Disease-specific motifs can be identified in circulating nucleic acids from live elk and cattle infected with transmissible spongiform encephalopathies
Bibliographic record
Abstract
To gain insight into the disease progression of transmissible spongiform encephalopathies (TSE), we searched for disease-specific patterns in circulating nucleic acids (CNA) in elk and cattle. In a 25-month time-course experiment, CNAs were isolated from blood samples of 24 elk (Cervus elaphus) orally challenged with chronic wasting disease (CWD) infectious material. In a separate experiment, blood-sample CNAs from 29 experimental cattle (Bos taurus) 40 months post-inoculation with clinical bovine spongiform encephalopathy (BSE) were analyzed according to the same protocol. Next-generation sequencing provided broad elucidation of sample CNAs: we detected infection-specific sequences as early as 11 months in elk (i.e. at least 3 months before the appearance of the first clinical signs) and we established CNA patterns related to BSE in cattle at least 4 months prior to clinical signs. In elk, a progression of CNA sequence patterns was found to precede and correlate with macro-observable disease progression, including delayed CWD progression in elk with PrP genotype LM. Some of the patterns identified contain transcription-factor-binding sites linked to endogenous retroviral integration. These patterns suggest that retroviruses may be connected to the manifestation of TSEs. Our results may become useful for the early diagnosis of TSE in live elk and cattle.
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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 | 0.001 | 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".