Results 291 to 300 of about 7,353,326 (373)

microRNAs as Biomarkers of Breast Cancer. [PDF]

open access: yesInt J Mol Sci
Jelski W, Okrasinska S, Mroczko B.
europepmc   +1 more source

Metal Nanoclusters for Cancer Imaging and Treatment

open access: yesAdvanced Functional Materials, EarlyView.
This review aims to provide a comprehensive summary and discussion of the core–shell design capabilities of metal nanoclusters (NCs) at the atomic level for cancer imaging and treatment. It offers essential insights into the design principles of metal NCs while also encouraging the exploration of other nanomaterials and their potential theranostic ...
Haiguang Zhu   +5 more
wiley   +1 more source

Genomic landscape of breast cancer in elderly patients. [PDF]

open access: yesNPJ Breast Cancer
Selenica P   +20 more
europepmc   +1 more source

Antibody‐Programmable Bimetallic Nanozymes for Transcriptional Blockade Therapy in HER2/ER‐Positive Breast Cancer

open access: yesAdvanced Functional Materials, EarlyView.
Zr‐Fe MOF@Ribociclib@Herceptin (ZFRH) efficiently targets/kills Human Epidermal Growth Factor Receptor 2/Estrogen Receptor‐positive (HER2/ER+) breast cancer cells. It combats tumors by: 1) Elevating ROS, altering redox balance; 2) Inhibiting transcription; 3) Inducing pyroptosis.
Hongkun Miao   +8 more
wiley   +1 more source

Role of Renin-Angiotensin System and Macrophages in Breast Cancer Microenvironment. [PDF]

open access: yesDiseases
Alamro AA   +5 more
europepmc   +1 more source

Biomaterial Strategies for Targeted Intracellular Delivery to Phagocytes

open access: yesAdvanced Functional Materials, EarlyView.
Phagocytes are essential to a functional immune system, and their behavior defines disease outcomes. Engineered particles offer a strategic opportunity to target phagocytes, harnessing inflammatory modulation in disease. By tuning features like size, shape, and surface, these systems can modulate immune responses and improve targeted treatment for a ...
Kaitlyn E. Woodworth   +2 more
wiley   +1 more source

Unleashing the Power of Machine Learning in Nanomedicine Formulation Development

open access: yesAdvanced Functional Materials, EarlyView.
A random forest machine learning model is able to make predictions on nanoparticle attributes of different nanomedicines (i.e. lipid nanoparticles, liposomes, or PLGA nanoparticles) based on microfluidic formulation parameters. Machine learning models are based on a database of nanoparticle formulations, and models are able to generate unique solutions
Thomas L. Moore   +7 more
wiley   +1 more source

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