Targeting Signaling Pathways of Hyaluronic Acid and Integrin Receptors by Synergistic Combination Nanocomposites Inhibits Systemic Metastases and Primary Triple Negative Breast Cancer
Bibliographic record
Abstract
Abstract Triple negative breast cancer (TNBC) has a poor prognosis due to its aggressive nature, high incidence of distant metastasis, and lack of targets for effective therapy. Therefore, a novel multifunctional biopolymer‐anticancer drug combination nanomedicine is designed for the prevention of spontaneous metastasis while treating primary TNBC. Oligomeric hyaluronic acid (oHA) and doxorubicin (DOX) at a synergistic ratio against TNBC cells are co‐loaded in a polymer‐lipid hybrid nanoparticle (PLN) which is then functionalized with an internalizing cyclic peptide iRGD (iRGD‐DOX‐oHA‐PLN). iRGD conjugation enhances cellular uptake and cytotoxicity in vitro and nanoparticle (NP) accumulation in human TNBC tumors that overexpress integrins. NP‐delivered oHA inhibits cell migration and invasion in vitro via the intracellular release of oHA that interacts with the receptor for hyaluronan mediated motility and downregulates the phospho‐extracellular signal‐regulated kinase (p‐ERK) signaling pathway. Intravenously injected iRGD‐DOX‐oHA‐PLN significantly inhibits the growth of primary TNBC tumors in a mouse model and prevents spontaneous metastasis to the lungs and lymph nodes, superior to free solutions of DOX, oHA, or both and NP formulations loaded with DOX or oHA. These results suggest that iRGD‐DOX‐oHA‐PLN can be a promising bioactive polymer‐drug combination nanomedicine for the treatment of TNBC and prevention of its spontaneous metastasis.
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) | 0.001 | 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".