Amrubicin encapsulated PLGA NPs inhibits the PI3K/AKT signaling pathway by activating PTEN and inducing apoptosis in TMZ-resistant Glioma
Bibliographic record
Abstract
Abstract Glioblastoma (GBM) remains a challenging malignancy due to its aggressive nature and the lack of efficacious therapeutic interventions. Nanotechnology-based approaches exhibit promise in GBM treatment; however, the successful translation of these strategies from preclinical models to clinical settings is hindered by inefficient nanoparticle clearance from vital organs. Addressing this concern, we investigated the therapeutic potential of amrubicin (AMR) encapsulated within poly (lactic-co-glycolic acid) nanoparticles (AMR-PLGA-NPs) in combating temozolomide (TMZ) resistant GBM. The study demonstrated that AMR-PLGA-NPs exerted a pronounced inhibitory effect on the cellular viability and migratory capacity of TMZ-resistant GBM cells. Furthermore, these nanoparticles exhibited considerable efficacy in downregulating the PI3K/AKT signaling pathway, thereby inducing apoptosis specifically in TMZ-resistant glioma cells and glioma stem-like cells through the activation of PTEN. Notably, in vivo experimentation revealed the ability of AMR-PLGA-NPs to traverse biological barriers within murine models. Collectively, these findings underscore the potential therapeutic utility of AMR-PLGA-NPs as a versatile nanoplatform for addressing the formidable challenges posed by GBM, particularly in mitigating drug resistance mechanisms. The study substantiates the stability and safety profile of AMR-PLGA-NPs, positioning them as a promising avenue for combating drug resistance in GBM therapeutics.
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".