An Easy New Modified Method for Detection of Antibacterial Susceptibility in Biofilm-Growing Bacteria
Bibliographic record
Abstract
Background: Biofilms are a major challenge in treating infections. Clinically, biofilms are often associated with chronic infections, so rapid and accurate methods of investigating the antibiotic susceptibility of biofilm bacteria are very important for faster diagnosis and treatment. In this study, a new modified microplate method was proposed to evaluate the antibiotic susceptibility of alive bacteria in the biofilm. Materials and Methods: The study was conducted on clinical isolates of Pseudomonas aeruginosa and standard strain over a period of one year. The susceptibility test was conducted according to a modified version of the Calgary biofilm device method. In the last step, to study the living bacteria in the biofilm, oxidative-fermentative (OF) medium was used to measure the acid production following the use of glucose by the bacteria. The biofilm-growing bacteria was determined by observing the color changing and also measuring optical density (OD) at 427 nm of OF medium. Results: The method in this study could evaluate the effect of antibiotics on biofilm bacteria based on glucose metabolism. The results indicated that this method can quickly and easily identify alive bacteria in biofilm at a low cost and without the need for any special devices. Conclusion: Although biofilms are involved in most incurable clinical cases, there is currently no guideline to assist physicians in treating biofilm-related infections. Therefore, it is necessary to establish a specific sensitivity test for biofilm or to approve a new method for routine use in diagnostic laboratories.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".