Publications by authors named "A Nazeri"
Background: Glioblastoma is the most aggressive adult primary brain cancer, characterized by significant heterogeneity, posing challenges for patient management, treatment planning, and clinical trial stratification.
Methods: We developed a highly reproducible, personalized prognostication and clinical subgrouping system using machine learning (ML) on routine clinical data, MRI, and molecular measures from 2,838 demographically diverse patients across 22 institutions and 3 continents. Patients were stratified into favorable, intermediate, and poor prognostic subgroups (I, II, III) using Kaplan-Meier analysis (Cox proportional model and hazard ratios [HR]).
Article Synopsis
- Tinnitus is highly prevalent and often linked with increased anxiety, making it important to understand the biological factors behind this connection, specifically the roles of proteins like Neuroligin 2 (NLGN2) and Brain-Derived Neurotrophic Factor (BDNF).
- This study is the first to explore changes in protein expression in the amygdala, which is crucial for anxiety, in relation to tinnitus-induced anxiety in rats.
- Results showed that rats with induced tinnitus had higher anxiety levels and increased NLGN2 alongside decreased BDNF in the amygdala compared to controls, highlighting these proteins as potential targets for tinnitus treatment.
Article Synopsis
- This review looks at how augmented reality (AR) and virtual reality (VR) can help people who have trouble hearing communicate better.
- Researchers found 22 studies focused mainly on nonverbal ways to communicate, like using sign language and visual signals.
- The studies suggest that AR and VR can help teach sign language and improve thinking and speech skills for those who are hard of hearing, but more research is needed to explore their full potential.
Background: Cerebrospinal fluid (CSF) circulation is essential in removing metabolic wastes from the brain and is an integral component of the glymphatic system. Abnormal CSF circulation is implicated in neurodegenerative diseases. Low b-value magnetic resonance imaging quantifies the variance of CSF motion, or pseudodiffusivity.
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- A new deep learning model called AmyloidPETNet was developed to classify brain PET scans as amyloid positive or negative, aiming to reduce reliance on radiologist expertise and costly MRI computations.
- The model was trained on 1538 PET scans and tested on various independent data sets, achieving an impressive area under the receiver operating characteristic curve (AUC) of up to 0.98, indicating strong performance across different tracers.
- Comparative analyses showed fair to good agreement between the model's classifications and visual assessments made by physicians, providing promising evidence for the model's clinical utility.