Identifying Biomarkers for Accurate Detection of Stress.

Substance use disorder (SUD) is a dangerous epidemic that develops out of recurrent use of alcohol and/or drugs and has the capability to severely damage one's brain and behaviour. Stress is an established risk factor in SUD's development of addiction and in reinstating drug seeking. Despite this expanding epidemic and the potential for its grave consequences, there are limited options available for management and treatment, as well as pharmacotherapies and psychosocial treatments. To this end, there is a need for new and improved devices dedicated to the detection, management, and treatment of SUD. In this paper, the negative effects of SUD-related stress were discussed, and based on that, a few significant biomarkers were selected from a set of eight features collected by a chest-worn device, RespiBAN Professional, on fifteen individuals. We used three machine learning classifiers on these optimal biomarkers to detect stress. Based on the accuracies, the best biomarkers to detect stress and those considered as features for classification were determined to be electrodermal activity (EDA), body temperature, and a chest-worn accelerometer. Additionally, the differences between mental stress and physical stress, as well as different administrations of meditation during the study, were identified and analysed. Challenges, implications, and applications were also discussed. In the near future, we aim to replicate the proposed methods in individuals with SUD.

Selection of Optimal Physiological Features for Accurate Detection of Stress.

Stress is an established risk factor in the development of addiction and in reinstating drug seeking. Substance use disorder (SUD) is a dangerous epidemic that affects the brain and behavior. Despite this growing epidemic and its subsequent consequences, there are limited management and treatment options, pharmacotherapies and psychosocial treatments available. To this end, there is a need for new and improved personalized devices and treatments for the detection and management of SUD. Based on documented negative effects of stress in SUD, in this paper, our objective was to select a few significant physiological features from a set of 8 features collected by a chest-worn RespiBAN Professional in 15 individuals. We used three machine learning classifiers on these optimal physiological features to detect stress. Our results indicate that best accuracies were achieved when electrodermal activity (EDA), body temperature and chest-worn accelerometer were considered as features for the classification. Challenges, implications and applications were discussed. In the near future, the proposed methods will be replicated in individuals with SUD.

Congenital heart defects and copy number variants associated with neurodevelopmental impairment.

A genetic etiology is identifiable in 20%-30% of patients with congenital heart defects (CHD). Chromosomal microarray analysis (CMA) can detect copy number variants (CNV) associated with CHD. In previous studies, the diagnostic yield of postnatal CMA testing ranged from 4% to 28% in CHD patients. However, incidental pathogenic CNV and variants of unknown significance are often discovered without any known association with CHD. The study objective was to describe the rate of pathogenic CNV associated with neurodevelopmental impairment (NDI) and compare clinical findings in CHD neonates with genetic results. A single-center retrospective review was performed on all consecutive newborns with CHD admitted to a tertiary neonatal intensive care unit from January 2013 to March 2019 (n = 525). CHD phenotypes were classified as per the National Birth Defect Prevention Study. CMA detected pathogenic CNV in 21.3% (61/287) of neonates, and karyotype or fluorescence in situ hybridization detected aneuploidies in an additional 11% of the overall cohort (58/525). Atrioventricular septal defects and conotruncal defects showed the highest diagnostic yield by CMA (28.6% and 27.2%, respectively). Among neonates with pathogenic CNV on CMA, 78.7% (48/61) were associated with NDI. Neonates with pathogenic CNV were smaller in length at birth compared to those with benign CNV or variants of unknown significance (p = 0.005) and were more likely to be discharged with an enteral feeding tube (p = 0.027). CMA can discover genetic variants associated with NDI and are common in neonates with CHD. Genetic testing in the neonatal period can heighten awareness of genetic risk for NDI.

Excretion and toxicity of gold-iron nanoparticles.

Though gold nanoparticles have been considered bio-inert, recent studies have questioned their safety. To reduce the potential for toxicity, we developed a nanoclustering of gold and iron oxide as a nanoparticle (nanorose) which biodegrades into subunits to facilitate rapid excretion. In this present study, we demonstrate acid and macrophage lysosomal degradation of nanorose via loss of the near-infrared optical shift, and clearance of the nanorose in vivo following i.v. administration in C57BL/6 mice by showing gold concentration is significantly reduced in 11 murine tissues in as little as 31 days (P < 0.01). Hematology and chemistry show no toxicity of nanorose injected mice up to 14 days after administration. We conclude that the clustering design of nanorose does enhance the excretion of these nanoparticles, and that this could be a viable strategy to limit the potential toxicity of gold nanoparticles for clinical applications. FROM THE CLINICAL EDITOR: The potential toxicity of nanomaterials is a critically important limiting factor in their more widespread clinical application. Gold nanoparticles have been classically considered bio-inert, but recent studies have questioned their safety. The authors of this study have developed a clustering gold and iron oxide nanoparticle (nanorose), which biodegrades into subunits to facilitate rapid excretion, resulting in reduced toxicity.