Breaking News
December 11, 2018 - Ezogabine treatment reduces motor neuron excitability in ALS patients, study shows
December 11, 2018 - One implant, two prices. It depends on who’s paying.
December 11, 2018 - Standardizing feeding practices improves growth trends for micro-preemies
December 11, 2018 - COPD Tied to Obesity in Male, Female Never-Smokers
December 11, 2018 - Flossing: Information for Caregivers
December 11, 2018 - Does breastfeeding hormone protect against type 2 diabetes?
December 11, 2018 - Krystal 2000 microplate design improves fluorescence and luminescence measurement
December 11, 2018 - FDA clears mobile medical app to help increase retention in recovery program for opioid use disorder
December 11, 2018 - Overcoming Challenges in High-Speed Centrifugation Experiments
December 11, 2018 - Study shows link between neighborhoods’ socioeconomic status and dietary choices
December 11, 2018 - Lower BMI before obesity surgery predicts greater post-operative weight loss, study finds
December 11, 2018 - Obesity May Be Driving Rise in Uterine Cancers
December 11, 2018 - Antioxidants may prevent cognitive impairment in diabetes
December 11, 2018 - Researchers identify potential diagnostic tool for Alzheimer’s disease
December 11, 2018 - Oral cancer prognostic signature identified
December 11, 2018 - How Can I Find Out What Caused My Miscarriage?
December 11, 2018 - Novel personalized medicine tool for assessing inherited colorectal cancer syndrome risk developed
December 11, 2018 - Study uncovers 11 new genes associated with epilepsy
December 11, 2018 - Filling research gaps could help develop more disability-inclusive workplaces
December 11, 2018 - Cartilage tissue engineering brings good news for patients with cartilage defects
December 11, 2018 - Novel 3D printing workflow helps predict leaky heart valves
December 11, 2018 - Imagination can help overcome fear and anxiety-related disorders, shows study
December 11, 2018 - Are caries linked to political regime?
December 11, 2018 - Leader in Diabetes Clinical Trials Wins Naomi Berrie Award
December 11, 2018 - Scientists discover cellular mechanism that triggers pneumonia in humans
December 11, 2018 - Increasing mental health problems related to drug use in over 55’s
December 11, 2018 - High-intensity interval exercise could help combat cognitive dysfunction in obese people
December 11, 2018 - Annual flu shot can save lives of heart failure patients
December 11, 2018 - Researchers compare health outcomes for VA and non-VA hospitals
December 11, 2018 - Recommendations Developed for Psoriatic Arthritis Treatment
December 11, 2018 - Genetic analysis links obesity with diabetes, coronary artery disease
December 11, 2018 - Study shows that having genetic information can affect how the body responds
December 11, 2018 - UNAIDS Report: 9 Million Are Likely HIV Positive And Don't Know It
December 11, 2018 - Lund University researchers succeed in obtaining dendritic cells by direct reprogramming
December 11, 2018 - Breast tumors recruit bone marrow cells to boost their growth, study reveals
December 11, 2018 - Updated breast cancer screening guideline highlights importance of shared decision-making
December 11, 2018 - EHR-related stress associated with physician burnout
December 11, 2018 - AHA: 12-Year-Old Heart Defect Survivor Inspires NFL Player’s Foundation
December 11, 2018 - Breast cancer patients who take heart drug with trastuzumab have less heart damage
December 11, 2018 - Providing aid to those humans – and animals – affected by the California fires
December 11, 2018 - Even without proof, CBD is finding a niche as a cure-all
December 11, 2018 - Drawing leads to better memory than writing
December 11, 2018 - Researchers report novel findings on plant hormone
December 10, 2018 - A Tale of Two Labels
December 10, 2018 - Triple combination cancer immunotherapy improves outcomes in preclinical melanoma model
December 10, 2018 - A 14-year-old explains what it’s like to get a new heart
December 10, 2018 - Team Players Honored with 2018 Baton Awards
December 10, 2018 - Global report highlights how the changing world is affecting children’s physical activity levels
December 10, 2018 - Genes play a role in physical activity and sleep
December 10, 2018 - DDT in Alaskan fish shown to increase risk of cancer
December 10, 2018 - Laws to curb use of cell phones have greatly reduced fatalities for motorcyclists
December 10, 2018 - Argenx Provides Detailed Data from Phase 2 Clinical Trial of Efgartigimod in Immune Thrombocytopenia and Phase 1/2 Clinical Trial of Cusatuzumab in Acute Myeloid Leukemia
December 10, 2018 - University of Maryland doctors treat first breast cancer patients with GammaPod radiotherapy
December 10, 2018 - The heartbeat seat: Demoing new well-being technologies in a car
December 10, 2018 - Leading Cancer Researcher to Direct Herbert Irving Comprehensive Cancer Center
December 10, 2018 - Researchers explore how glial cells develop in the brain from neural precursor cells
December 10, 2018 - Study compares pain-related diagnoses in First Nations and non-First Nations children, youth
December 10, 2018 - Experts address sleep disorders following traumatic brain injury
December 10, 2018 - Scientists find answers to how cancer spreads
December 10, 2018 - Study explores why older people read more slowly
December 10, 2018 - Smart life-collar could save lives of young children
December 10, 2018 - Asbestos found in most NHS hospitals finds BBC inquiry
December 10, 2018 - Researchers use new technique to probe hydrogen bonds
December 10, 2018 - Music improves social communication in autistic children
December 10, 2018 - Some Brain Tumors May Respond to Immunotherapy, New Study Suggests
December 10, 2018 - Banning junk food ads to combat childhood obesity
December 10, 2018 - Skin Autofluorescence Predicts T2DM, Heart Disease, Mortality
December 10, 2018 - Largest autism sequencing study to date yields 102 genes associated with ASD
December 10, 2018 - Statins associated with low risk of side effects
December 10, 2018 - Episodic memory tests help in predicting brain atrophy and Alzheimer’s disease
December 10, 2018 - Study explores how schools address adolescent self-harming practices
December 10, 2018 - Pregnancy in adolescence linked to increased risks of complications in young mothers
December 10, 2018 - Risk Analysis publishes special issue on communicating about Zika virus
December 10, 2018 - Botox May Help Prevent Post-Op A-Fib
December 10, 2018 - African-American mothers rate boys higher for ADHD
December 10, 2018 - Graphic warning labels cancel out cigarettes’ appeal to young people
December 10, 2018 - Australian researchers to study gas inhalational anaesthetic and likelihood of cancer return
December 10, 2018 - Individual neurons located within the brain have implications for psychiatric diseases
December 10, 2018 - Researchers improve bariatric surgery scoring system to extend prediction time for diabetic remission
December 10, 2018 - HPV type 16 or 18 associated with cervical cancer risk in young women
Research team diagnoses asthma with nasal brush test

Research team diagnoses asthma with nasal brush test

image_pdfDownload PDFimage_print
Study flow for the identification of a nasal brush-based classifier of asthma by machine learning analysis of RNAseq data. One hundred and ninety subjects with mild/moderate asthma and controls without asthma were recruited for phenotyping, nasal brushing, and RNA sequencing of nasal brushings. The RNAseq data generated were then a priori split into development and test sets. The development set was used for differential expression analysis and machine learning (involving feature selection, classification, and statistical analyses of classification performance) to identify an asthma classifier that can classify asthma from no asthma as accurately as possible. The asthma classifier was then evaluated on eight test sets, including (1) the RNAseq test set of independent subjects with and without asthma, (2) two external test sets of subjects with and without asthma with nasal gene expression profiled by microarray, and (3) five external test sets of subjects with non-asthma respiratory conditions (allergic rhinitis, upper respiratory infection, cystic fibrosis, and smoking) and nasal gene expression profiled by microarray. Figure drawn by Jill Gregory, Mount Sinai Health System, licensed under CC-BY-ND. Credit: Jill Gregory, Mount Sinai Health System, licensed under CC-BY-ND

Mount Sinai researchers have identified a genetic biomarker of asthma that can be tested for using a simple nasal brush and basic follow-up data analysis. This inexpensive diagnostic test can accurately identify mild to moderate asthma and differentiate it from other respiratory conditions such as allergic rhinitis, smoking, upper respiratory infection, and cystic fibrosis. The research team, led by a collaboration of clinical and computational scientists in the Department of Genetics and Genomic Sciences, the Icahn Institute for Genomics and Multiscale Biology, and the Department of Pediatrics at the Icahn School of Medicine at Mount Sinai, published their results in June 2018 in Scientific Reports.

“Mild to moderate asthma can be difficult to diagnose because symptoms change over time and can be complicated by other respiratory conditions,” said Dr. Supinda Bunyavanich, physician and researcher at the Icahn School of Medicine. “Our nasal brush test takes seconds to collect—for time-strapped clinicians, particularly primary care providers at the frontlines of asthma diagnosis, this could greatly improve patient outcomes through early and accurate diagnosis.”

Currently, pulmonary function testing (PFT) is the most reliable diagnostic tool for asthma. However, access to the equipment and expertise needed to perform these tests is not always prevalent in primary care settings where asthma is frequently diagnosed and treated. It is also difficult to differentiate between asthma and other respiratory diseases using PFT alone, while the nasal brush and subsequent analysis for this asthma biomarker provides a binary result of asthma or not asthma.

Data scientists leading the study applied machine learning algorithms to the genetic (RNA) data acquired from nasal brushes of patients with and without asthma. This robust data collection, and machine learning analysis identified a 90-gene biomarker indicative of asthma status. “One of the most exciting components of this study is demonstrating the power of machine learning when applied to biomedical data,” said Dr. Gaurav Pandey, who led data science efforts to develop the biomarker. “Collaborations between computational scientists and biomedical researchers and clinicians are advancing medicine at an inspiring pace—we have the power of insights we didn’t have many of in the past and that opens a window to an entirely new world of diagnostic tools and treatments”

Similar genetic biomarker tests are currently being used in other disease areas, including MammaPrint and Oncotype DX, both used for certain types of breast cancer prognosis. In fact, the Oncotype DX tool was used in the largest clinical trial of personalized breast cancer prognosis ever conducted, demonstrating that mammography testing is unnecessary to diagnose breast cancer in a large fraction of breast cancer patients. The positive clinical impact biomarker tests such as this have shown indicate great potential for further diagnostic tools based on biomarkers.

Dr. Bunyavanich says the next step to bringing this test into clinical practice is a study in a larger population of patients. “With prospective validation in large cohorts, our asthma biomarker could lead to the development of a minimally invasive test to aid asthma diagnosis at clinical frontlines where time and resources often preclude pulmonary function testing.”

Asthma affects 10 percent of children and adults in the United States. When undiagnosed, it can lead to restricted activity, emergency room visits, and hospitalizations. “We’re hopeful that further studies can help bring this test into primary care settings, transforming the ease and accuracy of diagnosing asthma and our ability as doctors to appropriately treat our patients,” said Dr. Bunyavanich.


Explore further:
Researchers identify common biological features of different types of asthma

More information:
Gaurav Pandey et al, A Nasal Brush-based Classifier of Asthma Identified by Machine Learning Analysis of Nasal RNA Sequence Data, Scientific Reports (2018). DOI: 10.1038/s41598-018-27189-4

Journal reference:
Scientific Reports

Provided by:
The Mount Sinai Hospital

Tagged with:

About author

Related Articles