Engineering biomimetic bacteria membrane-coated nanoparticles: an emerging anti-infection platform
Bibliographic record
Abstract
Bacterial infections represent a global challenge, posing a significant burden and life-threatening complications. Traditional therapeutic regimens, primarily antibiotics, although effective, face intrinsic obstacles, particularly antibiotic resistance which necessitates alternative approaches. In recent years, biomimetic nanosystems have demonstrated promising therapeutic outcomes in advanced medical protocols. In this context, bacterial membranes have become increasingly popular as accessible biomaterials for biomedical applications. Bacterial membranes show great promise as natural nanocoatings for biomedical engineering due to their excellent biomimetic properties, precision targeting, immune evasion potential, and unique therapeutic capabilities. This review highlights current breakthroughs in the design and application of bacterial membrane-coated nanoparticles (BMCNs). It focuses on the biosafety of BMCNs in terms of their potential therapeutic applications for drug delivery, as antibacterial agents, to inhibit pathogen adhesion, and in tissue regeneration. In addition, the current limitations and future outlook of BMCNs for clinical translation are discussed. Collectively, this review article serves as an updated resource that researchers can leverage while developing and applying BMCNs.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".