RETRACTED: Multi‐Stimuli Nanocomposite Therapeutic: Docetaxel Targeted Delivery and Synergies in Treatment of Human Breast Cancer Tumor
Post-publication record
Source: Retraction Watch, joined by DOI. OpenAlex records retraction as is_retracted, a boolean over a state space with at least four values, so it cannot express an expression of concern, a correction or a reinstatement; it reports them as false, which reads as “fine”.
Bibliographic record
Abstract
Abstract A versatile breast cancer‐targeting nanocomposite therapeutic combining docetaxel (DXL), polyvinyl alcohol (PVA) network for controlled release, and silica‐protected magnetic iron oxide nanoparticles (Fe 3 O 4 NPs) for targeted delivery and gold nanoparticles (AuNPs) for plasmonic photothermal therapy (PPTT) is presented in this work. First, the designed nanocomposite is magnetically directed for cancer‐targeted therapy confirmed by computerized tomography (CT) scans. Second, 10% DXL by mass is loaded into PVA, a pH and temperature responsive gel, for controlled release. Third, PPTT is confirmed with Au/Fe 3 O 4 /PVA‐10%DXL using a prototype circulation system and then for tumor treatment in vivo; Au/Fe 3 O 4 /PVA‐10%DXL is conveniently directed and the entrapped DXL is selectively released (≈96%) via the interaction of green and near‐infrared (NIR) light with the localized surface plasmon resonance of AuNPs. A 75% cell death is reported from in vitro studies with DXL doses as low as 20 µg mL −1 of Au/Fe 3 O 4 /PVA‐10%DXL, and a 70% tumor growth inhibition is demonstrated by in vivo experiments with the biosafety studies confirming minimal side effects to other organs. Overall, the developed Au/Fe 3 O 4 /PVA‐10%DXL has a strong potential to simultaneously enhance CT imaging contrast together with the targeted delivery of DXL.
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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".