Category Ranking

98%

Total Visits

921

Avg Visit Duration

2 minutes

Citations

20

Article Abstract

Breast cancer remains a major global health challenge. Great efforts have been made to develop advanced theranostic platforms for breast cancer. A critical limitation of the current approaches is insufficient tumor accumulation. To address this, we developed AS1411 aptamer-functionalized liposomes coloaded with indocyanine green (ICG) and chelerythrine (I/C@Lipo-A) for enhanced breast cancer theranostics. ICG serves as a dual-functional agent, enabling deep-tissue fluorescence (FL) imaging in the second near-infrared (NIR-II, 1000-1700 nm) window and photothermal therapy (PTT). Leveraging ICG's superior tissue penetration and reduced scattering in the NIR-II window, this system significantly improves tumor delineation and therapeutic precision. Meanwhile, chelerythrine (CHE), a natural benzophenanthridine alkaloid with potent anticancer activity, induces mitochondrial dysfunction, apoptosis, and reactive oxygen species (ROS) generation. However, their clinical application has been hindered by poor solubility and bioavailability. By coencapsulating ICG and CHE within tumor-targeting liposomes, we synergistically enhanced CHE's anticancer efficacy while enabling NIR-II FL imaging-guided PTT. This combinatorial strategy not only suppressed primary tumor growth but also inhibited distant metastases, significantly improving survival rates. Our work presents a robust, multifunctional platform for precision breast cancer theranostics, overcoming key barriers in drug delivery and imaging-guided therapy.

Download full-text PDF

Source
http://dx.doi.org/10.1021/acsabm.5c00779DOI Listing

Publication Analysis

Top Keywords

breast cancer
20
cancer theranostics
8
breast
5
cancer
5
nir-ii
4
nir-ii fluorescence
4
fluorescence imaging-guided
4
imaging-guided combinatorial
4
combinatorial therapy
4
therapy breast
4

Similar Publications

Purpose: Breast cancer remains a significant public health challenge globally, as well as in India, where it is the most frequently diagnosed cancer in females. Significant disparities in incidence, mortality, and access to health care across India's sociodemographically diverse population highlight the need for increased awareness, policy reform, and research.

Design: This review consolidates data from national cancer registries, global cancer databases, and institutional findings from a tertiary care center to examine the epidemiology, clinical challenges, and management gaps specific to India.

View Article and Find Full Text PDF

ObjectiveTo study the implications of implementing artificial intelligence (AI) as a decision support tool in the Norwegian breast cancer screening program concerning cost-effectiveness and time savings for radiologists.MethodsIn a decision tree model using recent data from AI vendors and the Cancer Registry of Norway, and assuming equal effectiveness of radiologists plus AI compared to standard practice, we simulated costs, effects and radiologist person-years over the next 20 years under different scenarios: 1) Assuming a €1 additional running cost of AI instead of the €3 assumed in the base case, 2) varying the AI-score thresholds for single vs. double readings, 3) varying the consensus and recall rates, and 4) reductions in the interval cancer rate compared to standard practice.

View Article and Find Full Text PDF

Background: Among childhood cancer survivors, germline rare variants in autosomal dominant cancer susceptibility genes (AD CSGs) could increase subsequent neoplasm (SNs) risks, but risks for rarer SNs and by age at onset are not well understood.

Methods: We pooled the Childhood Cancer Survivor Study and St Jude Lifetime Cohort (median follow-up = 29.7 years, range 7.

View Article and Find Full Text PDF

MicroRNAs (miRNAs) are critical regulators of gene expression in cancer biology, yet their spatial dynamics within tumor microenvironments (TMEs) remain underexplored due to technical limitations in current spatial transcriptomics (ST) technologies. To address this gap, we present STmiR, a novel XGBoost-based framework for spatially resolved miRNA activity prediction. STmiR integrates bulk RNA-seq data (TCGA and CCLE) with spatial transcriptomics profiles to model nonlinear miRNA-mRNA interactions, achieving high predictive accuracy (Spearman's ρ > 0.

View Article and Find Full Text PDF

Objective: This study employs integrated network toxicology and molecular docking to investigate the molecular basis underlying 4-nonylphenol (4-NP)-mediated enhancement of breast cancer susceptibility.

Methods: We integrated data from multiple databases, including ChEMBL, STITCH, Swiss Target Prediction, GeneCards, OMIM and TTD. Core compound-disease-associated target genes were identified through Protein-Protein Interaction (PPI) network analysis.

View Article and Find Full Text PDF