Emergence and Antimicrobial Resistance Patterns of ESBL-Producing Escherichia coli Isolated from Clinical and Subclinical Mastitis Cases in Egyptian Dairy Cattle
Bibliographic record
Abstract
The emergence and global spread of ESBL-producing Enterobacteriaceae is a growing public health concern. ESBL-producing E. coli ESBL-PE poses a significant health risk, particularly with the emergence of new variants carrying blaSHV, blaTEM and blaCTX-M genes. This study looked at antibiotic susceptibility, genotyping, and gene sequencing in order to discover ESBL-PE in milk from cows with subclinical and clinical mastitis in three Egyptian governorates. 186 milk samples were analyzed in this study in order to isolate E. coli and identify which ones developed ESBL Antibiotic resistance of ESBL- PE was assessed on Mueller-Hinton agar using 10 different commercial antibiotic disks. Resistance genes were genotyped using (PCR).The obtained sequences were evaluated using Chromas pro1.7 and the maximum-likelihood phylogenetic trees were constructed using MEGA11. E. coli was found in (n. 16/57) 28.07% and (n. 27/46) 58.69 %, while (ESBL- PE) were found in (13/57) 22.8% and (7/46) 15.2% with clinical mastitis and subclinical mastitis respectively. ESBL- PE showed greater resistance to ampicillin, amoxicillin and streptomycin at percentage of 91.67% while more sensitive to azithromycin , norfloxacin , and gentamicin by 83.33%. Results indicated that 8.3%, 16.7% and 8.3% of ESBL- PE were susceptible to ciprofloxacin, tetracycline, and amoxicillin. The blaTEM gene was recorded in GenBank as PQ45739. This study detected that ESBL- PE, harboring blaSHV and blaTEM antibiotic resistance genes, were identified in mastitis milk. Consistent monitoring of ESBL- PE and proactive measures are crucial to avoiding the future lay out of resistance genes.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".