Results 151 to 160 of about 1,239,936 (299)
Digital Twin Pathology of Skin Cancer. [PDF]
Cockerell CJ, Goldust M.
europepmc +1 more source
Bioprinting Organs—Science or Fiction?—A Review From Students to Students
Bioprinting artificial organs has the potential to revolutionize the medical field. This is a comprehensive review of the bioprinting workflow delving into the latest advancements in bioinks, materials and bioprinting techniques, exploring the critical stages of tissue maturation and functionality.
Nicoletta Murenu +18 more
wiley +1 more source
Application of transcriptomics techniques in skin cancer. [PDF]
Ma Y, Wang F, Xu S.
europepmc +1 more source
Despite significant efforts in developing novel biomaterials to regenerate tissue, only a few of them have successfully reached clinical use. It has become clear that the next generation of biomaterials must be multifunctional. Smart biomaterials can respond to environmental or external stimuli, interact in a spatial‐temporal manner, and trigger ...
Sonya Ghanavati +12 more
wiley +1 more source
Biomaterials for Improving Skin Penetration in Treatment of Skin Cancer. [PDF]
Secci D +3 more
europepmc +1 more source
Bioengineering tumor‐stroma pancreatic cancer models with potential to emulate the native immunosuppressive tumor microenvironment. Cancer‐on‐a‐bead models exhibited higher relevance for screening immunotherapies and modulating the protumoral PDAC microenvironment. ABSTRACT Pancreatic cancer is notably resistant to treatment, primarily due to its dense
Maria V. Monteiro +4 more
wiley +1 more source
Skin cancer risk in hereditary mixed cancer syndromes. [PDF]
Nikkola V +5 more
europepmc +1 more source
Microphysiological Systems of Lymphatics and Immune Organs
This review surveys recent progress in engineering lymphatic microenvironments and immune organoids within microphysiological systems, emphasizing innovative strategies to recreate the biochemical and biophysical complexity of native lymphatic tissues.
Ishita Jain +2 more
wiley +1 more source
Multimodal deep learning ensemble framework for skin cancer detection. [PDF]
Saeed MA, Afify YM, Badr NL, Helal NA.
europepmc +1 more source

