Biofilm Formation by Shiga Toxin-Producing Escherichia coli on Stainless Steel Coupons as Affected by Temperature and Incubation Time
Bibliographic record
Abstract
Forming biofilm is a strategy utilized by Shiga toxin-producing Escherichia coli (STEC) to survive and persist in food processing environments. We investigated the biofilm-forming potential of STEC strains from 10 clinically important serogroups on stainless steel at 22 °C or 13 °C after 24, 48, and 72 h of incubation. Results from crystal violet staining, plate counts, and scanning electron microscopy (SEM) identified a single isolate from each of the O113, O145, O91, O157, and O121 serogroups that was capable of forming strong or moderate biofilms on stainless steel at 22 °C. However, the biofilm-forming strength of these five strains was reduced when incubation time progressed. Moreover, we found that these strains formed a dense pellicle at the air-liquid interface on stainless steel, which suggests that oxygen was conducive to biofilm formation. At 13 °C, biofilm formation by these strains decreased (P < 0.05), but gradually increased over time. Overall, STEC biofilm formation was most prominent at 22 °C up to 24 h. The findings in this study identify the environmental conditions that may promote STEC biofilm formation in food processing facilities and suggest that the ability of specific strains to form biofilms contributes to their persistence within these environments.
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 | 0.000 |
| 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".