Physically stimulus-responsive nanoparticles for therapy and diagnosis
Bibliographic record
Abstract
Nanoparticles offer numerous advantages in various fields of science, particularly in medicine. Over recent years, the use of nanoparticles in disease diagnosis and treatments has increased dramatically by the development of stimuli-responsive nano-systems, which can respond to internal or external stimuli. In the last 10 years, many preclinical studies were performed on physically triggered nano-systems to develop and optimize stable, precise, and selective therapeutic or diagnostic agents. In this regard, the systems must meet the requirements of efficacy, toxicity, pharmacokinetics, and safety before clinical investigation. Several undesired aspects need to be addressed to successfully translate these physical stimuli-responsive nano-systems, as biomaterials, into clinical practice. These have to be commonly taken into account when developing physically triggered systems; thus, also applicable for nano-systems based on nanomaterials. This review focuses on physically triggered nano-systems (PTNSs), with diagnostic or therapeutic and theranostic applications. Several types of physically triggered nano-systems based on polymeric micelles and hydrogels, mesoporous silica, and magnets are reviewed and discussed in various aspects.
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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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".