Antimicrobial activity and mechanism of peptide CM4 against<i>Pseudomonas aeruginosa</i>
Bibliographic record
Abstract
Antibacterial peptide CM4 (ABP-CM4) is a small cationic peptide with broad-spectrum activities against bacteria, fungi and tumor cells and may possibly be used as an antimicrobial agent. In this study, a C-terminal amidated antibacterial peptide ABP-CM4 (ABP-CM4N) with the strongest antibacterial activity was obtained through screening the antibacterial activities of ABP-CM4 with different modifications. The minimal inhibitory concentration of ABP-CM4N was 8 μM against P. aeruginosa (ATCC 27853) which was lower than that of ABP-CM4 (16 μM). The strengthened antimicrobial activity of ABP-CM4N may be associated with the increased membrane binding capacity, being two times that of ABP-CM4 (p < 0.001). The antibacterial mechanism of ABP-CM4N to Pseudomonas aeruginosa was examined by means of cell membrane integrity analysiss, the intracellular ultrastructure change observation and E. coli genomic DNA binding assay. It was found that ABP-CM4N had the same antimicrobial mechanism as ABP-CM4, and the aim of the antimicrobial mechanism was mainly to destroy the cell membrane which caused nucleic acid or protein leakage, and secondly to interact with E. coli genomic DNA after penetrating the cell membrane. Furthermore, in vitro ABP-CM4N showed a better bacteriostatic activity in meats, with the treated samples showing two to three times less positive colonies than ABP-CM4.
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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".