Monitoring the battleground: Antimicrobial resistance, antibiofilm patterns, and virulence factors of wound bacterial isolates from patients in hospital system
Bibliographic record
Abstract
ABSTRACT Extensive use of antibiotics in the treatment of bacterial infections has led to a challenge of antibiotic resistance, contributing to morbidity and mortality. Herein, evaluation of bacterial isolates from patients with wound discharge was performed and drug susceptibility patterns examined, with a goal of deciphering antibacterial resistance. A cross-sectional study was conducted between March to June 2022, in which samples were collected from patients with chronic wounds and were inoculated into appropriate media for identification and characterization. The bacterial pathogens were identified using standard microbiological methods. Shockingly, the majority of wound isolates showed positive growth in microbial analysis with high prevalence in male candidates. Further, Staphylococcus aureus 28 (20.7%) was identified as the most predominant pathogen followed by Klebsiella spp . 20 (14.8%), P. aeruginosa spp . 10 (14.8%) and lastly E. coli 6 (4.4%) bacteria in the wound isolates while cotrimoxazole 13 (48.1%) followed by clindamycin 7 (25.9%) and erythromycin 7 (25.9%) were the most antibacterial resistant drugs to both Gram positive and Gram-negative bacteria. Out of the four isolates, 3 (75%) isolates were able to produce the haemolysin and protease and 2 (50%) isolates were able to produce the lipase and phospholipase. The findings herein form a clinical basis for identification of antimicrobial resistance in chronic wounds that can be applied in responsible use of antibacterial in chronic wound management and as an illumination in development of more potent antibiotics for chronic wound treatment
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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 | 0.000 | 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 | 0.001 |
| 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 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".