Examining the Survival of A(H5N1) Influenza Virus in Thermised Whole Cow Milk
Bibliographic record
Abstract
Abstract The recent spillover events of highly pathogenic avian influenza (HPAI) A(H5N1) clade 2.3.4.4b to dairy cattle, and high viral shedding in the milk from infected animals, has created concern that milk and dairy products could be a route for human infection. It has been demonstrated that pasteurization is effective in inactivating A(H5N1) in milk. However, multiple dairy products are made with unpasteurized but thermised milk. The aim of this study was to examine whether some conditions commonly used for thermisation are effective against inactivation of A(H5N1) in whole milk. For this purpose, we artificially inoculated whole raw cow milk with 6.5 log 10 EID 50 A(H5N1) and heated for 15 seconds at 60°C, 63°C and 66°C, the viral infectivity was tested using embryonated chicken eggs. We observed over 4 and 5 log 10 reduction in viral infectivity at 60°C and 63°C, respectively. The viral infectivity was reduced to below the detection limit at 66°C. We also calculated the D-values, the time required to reduce the viral titer by one log 10 , for each treatment and as expected, we observed a decrease in D-values with increasing thermisation temperature. These data demonstrate that thermisation is effective in reducing the viral load and thus they allow for informed risk assessment of A(H5N1) contaminated dairy products made from thermized milk.
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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 | 0.000 |
| 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".