Biocontrol of Listeria monocytogenes and Escherichia coli O157:H7 in Meat by Using Phages Immobilized on Modified Cellulose Membranes
Bibliographic record
Abstract
The ability of phages to specifically interact with and lyse their host bacteria makes them ideal antibacterial agents. The range of applications of bacteriophage can be extended by their immobilization on inert surfaces. A novel method for the oriented immobilization of bacteriophage has been developed. The method was based on charge differences between the bacteriophage head, which exhibits an overall net negative charge, and the tail fibers, which possess an overall net positive charge. Hence, the head would be more likely to attach to positively charged surfaces, leaving the tails free to capture and lyse bacteria. Cellulose membranes modified so that they had a positive surface charge were used as the support for phage immobilization. It was established that the number of infective phages immobilized on the positively charged cellulose membranes was significantly higher than that on unmodified membranes. Cocktails of phages active against Listeria or Escherichia coli immobilized on these membranes were shown to effectively control the growth of L. monocytogenes and E. coli O157:H7 in ready-to-eat and raw meat, respectively, under different storage temperatures and packaging conditions. The phage storage stability was investigated to further extend their industrial applications. It was shown that lyophilization can be used as a phage-drying method to maintain their infectivity on the newly developed bioactive materials. In conclusion, utilizing the charge difference between phage heads and tails provided a simple technique for oriented immobilization applicable to a wide range of phages and allowed the retention of infectivity.
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) | 0.000 | 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 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".