tubes or immediate processing. We investigated the feasibility of using residual plasma from heparin separators, which are routinely used in clinical chemistry, as an accessible and underutilized source for cfDNA testing. Methods We analyzed matched plasma samples collected in EDTA, Streck, and heparin separators in a Healthy Cohort (n = 5) and matched samples collected in EDTA and heparin separators from a Hospital Cohort derived from viral PCR-positive patients (n = 34). Whole-genome sequencing and genome-wide enriched methylation sequencing were performed to evaluate concordance across multiple benchmarks, including metagenomics, chromosomal copy number, methylome, and fragmentomics. Results In the Healthy Cohort, methylation patterns were correlated (Pearson’s r = 0.92–0.93) between tube types, and fragmentation features were preserved with a modal size peak at 166 bp and a consistent top 10 end motif ranking across tube types (n = 5). In the Hospital Cohort, heparin separators showed a strong concordance with matched EDTA tubes for viral detection (n = 34, Pearson’s r = 0.99), copy number alteration profiling (n = 6, Pearson’s r = 0.86-0.98), and methylation patterns (n = 12, r = 0.83-0.93). Conclusions Residual plasma from routine clinical chemistry tests can provide a vast, untapped resource for cfDNA analysis.
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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 | 0.004 | 0.007 |
| 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 | 0.002 | 0.002 |
| 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".