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A Brief Introduction to Virology
Published in Rae-Ellen W. Kavey, Allison B. Kavey, Viral Pandemics, 2020
Rae-Ellen W. Kavey, Allison B. Kavey
Between the 1930s and the 1950s, progressive understanding of viruses was inextricably linked to progress in imaging, genetics and molecular biology. Invention of the electron microscope (EM) in 1931 gave scientists the first images of viruses, which were seen to be extremely small, roughly 100–300 times smaller than bacteria, ranging from 20 to 400 nanometers in diameter.13 In 1935, examination of the infamous tobacco mosaic virus revealed the very simple basic structure of all viruses: a protective protein shell or capsid around a nucleic-acid core of either DNA or RNA, the viral genome, as depicted in Figure 1.2.14 This work showed that viruses were actually particles, not cells, with the capsid composed of protein subunits called capsomeres; the arrangement of capsomeres around the viral genome determined the shape of the virion. In some viruses, an outer layer was seen to envelope the capsid.15 As the smallest and simplest of known infectious agents, viruses were famously described as “a piece of bad news wrapped up in protein”.16
Big Data-Based Decision Support Systems for Hadron Therapy
Published in Manjit Dosanjh, Jacques Bernier, Advances in Particle Therapy, 2018
Yvonka van Wijk, Cary Oberije, Erik Roelofs, Philippe Lambin
Modern health care aspires to optimise personalised cancer therapy and faces many challenges in this respect. The increase in available treatment options and the diversity in patients prove incredibly problematic for individualised decision making. However, DSS developed using RLHC have the potential to bring us one step closer to realising that goal. An essential step that needs to be taken is the standardisation of data acquisition, including data concerning treatment, clinical features, imaging, genetics and outcome. Also, the clinical assessment of developed DSS is critical, as well as standardising the development of robust prediction models.
Prevention in the era of optimized patient flow, criminalization of serious neuropsychiatric disease, and anemic occupational and student health services
Published in John A. Liebert, William J. Birnes, Psychiatric Criminology, 2016
John A. Liebert, William J. Birnes
The epigenetics of schizophrenia is made more complex because of the likelihood of their interactions with many variables, such as age, determining being turned off or on. How the timed mix of those on and those off likely determines risk but not the emergence of schizophrenia. Additional work also found a significant correlation between autoimmune diseases and schizophrenia, further strengthening the genetic risk basis of this debilitating psychiatric disorder. The current belief is that genes nudge the person toward that first psychotic episode, but there may be environmental triggers that bring it out—thus manifesting the phenotype from the genotype. Do all children of parents who contract lung cancer get the disease? No. But do these children of parents with lung cancer who smoke have a higher probability of contracting the disease? Yes. And the same goes for heart disease and diabetes. Clearly, though genes likely predispose a person for a certain disease, they are not sufficient to cause it. Environmental factors, such as psychological trauma and loss, are necessary, although oftentimes clinically invisible from the patient's history. Few experts in the study of schizophrenia still subscribe to “sick family” or schizogenic mothering as sufficient causation for the profound deterioration and mental disorganization of this debilitating psychiatric disorder as subscribed to in departments of psychiatry during the 1960s and 1970s (Cardiff University 2014).Risk factors for psychosis have been identified at the level of single genes, and in relation to disorders caused by these. For example, the lifetime risk of psychosis in velo-cardio-facial syndrome is around 30%. In general, psychiatric risk is likely to be determined by contributions from many genes which are individually of small effect: genome-wide association studies are locating such genes including ZN804A which influences the risk of schizophrenia and CACNA1C which modulates the risk of bipolar disease. Variations in copy numbers are proving to be common risk factors for disorders previously regarded as distinct, for example, autism, schizophrenia and learning disability. The path leading from genotype to psychiatric phenotype will undoubtedly be a complex one in which gene–gene and gene–environment interactions will play a key role. Imaging genetics is identifying the effects of genetic variation on patterns of brain activity.(Zeman 2014)
Impact of ZNF804A rs1344706 or CACNA1C rs1006737 polymorphisms on cognition in patients with severe mental disorders: A systematic review and meta-analysis
Published in The World Journal of Biological Psychiatry, 2023
Ana Cecília Novaes de Oliveira Roldan, Luiz Carlos Cantanhede Fernandes Júnior, Carlos Eduardo Coral de Oliveira, Sandra Odebrecht Vargas Nunes
The imaging genetics approaches have indicated that effect sizes of polymorphisms might not be strong enough to be detected with sample sizes typically enrolled in imaging genetics cohorts. A meta-analysis reported that there is no significant spatial convergence of imaging genetics findings on the most frequently studied association, that is, that between COMT Val158Met genotype and working memory-related activations in SCZ patients (Nickl-Jockschat et al. 2015). Conversely, some studies reported that there is an association between changes in brain structure and CACNA1C and ZNF804 polymorphisms risk in SCZ and BD (Nenadic et al. 2015; Wei et al. 2015; Shonibare et al. 2021). Imaging genetics builds a bridge to understand the behavioural phenotypes, symptoms and cognition clinically observed, thus structural brain imaging genetics found that ZNF804A showed association with the anterior cingulate cortex and dorsolateral prefrontal cortex coupling during attention and cognitive control in SCZ and CACNA1C risk allele carriers showed increased activation in the bilateral hippocampus, which was in line in BD (Jiang et al. 2019).
Cortico-limbic connectivity as a possible biomarker for bipolar disorder: where are we now?
Published in Expert Review of Neurotherapeutics, 2019
Benedetta Vai, Carlotta Bertocchi, Francesco Benedetti
BD is highly heritable, and genetic influences explain 60–85% of the risk to develop BD [124,125]. Recently, there has been wide interest in candidate-gene and GWAS studies, leading to a rapid increase of risk genes for BD [25]. By integrating genetic and epigenetic variation and neuroimaging techniques, imaging genetics can help in elucidating the quantitative and mechanistic neural mechanisms affected by the risk genes [126]. This conceptual framework identifies brain function and structure as ‘intermediate phenotypes’ between the genetic vulnerability and the disorder, closer to biological pathways than phenotype itself, and thus widely affected by genetic risk variations [26]. Notably, insights provided by meta-analyses support the hereditability of neuroimaging abnormalities in BD detecting in first-degree relatives [77,127]. Imaging genetic studies of single risk variants in BD consistently found significant effects of loci near CACNA1Z, ANK3, 5-HTTLPR, NGR1, BDNF on corticolimbic structure and function (see [128]). In terms of connectivity, recent studies confirmed abnormalities in SC and FC during both RS and emotional or cognitive tasks execution in high genetic risk subject and first-degree relatives [78,110,129,130].