Results 191 to 200 of about 309,378 (343)

T‐Cell Exhaustion in the Tumor Microenvironment: Subcellular Dysfunction, Pan‐Cancer Characteristics, and Therapeutic Interventions

open access: yesAdvanced Science, EarlyView.
This study elucidates the mechanisms of subcellular multidimensional collapse in exhausted T cells. By specifically targeting the nucleus, mitochondria, and endoplasmic reticulum, strategic interventions can effectively remodel the compromised organelle network. This integrated approach drives comprehensive T cell resuscitation, ultimately establishing
Mingxing Wang   +9 more
wiley   +1 more source

A centralized interactive tool for genomic data on heat shock protein 90 in Fagaceae species. [PDF]

open access: yesBMC Genomics
Postigo-Luque R   +4 more
europepmc   +1 more source

A Plug‐and‐Play Platform for Customizing Multivalent Degraders and Degrader‐Drug Conjugates

open access: yesAdvanced Science, EarlyView.
Membrane proteins remain challenging targets for conventional TPD approaches. Here, the authors develop UPTAB, a modular platform leveraging ultrahigh‐affinity orthogonal Im/CL protein pairs for lysosomal degradation of membrane proteins. Mono‐targeted (Type‐I), dual‐targeted (Type‐II), and tri‐targeted (Type‐III) UPTABs enable simultaneous degradation
Mengqing Zhao   +7 more
wiley   +1 more source

Figure 3 in The heat shock protein 90 of Toxoplasma gondii is essential for invasion of host cells and tachyzoite growth

open access: green, 2017
Hongchao Sun   +6 more
openalex   +1 more source

Hydroxamic Acid Analogue Histone Deacetylase Inhibitors Attenuate Estrogen Receptor-α Levels and Transcriptional Activity: A Result of Hyperacetylation and Inhibition of Chaperone Function of Heat Shock Protein 90 [PDF]

open access: bronze, 2007
Warren Fiskus   +10 more
openalex   +1 more source

Machine Learning Interatomic Potentials for Energy Materials: Architectures, Training Strategies, and Applications

open access: yesAdvanced Energy Materials, EarlyView.
Machine learning interatomic potentials bridge quantum accuracy and computational efficiency for materials discovery. Architectures from Gaussian process regression to equivariant graph neural networks, training strategies including active learning and foundation models, and applications in solid‐state electrolytes, batteries, electrocatalysts ...
In Kee Park   +19 more
wiley   +1 more source

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