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Nanocarriers for Brain Targeting
Published in Raj K. Keservani, Anil K. Sharma, Rajesh K. Kesharwani, Nanocarriers for Brain Targeting, 2019
B. A. Aderibigbe, I. A. Aderibigbe, A. P. I. Popoola
Schizophrenia is a genetic brain disorder that involves episodes of psychosis and altered brain function (Frackenburg, 2017; Hannon et al., 2016). A new study revealed that the disease is linked to pruning away of the parts of the brain (Sekar et al., 2016). According to WHO, 21 million people are living with the disease (Schizophrenia). The disease is treatable.
Drugs for Treatment of Neurological and Psychological Conditions
Published in Richard J. Sundberg, The Chemical Century, 2017
Antipsychotic drugs are those used in the more severe types of mental illness, including schizophrenia, manic-depressive bipolar disorder, psychotic depression, and some types of dementia. The term psychotic implies disordered thoughts and behavior and dissociation from reality, such as delusions and hallucinations.
A deep learning approach for diagnosing schizophrenic patients
Published in Journal of Experimental & Theoretical Artificial Intelligence, 2019
Srivathsan Srinivasagopalan, Justin Barry, Varadraj Gurupur, Sharma Thankachan
Magnetic Resonance Imaging (MRI) is a non-invasive imaging modality that returns valuable information about the physiology of the human brain, including size, shape, and tissue structure (Bois, Whalley, McIntosh, & Lawrie, 2015). MRI captures either structural or functional information. Functional MRI (fMRI) utilizes Blood-oxygen-level-dependent (BOLD) signals to capture an approximate measurement of activity between remote regions in the brain (Demirci & Calhoun, 2009). Structural MRI (sMRI) provides information on varying characteristics of brain tissue such as gray matter, white matter, and cerebrospinal fluid (Vemuri & Jack, 2010). The challenge with using sMRI data to diagnose based on structural changes brought on by SCZ is the overlap in structural change brought on by factors closely linked with SCZ such as alcoholism and anti-psychosis medication (Bois et al., 2015). Previous studies have shown that the combination of fMRI and sMRI data can be used in conjunction with a deep learning autoencoder to classify mental disorders including SCZ (Patel, Aggarwal, & Gupta, 2016; Silva et al., 2014; Zeng et al., 2018). In one such study (Patel et al., 2016), researchers used an autoencoder, four-layers deep in encoding and decoding, to learn the features of the input data, then used SVM to classify the data with 92% accuracy.
Advanced 4D-bioprinting technologies for brain tissue modeling and study
Published in International Journal of Smart and Nano Materials, 2019
Timothy J. Esworthy, Shida Miao, Se-Jun Lee, Xuan Zhou, Haitao Cui, Yi Y. Zuo, Lijie Grace Zhang
The process by which the cortical tissues of the brain enfold in order to form its wrinkled topology has been the subject of extensive study over the past several decades, yet the exact mechanisms which guide this process remain poorly understood. However, it has been found that the manner in which the cortical tissues fold has a critical effect on conventional neurological development. Therein, aberrant folding has been shown to be correlated with the presentation of certain neurological disorders, such as autism, schizophrenia, and some forms of psychosis [1–6]. Both theoretical and computation models have been proposed in an attempt to give a general description of the mechanics of neural tissue folding; however, a unified mechanism has yet to be fully accepted [7–14]. For a more comprehensive discussion of the various proposed computational models of cortical folding, the reader is referred to the works of Bayly et al. (2014) and Striedter et al. (2015) [15,16]. Recently, abiotic materials-based studies have challenged, verified, and extended existing theoretical models of cortical folding [17,18]. However, since these materials-based studies do not incorporate living cells, they largely cannot account for the potential unforeseen effects that cells and their physiological processes might have on the mechanics of tissue development such as stiffening and folding [14,19–21]. Therefore, these materials-based studies arguably best serve as a general description of the likely mechanisms which underlie cortical folding, rather than a fully comprehensive account of the phenomenon as a whole.
LooseLeaf, a Mobile-Based Application to Monitor Cannabis Use and Cannabis-Related Experiences for Youth at Clinical High-Risk for Psychosis: Development and User Acceptance Testing
Published in International Journal of Human–Computer Interaction, 2021
Olga Santesteban Echarri, GaHyung Kim, Preston Haffey, Jacky Tang, Jean Addington
Cannabis use is more prevalent in CHR individuals compared to healthy controls (Farris et al., 2020). There is evidence to suggest that there is a dose-response relation between cannabis use and psychosis risk (Kraan et al., 2016). CHR individuals with cannabis dependence are more likely to develop psychosis within a year (Kristensen & Cadenhead, 2007), and cannabis use in CHR individuals is associated with greater attenuated psychotic symptom severity (Carney et al., 2017). However, evidence is lacking with respect to frequency, dosage, and association with symptoms in CHR individuals. Most studies are limited by the reliance on retrospective information and are thus unable to obtain an accurate picture of the daily use of cannabis (Farris et al., 2020).