Enhancement of anti-bactericidal and anti-biofilm activities of silver nanoparticles against multidrug-resistant enteric pathogens isolated from children with diarrhea
Bibliographic record
Abstract
Background: Gastroenteritis has a significant mortality andmorbidity incidence in children globally. Antimicrobial resistance in Enterobacteriaceaeis a serious public health problem, especially in developing countries.Objective: The present study aimed to evaluate the anti-bacterial and anti-biofilmactivities of AgNPs alone and in combination with kanamycin against multidrug-resistant Enterobacteriaceae and P. aeruginosa isolated from diarrheal children.Materials and methods: 90 Enterobacteriaceae and P. aeruginosa isolates fromdiarrheal children were evaluated against 10 antibiotics. Minimum inhibitory doses ofAgNPs and kanamycin were determined using broth microdilution, synergistic wasdetermined using Checkerboard dilution tests, and the Calgary technique was used toanalyze biofilm development.Results: A total of 90 stool cultures were conducted for bacteria associated withdiarrhea among children attending some Baghdad hospitals. The findings revealed thatbacterial diarrhea was most often caused by E. coli 31 (34.5%), followed by S. typhi 19(21.1%), K. pneumoniae 14 (15.5%), P. aeruginosa 11 (12.2%) and S. sonnei 6 (6.7%),and significant variations between the strain's species were discovered using statisticalanalysis (P < 0.05). The present study's findings revealed that bacteria isolated fromchildren with diarrhea were spread significantly in age groups of 37–48 months andsignificantly different between age groups (P < 0.05), with a male/female ratio of0.57/1. Imipenem and amikacin were the most active antibiotics compared to penicillin,which was the least effective antibiotic. The combination of sublethal doses of AgNPswith sub-MIC (½MIC) of kanamycin exhibited substantial synergistic bactericidaleffects against MDR-Enterobacteriaceae. AgNPs inhibited biofilm formation by55%–65% for diarrhea-causing bacteria, while the combination of AgNPs withkanamycin demonstrated the strongest biofilm inhibition of around 80%–90% againstMDR-Enterobacteriaceae with a highly significant variation (P < 0.05).Conclusions: The outcomes of the research shows that the combination of AgNPs withkanamycin has remarkable synergistic bactericidal and anti-biofilm effectivenessagainst MDR-Enterobacteriaceae isolated from diarrheal children.
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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 | 0.000 |
| 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) | 0.001 | 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".