Effect of Certain Disinfectants and Antibiotics on the Biofilm Formed by <i>Staphylococcus Aureus</i> Isolated from Medical Devices at the University Hospital Center of Sidi Bel Abbes (Algeria)
Bibliographic record
Abstract
Staphylococcus aureus is one of the species that are most frequently isolated from medical devices. The ability to produce a biofilm is an important step in the pathogenesis of these staphylococci; biofilm formation is strongly dependent on the environmental conditions and also on the antibiotics and disinfectants used in the treatment of infections. In this study, 28 staphylococcus aureus isolated from medical devices at the University Hospital Center of Sidi Bel Abbes (in Northwestern Algeria) were analyzed to detect the formation of biofilm by culture on Red Congo Agar (RCA). The Tube Method (TM) and tissue Culture Plate (TCP) techniques were also used to investigate the effect of penicillin, ethanol and Betadine on the preformed biofilm. It has been found that 19 strains produced a bacterial slime on the Congo red medium, 7 strains produced a biofilm by the tube method, 2 of which are highly productive. In addition, 9 strains produced a biofilm on polystyrene micro-plates; this number was higher in the presence of penicillin and ethanol with 19 and 11 biofilm producing strains, respectively. On the other hand, no biofilm was formed in the presence of Betadine. It is important to examine the response of biofilms following an imposed external constraint such as disinfectants and antibiotics in order to develop new strategies to combat bacterial biofilms but also to better control their formation.
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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) | 0.000 | 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) | 0.002 | 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".