Category Ranking

98%

Total Visits

921

Avg Visit Duration

2 minutes

Citations

20

Article Abstract

Chemotherapy is widely used for cancer therapy; however, its efficacy is limited due to poor targeting specificity and severe side effects. Currently, the next generations of delivery systems with multitasking potential have attracted significant attention for cancer therapy. This study reports on the design and synthesis of a multifunctional nanoplatform based on niosomes (NIO) coloaded with paclitaxel (PTX), a chemotherapeutic drug commonly used to treat breast cancer, and sodium oxamate (SO), a glycolytic inhibitor to enhance the cytotoxicity of anticancer drug, along with quantum dots (QD) as bioimaging agents, and hyaluronic acid (HA) coating for active targeting. HN@QPS nanoparticles with a size of ∼150 nm and a surface charge of -39.9 mV with more than 90% EE for PTX were synthesized. Codelivery of SO with PTX remarkably boosted the anticancer effects of PTX, achieving IC values of 1-5 and >0.5 ppm for HN@QP and HN@QPS, respectively. Further, HN@QPS treatment enhanced the apoptosis rate by more than 70% in MCF-7 breast cancer cells without significant cytotoxicity on HHF-2 normal cells. Also, quantification of mitochondrial fluorescence showed efficient toxicity against MCF-7 cells. Moreover, the cellular uptake evaluation demonstrated an improved uptake of HN@Q in MCF-7 cells. Taken together, this preliminary research indicated the potential of HN@QPS as an efficient targeted-dual drug delivery nanotheranostic against breast cancer cells.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10918778PMC
http://dx.doi.org/10.1021/acsomega.3c09782DOI Listing

Publication Analysis

Top Keywords

breast cancer
16
cancer therapy
8
cancer cells
8
mcf-7 cells
8
cancer
6
cells
5
hyaluronic acid-targeted
4
acid-targeted niosomes
4
niosomes effective
4
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