CTL–doxorubicin (DOX)–gold complex nanoparticles (DOX–AuGCs): from synthesis to enhancement of therapeutic effect on liver cancer model
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
In this work, we bring back a rapid way to conceive doxorubicin (DOX) hybrid gold nanoparticles, in which DOX and Au(iii) ions were complexed with a hydrochloride-lactose-modified chitosan, named CTL and dicarboxylic acid-terminated polyethylene-glycol (PEG), leading to hybrid polymer-sugar-metal nanoparticles (DOX-AuGSs). All formulations were assessed by spectroscopic techniques (Raman and UV-Vis) and transmission electron microscopy (TEM). To estimate the therapeutic effect of DOX-AuGSs in liver cancer, murine HepG2 cells were used to induce a hepatic carcinoma model in nude mice. The survival time of the tumor-bearing mice, body weight and tumor volume were measured and recorded. The cytokines were used to detect the serum inflammatory factors, and the blood cell analyzer was used to determine the blood cell content of different groups of nude mice. The outcomes demonstrate that DOX-AuGCs significantly suppressed the tumor growth derived from human HepG2 injection and reduce the tumor index without affecting the body weight of mice. Moreover, DOX-AuGCs significantly reduced the serum levels of cytokines IL-6, TNF-α and IL-12 P70. Finally, a histological analysis of the heart tissue sections indicated that DOX-AuGCs significantly reduce the chronic myocardial toxicity of DOX during the period of treatment.
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) | 0.000 | 0.000 |
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".