Identification and frequency of the associated genes with virulence and antibiotic resistance of <i>Escherichia coli</i> isolated from cow's milk presenting mastitis pathology
Bibliographic record
Abstract
Antimicrobial resistance, genotype, and virulence gene content of Escherichia coli isolated from bovine clinical mastitis in Tehran Province, Iran, were studied. Two hundred seven (207) milk samples from individual cows presenting mastitis symptoms collected from different dairy farms were used to determine the presence of specific genes of E. coli responsible for this pathology. Multiplex PCR was used to differentiate E. coli isolates into different phylogenetic groups/subgroups and to detect their virulence and involved resistance genes. All the isolated strains were tested for the susceptibility to 21 antimicrobial agents. The results showed that E. coli was detected in 42 (20.3%) samples and 69% of them belonged to the phylogenetic groups A and B1. The phylogenetic subgroup A1 (31%) and subgroup B1 (28.6%) demonstrated the highest prevalence of virulence genes (f17c-A, and eae (n = 6), f17b-A, and f17d-A (n = 5), afaD-8, afaE-8, aucD, and bfpA (n = 4), clpG and VT (n = 2), and LT and ST genes (n = 1)). The highest antimicrobial resistance was observed for tetracycline (45.2%) followed by streptomycin (26.2%). The antimicrobial resistance genes tetB (31%), tetA (28.6%), and aadA (26.2%) were the most prevalent. Moreover, integron class 1 and 2 were found in 24 (57.1%) and 8 (19%) of the E. coli isolates.
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 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".