Effectiveness of a short video-based educational intervention on factors related to clinical trial participation in adolescents and young adults: a pre-test/post-test design

Abstract Background Poor clinical trial enrollment continues to be pervasive and is especially problematic among young adults and youth, and among minorities. Efforts to address barriers to enrollment have been predominantly focused on adult diseased populations. Because older adults may already have established attitudes, it is imperative to identify strategies that target adolescents and young adults. The purpose of this study was to test the effectiveness of an educational video on factors related to clinical trial participation among a healthy adolescent and young adult population. Methods Participants completed a 49-item pre-test, viewed a 10-min video, and completed a 45-item post-test to assess changes in attitudes, knowledge, self-efficacy, receptivity to, and intention to participate (primary outcome) in clinical trials. Descriptive statistics, paired samples t-tests, and Wilcoxon signed-rank tests were conducted. Results The final analyses included 935 participants. The mean age was 20.7 years, with almost 70% aged 18 to 20 years. The majority were female (73%), non-Hispanic (92.2%), white (70%), or African American (20%). Participants indicated a higher intention to participate in a clinical trial (p < 0.0001) and receptivity to hearing more about a clinical trial (p < 0.0001) after seeing the video. Intention to participate (definitely yes and probably yes) increased by an absolute 18% (95% confidence interval 15–22%). There were significant improvements in attitudes, knowledge, and self-efficacy scores for all participants (p < 0.0001). Conclusions The results of this study showed strong evidence for the effectiveness of a brief intervention on factors related to participation in clinical trials. This supports the use of a brief intervention, in a traditional educational setting, to impact the immediate attitudes, knowledge, self-efficacy, and intention to participate in clinical trial research among diverse, healthy adolescents and young adults.https://deepblue.lib.umich.edu/bitstream/2027.42/146769/1/13063_2018_Article_3097.pd

The Analysis of Commercially Available Kratom Products in Richmond, Virginia

Kratom is a novel psychoactive substance that has gained popularity within the past ten years. Originating from Southeast Asia, the leaves of the Mitragyna speciosa tree contain two principal alkaloids, mitragynine and 7-hydroxymitragynine, that play a key role in opioid-like effects. Twenty-nine kratom products were obtained from tobacco shops in the Richmond, Virginia area, including powders, teas, capsules, extracts, and a carbonated beverage. Samples were analyzed using Direct Analysis in Real Time-Mass Spectrometry (DART-MS) for kratom alkaloids, labeled ingredients, and other possible organic compounds. Inductively Coupled Plasma-Optical Emission Spectroscopy (ICP-OES) was used to quantitate aluminum, arsenic, copper, iron, magnesium, nickel, and lead with yttrium as the internal standard. Mitragynine and 7-hydroxymitragynine were present in every kratom sample. Kratom tea samples were found to have up to 20 times the tolerable upper intake of manganese. Overexposure to manganese can lead to Parkinsonian symptoms including tremors, dystonia, and facial muscle spasms. Gas Chromatography-Mass Spectrometry (GC-MS) was used to qualitatively confirm the presence of alkaloids and differentiate diastereomers. One non-kratom product was analyzed and was found to contain phenibut, an anxiolytic and nootropic substance. Phenibut was not listed on the label of this product. This work contributes to bring attention to the absence of quality control standards on kratom manufacturers as well as proper labeling of products sold at smoke and tobacco shops, prompting a public health concern due to the association of toxic metal levels in commercial kratom products.https://scholarscompass.vcu.edu/gradposters/1175/thumbnail.jp

CFCI3 (CFC-11): UV Absorption Spectrum Temperature Dependence Measurements and the Impact on Atmospheric Lifetime and Uncertainty

CFCl3 (CFC-11) is both an atmospheric ozone-depleting and potent greenhouse gas that is removed primarily via stratospheric UV photolysis. Uncertainty in the temperature dependence of its UV absorption spectrum is a significant contributing factor to the overall uncertainty in its global lifetime and, thus, model calculations of stratospheric ozone recovery and climate change. In this work, the CFC-11 UV absorption spectrum was measured over a range of wavelength (184.95 - 230 nm) and temperature (216 - 296 K). We report a spectrum temperature dependence that is less than currently recommended for use in atmospheric models. The impact on its atmospheric lifetime was quantified using a 2-D model and the spectrum parameterization developed in this work. The obtained global annually averaged lifetime was 58.1 +- 0.7 years (2 sigma uncertainty due solely to the spectrum uncertainty). The lifetime is slightly reduced and the uncertainty significantly reduced from that obtained using current spectrum recommendation

A reduced complexity numerical method for optimal gate synthesis

Although quantum computers have the potential to efficiently solve certain problems considered difficult by known classical approaches, the design of a quantum circuit remains computationally difficult. It is known that the optimal gate design problem is equivalent to the solution of an associated optimal control problem, the solution to which is also computationally intensive. Hence, in this article, we introduce the application of a class of numerical methods (termed the max-plus curse of dimensionality free techniques) that determine the optimal control thereby synthesizing the desired unitary gate. The application of this technique to quantum systems has a growth in complexity that depends on the cardinality of the control set approximation rather than the much larger growth with respect to spatial dimensions in approaches based on gridding of the space, used in previous literature. This technique is demonstrated by obtaining an approximate solution for the gate synthesis on $SU(4)$- a problem that is computationally intractable by grid based approaches.Comment: 8 pages, 4 figure

NF3: UV Absorption Spectrum Temperature Dependence and the Atmospheric and Climate Forcing Implications

Nitrogen trifluoride (NF3) is an atmospherically persistent greenhouse gas that is primarily removed by UV photolysis and reaction with O((sup 1)D) atoms. In this work, the NF3 gas-phase UV absorption spectrum, sigma(delta,T), was measured at 16 wavelengths between 184.95 and 250 nm at temperatures between 212 and 296 K. A significant spectrum temperature dependence was observed in the wavelength region most relevant to atmospheric photolysis (200-220 nm) with a decrease in sigma(210 nm,T) of approximately 45 percent between 296 and 212 K. Atmospheric photolysis rates and global annually averaged lifetimes of NF3 were calculated using the Goddard Space Flight Center 2-D model and the sigma(delta,T) parameterization developed in this work. Including the UV absorption spectrum temperature dependence increased the stratospheric photolysis lifetime from 610 to 762 years and the total global lifetime from 484 to 585 years; the NF3 global warming potentials on the 20-, 100-, and 500-year time horizons increased less than 0.3, 1.1, and 6.5 percent to 13,300, 17,700, and 19,700, respectively

UV Absorption Cross Sections of Nitrous Oxide (N2O) and Carbon Tetrachloride (CCl4) Between 210 and 350 K and the Atmospheric Implications

Absorption cross sections of nitrous oxide (N2O) and carbon tetrachloride (CCl4) are reported at five atomic UV lines (184.95, 202.548, 206.200, 213.857, and 228.8 nm) at 27 temperatures in the range 210-350 K. In addition, UV absorption spectra of CCl4 are reported between 200-235 nm as a function of temperature (225-350 K). The results from this work are critically compared with results from earlier studies. For N2O, the present results are in good agreement with the current JPL recommendation enabling a reduction in the estimated uncertainty in the N2O atmospheric photolysis rate. For CCl4, the present cross section results are systematically greater than the current recommendation at the reduced temperatures most relevant to stratospheric photolysis. The new cross sections result in a 5-7% increase in the modeled CCl4 photolysis loss, and a slight decrease in the stratospheric lifetime, from 51 to 50 years, for present day conditions. The corresponding changes in modeled inorganic chlorine and ozone in the stratosphere are quite small. A CCl4 cross section parameterization for use in 37 atmospheric model calculations is presented

Risk of skin cancer after neonatal phototherapy : retrospective cohort study

Peer reviewedPostprin

A Quantum Langevin Formulation of Risk-Sensitive Optimal Control

In this paper we formulate a risk-sensitive optimal control problem for continuously monitored open quantum systems modelled by quantum Langevin equations. The optimal controller is expressed in terms of a modified conditional state, which we call a risk-sensitive state, that represents measurement knowledge tempered by the control purpose. One of the two components of the optimal controller is dynamic, a filter that computes the risk-sensitive state. The second component is an optimal control feedback function that is found by solving the dynamic programming equation. The optimal controller can be implemented using classical electronics. The ideas are illustrated using an example of feedback control of a two-level atom

Prediction of mechanistic subtypes of Parkinson’s using patient-derived stem cell models

Parkinson’s disease is a common, incurable neurodegenerative disorder that is clinically heterogeneous: it is likely that different cellular mechanisms drive the pathology in different individuals. So far it has not been possible to define the cellular mechanism underlying the neurodegenerative disease in life. We generated a machine learning-based model that can simultaneously predict the presence of disease and its primary mechanistic subtype in human neurons. We used stem cell technology to derive control or patient-derived neurons, and generated different disease subtypes through chemical induction or the presence of mutation. Multidimensional fluorescent labelling of organelles was performed in healthy control neurons and in four different disease subtypes, and both the quantitative single-cell fluorescence features and the images were used to independently train a series of classifiers to build deep neural networks. Quantitative cellular profile-based classifiers achieve an accuracy of 82%, whereas image-based deep neural networks predict control and four distinct disease subtypes with an accuracy of 95%. The machine learning-trained classifiers achieve their accuracy across all subtypes, using the organellar features of the mitochondria with the additional contribution of the lysosomes, confirming the biological importance of these pathways in Parkinson’s. Altogether, we show that machine learning approaches applied to patient-derived cells are highly accurate at predicting disease subtypes, providing proof of concept that this approach may enable mechanistic stratification and precision medicine approaches in the future