Results 91 to 100 of about 6,484,256 (294)
GENETIC FACTORS AFFECTING MATING TYPE FREQUENCIES IN VARIETY 1 OF TETRAHYMENA PYRIFORMIS [PDF]
David L. Nanney
openalex +1 more source
TRAF2 binds to TIFA via a novel motif and contributes to its autophagic degradation
TRAF family members couple receptor signalling complexes to downstream outputs, but how they interact with these complexes is not always clear. Here, we show that during ADP‐heptose signalling, TRAF2 binding to TIFA requires two short sequence motifs in the C‐terminal tail of TIFA, which are distinct from the TRAF6 binding motif.
Tom Snelling+4 more
wiley +1 more source
Etiology of Type 1 Diabetes [PDF]
Recent genetic mapping and gene-phenotype studies have revealed the genetic architecture of type 1 diabetes. At least ten genes so far can be singled out as strong causal candidates. The known functions of these genes indicate the primary etiological pathways of this disease, including HLA class II and I molecules binding to preproinsulin peptides and ...
openaire +3 more sources
Exposure to common noxious agents (1), including allergens, pollutants, and micro‐nanoplastics, can cause epithelial barrier damage (2) in our body's protective linings. This may trigger an immune response to our microbiome (3). The epithelial barrier theory explains how this process can lead to chronic noncommunicable diseases (4) affecting organs ...
Can Zeyneloglu+17 more
wiley +1 more source
Contiguous Hypergeometric Functions of the Type 3F2(1) [PDF]
W. N. Bailey
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Three‐dimensional (3D) biological systems have become key tools in lymphoma research, offering reliable in vitro and ex vivo platforms to explore pathogenesis and support precision medicine. This review highlights current 3D non‐Hodgkin lymphoma models, detailing their features, advantages, and limitations, and provides a broad perspective on future ...
Carla Faria+3 more
wiley +1 more source
STAPHYLOCOCCAL INFECTIONS AT SINGAPORE<xref ref-type="fn" rid="fn1"><sup>1</sup></xref> [PDF]
WILLIAM HUGHES
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From omics to AI—mapping the pathogenic pathways in type 2 diabetes
Integrating multi‐omics data with AI‐based modelling (unsupervised and supervised machine learning) identify optimal patient clusters, informing AI‐driven accurate risk stratification. Digital twins simulate individual trajectories in real time, guiding precision medicine by matching patients to targeted therapies.
Siobhán O'Sullivan+2 more
wiley +1 more source