Author Interviews, Exercise - Fitness, JAMA, Mental Health Research, Pediatrics, UCLA / 29.05.2019
Team Sports Benefits Teens With a Troubled Childhood
MedicalResearch.com Interview with:
Molly C. Easterlin, MD
Fellow, UCLA National Clinician Scholars Program
Clinical Instructor, Pediatrics, Cedars-Sinai Medical Center
MedicalResearch.com: What is the background for this study?
Response: Adverse childhood experiences or ACEs (including physical or emotional neglect or abuse, sexual abuse, domestic abuse, exposure to household substance misuse or mental illness, parental separation or divorce, and parental incarceration) are common with about half of children experiencing 1 and one-quarter of children experiencing 2 or more.
Children exposed to adverse childhood experiences have worse mental health throughout life, including higher rates of depression and anxiety. However, little is known about what factors improve long-term mental health in those exposed to ACEs. Additionally, as far as we are aware, no studies have looked at team sports participation as a potential factor that may be associated with improved mental health among those with adverse childhood experiences.
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Jasleen Grewal, BSc.
Genome Sciences Centre
British Columbia Cancer Research Centre
Vancouver, British Columbia, Canada
MedicalResearch.com: What is the background for this study?
Response: Cancer diagnosis requires manual analysis of tissue appearance, histology, and protein expression. However, there are certain types of cancers, known as cancers of unknown primary, that are difficult to diagnose based purely on their appearance and a small set of proteins. In our precision medicine oncogenomics program, we needed an accurate approach to confirm diagnosis of biopsied samples and determine candidate tumour types for where the primary site of the cancer was uncertain. We developed a machine learning approach, trained on the gene expression data of over 10,688 individual tumours and healthy tissues, that has been able to achieve this task with high accuracy.
Genome sequencing offers a high-resolution view of the biological landscape of cancers. RNA-Seq in particular quantifies how much each gene is expressed in a given sample. In this study, we used the entire transcriptome, spanning 17,688 genes in the human genome, to train a machine learning method for cancer diagnosis. The resultant method, SCOPE, takes in the entire transcriptome and outputs an interpretable confidence score from across a set of 40 different cancer types and 26 healthy tissues.