Effectiveness of Pasteurization for the Inactivation of H5N1 Influenza Virus in Raw Whole Milk
Bibliographic record
Abstract
Abstract Highly pathogenic avian influenza (HPAI) clade 2.3.4.4b H5Nx viruses continue to cause episodic incursions and have been detected in more than 12 taxonomic orders encompassing more than 80 avian species, land and marine mammals, including recent detections in dairy cattle. The HPAI H5N1 spillover to these important livestock species creates a new interface for human exposure and raises food safety concerns. Presence of H5N1 genetic material in one out of five retail pasteurized milk samples in the USA has prompted the evaluation of pasteurization processes for the inactivation of influenza viruses. Our study examined whether pasteurization could effectively inactivate HPAI H5N1 inoculated raw whole milk samples. We heated 1 mL of non-homogenized cow’s milk samples to attain an internal temperature of 63°C or 72°C and spiked with 6.3 log EID 50 of clade 2.3.4.4b H5N1 virus. Complete inactivation was achieved after incubation of the H5N1 spiked raw milk at 63°C for 30 minutes. In addition, complete viral inactivation was observed in seven out of eight replicates of raw milk samples treated at 72°C for 15 seconds. In one replicate, a 4.56 log reduction was achieved, which is about 1 log higher than the average viral quantities detected in bulk tanks in affected areas. Therefore, we conclude that pasteurization of milk is an effective strategy for mitigation of risk of human exposure to milk contaminated with H5N1 virus.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".