Antibiotic-loaded lipid-based nanocarrier: A promising strategy to overcome bacterial infection
Bibliographic record
Abstract
According to the World Health Organization (WHO) and Centers for Disease Control and Prevention (CDC), bacterial infections are one of the greatest threats to global health, food production, and life expectancy. In this sense, the development of innovative formulations aiming at greater therapeutic efficacy, safety, and shorter treatment duration compared to conventional products is urgently needed. Lipid-based nanocarriers (LBNs) have demonstrated the potential to enhance the effectiveness of available antibiotics. Among them, liposome, nanoemulsion, solid lipid nanoparticle (SLN), and nanostructured lipid carrier (NLC) are the most promising due to their solid technical background for laboratory and industrial production. This review describes recent advances in developing antibiotic-loaded LBNs against susceptible and resistant bacterial strains and biofilm. LBNs revealed to be a promising alternative to deliver antibiotics due to their superior characteristics compared to conventional preparations, including their modified drug release, improved bioavailability, drug protection against chemical or enzymatic degradation, greater drug loading capacity, and biocompatibility. Antibiotic-loaded LBNs can improve current clinical drug therapy, bring innovative products and rescue discarded antibiotics. Thus, antibiotic-loaded LBNs have potential to open a window of opportunities to continue saving millions of lives and prevent the devastating impact of bacterial infection.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".