Preparing for another Ebola Outbreak: The impact of viral inactivation methods on commonly measured biochemistry analytes in plasma and urine
Bibliographic record
Abstract
INTRODUCTION: Infectious specimens containing viruses like Ebola require sample manipulation to ensure the safety of laboratory staff, which may negatively impact biochemistry test results. We evaluated the impact of viral inactivation methods on 25 biochemistry analytes in plasma, and seven biochemistry analytes in urine. METHODS: Fifteen lithium heparinized plasma specimens with and without gel underwent the following viral inactivation methods: 1) untreated, 2) Triton X-100 treatment, 2) heated for 60 min then Triton X-100 treatment, 3) heated for 60 min, 4) heated for 75 min, and 5) heated for 90 min. Electrolytes, protein, enzymes, glucose, as well as hepatic and renal markers were measured on the Roche Cobas e601, c502 or c702. Urinalysis analytes were measured on the Siemens CLINITEK. Acceptable recovery was based on Institute for Quality Management in Healthcare 2021 guidelines or ± 1 for urinalysis. RESULTS: Potassium and lactate dehydrogenase were impacted by the presence of gel. Viral inactivation with Triton X-100 had minimal impact on the biochemistry results. Heat inactivation resulted in significant negative bias in alanine aminotransferase, alkaline phosphatase, gamma-glutamyl transferase, creatinine, total protein, amylase, lactate dehydrogenase and creatine kinase. Positive bias in phosphate, aspartate transaminase, total bilirubin, and uric acid were observed after heat inactivation. CONCLUSION: Reliable results for commonly measured electrolytes, enzymes and proteins can be obtained after viral inactivation by Triton X-100 treatment at room temperature. However, heat inactivation has significant negative impact on routine biochemistry enzymes and alternative testing processes should be explored.
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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 | 0.012 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".