Hunting for mpox (monkeypox) mimickers: Use of the Biofire meningitis/encephalitis panel on lesion swabs to support alternative viral diagnoses
Bibliographic record
Abstract
BACKGROUND: Mpox (formerly monkeypox) is an emerging zoonotic disease of public health concern that presents as a rash mimicking other common viral exanthems. Unlike traditional testing algorithms relying on several assays, the BioFire FilmArray meningitis/encephalitis (ME) panel simultaneously detects common viruses causing rashes; however, Biofire ME is only licensed for testing on cerebral spinal fluid. OBJECTIVES: This study evaluated use of the Biofire ME panel for detection and discrimination of herpes simplex virus types 1 and 2 (HSV-1 and HSV-2), varicella zoster virus (VZV), human herpesviruses type 6 (HHV-6), enteroviruses (EVs), and human paraechoviruses (HPeVs) from a dermal or mucocutaneous swabs collected in universal transport media (UTM). STUDY DESIGN: Results of the BioFire ME panel were compared against methods used during clinical testing. Ten-fold serial dilutions in UTM of cultured viruses were used to compare analytical sensitivity, and analytical specificity was assessed using panels of microorganisms in UTM. Clinical sensitivity and specificity were assessed using 20 positive specimens each for HHV-1, HHV-2, HHV-6, VZV, EVs, and HPeV, as well as 35 known negative specimens that included 15 mpox-positive specimens. RESULTS: Biofire ME was as sensitive as comparator methods, and correctly discriminated all HSV-1, HSV-2, VZV, HHV-6, EVs, and HPeVs from mpox and mpox-mimickers. Cross-reaction between EV and rhinoviruses A, B, and C were noted in the specificity panel. CONCLUSIONS: Swabs in UTM collected for mpox testing are suitable for use on the Biofire ME panel, allowing more streamlined diagnostic testing for viral exanthems in patients under investigation for mpox infection.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".