Hepatitis A virus attachment to agri-food surfaces using immunological, virological and thermodynamic assays
Bibliographic record
Abstract
AIMS: This study was designed to investigate the ability of hepatitis A virus (HAV) to attach to various food contact surfaces. METHODS AND RESULTS: HAV attachment was demonstrated after elution of attached viruses from solid surfaces by an immunofluorescent method using anti-HAV-specific antibodies and confocal microscopy. Attachment and survival of HAV on stainless steel, copper, polythene and polyvinyl chloride (PVC) at 20 and 4 degrees C after 2 and 4 h were quantified by plaque assay. HAV was shown to attach almost instantaneously to all four surfaces tested. Attachment of HAV depended on initial viral concentration and was slightly greater at 4 degrees C. The total surface energy (gammaTOT), nonpolar Lifshitz-Van der Waals (gammaLW) and polar short range (gammaSR) hydrogen-bonding components for HAV and each surface as well as total free energy of the system were determined by contact angle measurements using an extended Young equation [Young (1805) Philosophical Transactions of The Royal Society (London) 95, 65-87). The calculation of these parameters predicted the favourable conditions for attachment of HAV to all four surfaces tested. CONCLUSION: HAV particles attach to stainless steel, copper, polythene and PVC at 20 and 4 degrees C and the total free energy of the interaction is optimal for this attachment. SIGNIFICANCE AND IMPACT OF THE STUDY: Comprehension of viral attachment to the solid surfaces will permit to successfully disinfect these surfaces and to establish a better surveillance programme for control of viral food-borne illnesses.
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 | 0.000 | 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".