Effectiveness of TRIzol in Inactivating Animal Pathogens
Bibliographic record
Abstract
Introduction: Safe handling of biological samples sourced from wild ecosystems is a pressing concern for scientists in disparate fields, including ecology and evolution, OneHealth initiatives, bioresources, geography, veterinary medicine, conservation, and many others. This is especially relevant given the growing global research community and collaborative networks that often span international borders. Treatments to inactivate potential pathogens of concern during transportation and analysis of biospecimens while preserving molecular structures of interest are necessary. Objective: We provide a detailed resource on the effectiveness and limitations of TRIzol™ Reagent, a product commonly used in molecular biology to inactivate bacterial and viral pathogens found in wild animals. Methods: By literature review, we evaluate the mode of action of TRIzol Reagent and its main components on bacterial and viral structures. We also synthesize peer-reviewed literature on the effectiveness of TRIzol in inactivating a broad range of infectious bacteria and viruses. Key Findings: TRIzol Reagent inactivation is based on phenol, chaotropic salts, and sodium acetate. We find evidence of widespread efficacy in deactivating bacteria and a broad range of enveloped viruses. The efficacy against a subset of potential pathogens, including some nonenveloped viruses, remains uncertain. Conclusion: Available evidence suggests that TRIzol Reagent is effective in inactivating a broad spectrum of bacteria and viruses from cells, tissues, and liquids in biological samples when the matrices are exposed to at least 10 min at room temperature to the reagent. We highlight areas that require additional research and discuss implications for laboratory protocols.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".