Multidrug-resistant Campylobacter jejuni, Campylobacter coli and Campylobacter lari isolated from asymptomatic school-going children in Kibera slum, Kenya
Bibliographic record
Abstract
<ns4:p> <ns4:bold>Background:</ns4:bold> The objective of this study was to determine the prevalence of thermophilic <ns4:italic>Campylobacter</ns4:italic> spp. in asymptomatic school-going children and establish the antibiotic resistant patterns of the isolates towards the drugs used to treat campylobacteriosis, including macrolides, quinolones and tetracycline. <ns4:italic>Campylobacter</ns4:italic> spp. are a leading cause of enteric illness and have only recently shown resistant to antibiotics. </ns4:p> <ns4:p> <ns4:bold>Methods:</ns4:bold> This study isolated <ns4:italic>Campylobacter</ns4:italic> spp., including <ns4:italic>Campylobacter coli</ns4:italic> , <ns4:italic>Campylobacter jejuni</ns4:italic> and <ns4:italic>Campylobacter lari</ns4:italic> , in stool samples from asymptomatic school-going children in one of the biggest urban slums in Kenya. The disc diffusion method using EUCAST breakpoints was used to identify antibiotic-resistant isolates, which were further tested for genes encoding for tetracycline resistances using primer-specific polymerase chain reaction. </ns4:p> <ns4:p> <ns4:bold>Results:</ns4:bold> In total, 580 stool samples were collected from 11 primary schools considering both gender and age. Subjecting 294 biochemically characterized <ns4:italic>Campylobacter</ns4:italic> spp. isolates to genus-specific PCR, 106 (18.27% of stool samples) isolates were confirmed <ns4:italic>Campylobacter</ns4:italic> spp. Out of the 106 isolates, 28 (4.83%) were <ns4:italic>Campylobacter</ns4:italic> <ns4:italic>coli</ns4:italic> , 44 (7.58%) were <ns4:italic>Campylobacter jejuni</ns4:italic> while 11 (1.89%) were <ns4:italic>Campylobacter</ns4:italic> <ns4:italic>lari</ns4:italic> . <ns4:italic>Campylobacter jejuni</ns4:italic> had the highest number of isolates that were multi-drug resistant, with 26 out of the 28 tested isolates being resistant to ciprofloxacin (5 mg), nalidixic acid (30 mg), tetracycline (30 mg) and erythromycin (15 mg). </ns4:p> <ns4:p> <ns4:bold>Conclusions:</ns4:bold> In conclusion, a one-health approach, which considers overlaps in environment, animals and human ecosystems, is recommended in addressing campylobacteriosis in humans, since animals are the main reservoirs and environmental contamination is evident. </ns4:p>
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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; both teacher heads agree on what is shown here.
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".