#Neuroscience #CognitivePhysiology #Neuropharmacology #Neuropathology #Alzheimers #sflorg
https://www.sflorg.com/2026/05/ns05182602.html
RE: https://fediscience.org/@eLife/116238133569931871
In mice, but very interesting. A commentary on the research paper:
"Microglia replacement by ER-Hoxb8 conditionally immortalized macrophages provides insight into Aicardi–Goutières syndrome neuropathology", Nemec et al. 2026
https://elifesciences.org/articles/102900
The Science Behind Chronic Traumatic Encephalopathy (CTE)
#BrainHealth #CTE #Neurodegeneration #TraumaticBrainInjury #TauProtein #NeuroPathology #BrainScience #ConcussionAwareness #Neuroscience #ProtectYourBrain
How to Build a Human Brain by Lynne Barker, 2024
How to Build a Human Brain takes a developmental approach to understanding brain structure and function. It guides readers through the evolution of the human brain, from its cellular building blocks, up to hind brain structures and functions, and through to neocortex and associated functions.
@bookstodon
#books
#nonfiction
#brain
#neuroscience
#neuroanatomy
#neuropathology
Monika Pytlarz @SanoScience work in collaboration with the #neurobiology group @NenckiInstitute on automatic #brain #tumor grading, showing insights on how the evolution of high grades is probably related to myelinating cells.
We investigated different #deeplearning architectures, and whole-slide classification versus individual cell neighbourhood analysis
https://link.springer.com/article/10.1007/s10278-024-01008-x
Gliomas are primary brain tumors that arise from neural stem cells, or glial precursors. Diagnosis of glioma is based on histological evaluation of pathological cell features and molecular markers. Gliomas are infiltrated by myeloid cells that accumulate preferentially in malignant tumors, and their abundance inversely correlates with survival, which is of interest for cancer immunotherapies. To avoid time-consuming and laborious manual examination of images, a deep learning approach for automatic multiclass classification of tumor grades was proposed. As an alternative way of investigating characteristics of brain tumor grades, we implemented a protocol for learning, discovering, and quantifying tumor microenvironment elements on our glioma dataset. Using only single-stained biopsies we derived characteristic differentiating tumor microenvironment phenotypic neighborhoods. The study was complicated by the small size of the available human leukocyte antigen stained on glioma tissue microarray dataset — 206 images of 5 classes — as well as imbalanced data distribution. This challenge was addressed by image augmentation for underrepresented classes. In practice, we considered two scenarios, a whole slide supervised learning classification, and an unsupervised cell-to-cell analysis looking for patterns of the microenvironment. In the supervised learning investigation, we evaluated 6 distinct model architectures. Experiments revealed that a DenseNet121 architecture surpasses the baseline’s accuracy by a significant margin of 9% for the test set, achieving a score of 69%, increasing accuracy in discerning challenging WHO grade 2 and 3 cases. All experiments have been carried out in a cross-validation manner. The tumor microenvironment analysis suggested an important role for myeloid cells and their accumulation in the context of characterizing glioma grades. Those promising approaches can be used as an additional diagnostic tool to improve assessment during intraoperative examination or subtyping tissues for treatment selection, potentially easing the workflow of pathologists and oncologists. Graphical Abstract
Bystander activated #CD8 #Tcells mediate #neuropathology during #viral infection via antigen-independent #cytotoxicity
Many viral infections are linked to the development of neurological disorders. Here, Balint et al use a mouse model of Zika virus infection to show that it is immune cells (NKG2D+CD8+ T cells) that cause infection-associated paralysis, rather than the virus itself.