Nanoparticles—Attractive Carriers of Antimicrobial Essential Oils
Bibliographic record
Abstract
Microbial pathogens are the most prevalent cause of chronic infections and fatalities around the world. Antimicrobial agents including antibiotics have been frequently utilized in the treatment of infections due to their exceptional outcomes. However, their widespread use has resulted in the emergence of multidrug-resistant strains of bacteria, fungi, viruses, and parasites. Furthermore, due to inherent resistance to antimicrobial drugs and the host defence system, the advent of new infectious diseases, chronic infections, and the occurrence of biofilms pose a tougher challenge to the current treatment line. Essential oils (EOs) and their biologically and structurally diverse constituents provide a distinctive, inexhaustible, and novel source of antibacterial, antiviral, antifungal, and antiparasitic agents. However, due to their volatile nature, chemical susceptibility, and poor solubility, their development as antimicrobials is limited. Nanoparticles composed of biodegradable polymeric and inorganic materials have been studied extensively to overcome these limitations. Nanoparticles are being investigated as nanocarriers for antimicrobial delivery, antimicrobial coatings for food products, implantable devices, and medicinal materials in dressings and packaging materials due to their intrinsic capacity to overcome microbial resistance. Essential oil-loaded nanoparticles may offer the potential benefits of synergism in antimicrobial activity, high loading capacity, increased solubility, decreased volatility, chemical stability, and enhancement of the bioavailability and shelf life of EOs and their constituents. This review focuses on the potentiation of the antimicrobial activity of essential oils and their constituents in nanoparticulate delivery systems for a wide range of applications, such as food preservation, packaging, and alternative treatments for infectious diseases.
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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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".