Some aspects of biofilm formation by enteropathogenic Escherichia coli
Bibliographic record
Abstract
Microorganisms growing as collective communities called 'biofilms' are widely recognised today. Biofilms impact almost all aspects of human life: personal, medical and industrial. Escherichia coli are model organisms for the study of biofi lms. In this work, eight enteropathogenic E. coli (EPEe) and three Shigella sonnei strains (a bacterium that is very similar to E. coli and is often implicated in diseases that are similar to those caused by EPEe) were investigated for their biofilm fonning abilities in relation to a number of factors. These included nutrient availabi lity, pH, temperature, time, sodium chloride concentration and iron avai lability. E. coli were able to form biofilms under all conditions tested; however, S. sonne; was not able to form any biofi lms under any of the tested settings and therefore studies on this microorganism were discontinued. The results of the investigations into biofilm-fonning abilities directed the subsequent research. These included est imation of antibiotic resistance, examination of structural development of biofilrns and the inspection of biofi lm behaviour at genetic and proteomic levels. A modification of Calgary Biofilrn Device was employed to estimate resistance to nine antibiotics in biofi lm form. A Stovall Flow Cell was employed to study the structure of biofilms formed by one (selected) strain E. coli NCiMB 555 which was transformed to express gfp in order to visualize the biotilm morphology. E. coli NelMB 555 (un-transformed) biofilms were also grown to study proteins expression and gene regulation of six targeted genes. Of these four genes were up-regulated, one was down-regulated and onc was expressed at a similar level to planktonic forms. This study contributes to the existing knowledge of the biofilm formation by E. co/; under a number of conditions, their antibiotic resistance and gene- and protein-expression associated. The findings of the work have highlighted the need for more work especially in the field of antibiotic resistance. 4
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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) | 0.000 | 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 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".