Results 161 to 170 of about 28,711 (238)
Correlating milk cytokines and somatic cell counts to intramammary infections in dairy sheep. [PDF]
Franzoni G +13 more
europepmc +1 more source
Controlled syntheses of lanthanide coordination polymers based on the dihydroxybenzoquinone (DHBQ) organic linker afforded large single crystals of Ln‐DHBQ CPs (Ln = Yb, Nd). A novel structural variant of Yb‐DHBQ is identified by means of single crystal diffraction analysis.
Marina I. Schönherr +7 more
wiley +1 more source
Functional analysis and identification of miRNAs associated with lipid metabolism from milk-derived exosomes. [PDF]
Lu X +6 more
europepmc +1 more source
application/pdf This observational study aimed to use somatic cell score (SCS) and differential somatic cell count (DSCC), the combined proportion of polymorphonuclear leukocytes and lymphocytes in somatic cells, to investigate how mastitis affected milk production.
openaire
Exploring the photocatalytic reverse water–gas shift (RWGS) reaction on doped SrTiO3 nanoparticle films, reveals that normalizing catalytic rates by the catalyst's specific surface area (SSA) disentangled surface area effects from the catalyst's intrinsic material properties.
Dikshita Bhattacharyya +6 more
wiley +1 more source
Molecular approach to cytopenia and bone marrow failure. [PDF]
Jain J, Borate U.
europepmc +1 more source
This study examines how pore shape and manufacturing‐induced deviations affect the mechanical properties of 3D‐printed lattice materials with constant porosity. Combining µ‐CT analysis, FEM, and compression testing, the authors show that structural imperfections reduce stiffness and strength, while bulk material inhomogeneities probably enhance ...
Oliver Walker +5 more
wiley +1 more source
First Case of Neuroblastoma-Like Somatic-Type Malignancy Arising in the Teratomatous Component of a Mixed Non-Seminomatous Testicular Germ Cell Tumor in an Adult. [PDF]
Ziade K +4 more
europepmc +1 more source
Unleashing the Power of Machine Learning in Nanomedicine Formulation Development
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
A Review of CEBPA's Role in Hereditary Leukemia. [PDF]
Rakiewicz T, Palmisiano N.
europepmc +1 more source

