Mimicking Nature‘S Nanocarrier: Synthetic Low-Density Lipoprotein-Like Nanoparticles for Cancer-Drug Delivery
Bibliographic record
Abstract
Evaluation of: Nikanjam M, Blakely EA, Bjornstad KA, Shu X, Budinger TF, Forte TK: Synthetic nano-low density lipoprotein as targeted drug delivery vehicle for glioblastoma multiforme. Int. J. Pharm. 3287, 86–94 (2007) [1]. Low-density lipoproteins have long been recognized as a viable nanocarrier for targeted delivery of drug and imaging agents. Many groups have published promising initial findings; however, progress in this field has been impeded by the need to isolate low-density lipoproteins from fresh donor plasma. In a recent paper by Nikanjam and colleagues, synthetic low-density lipoprotein-like nanoparticles were prepared from commercial lipids and a bifunctional synthetic peptide containing the low-density lipoprotein receptor-binding domain and the lipid-binding motif. These particles were shown to behave similarly to native low-density lipoproteins and also to bind to the low-density lipoprotein receptor on cancer cells. Herein, we evaluate the utility of this novel delivery vehicle and discuss what role this technology may have in nanomedicine.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.004 | 0.002 |
| 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".