Genotypic detection of <i>β</i>-lactamase-producing <i>Escherichia coli</i> isolates obtained from Seven Crater Lakes of San Pablo, Laguna, Philippines
Bibliographic record
Abstract
Abstract The extended-spectrum β-lactamase (ESBL)-producing Escherichia coli is becoming a global public health concern. More comprehensive surveillance of β-lactam resistance in E. coli would improve monitoring strategies and control resistance transmission in contaminated environments. This study investigated the prevalence of β-lactamase genes in E. coli isolated from the Seven Crater Lakes in San Pablo, Laguna, Philippines. Water samples from lakes were collected for the isolation of E. coli (n = 846) and molecular characterization by detecting the presence of the uidA gene. The isolates were then tested for the presence of β-lactamase genes using PCR. Among the screened genes, blaAmpC was the most dominant (91%). Other β-lactamase genes such as blaTEM, blaSHV, and blaCTXM were also detected with percentage occurrence of 34, 5, and 1%, respectively. Multiple genes within individual isolates were also observed, wherein blaTEM/AmpC was the most prevalent gene combination. Moreover, a significant negative correlation between blaAmpC with blaSHV and blaCTXM was depicted in this study. Overall, these findings demonstrate the presence of β-lactamase genes in E. coli in the Seven Crater Lakes of San Pablo and can be used in developing effective strategies to control antibiotic resistance in environmental waters.
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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) | 0.000 | 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".