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Central Nervous System

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Code-free machine learning for classification of central nervous system histopathology images.

Journal of neuropathology and experimental neurology
Machine learning (ML), an application of artificial intelligence, is currently transforming the analysis of biomedical data and specifically of biomedical images including histopathology. The promises of this technology contrast, however, with its cu...

Deep learning-based overall survival prediction model in patients with rare cancer: a case study for primary central nervous system lymphoma.

International journal of computer assisted radiology and surgery
PURPOSE: Primary central nervous system lymphoma (PCNSL) is a rare, aggressive form of extranodal non-Hodgkin lymphoma. To predict the overall survival (OS) in advance is of utmost importance as it has the potential to aid clinical decision-making. T...

A comprehensive dataset of annotated brain metastasis MR images with clinical and radiomic data.

Scientific data
Brain metastasis (BM) is one of the main complications of many cancers, and the most frequent malignancy of the central nervous system. Imaging studies of BMs are routinely used for diagnosis of disease, treatment planning and follow-up. Artificial I...

Neuroimaging Scoring Tools to Differentiate Inflammatory Central Nervous System Small-Vessel Vasculitis: A Need for Artificial Intelligence/Machine Learning?-A Scoping Review.

Tomography (Ann Arbor, Mich.)
Neuroimaging has a key role in identifying small-vessel vasculitis from common diseases it mimics, such as multiple sclerosis. Oftentimes, a multitude of these conditions present similarly, and thus diagnosis is difficult. To date, there is no standa...

Inference of network connectivity from temporally binned spike trains.

Journal of neuroscience methods
BACKGROUND: Processing neural activity to reconstruct network connectivity is a central focus of neuroscience, yet the spatiotemporal requisites of biological nervous systems are challenging for current neuronal sensing modalities. Consequently, meth...

Integrating large-scale single-cell RNA sequencing in central nervous system disease using self-supervised contrastive learning.

Communications biology
The central nervous system (CNS) comprises a diverse range of brain cell types with distinct functions and gene expression profiles. Although single-cell RNA sequencing (scRNA-seq) provides new insights into the brain cell atlases, integrating large-...

Identification of Immune-Related Biomarkers of Schizophrenia in the Central Nervous System Using Bioinformatic Methods and Machine Learning Algorithms.

Molecular neurobiology
Schizophrenia is a disastrous mental disorder. Identification of diagnostic biomarkers and therapeutic targets is of significant importance. In this study, five datasets of schizophrenia post-mortem prefrontal cortex samples were downloaded from the ...

During haptic communication, the central nervous system compensates distinctly for delay and noise.

PLoS computational biology
Physically connected humans have been shown to exploit the exchange of haptic forces and tactile information to improve their performance in joint action tasks. As human interactions are increasingly mediated through robots and networks it is importa...

Unveiling CNS cell morphology with deep learning: A gateway to anti-inflammatory compound screening.

PloS one
Deciphering the complex relationships between cellular morphology and phenotypic manifestations is crucial for understanding cell behavior, particularly in the context of neuropathological states. Despite its importance, the application of advanced i...

Artificial intelligence in central-peripheral interaction organ crosstalk: the future of drug discovery and clinical trials.

Pharmacological research
Drug discovery before the 20th century often focused on single genes, molecules, cells, or organs, failing to capture the complexity of biological systems. The emergence of protein-protein interaction network studies in 2001 marked a turning point an...