Biofilm Formation in Dairy: A Food Safety Concern—Recent progress in antibiofilm strategies in the dairy industry
Bibliographic record
Abstract
Biofilm formation allows microorganisms including bacteria to persist on abiotic or biotic surfaces, to resist treatments with biocides (disinfectants and antibiotics), and to evade the immune response in animal hosts much more than they do in the planktonic form. Bacteria able to form biofilm can be troublesome in the dairy industry, both by causing clinical symptoms in livestock and by colonizing milking devices and milk processing equipment, resulting in dairy products of lower quality and sometimes raising serious food safety issues. In fact, most of the bacterial species isolated frequently in the dairy chain have the ability to form biofilm. Common examples include Staphylococcus aureus and other staphylococci that frequently infect mammary glands but also Bacillus spp., Listeria monocytogenes, and Pseudomonas spp., which cause spoilage of dairy products and sometimes foodborne illnesses. The economic losses due to biofilm formation in the dairy industry are considerable, and scientists are constantly solicited to develop new antibiofilm strategies, especially using biocides of natural origin. Although the number of studies in this subject area has exploded in recent years, the in vivo efficacy of most novel approaches remains to be explored. Used alone or to increase the efficacy of disinfectants or antibiotics, they could allow the implementation of strategies having less impact on the environment. Their use is expected to lead to less reliance on antibiotics to treat intramammary infections in dairy farms and the use of lower concentrations of chemical disinfectants in dairy processing plants.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| 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".