AIMC Topic: Neurodegenerative Diseases

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msQSM: Morphology-based self-supervised deep learning for quantitative susceptibility mapping.

NeuroImage
Quantitative susceptibility mapping (QSM) has been applied to the measurement of iron deposition and the auxiliary diagnosis of neurodegenerative disease. There still exists a dipole inversion problem in QSM reconstruction. Recently, deep learning ap...

Explainable classification of Parkinson's disease using deep learning trained on a large multi-center database of T1-weighted MRI datasets.

NeuroImage. Clinical
INTRODUCTION: Parkinson's disease (PD) is a severe neurodegenerative disease that affects millions of people. Early diagnosis is important to facilitate prompt interventions to slow down disease progression. However, accurate PD diagnosis can be chal...

Bifunctional robots inducing targeted protein degradation.

European journal of medicinal chemistry
The gaining importance of Targeted Protein Degradation (TPD) and PROTACs (PROteolysis-TArgeting Chimeras) have drawn the scientific community's attention. PROTACs are considered bifunctional robots owing to their avidity for the protein of interest (...

Diagnosis of Alzheimer Disease and Tauopathies on Whole-Slide Histopathology Images Using a Weakly Supervised Deep Learning Algorithm.

Laboratory investigation; a journal of technical methods and pathology
Neuropathologic assessment during autopsy is the gold standard for diagnosing neurodegenerative disorders. Neurodegenerative conditions, such as Alzheimer disease (AD) neuropathological change, are a continuous process from normal aging rather than c...

Restoring arm function with a soft robotic wearable for individuals with amyotrophic lateral sclerosis.

Science translational medicine
Despite promising results in the rehabilitation field, it remains unclear whether upper limb robotic wearables, e.g., for people with physical impairments resulting from neurodegenerative disease, can be made portable and suitable for everyday use. W...

Neurodegenerative disease of the brain: a survey of interdisciplinary approaches.

Journal of the Royal Society, Interface
Neurodegenerative diseases of the brain pose a major and increasing global health challenge, with only limited progress made in developing effective therapies over the last decade. Interdisciplinary research is improving understanding of these diseas...

Multi-speed transformer network for neurodegenerative disease assessment and activity recognition.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Neurodegenerative diseases are the most frequent age-related diseases. This type of disease, if not discovered in the initial stage, will compromise the quality of life of the affected subject. Thus, a timely diagnosis is of...

Adopting transfer learning for neuroimaging: a comparative analysis with a custom 3D convolution neural network model.

BMC medical informatics and decision making
BACKGROUND: In recent years, neuroimaging with deep learning (DL) algorithms have made remarkable advances in the diagnosis of neurodegenerative disorders. However, applying DL in different medical domains is usually challenged by lack of labeled dat...

3-Dimensional Immunostaining and Automated Deep-Learning Based Analysis of Nerve Degeneration.

International journal of molecular sciences
Multiple sclerosis (MS) is an autoimmune and neurodegenerative disease driven by inflammation and demyelination in the brain, spinal cord, and optic nerve. Optic neuritis, characterized by inflammation and demyelination of the optic nerve, is a sympt...

Identifying an Optimal Neuroinflammation Treatment Using a Nanoligomer Discovery Engine.

ACS chemical neuroscience
Acute activation of innate immune response in the brain, or neuroinflammation, protects this vital organ from a range of external pathogens and promotes healing after traumatic brain injury. However, chronic neuroinflammation leading to the activatio...