Contribution of biofilm and Cell Surface Hydrophobicity in the resistance of Pseudomonas aeruginosa from some Healthcare settings of Yaounde
Bibliographic record
Abstract
Antibiotic resistance (ABR) is one of the priority problems around the world which affects the evolution of infectious diseases and leads to economic losses with a considerable impact on increased mortality and morbidity in low-resource countries. One of the bacteria most involved in resistance is Pseudomonas aeruginosa. It is a bacteria belonging to the ESKAPE group, for which there is an urgent and critical need for antibiotic development. The circulation of this bacteria in several countries has been the cause of many infections affecting all ages, patients and healthcare staff, and has promoted the spread of resistance in environmental and animal health. To investigate the determinants of resistance in Pseudomonsa aeruginosa, the aim of this work was to assess the correlation between biofilm, Cell Surface Hydrophobicity (CSH) and antibiotic resistance in clinical isolates of Pseudomonas aeruginosa circulating in Yaounde. Methods: We conducted a cross sectional study from March 2019 to October 2021. We collected various samples including pus, urine, lochia, wound, bedsore and blood, from patients of the General Hospital of Yaounde (GHY), Central Hospital of Yaounde (CHY), University Teaching Hospital of Yaounde (UTHY) and Centre Pasteur du Cameroun (CPC). The hydrophobicity of the bacterial surfaces was determined by measuring the percentage of adhesion to xylene. Biofilm formation was assessed using the TCP method. The correlation between CSH, biofilm production and ABR were determined. Results: We included 300 patients in this study. The most represented patients were men (50.67%). P. aeruginosa was isolated from the four settings and the prevalence was 10%. A large proportion of P.aeruginosa were biofilm producer with 30% being strong biofilm producer, 53.33% were weak biofilm producer and 16.67% were non-biofilm producers. Twenty percent of P. aeruginosa were highly hydrophobes while, 23.33% were moderately hydrophobes and 56.67 were low hydrophobes. The activities of most antibiotics were negatively correlated with biofilms and hydrophobicity. Isolates from lochia and wounds were biofilm producers and showed multidrug resistance while isolates from urine and pus was very hydrophobic. Conclusion: These findings should serve as evidence base that biofilms and hydrophobicity are factors that contribute to antibiotic resistance in Pseudomonas aeruginosa. This demonstrates the need to consider these virulence factors, as well as the nature of the sample or excipient, during patient treatment, especially during empirical antibiotic therapy.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.017 | 0.012 |
| 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 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".