Identification of circulating microRNA signatures as potential biomarkers in the serum of elk infected with chronic wasting disease
Bibliographic record
Abstract
Chronic wasting disease (CWD) is an emerging infectious prion disorder that is spreading rapidly in wild populations of cervids in North America. The risk of zoonotic transmission of CWD is as yet unclear but a high priority must be to minimize further spread of the disease. No simple diagnostic tests are available to detect CWD quickly or in live animals; therefore, easily accessible biomarkers may be useful in identifying infected animals. MicroRNAs (miRNAs) are a class of small, non-coding RNA molecules that circulate in blood and are promising biomarkers for several infectious diseases. In this study we used next-generation sequencing to characterize the serum miRNA profiles of 35 naturally infected elk that tested positive for CWD in addition to 35 elk that tested negative for CWD. A total of 21 miRNAs that are highly conserved amongst mammals were altered in abundance in sera, irrespective of hemolysis in the samples. A number of these miRNAs have previously been associated with prion diseases. Receiver operating characteristic (ROC) curve analysis was performed to evaluate the discriminative potential of these miRNAs as biomarkers for the diagnosis of CWD. We also determined that a subgroup of 6 of these miRNAs were consistently altered in abundance in serum from hamsters experimentally infected with scrapie. This suggests that common miRNA candidate biomarkers could be selected for prion diseases in multiple species. Furthermore, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses pointed to a strong correlation for 3 of these miRNAs, miR-148a-3p, miR-186-5p, miR-30e-3p, with prion disease.
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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 | 0.000 | 0.000 |
| 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 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".