Bibliographic record
Abstract
Immuno-nanoparticles of poly(ᴅ,ʟ-lactide-co-2-methyl-2-carboxytrimethylene carbonate)-g-poly(ethylene glycol) (poly(LA-co-TMCC)-g-PEG) have been used to target breast cancer cells through the specific binding of trastuzumab antibodies to over-expressed human epidermal growth factor receptor 2 (HER2). Small interfering RNA (siRNA) and antisense oligonucleotides (AONs) disrupt the synthesis of select proteins. It is hypothesized that oligonucleotides coupled to polymeric immuno-nanoparticles can be used for gene silencing and specifically to target luciferase. The first objective is to demonstrate the capacity to create dual functional micelles with antibodies and oligonucleotides. The second objective is in vitro testing of the nanoparticle for gene silencing activity. Oligonucleotides are conjugated to the nanoparticle by sequential click reactions of Diels Alder chemistry and copper catalyzed azide-alkyne cycloadditions, respectively. A luciferase assay is used to quantify knockdown of luciferase levels in SKOV-3luc cells (HER2+, luc+). When used in conjunction with a targeted drug delivery vehicle, the nanoparticles provide selective interactions with SKOV-3luc cells.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".