Analysis of Antibiotic Resistance in Bacteria Isolated from the Surface Microlayer and Underlying Water of an Estuarine Environment
Bibliographic record
Abstract
We compared the prevalence of cultivable antibiotic-resistant bacteria and resistance genes in the surface microlayer (SML) and underlying waters (UW) of an estuary. Prevalence of resistant bacteria was determined in antibiotic-supplemented agar. Bacterial isolates from the UW (n=91) and SML (n=80), selected in media without antibiotic, were characterized concerning susceptibility against nine antibiotics. The presence of genes bla(TEM), bla(OXA-B), bla(SHV), bla(IMP), tet(A), tet(B), tet(E), tet(M), cat, sul1, sul2, sul3, aadA, IntI1, IntI2, and IntI3 was assessed by PCR. The variable regions of integrons were sequenced. Ampicillin- and streptomycin-resistant bacteria were significantly more prevalent in SML. Resistance levels among the bacterial collections were generally low, preventing detection of significant differences between SML and UW. The tet(E) gene was detected in two Aeromonas isolates and tet(M) was detected in a Pseudomonas isolate. Gene sul1 was amplified from three Aeromonas isolates. Prevalence of intI genes was 2.11%. Cassette arrays contained genes encoding resistance to aminoglycosides and chloramphenicol. A higher prevalence of antibiotic-resistant bacteria in the SML, although only detectable when bacteria were selected in antibiotic-supplemented agar, suggests that SML conditions select for antibiotic resistance. Results also showed that antibiotic resistance was uncommon among estuarine bacteria and the resistance mechanisms are probably predominantly intrinsic.
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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) | 0.000 | 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 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".