Distribution of Antibiotic Resistance Genes in Gram Negative Bacteria Isolated from Contact Surfaces at Slaughterhouses and Butcheries
Bibliographic record
Abstract
BACKGROUND: Cross contamination of contact surfaces in slaughterhouses and meat shops (butcheries) may contain antibiotic resistance genes that pose a serious public health challenge. AIMS: The present study aimed to assess prevalence of antibiotic resistance genes in Gram-negative strains isolated from contact surfaces at slaughterhouses and meat shops. METHODS: A total of 71 swab samples were collected from different contact surfaces at slaughterhouses and meat shops and cultured for bacterial identification, antibiotic resistance testing, and resistance gene detection.  Polymerase chain reaction (PCR) was used to detect strA-strB, sul2, aadA1, tet(B), tet(A), cat, sul3, and blaSHV resistant genes in Gram-negative bacteria. RESULTS: The results showed that the most common Gram-negative bacteria were Klebsiella pneumoniae (41.2%) and Escherichia coli (35.3%) followed by Enterobacter cloacae (5.9%). Resistance was most prevalent against trimethoprim-sulfamethoxazole (88.1%), ampicillin (85.3%), and cefuroxime (70.6%). The most prevalent antibiotic resistance gene was aadA1 (88.2%), followed by tet (A) (73.5%), and sul3 (70.6%). The results also indicated that 29.4% of the bacteria carried one resistance gene, 44.1% carried two genes, 20.6% carried more than two genes. CONCLUSIONS: The findings of this study emphasize the critical role of hygiene and antimicrobial stewardship in slaughterhouses and butcheries. The high prevalence of Gram-negative bacteria and their resistance to multiple antibiotics highlight the urgent need for improved sanitation practices and responsible antibiotic usage. Future studies should focus on whole-genome sequencing to further investigate resistance mechanisms and explore alternative disinfection strategies to reduce bacterial contamination in meat-processing facilities.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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 teacher head, 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".