Strategies in biomimetic surface engineering of nanoparticles for biomedical applications
Bibliographic record
Abstract
Engineered nanoparticles (NPs) play an increasingly important role in biomedical sciences and in nanomedicine. Yet, in spite of significant advances, it remains difficult to construct drug-loaded NPs with precisely defined therapeutic effects, in terms of release time and spatial targeting. The body is a highly complex system that imposes multiple physiological and cellular barriers to foreign objects. Upon injection in the blood stream or following oral administation, NPs have to bypass numerous barriers prior to reaching their intended target. A particularly successful design strategy consists in masking the NP to the biological environment by covering it with an outer surface mimicking the composition and functionality of the cell's external membrane. This review describes this biomimetic approach. First, we outline key features of the composition and function of the cell membrane. Then, we present recent developments in the fabrication of molecules that mimic biomolecules present on the cell membrane, such as proteins, peptides, and carbohydrates. We present effective strategies to link such bioactive molecules to the NPs surface and we highlight the power of this approach by presenting some exciting examples of biomimetically engineered NPs useful for multimodal diagnostics and for target-specific drug/gene delivery applications. Finally, critical directions for future research and applications of biomimetic NPs are suggested to the readers.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".