Equivariant 3D Conditional Diffusion Model for Molecular Linker Design

Equivariant 3D Conditional Diffusion Model for Molecular Linker Design

Chemistry, Medicinal Chemistry, Computational Chemistry, Information Science, Artificial Intelligence, Medicine, Pharmacy,
molecular linker design   drug design   generative model  

From early drug discovery researchers face a daunting challenge – to find drug-like candidate molecules among approximately 10^60 possible molecular structures. One successful solution is to start from smaller “fragment” molecules, a strategy known as fragment-based drug design (FBDD). In the FBDD process, the first step is to computationally scree...

Targeting TGFβ-activated kinase-1 activation in microglia reduces CAR T immune effector cell-associated neurotoxicity syndrome

Medicine, Rheumatology and Immunology, Neurology, Life Sciences, Immunology,
microglia   TGFβ-activated kinase-1   CAR T cells   neurotoxicity syndrome  

In this study, researchers explored the role of the TAK1 activation pathway in CAR T cell therapy-associated immune effector cell-associated neurotoxicity syndrome (ICANS). They established a mouse ICANS model and found that following the transfer of CAR19 T cells, cerebellar cells were activated, underwent morphological changes, and expressed more...

Targeted Activation of Ferroptosis in Colorectal Cancer via LGR4 Targeting Overcomes Acquired Drug Resistance

Chemistry, Medicinal Chemistry, Medicine, Oncology, Life Sciences, Cell Biology, Biochemistry,
Colorectal Cancer   Ferroptosis   Acquired Drug Resistance   LGR4 Targeting   WNT Signaling Pathway  

Overcoming Acquired Resistance in Colorectal Cancer by Targeting LGR4 Research Background: Acquired drug resistance is a major obstacle in cancer treatment and a leading cause of cancer-related deaths. However, the mechanisms of resistance are diverse, and how to specifically target resistant cancer cells remains a significant clinical challenge. A...

Identification of a clinically efficacious CAR T cell subset in diffuse large B cell lymphoma by dynamic multidimensional single-cell profiling

Identification of a clinically efficacious CAR T cell subset in diffuse large B cell lymphoma by dynamic multidimensional single-cell profiling

Medicine, Rheumatology and Immunology, Oncology, Life Sciences, Immunology,
CAR T cells   Diffuse large B cell lymphoma   Single-cell analysis   Cell function   Clinical response  

Utilizing Dynamic Single-Cell Analysis to Discover a Clinically Effective Chimeric Antigen Receptor T-Cell Subset for Treating Diffuse Large B-Cell Lymphoma Research Background Chimeric antigen receptor (CAR) T-cell therapy has been proven to be an effective treatment for B-cell malignancies. However, it remains challenging to predict individual cl...

Temporal Changes in Treatment and Late Mortality and Morbidity in Adult Survivors of Childhood Glioma: A Report from the Childhood Cancer Survivor Study

Temporal Changes in Treatment and Late Mortality and Morbidity in Adult Survivors of Childhood Glioma: A Report from the Childhood Cancer Survivor Study

Medicine, Pediatrics, Neurosurgery, Oncology,
childhood glioma   late mortality   morbidity   radiation therapy   chemotherapy  

This is a long-term outcome study on survivors of pediatric glioma. The main purpose of the study was to evaluate the impact of changes in treatment approaches for pediatric glioma over the past few decades on long-term mortality, chronic health conditions, and subsequent tumor risk among survivors. Background: In the past, treatment for pediatric ...

Hyperbolic secant representation of the logistic function: Application to probabilistic multiple instance learning for CT intracranial hemorrhage detection

Information Science, Artificial Intelligence, Medicine, Neurology, Medical Imaging, Life Sciences, Bioengineering,
multiple instance learning   gaussian processes   bayesian variational inference   intracranial hemorrhage detection  

There has long been a problem of “weak supervision” in the field of artificial intelligence, where only part of the labels are observable in the training data, while the remaining labels are unknown. Multiple Instance Learning (MIL) is a paradigm to address this issue. In MIL, the training data is divided into multiple “bags”, each containing multi...