Allergies, Author Interviews, JAMA, Nutrition, Pediatrics / 22.10.2019
Avoiding Cow’s Milk Formula at Birth May Reduce Food Allergies
MedicalResearch.com Interview with:
Mitsuyoshi Urashima MD, PhD, MPH
Professor of Molecular Epidemiology
Jikei University School of Medicine
Tokyo, JAPAN
MedicalResearch.com: What is the background for this study?
Response: IgE-mediated food allergy is becoming a global concern, because its prevalence and severity are worsening. Many Japanese maternity wards encourage breastfeeding, but allow mothers or nurses to supplement breastfeeding with cow’s milk formula, e.g., approximately 6 to 10 hours after birth or even earlier, based on maternal preferences, but not based on clinical evidence. However, more than 20 to 30 years ago, sugar water was given instead of cow’s milk formula supplement at birth. Thus, we hypothesized that early exposure to cow’s milk formula at birth is, at least in part, associated with the recent increase in children with food allergy.
Therefore, a randomized clinical trial, named ABC (Atopy induced by Breast feeding or Cow's milk formula), was conducted to assess whether the risk of cow’s milk formula sensitization and food allergy is decreased by either avoiding or supplementing cow’s milk formula at birth. Immediately after birth, newborns were randomly assigned (1:1 ratio) to either breastfeeding with or without amino acid-based elemental formula for at least the first 3 days of life (breastfeeding ± elemental formula), or breastfeeding supplemented with cow’s milk formula (≥5 mL/day) from the first day of life to 5 months of age (breastfeeding + cow's milk formula).
Dr. Helen Marsden PhD
Skin Analytics Limited
London, United Kingdom
MedicalResearch.com: What is the background for this study?
Response: In this technology age, with the explosion of interest and applications using Artificial Intelligence, it is easy to accept the output of a technology-based test - such as a smartphone app designed to identify skin cancer - without thinking too much about it. In reality, technology is only as good as the way it has been developed, tested and validated. In particular, AI algorithms are prone to a lack of “generalisation” - i.e. their performance drops when presented with data it has not seen before. In the medical field, and particularly in areas where AI is being developed to direct a patient’s diagnosis or care, this is particularly problematic. Inappropriate diagnosis or advice to patients can lead to false reassurance, heightened concern and pressure on NHS services, or worse. It is concerning, therefore, that there are a large number of smartphone apps available that provide an assessment of skin lesions, including some that provide an estimate of the probability of malignancy, that have not been assessed for diagnostic accuracy.
Skin Analytics has developed an AI-based algorithm, named: Deep Ensemble for Recognition of Malignancy (DERM), for use as a decision support tool for healthcare providers. DERM determines the likelihood of skin cancer from dermoscopic images of skin lesions. It was developed using deep learning techniques that identify and assess features of these lesions which are associated with melanoma, using over 7,000 archived dermoscopic images. Using these images, it was shown to identify melanoma with similar accuracy to specialist physicians. However, to prove the algorithm could be used in a real life clinical setting, Skin Analytics set out to conduct a clinical validation study.
Dr. Hongying (Daisy) Dai[/caption]
Hongying (Daisy) Dai, PhD
Associate Professor
Department of Biostatistics | College of Public Health
University of Nebraska Medical Center
MedicalResearch.com: What is the background for this study?
Response: Although marijuana is still classified as a Schedule I drug at the Federal level, as of June 2019, 33 states and the District of Columbia have legalized one or more forms of marijuana; 11 states and the District of Columbia have approved both medical and recreational uses. Public opinion on marijuana has changed dramatically over the last two decades and support for legalization has doubled since 2010. However, very little is known about the prevalence and patterns of marijuana use among adults with medical conditions.
This study analyzed the 2016 and 2017 Behavioral Risk Factor Surveillance System data to report the prevalence and patterns of marijuana use among adults with self-reported medical conditions. 
