Improved accuracy when screening for human growth disorders by likelihood ratios

Since ancient times plant and animal tissues have been used as medicines. In the 20th century growth hormone as a purified extraction from human pituitaries was still used to treat growth disorders. Since the genetic engineering of host cells became possible, a new generation of medicines obtained using recombinant DNA (rDNA) technology has emerged. These medicines have been named ‘biopharmaceuticals’. The first biopharmaceutical growth hormones were patented in the 1980s, so already over two decades of clinical experience support the development of a new, off-patent growth hormone preparation obtained by rDNA technology. The European Medicines Agency (EMEA) has put in place a centralised procedure for the approval of new biopharmaceuticals. This procedure includes testing comparability with a reference product and demands post-approval pharmacovigilance. Omnitrope® was the first off-patent recombinant human growth hormone (rhGH) approved on the basis of the biosimilar pathway; it underwent a very demanding approval procedure in 2006 and is now used for several indications in Europe, the US, Canada, Japan, Australia and other countries where it has received marketing approval.

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Effects of Growth Hormone and Insulin-Like Growth Factor I on Muscle in Mouse Models of Human Growth Disorders

Hormone Research in Paediatrics ◽

10.1159/000096620 ◽

2006 ◽

Vol 66 (1) ◽

pp. 26-34 ◽

Cited By ~ 5

Author(s):

Ryan P. Clark ◽

Mark Schuenke ◽

Stephanie M. Keeton ◽

Robert S. Staron ◽

John J. Kopchick

Keyword(s):

Growth Hormone ◽

Growth Factor ◽

Mouse Models ◽

Human Growth ◽

Growth Disorders ◽

Insulin Like Growth Factor ◽

Factor I ◽

Growth Factor I

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Identifying Situational, Individual, and Demographic Determinants of Social Anxiety Using a Naive Bayesian Classification Algorithm

10.31234/osf.io/29ewp ◽

2019 ◽

Author(s):

Alexander Krieg ◽

Kelsie Okamura ◽

Charmaine Higa McMillan

Keyword(s):

Social Anxiety ◽

Undergraduate Students ◽

Predictive Accuracy ◽

Learning Algorithm ◽

Community Sample ◽

Bayesian Classification ◽

Likelihood Ratios ◽

Naive Bayesian ◽

Naïve Bayesian ◽

Improved Accuracy

Social situations may play a role in the conceptualization and treatment of social anxiety disorder. However, situational variance is rarely examined and has not yet been directly compared to dispositional or demographic variance. We used situation-sampling methodology and naive Bayesian classification to determine the predictive power of situational features compared to individual and demographic variables. In Study 1, a sub-sample of 309 undergraduate students responded to random subsets of 680 situations to train a machine-learning algorithm that predicted whether a participant would endorse situational social anxiety. The predictions were then tested on the remaining participants in order to generate accuracy estimates. Situational variance alone improved accuracy by 21.39% (from 50%) whereas the addition of individual variance (+3.8%) and demographic information (-1.11%), did not add to the predictive accuracy. Situational features were rank-ordered by their likelihood ratios, and were grammatically edited to reflect the top, middle, and bottom features. In Study 2, a community sample of 211 people responded to these new situations and endorsement rate was compared to predictive accuracy estimations. We found consistent accuracy for high and middle social anxiety-provoking situations indicating that situational features may be particularly relevant when treating clients with higher levels of anxiety.

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Application of Genomics to the Study of Human Growth Disorders

Translational Bioinformatics - Pediatric Biomedical Informatics ◽

10.1007/978-981-10-1104-7_18 ◽

2016 ◽

pp. 363-384

Author(s):

Michael H. Guo ◽

Andrew Dauber

Keyword(s):

Human Growth ◽

Growth Disorders

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Human growth, growth disorders and treatments

Rapid Revision in Endocrinology ◽

10.1201/9781315378435-6 ◽

2017 ◽

pp. 42-45

Author(s):

Ben Greenstein

Keyword(s):

Human Growth ◽

Growth Disorders

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Insights and Implications of Genome-Wide Association Studies of Height

The Journal of Clinical Endocrinology & Metabolism ◽

10.1210/jc.2018-01126 ◽

2018 ◽

Vol 103 (9) ◽

pp. 3155-3168 ◽

Cited By ~ 9

Author(s):

Michael H Guo ◽

Joel N Hirschhorn ◽

Andrew Dauber

Keyword(s):

Adult Height ◽

Association Studies ◽

Evidence Synthesis ◽

Human Growth ◽

Biological Pathways ◽

Genome Wide Association ◽

Growth Disorders ◽

Genome Wide Association Studies ◽

Growth Factor Signaling ◽

Genome Wide

Abstract Context In the last decade, genome-wide association studies (GWASs) have catalyzed our understanding of the genetics of height and have identified hundreds of regions of the genome associated with adult height and other height-related body measurements. Evidence Acquisition GWASs related to height were identified via PubMed search and a review of the GWAS catalog. Evidence Synthesis The GWAS results demonstrate that height is highly polygenic: that is, many thousands of genetic variants distributed across the genome each contribute to an individual’s height. These height-associated regions of the genome are enriched for genes in known biological pathways involved in growth, such as fibroblast growth factor signaling, as well as for genes expressed in relevant tissues, such as the growth plate. GWASs can also uncover previously unappreciated biological pathways, such as theSTC2/PAPPA/IGFBP4 pathway. The genes implicated by GWASs are often the same genes that are the genetic causes of Mendelian growth disorders or skeletal dysplasias, and GWAS results can provide complementary information about these disorders. Conclusions Here, we review the rationale behind GWASs and what we have learned from GWASs for height, including how it has enhanced our understanding of the underlying biology of human growth. We also highlight the implications of GWASs in terms of prediction of adult height and our understanding of Mendelian growth disorders.

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