Disease-driven domain generalization for neuroimaging-based assessment of Alzheimer's disease.

Development of deep learning models to evaluate structural brain changes caused by cognitive impairment in MRI scans holds significant translational value. The efficacy of these models often encounters challenges due to variabilities arising from different data generation protocols, imaging equipment, radiological artifacts, and shifts in demographic distributions. Domain generalization (DG) techniques show promise in addressing these challenges by enabling the model to learn from one or more source domains and apply this knowledge to new, unseen target domains.

Disease-driven domain generalization for neuroimaging-based assessment of Alzheimer's disease.

Development of deep learning models to assess the degree of cognitive impairment on magnetic resonance imaging (MRI) scans has high translational significance. Performance of such models is often affected by potential variabilities stemming from independent protocols for data generation, imaging equipment, radiology artifacts, and demographic distributional shifts. Domain generalization (DG) frameworks have the potential to overcome these issues by learning signal from one or more source domains that can be transferable to unseen target domains.

Neural Differentiation of Induced Pluripotent Stem Cells for a Xenogeneic Material-Free 3D Neurological Disease Model Neurulation from Pluripotent Cells Using a Human Hydrogel.

Alzheimer's Disease (AD) is characterized by synapse and neuronal loss and the accumulation of neurofibrillary tangles and Amyloid β plaques. Despite significant research efforts to understand the late stages of the disease, its etiology remains largely unknown. This is in part because of the imprecise AD models in current use.

Methods and Protocols for Using Extracellular Vesicles as Delivery Vehicles in Neuronal Research.

Extracellular vesicles (EVs) transport nucleic acids, proteins, and lipid molecules for intercellular communication. The biomolecular cargo from EVs can modify the recipient cell genetically, physiologically, and pathologically. This innate ability of EVs can be harnessed to deliver the cargo of interest to a specific organ or a cell type.

Coculture with Neural Stem Cells May Shift the Transcription Profile of Glioblastoma Multiforme towards Cancer-Specific Stemness.

Glioblastoma multiforme (GBM) possesses a small but significant population of cancer stem cells (CSCs) thought to play a role in its invasiveness, recurrence, and metastasis. The CSCs display transcriptional profiles for multipotency, self-renewal, tumorigenesis, and therapy resistance. There are two possible theories regarding the origin of CSCs in the context of neural stem cells (NSCs); i.

Exposure to a Pathological Condition May Be Required for the Cells to Secrete Exosomes Containing mtDNA Aberration.

Exosomes, nanovesicles secreted by all cells, carry out intercellular communication by transmitting biologically active cargo comprising DNA, RNA, and proteins. These biomolecules reflect the status of their parent cells and can be altered by pathological conditions. Therefore, the researchers have been investigating differential sequences and quantities of DNA associated with exosomes as valuable biomarkers of diseases.