Evaluation and Clinical Validation of an Alcohol-Based Transport Medium for Preservation and Inactivation of Respiratory Viruses
Bibliographic record
Abstract
The clinical and public health importance of influenza and other respiratory viruses has accelerated the development of highly sensitive molecular diagnostics, but data are limited regarding preanalytical stages of diagnostic testing. We evaluated CyMol, an alcohol-based transport medium, for its ability to maintain specimen integrity for up to 21 days of storage at various temperatures; for its ability to inactivate virus; and for its compatibility with antigen- or nucleic acid-based diagnostics for respiratory viruses in clinical samples. In mock-infected samples, both universal transport medium (UTM-RT) and CyMol maintained equivalent viral quantities for at least 14 days at room temperature or colder, whereas a dry swab collection maintained viral quantities only if refrigerated or frozen. CyMol inactivated influenza virus within 5 min of sample immersion. UTM-RT- and CyMol-collected nasal swab specimens from 73 symptomatic students attending a campus health clinic were positive for a respiratory virus in 56.2% of subjects by multiplex PCR testing, including influenza A and B viruses, rhinovirus/enteroviruses, coronaviruses, respiratory syncytial virus, parainfluenza viruses, metapneumovirus, and adenovirus. Detection by PCR was equivalent in UTM-RT- and CyMol-collected specimens and in self- and staff-collected swabs. Direct fluorescent antibody (DFA) testing was substantially less sensitive (23.3%) than multiplex PCR, and DFA testing from UTM-RT-collected swabs was more sensitive than that from CyMol-collected swabs. These data indicate that an alcohol-based transport medium such as CyMol preserves respiratory virus integrity, rapidly inactivates viruses, and is compatible with PCR-based respiratory diagnostics.
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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 | 0.004 | 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 | 0.001 |
| 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 | 0.000 |
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".