Variability in biofilm formation dynamics by <i>Salmonella enterica</i> isolated from animal-origin foods, plant-based foods, environment, clinical, and unspecified food sources: a 3-day in vitro study in tryptic soy broth at ambient temperature
Bibliographic record
Abstract
Bacterial biofilm production is linked to its adaptive capacity to environments throughout its lifecycle. This study aimed to assess the variability in biofilm formation (BF) dynamic by Salmonella enterica and to explore the potential impact of the cell’s prior history, primarily shaped by strain and its isolation source. In vitro BF of 141 S. enterica strains isolated from animal-origin foods, plant-based foods, unspecified food sources, the environment, and clinical cases, was evaluated using the crystal violet assay at 25 °C for up to 72 h. Kruskal–Wallis test was used to assess the effect of time, source, and strain. The Aryani method was used to characterize microbial response variability. The BF capacity of S. enterica strains ranged from 0.07 to 2.3, 0.07 to 2.7, and 0.06 to 2.7OD595nm at 24, 48, and 72 h, respectively. At 24 h (66.0%; 93/141) and 48 h (56.0%; 79/141), most isolates were classified as nonbiofilm producers, while at 72 h, the majority were weak biofilm producers (39.7%; 56/141). Time, strain, and isolation source significantly influenced BF, with an overall increase in BF occurring over time, and clinical strains being the highest biofilm producers. Strain to strain variability was the highest contributor to the total variance ([Formula: see text] = 0.18OD595nm 2, [Formula: see text] = 0.23OD595nm 2, [Formula: see text] = 0.26OD595nm 2). Analysis of variability between and within isolation source groups revealed the highest variability among clinical isolates ([Formula: see text] = 1.08OD595nm 2, [Formula: see text] = 1.36OD595nm 2, [Formula: see text] = 1.38OD595nm 2). Although BF was statistically associated with the strain and its isolation source, the high variability observed within these factors suggests that they alone are insufficient to explain how the cell’s prior history influences BF. A more comprehensive undertanding on BF will require considering additional intrinsic and extrinsic factors.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 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".