Combined use of eBeam irradiation and the potential probiotic Lactobacillus rhamnosus Vahe for control of foodborne pathogen Klebsiella pneumoniae
Bibliographic record
Abstract
Abstract Purpose The implementation of electron beam radiation coupled with the use of probiotics is one of the newest food processing technologies that may be used to ensure food safety and improve shelf life of food products. The purpose of this study was to evaluate the effect of 50–150-Gy electron beam irradiation on the antimicrobial activity of the putative probiotic strain Lactobacillus rhamnosus Vahe. Methods Low-dose electron beam irradiation of lactobacilli cells was performed using the Advanced Research Electron Accelerator Laboratory’s electron accelerator, and the agar well diffusion method and Verhulst logistic function were used to evaluate the effect of radiation on anti–Klebsiella pneumoniae activity of the cell free supernatant of L. rhamnosus Vahe cells in vitro. Results Our results suggest that 50–150-Gy electron beam irradiation decreases the viability of the investigated lactobacilli, but does not significantly change the probiotic’s activity against K. pneumoniae. Conclusions Results indicate that the combined use of irradiation and L. rhamnosus Vahe might be suggested for non-thermal food sterilizing technologies.
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 | 0.000 |
| 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".