Inhibiting HER3 Hyperphosphorylation in HER2‐Overexpressing Breast Cancer through Multimodal Therapy with Branched Gold Nanoshells
Bibliographic record
Abstract
Abstract Treatment failure in breast cancers overexpressing human epidermal growth factor receptor 2 (HER2) is associated mainly to the upregulation of human epidermal growth factor receptor 3 (HER3) oncoprotein linked to chemoresitence. Therefore, to increase patient survival, here a multimodal theranostic nanoplatform targeting both HER2 and HER3 is developed. This consists of doxorubicin‐loaded branched gold nanoshells functionalized with the near‐infrared (NIR) fluorescent dye indocyanine green, a small interfering RNA (siRNA) against HER3, and the HER2‐specific antibody Transtuzumab, able to provide a combined therapeutic outcome (chemo‐ and photothermal activities, RNA silencing, and immune response). In vitro assays in HER2+/HER3+ SKBR‐3 breast cancer cells have shown an effective silencing of HER3 by the released siRNA and an inhibition of HER2 oncoproteins provided by Trastuzumab, along with a decrease of the serine/threonine protein kinase Akt (p‐AKT) typically associated with cell survival and proliferation, which helps to overcome doxorubicin chemoresistance. Conversely, adding the NIR light therapy, an increment in p‐AKT concentration is observed, although HER2/HER3 inhibitions are maintained for 72 h. Finally, in vivo studies in a tumor‐bearing mice model display a significant progressively decrease of the tumor volume after nanoparticle administration and subsequent NIR light irradiation, confirming the potential efficacy of the hybrid nanocarrier.
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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".