Results 251 to 260 of about 1,587,717 (295)
ABSTRACT Background Neurodegeneration with brain iron accumulation (NBIA) comprises a genetically and clinically heterogeneous group of rare neurological disorders characterized particularly by iron accumulation in the basal ganglia. To date, 15 genes have been associated with NBIA.
Seda Susgun +95 more
wiley +1 more source
First development of a 100 K SNP array for small yellow croaker and its application in genomic selection for growth and disease resistance. [PDF]
Liu F +6 more
europepmc +1 more source
Decreased Serum 5‐HT: Clinical Correlates and Regulatory Role in NMJ of MG
ABSTRACT Objective Although 5‐Hydroxytryptamine (5‐HT) indirectly stimulates muscle contraction and participates in regulating Acetylcholine receptor (AChR) cluster homeostasis in cellular, animal, and clinical studies, evidence regarding its potential to modulate muscle contraction in myasthenia gravis (MG) remains limited.
Xinru Shen +18 more
wiley +1 more source
Quantitive disease resistance (QDR): The alternative to "all-or-nothing" strategy in plant immunity. [PDF]
Ng PQ.
europepmc +1 more source
ABSTRACT Aims This study aimed to explore the relationship between stress‐induced hyperglycemia (SIH) and in‐hospital medical complications in patients with acute stroke. Methods We enrolled 865,765 patients with acute stroke from the Chinese Stroke Center Alliance cohort.
Xintong Song +6 more
wiley +1 more source
Co-option of transcription factors drives evolution of quantitative disease resistance against a necrotrophic pathogen. [PDF]
Einspanier S +3 more
europepmc +1 more source
ABSTRACT Objectives To evaluate the utility of cerebrospinal fluid (CSF) biomarkers—matrix metalloproteinase‐9 (MMP‐9), tissue inhibitor of metalloproteinases‐1 (TIMP‐1), the MMP‐9/TIMP‐1 ratio, and osteopontin (OPN)—as indicators of blood–brain barrier (BBB) integrity and disease activity in people with relapsing–remitting multiple sclerosis (pwMS ...
Ivan Pavlovic +6 more
wiley +1 more source
Integrating multi-omics and machine learning for disease resistance prediction in legumes. [PDF]
Mohamedikbal S +4 more
europepmc +1 more source

