POTENTIAL OF NISIN LOADED LIPID NANOPARTICLES ON INHIBITION OF ENTEROBACTER CLOACAE BIOFILM FORMATION
Bibliographic record
Abstract
The food borne pathogen Enterobacter cloacae contribute to food borne illness in humans.Biofilm formation in Enterobacter cloacae makes them more resistant to antibiotics.The main goal of the research is to prevent biofilm-forming Enterobacter cloacae by encapsulating nisin in liposomes using nanotechnology.The isolate was identified by 16S rRNA gene sequencing, and the biofilm-formed were characterized.Nisin was selected based on sensitivity testing.A microvesicle encapsulation method was used to encapsulate nisin in liposomes.Bacterial control was determined by colony forming units in an in vitro bioassay.Inhibition and eradication of Enterobacter cloacae was investigated using a microbial biofilm highthroughput antimicrobial susceptibility test using the Calgary biofilm apparatus.The food borne pathogen Enterobacter cloaca was isolated from the skin of grapes.After characterizing the biofilm formation on the isolate, the results showed that Enterobacter cloacae has the highest biofilm formation in tryptic broth (TSB) and brain heart infusion medium (BHI).In the antibiotic susceptibility test, the isolate is inhibited by antibiotics when presented in high concentrations.High-throughput analysis was performed using the Calgary biofilm apparatus, and the results showed that the nisinloaded liposome exhibited good inhibition compared to antibiotics.One mM concentration of nisin (3.3 mg/10 mL) was used to encapsulate them in liposomes using a microvesicle encapsulation method.The results showed a tremendous inhibition of the food borne pathogen Enterobacter cloacae by the colony-forming units.This liposomal encapsulation of nisin promises high inhibition and can also be used for food safety.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".