Bibliographic record
Abstract
Engineered nanoparticles are emerging as useful tools for different purposes in life sciences, medicine and agriculture. Nanomedicine, an emerging discipline, involves the application of nanotechnology (usually regarded within the size range of 1-1000 nm) in the design of systems and devices that can facilitate our understanding of disease pathophysiology, nano-imaging, nanomedicines and nano-diagnostics. Among the different nanomaterials used to construct nanoparticles, are organic polymers, co-polymers and metals. Some of these materials can self assemble, and depending on the conditions under which the self-assembly process occurs, a vast array of shapes can be formed. Frequently, the nanoparticle morphology is spherical or tubular, mimicking the shape, but thus far, not the functions of subcellular organelles. We discuss here several representative nanoparticles, made of block copolymers and metals, highlighting some of their current uses, advantages and limitations in medicine. Nano-oncology and nano-neurosciences will also be discussed in more detail in the context of the intracellular fate of nanoparticles and possible long-term consequences on cell functions.
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 | 0.010 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.010 | 0.026 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.011 | 0.013 |
| Insufficient payload (model declined to judge) | 0.042 | 0.018 |
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".