Results 131 to 140 of about 2,891,562 (358)
Subcutaneous implantation of murine Panc02 pancreatic cancer cells depleted of sST2, a soluble decoy receptor for the proinflammatory interleukin‐33 (IL‐33), leads to a decreased number of GLUT4‐positive cancer‐associated adipocytes, reduced levels of the anti‐inflammatory molecule adiponectin, increased phosphorylation of IκBα, elevated Cxcl3 ...
Miho Akimoto+5 more
wiley +1 more source
Innovations and Experiments in Uses of Health Manpower. The Effect of Licensure Laws
Edward H. Forgotson, John L. Cook
openalex +2 more sources
Mitochondrial DNA disorders in neuromuscular diseases in diverse populations
Abstract Neuromuscular features are common in mitochondrial DNA (mtDNA) disorders. The genetic architecture of mtDNA disorders in diverse populations is poorly understood. We analysed mtDNA variants from whole‐exome sequencing data in neuromuscular patients from South Africa, Brazil, India, Turkey and Zambia. In 998 individuals, there were two definite
Fei Gao+34 more
wiley +1 more source
The nature and variety of innovation
What is our current understanding of innovation and how many types of innovation do we know? Broadly, innovation landscapes are characterized by well-established categories, such as product, process, organizational, and marketing innovation, explained ...
Mónica Edwards-Schachter
doaj
Wax & Gold: Tradition and Innovation in Ethiopian Culture. D
Allan Hoben
openalex +1 more source
ABSTRACT C‐truncating variants in the charged multivesicular body protein 2B (CHMP2B) gene are a rare cause of frontotemporal lobar degeneration (FTLD), previously identified only in Denmark, Belgium, and China. We report a novel CHMP2B splice‐site variant (c.35‐1G>A) associated with familial FTLD in Spain. The cases were two monozygotic male twins who
Sara Rubio‐Guerra+17 more
wiley +1 more source
Expanding Anthropology: A Narrative of Innovation in the A-level Curriculum
David A. Bennett
openalex +2 more sources
Precision‐Optimised Post‐Stroke Prognoses
ABSTRACT Background Current medicine cannot confidently predict who will recover from post‐stroke impairments. Researchers have sought to bridge this gap by treating the post‐stroke prognostic problem as a machine learning problem, reporting prediction error metrics across samples of patients whose outcomes are known.
Thomas M. H. Hope+4 more
wiley +1 more source