Applying the concept of liquid biopsy to monitor the microbial biodiversity of marine coastal ecosystems
Bibliographic record
Abstract
Liquid biopsy (LB) is a concept that is rapidly gaining ground in the biomedical field. Its concept is largely based on the detection of circulating cell-free DNA (ccfDNA) fragments that are mostly released as small fragments following cell death in various tissues. A small percentage of these fragments are from foreign (nonself) tissues or organisms. In the present work, we applied this concept to mussels, a sentinel species known for its high filtration capacity of seawater. We exploited the capacity of mussels to be used as natural filters to capture environmental DNA fragments of different origins to provide information on the biodiversity of marine coastal ecosystems. Our results showed that hemolymph of mussels contains DNA fragments that varied considerably in size, ranging from 1 to 5 kb. Shotgun sequencing revealed that a significant amount of DNA fragments had a nonself microbial origin. Among these, we found DNA fragments derived from bacteria, archaea, and viruses, including viruses known to infect a variety of hosts that commonly populate coastal marine ecosystems. Taken together, our study shows that the concept of LB applied to mussels provides a rich and yet unexplored source of knowledge regarding the microbial biodiversity of a marine coastal ecosystem.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".