Relationship between Desiccation Tolerance and Biofilm Formation in Shiga Toxin-Producing Escherichia coli
Bibliographic record
Abstract
Shiga toxin-producing Escherichia coli (STEC) is a major concern in the food industry and requires effective control measures to prevent foodborne illnesses. Previous studies have demonstrated increased difficulty in the control of biofilm-forming STEC. Desiccation, achieved through osmotic stress and water removal, has emerged as a potential antimicrobial hurdle. This study focused on 254 genetically diverse E. coli strains collected from cattle, carcass hides, hide-off carcasses, and processing equipment. Of these, 141 (55.51%) were STEC and 113 (44.48%) were generic E. coli. The biofilm-forming capabilities of these isolates were assessed, and their desiccation tolerance was investigated to understand the relationships between growth temperature, relative humidity (RH), and bacterial survival. Only 28% of the STEC isolates had the ability to form biofilms, compared to 60% of the generic E. coli. Stainless steel surfaces were exposed to different combinations of temperature (0 °C or 35 °C) and relative humidity (75% or 100%), and the bacterial attachment and survival rates were measured over 72 h and compared to controls. The results revealed that all the strains exposed to 75% relative humidity (RH) at any temperature had reduced growth (p < 0.001). In contrast, 35 °C and 100% RH supported bacterial proliferation, except for isolates forming the strongest biofilms. The ability of E. coli to form a biofilm did not impact growth reduction at 75% RH. Therefore, desiccation treatment at 75% RH at temperatures of 0 °C or 35 °C holds promise as a novel antimicrobial hurdle for the removal of biofilm-forming E. coli from challenging-to-clean surfaces and equipment within food processing facilities.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".