molecular brain
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2021 ◽  
Author(s):  
Miya John ◽  
Padmashree Rao ◽  
Humaira Noor ◽  
Caroline Ford

AbstractThe cell surface receptor ROR1 is a therapeutic target of growing interest in oncology; however, its role in glioma has not been established thus far. This study analyzed associations between ROR1 mRNA expression and clinical outcomes, and histological and molecular subtypes in four independent glioma (grades II-IV) transcriptomic datasets (The Cancer Genome Atlas-GBMLGG, Chinese Glioma Genome Atlas, Repository for Molecular Brain Neoplasia, and GSE16011), encompassing a total of 2,388 cases. The data strongly suggests that ROR1 may be associated with poorer outcomes and more aggressive disease. Taken together, ROR1 should be further examined as a novel putative druggable target for glioma, a cancer that currently has very limited therapeutic options.


NeuroImage ◽  
2021 ◽  
Vol 231 ◽  
pp. 117819
Author(s):  
Valentin Ourry ◽  
Julie Gonneaud ◽  
Brigitte Landeau ◽  
Inès Moulinet ◽  
Edelweiss Touron ◽  
...  

2021 ◽  
Author(s):  
Tudor M. Ionescu ◽  
Mario Amend ◽  
Rakibul Hafiz ◽  
Bharat B. Biswal ◽  
Andreas Maurer ◽  
...  

AbstractThe molecular substrate of resting-state functional connectivity (rs-FC) remains poorly understood. We aimed to elucidate interactions of dopamine D2 receptor (D2R) and serotonin transporter (SERT) availabilities in main dopaminergic and serotonergic projection areas with the default-mode network (DMN) and two other resting-state networks (RSNs), the salience (SN) and sensorimotor networks (SMN). We performed simultaneous PET/fMRI scans in rats using [11C]raclopride and [11C]DASB to image D2R and SERT distributions, showing for the first time direct relationships between rs-FC and molecular properties of the rodent brain. We found negative associations between CPu D2R availability and all RSNs investigated. Strikingly, medial prefrontal SERT correlated both positively with anterior DMN rs-FC and negatively with rs-FC between the other networks, underlining serotonin’s intricate role in this region. By further elucidating the link between molecular brain properties and its network-level function, our data support future diagnostic and therapeutic strategies.TeaserSimultaneous PET/fMRI indicates direct associations between monoaminergic neurotransmission and brain functional networks.


2021 ◽  
Vol 13 ◽  
Author(s):  
Alba Castells-Sánchez ◽  
Francesca Roig-Coll ◽  
Rosalia Dacosta-Aguayo ◽  
Noemí Lamonja-Vicente ◽  
Angelika K. Sawicka ◽  
...  

Background: Although exercise is known to have a neuroprotective effect in aging, the mediators underlying the exercise-cognition association remain poorly understood. In this paper we aimed to study the molecular, brain, and behavioral changes related to physical activity and their potential role as mediators.Methods: We obtained demographic, physical activity outcomes [sportive physical activity and cardiorespiratory fitness (CRF)], plasma biomarkers (TNF-α, ICAM-1, HGF, SDF1-α, and BDNF), structural-MRI (brain volume areas), psychological and sleep health (mood, depressive and distress symptoms, and sleep quality), and multi-domain cognitive data from 115 adults aged 50–70 years. We conducted linear regression models and mediation analyses stratifying results by sex in a final sample of 104 individuals [65 women (age = 56.75 ± 4.96) and 39 men (age = 58.59 ± 5.86)].Results: Women engaging in greater amounts of exercising showed lower TNF-α levels and greater dorsolateral prefrontal cortex and temporal lobe volumes. Men engaging in greater amounts of exercise showed greater temporal lobe volumes. CRF levels were not related to any of the analyzed outcomes in women but in men higher CRF was associated with lower TNF-α, HGF and ventricle volumes, greater volume of temporal and parietal lobes and fewer depressive symptoms and better mood. In men, reduced TNF-α and HGF levels mediated brain and cognitive CRF-related benefits.Conclusion: Our results show that exercise is a promising approach for influencing inflammation and brain volume and also contributes to ongoing discussions about the physiological mediators for the association between CRF and cognition in men.


2020 ◽  
Vol 22 (Supplement_3) ◽  
pp. iii425-iii425
Author(s):  
Martin Sill ◽  
Felix Sahm ◽  
Daniel Schrimpf ◽  
David Capper ◽  
Stefan M Pfister ◽  
...  

Abstract Tumors of the CNS represent one of the most complex groups of human cancer, with a vast number of different entities occurring across a spectrum of ages and anatomic locations. This heterogeneity makes accurate diagnosis challenging, with the current gold standard relying on multiple subjective elements. We recently proposed a classification algorithm based on tumor DNA methylation profiling as an objective way to assign samples to over 80 distinct molecular classes. Here we present a substantial update to our machine learning-based algorithm, with more than 170 molecular classes now being represented amongst the 5,915 samples in our reference cohort. These new classes include further subclassification of known groups such as medulloblastoma and ependymoma, as well as multiple new molecular entities described here for the first time. A further improvement is the introduction of a more rationally layered output, making use of ‘families’ of closely-related molecular classes to improve the compatibility with the current WHO classification of CNS tumors. This approach is designed to increase the clinical relevance of the primary output, while also retaining the full information content from the random forest-driven classification. Benchmarking our new algorithm by cross-validation and on an independent validation cohort indicates a retention of the excellent accuracy of diagnosis (error-rate < 4%), with a significant improvement in the proportion of confidently classifiable tumors compared with our previous tool. We believe that this approach, freely accessible through an online web portal, has the potential to enhance diagnostic precision and thereby support clinical care for brain tumor patients.


NeuroImage ◽  
2020 ◽  
Vol 219 ◽  
pp. 117023 ◽  
Author(s):  
Sara Tremblay ◽  
Lauri Tuominen ◽  
Vanessa Zayed ◽  
Alvaro Pascual-Leone ◽  
Juho Joutsa

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