Detection of Circulating Parasite-Derived MicroRNAs in Filarial Infections
Bibliographic record
Abstract
Filarial nematodes cause chronic and profoundly debilitating diseases in both humans and animals. Applications of novel technology are providing unprecedented opportunities to improve diagnosis and our understanding of the molecular basis for host-parasite interactions. As a first step, we investigated the presence of circulating miRNAs released by filarial nematodes into the host bloodstream. miRNA deep-sequencing combined with bioinformatics revealed over 200 mature miRNA sequences of potential nematode origin in Dirofilaria immitis-infected dog plasma in two independent analyses, and 21 in Onchocerca volvulus-infected human serum. Total RNA obtained from D. immitis-infected dog plasma was subjected to stem-loop RT-qPCR assays targeting two detected miRNA candidates, miR-71 and miR-34. Additionally, Brugia pahangi-infected dog samples were included in the analysis, as these miRNAs were previously detected in extracts prepared from this species. The presence of miR-71 and miR-34 discriminated infected samples (both species) from uninfected samples, in which no specific miRNA amplification occurred. However, absolute miRNA copy numbers were not significantly correlated with microfilaraemia for either parasite. This may be due to the imprecision of mf counts to estimate infection intensity or to miRNA contributions from the unknown number of adult worms present. Nonetheless, parasite-derived circulating miRNAs are found in plasma or serum even for those species that do not live in the bloodstream.
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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 | 0.001 |
| 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) | 0.001 | 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".