Histones from Avian Erythrocytes Exhibit Antibiofilm activity against methicillin-sensitive and methicillin-resistant Staphylococcus aureus
Bibliographic record
Abstract
Staphylococcus aureus, a human pathogen associated with many illnesses and post-surgical infections, can resist treatment due to the emergence of antibiotic-resistant strains and through biofilm formation. The current treatments for chronic biofilm infections are antibiotics and/or surgical removal of the contaminated medical device. Due to higher morbidity and mortality rates associated with overuse/misuse of antibiotics, alternate treatments are essential. This study reports the antibiofilm activity of avian erythrocyte histones against methicillin-sensitive Staphylococcus aureus (MSSA) and methicillin-resistant Staphylococcus aureus (MRSA). Fluorescence and scanning electron microscopy revealed membrane damage to bacteria in histone-treated biofilms. Histones and indolicidin (positive control) increased the expression of apsS and apsR, which are associated with the Antimicrobial Peptide (AMP) sensor/regulator system in S. aureus. The expression of dltB, and vraF, associated with AMP resistance mechanisms, were under histone inducible control in the biofilm-embedded bacterial cells. The time kill kinetics for histones against S. aureus revealed a rapid biocidal activity (<5 min). Purified erythrocyte-specific histone H5 possessed 3-4 fold enhanced antimicrobial activity against planktonic cells compared to the histone mixture (H1, H2A, H2B, H3, H4, H5). These results demonstrate the promise of histones and histone-like derivatives as novel antibiotics against pathogens in their planktonic and biofilm forms.
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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".