Water and foodborne bacterial pathogens isolated from hospitalized children are extensively resistance against commonly used Penicillin-type antibiotics in developing countries
Bibliographic record
Abstract
Diarrheal diseases pose a significant risk to children under the age of five, ranking as the second leading cause of death within this demographic. This threat is exacerbated by the emergence of multidrug-resistant enteric bacteria, contributing to the ongoing global health crisis. This study aims to identify prevalent bacteria responsible for diarrhea and evaluate their susceptibility to antibiotics in children under five receiving medical attention at a model Kenyan hospital; Muhoroni County Hospital. Varied approaches comprising biochemical analyses, microbial monitoring and susceptibility testing were conducted using conventional antibiotics on carefully collected patients’ samples to assess respective trends. Enteric bacterial pathogens were identified in 43 out of 196 samples (21.9%), with Escherichia coli constituting 25 out of 43 (58.1%), Shigella spp. accounting for 11 out of 43 (25.6%), and Salmonella spp. making up 7 out of 43 (16.3%). All isolated E. coli, Shigella spp., and Salmonella spp. demonstrated susceptibility to Ceftriaxone. Notably, 14 out of 25 (56%), E. coli isolates and 13 out of 25 (52%) E. coli isolates, 11 out of 11 (100%) and 10 out of 11(91%) Shigella spp. isolates, and finally 6 out of 7 (86%) and 5 out of 7(71%) Salmonella spp. showed resistance to Amoxicillin and Ampicillin respectively, commonly prescribed drugs for children under five years. The findings underscore the prevalence of enteric bacterial pathogens contributing to diarrhea in children under five, emphasizing the urgent need for targeted interventions. Furthermore, the observed resistance to commonly prescribed antibiotics highlights the imperative for ongoing surveillance and development of alternative treatment strategies to address the evolving landscape of antimicrobial resistance in pediatric healthcare.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".