Detection of viral RNA in tissues following plasma clearance from an Ebola virus infected patient
Bibliographic record
Abstract
Purpose: An unprecedented Ebola outbreak occurred in 2014-2015 in West Africa. A better understanding of the EBOV life cycle is fundamental to develop new countermeasures, as well as to fully comprehend the pathways of inter-human transmission. We have explored the possibility of viral persistence in different body fluids’ samples obtained from a Health Care Worker (HCW) infected in Sierra Leone and treated at INMI L. Spallanzani, Italy. To evaluate whether the virus was in a replicative status or simply derived from blood spill over, we compared the trends of EBOV-specific negative sense genomic RNA (neg-RNA), positive sense RNA (pos-RNA) and total viral RNA in different clinical samples. Methods & Materials: Clinical samples were inactivated in BSL4 facility and RNA was extracted with QIAamp Viral-RNA Mini-Kit (Qiagen). Quantification of total viral RNA was performed with the reference test Altona-FilovirusScreen Kit1.0 (Altona Diagnostics). To measure EBOV-specific negative sense genomic RNA (neg-RNA) and positive sense RNA (pos-RNA) [including both replication intermediate (cRNA) and messenger RNA (mRNA)], L-gene specific reverse or forward primers were used in the reverse transcription step Results: The clinical samples analyzed (sputum, nasopharyngeal swab, ocular swab, urine and plasma) show different trends in neg-RNA, pos-RNA and total viral RNA levels. In Nasopharyngeal swab, Ocular Swab and Urine, total viral RNA is the only one detectable, until Day 12, 5 and 15 after hospitalization, respectively. On the contrary, in plasma, total viral RNA, neg-RNA and pos-RNA levels simultaneously decreased, starting from day 3 and becoming undetectable at Day 6. In Sputum, pos-RNA levels decreased since Day 8, persisting at detectable levels up to Day 10, coherently with total viral RNA and neg-RNA levels, which start decreasing at Day 10 and become undetectable at Day 11. Conclusion: The presence of viral RNA and replication markers (pos-RNA and neg-RNA) in sputum might be of relevance in future analysis regarding the persistence of the virus in the upper respiratory tract, even after viral clearance from plasma. These results should be taken under further investigation in order to better understand the role of the respiratory tract for possible involvement in viral shedding, viral replication site or as a viral reservoir.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".