AgNP-Containing Niosomes Functionalized with Fucoidan Potentiated the Intracellular Killing of Mycobacterium abscessus in Macrophages
Bibliographic record
Abstract
Intracellular pathogens represent a challenge for therapy because the antibiotics used need to diffuse into the cytoplasm to target the pathogens. The situation is more complicated in the mycobacteria family because members of this family infect and multiply within macrophages, the cells responsible for clearing microorganisms in the body. In addition, mycobacteria members are enclosed inside pathogen-containing vesicles or phagosomes. The treatments of these pathogens are aggravated when these pathogens acquire resistance to antibiotic molecules. As a result, new antimicrobial alternatives are needed. Niosomes are vesicles composed of cholesterol and nonionic surfactants that can be used for antibiotic encapsulation and delivery. The current study developed a systematic formulation of niosomes to determine the best option for niosome functionalizing for precise delivery to the intracellular pathogen Mycobacterium abscessus. Silver nanoparticles (AgNPs) were synthesized using gallic acid as an antibacterial agent. Then, niosomes were prepared and characterized, following the encapsulation of AgNPs functionalized with a single-chain antibody screened against the cell wall glycopeptidolipid of Mycobacterium abscessus. For a precise delivery of the cargo into macrophages, the niosomes were also functionalized with the polysaccharide fucoidan, taken specifically by the scavenger receptor class A expressed on the surface of macrophages. Results of the study showed a steady decrease in the intracellular pathogen load after 48 h post-infection. In conclusion, this system could be developed into a platform to target other types of intracellular pathogens and as an option for antimicrobial therapy.
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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".