Prevalence and Molecular Characterization of Extended Spectrum Beta Lactamase Bacteria Causing Urinary Tract Infections in Pregnant Mothers at Itojo Hospital, South Western Uganda
Bibliographic record
Abstract
Background Extended-spectrum β-lactamase (ESBL) producing bacteria pose a global challenge because of resistance developing against a wide range of antimicrobial agents that complicate available treatment options. Thus, identifying the prevalent bacterial species producing ESBL enzymes and understanding how they are susceptible to antibiotics is necessary internationally to inform the effective treatment guidelines. Objective To determine the prevalence and molecular characterization of ESBL bacteria causing Urinary Tract Infections (UTIs) in women who are pregnant at the Itojo Hospital, Ntungamo District. Methods We conducted cross-sectional study where we collected and analyzed 340 urine samples. We did antimicrobial susceptibility testing using the Kirby Bauer disk diffusion method. Isolates were screened for ESBL production and confirmed using the combination disk test (CDT). Genotypic characterization was confirmed using multiplex PCR to detect blaTEM, blaCTX-M and blaSHV genes. Results The prevalence of ESBL – producing bacteria was 29.7% (101/340). Escherichia coli (35.6%) and Klebsiella species (32.7%) were predominant ESBL producers. Genotypic analysis revealed blaTEM (49.5%) and blaCTX-M (30.7%) as the most prevalent genes, while blaSHV was less common (7.9%) Conclusion The high prevalence of ESBL–producing bacteria and their resistance to commonly used antibiotics highlight the need for targeted antibiotic therapy, antimicrobial stewardship, and regular molecular surveillance.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".