Nanostructures in Nanomedicine: Critical Issues and Perspectives
Bibliographic record
Abstract
In the last decades the development of novel smart nanomaterials provide versatile tuneable platforms for the investigation and manipulation of several biological tasks with low invasiveness in tissues and biological systems.As a matter of fact, a large variety of smart integrated nanostructured systems have proven their effectiveness for various types of biomedical applications, including stimuli-responsive organic and metal nanoparticles as well as hybrid (organic/inorganic) nanostructures.These novel nanostructures allow the possibility to include a diagnostic imaging system with the monitoring of the temporal evolution of the response of the disease in patients.The development of integrated medical nano-devices, that includes early diagnostics functions, allow to attain advanced profiling of the health (and disease) of individual patient, thus providing new methods for personalized health monitoring and preventative medicine.However, although the good performance of these novel nano-platform against a large number of specific diseases, a number of inherent drawbacks and critical issues are still present.This circumstance limit their translation in the clinic experience.Much efforts are currently being directed at bridging the gap to put these smart nano-platforms into practice, by a deeper investigation of their safety, therapeutic efficacy, and a detailed understanding of their physico-chemical behaviour.
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 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 | 0.008 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.004 | 0.010 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 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".