Novel Pentafluorosulfanyl-containing Triclocarban Analogs selectively kill Gram-positive bacteria
Bibliographic record
Abstract
Abstract The antibacterial and antibiofilm efficacy of our novel pentafluorosulfanyl-containing triclocarban analogs was explored against seven different Gram-positive and Gram-negative indicator strains. After initial screening, they had bactericidal and bacteriostatic activity against Gram-positive bacteria, especially Staphylococcus aureus and MRSA (methicillin–resistant staphylococcus aureus) in a very low concentration. Our results were compared with the most common antibiotic being used (Ciprofloxacin and Gentamycin); the novel components had significantly better antibacterial and antibiofilm activity in lower concentrations in comparison to the antibiotics. For instance, EBP- 59 minimum inhibitory concentration was < 0.0003 mM, while ciprofloxacin 0.08 mM. Further antibacterial activity of novel components was surveyed against 10 clinical antibiotic resistance MRSA isolates. Again, novel components had significantly better antibacterial and antibiofilm activity in comparison with antibiotics. Mechanistic studies have revealed that none of these novel compounds exhibit any effect on the reduced thiol, disrupting iron sulfur clusters, or hydrogen peroxide pathways. Instead, their impact is attributed to the disruption of the Gram-positive bacterial cell membrane. Toxicity and safety testing on tissue cell culture showed promising results for the safety of components to the host.
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".