Comparison of Phenotypic Characteristics and Antimicrobial Resistance Patterns of Clinical Escherichia coli Collected From Two Unrelated Geographical Areas
Bibliographic record
Abstract
BACKGROUND: Antimicrobial resistance among pathogenic Escherichia coli is an increasing problem especially in developing countries. AIMS: To compare between resistance patterns of E. coli collected from two unrelated geographical areas. METHODS: A descriptive comparative study was conducted between May 2010 and August 2011. E. coli (n= 402) collected from hospitals in Khartoum state, Sudan and in Aseer region, Saudi Arabia were studied. Identification and antimicrobial susceptibility testing of isolates were performed following standard methods. Multi-drug resistance (MDR) was defined as non-susceptibility to ? three antimicrobials. RESULTS: Of the 402 E. coli isolates studied, MDR patterns were significantly higher among isolates from Sudan than Saudi Arabia [92.2% (214/232) vs. 70.6% (120/170)] (p = 0.000). The resistance rates of E. coli isolates were recorded as follows (Sudan and Saudi Arabia): High to moderate resistance to amoxicillin (97.7% and 94.2%), trimethoprim-sulfamethoxazole (88.3% and 82.5%), tetracycline (77.1% and 74.2%), amoxicillin- clavulanic acid (51.4% and 70%), ceftriaxone (64% and 52.4%) and ciprofloxacin (58.4% and 40%). Low resistance was to ceftazidime (35% and 20%), gentamicin (35% and 17.5%) and nitrofurantoin (22.4% and 11.7%). Resistance to amikacin was uncommon (1.9% and 5%). Significant differences (p < 0.05) in resistance rates of isolates between both countries in term to patient's gender and age. The most frequent MDR phenotypes among isolates were to 7(15.9%) in Khartoum state and to 3(20.8%) in Aseer region. CONCLUSIONS: Variation and emerging of antimicrobial resistance among pathogenic E. coli isolates was observed in both regions. Continuous monitoring of resistance profiles, locally and international surveillance programs are required.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".