Tolerance of Biofilms to Antimicrobials and Significance to Antibiotic Resistance in Wounds.
Bibliographic record
Abstract
A biofilm is a community of microorganisms that adhere to each other and to surfaces and secrete extracellular polymeric substances (EPS) encasing themselves in a matrix. Biofilms are a major healthcare concern, as they can form on medical devices leading to infection. Additionally, there is growing evidence to show their ability to form in chronic wounds, which leads to delayed wound healing and inflammation. Due to a number of reasons, such as formation of the EPS resulting in sub-inhibitory concentrations of antimicrobials reaching the bacterial cells, slow growth rate of bacterial cells rendering some antibiotics ineffective, and the presence of persister cells, biofilms show increased tolerance to many antimicrobials and antibiotics. Additionally, studies have started to emerge showing a link between resistance to antimicrobials and antibiotics. Cross-resistance can be attributed to a number of factors, for example, increased expression of multidrug efflux pumps that efflux a wide range of substrates and horizontal gene transfer of genetic material encoding multiple resistance genes between different species within the polymicrobial biofilm. Antimicrobial resistance is an increasing threat caused by multiple factors including cross-resistance, and it is a global health concern. This review focuses on current research on antimicrobial and antibiotic resistance and cross-resistance found between antimicrobials and antibiotics commonly used in woundcare to evaluate the significance of this acquired antibiotic resistance. Furthermore, the review discusses the significance of antimicrobial tolerance and the role biofilms play in enhancing antibiotic resistance.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".