Application of bacteriophages to reduce biofilms formed by hydrogen sulfide producing bacteria on surfaces in a rendering plant
Bibliographic record
Abstract
Hydrogen sulfide producing bacteria (SPB) in raw animal by-products are likely to grow and form biofilms in the rendering processing environments, resulting in the release of harmful hydrogen sulfide (H2S) gas. The objective of this study was to reduce SPB biofilms formed on different surfaces typically found in rendering plants by applying a bacteriophage cocktail. Using a 96-well microplate method, we determined that 3 SPB strains of Citrobacter freundii and Hafnia alvei are strong biofilm formers. Application of 9 bacteriophages (10(7) PFU/mL) from families of Siphoviridae and Myoviridae resulted in a 33%-70% reduction of biofilm formation by each SPB strain. On stainless steel and plastic templates, phage treatment (10(8) PFU/mL) reduced the attached cells of a mixed SPB culture (no biofilm) by 2.3 and 2.7 log CFU/cm(2) within 6 h at 30 °C, respectively, as compared with 2 and 1.5 log CFU/cm(2) reductions of SPB biofilms within 6 h at 30 °C. Phage treatment was also applied to indigenous SPB biofilms formed on the environmental surface, stainless steel, high-density polyethylene plastic, and rubber templates in a rendering plant. With phage treatment (10(9) PFU/mL), SPB biofilms were reduced by 0.7-1.4, 0.3-0.6, and 0.2-0.6 log CFU/cm(2) in spring, summer, and fall trials, respectively. Our study demonstrated that bacteriophages could effectively reduce the selected SPB strains either attached to or in formed biofilms on various surfaces and could to some extent reduce the indigenous SPB biofilms on the surfaces in the rendering environment.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".