Notes

Self-Exclusion from Gambling-A Measure of COVID-19 Impact on Wagering within a Remarkably Online-Based Betting Market?
Some parents engage in the potentially unhealthy and morally debateable parenting practice of giving prescription stimulant drugs to healthy children to boost their school and extracurricular performance. However, the parents' underlying reasoning remains unexamined. This web-based study (NRespondents = 1360) simultaneously investigates eight experimentally-varied situational (dis-)incentives (e.g., financial gains and drug properties) within a factorial vignette survey (NVignettes = 256), Machiavellianism as a measured socially relevant personality trait, and possible interaction effects. Results show that approximately 40% of the described situations (NEvaluations = 5440) provoked some willingness to medicate healthy children. Multilevel mixed-effect models revealed that this willingness was higher, for example, with increasing financial gains and weaker side effects. Machiavellians disclosed a higher willingness. They were more responsive to financial gains and threats (e.g., probable side effects). Respondents' sex, age, ethnicity, and experience with prescription drugs also had effects. Prevention measures might emphasize the dangers and limited potential of prescription drugs for healthy children.This study examines the association between sibling gender configuration and second-born siblings' choice of so-called STEM educational fields (i.e., science, technology, engineering, and mathematics) in Swedish two-child families. Using population data from administrative registers in Sweden, the findings show that younger siblings, net of parental characteristics, are more likely to choose a STEM field if their older sibling has attended or is already attending a STEM program. Moreover, a gender difference is clear concerning the choice of a STEM field among younger siblings Girls are more likely to choose a STEM field if they have an older sister who has attended a STEM program, than if they have an older brother in a similar program. However, the corresponding results are not found for boys. Given that STEM fields are markedly male-dominated at tertiary level, this indicates an importance of a same-sex role model for young girls contemplating gender-atypical educational choices.The measurement of respondents' attitudes is key in social science research and many adjacent research fields. A common method to measure this information is to use Likert-type questions that consist of a statement that is evaluated with a rating scale. As shown by previous research, the scale design of Likert-type questions can have a profound impact on respondents' answer behavior. In this study, we therefore investigate the measurement properties of scales that systematically vary with respect to polarity (i.e., unipolar and bipolar) and labeling (i.e., completely and end). We conducted a survey experiment in a probability-based online panel (N = 4851) and used questions on income (in)equality that were adopted from the European Social Survey (ESS). The results reveal considerable differences between the scales under investigation. They show that end labeled unipolar and bipolar scales accomplish the criteria of equidistance best. Dihexa price Completely labeled bipolar scales, in contrast, only show a poor performance in terms of equidistance. Completely labeled unipolar scales are somewhere in between. Overall, our findings suggest that researchers should be careful when using survey data measured with (slightly) different scales because the results might not be comparable.A need exists to accurately estimate overdose risk and improve understanding of how to deliver treatments and interventions in people with opioid use disorder in a way that reduces such risk. We consider opportunities for predictive analytics and routinely collected administrative data to evaluate how overdose could be reduced among people with opioid use disorder. Specifically, we summarise global trends in opioid use and overdoses; describe the use of big data in research into opioid overdose; consider the potential for predictive modelling, including machine learning, for prevention and monitoring of opioid overdoses; and outline the challenges and risks relating to the use of big data and machine learning in reducing harms that are related to opioid use. Future research for improving the coverage and provision of existing interventions, treatments, and resources for opioid use disorder requires collaboration of multiple agencies. Predictive modelling could transport the concept of stratified medicine to public health through novel methods, such as predictive modelling and emulated trials for evaluating diagnoses and prognoses of opioid use disorder, predicting treatment response, and providing targeted treatment recommendations.
The tumour stroma microenvironment plays an important part in disease progression and its composition can influence treatment response and outcomes. Histological evaluation of tumour stroma is limited by access to tissue, spatial heterogeneity, and temporal evolution. We aimed to develop a radiological signature for non-invasive assessment of tumour stroma and treatment outcomes.

In this multicentre, retrospective study, we analysed CT images and outcome data of 2209 patients with resected gastric cancer from five independent cohorts recruited from two centres (Nanfang Hospital of Southern Medical University [Guangzhou, China] and Sun Yat-sen University Cancer Center [Guangzhou, China]). Patients with histologically confirmed gastric cancer, at least 15 lymph nodes harvested, preoperative abdominal CT available, and complete clinicopathological and follow-up data were eligible for inclusion. Tumour tissue was collected for patients in the training cohort (321 patients), internal validation cohort one (246a to refine prognosis and inform treatment decisions of patients with gastric cancer.

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Streptococcus pneumoniae, Haemophilus influenzae, and Neisseria meningitidis, which are typically transmitted via respiratory droplets, are leading causes of invasive diseases, including bacteraemic pneumonia and meningitis, and of secondary infections subsequent to post-viral respiratory disease. The aim of this study was to investigate the incidence of invasive disease due to these pathogens during the early months of the COVID-19 pandemic.

In this prospective analysis of surveillance data, laboratories in 26 countries and territories across six continents submitted data on cases of invasive disease due to S pneumoniae, H influenzae, and N meningitidis from Jan 1, 2018, to May, 31, 2020, as part of the Invasive Respiratory Infection Surveillance (IRIS) Initiative. Numbers of weekly cases in 2020 were compared with corresponding data for 2018 and 2019. Data for invasive disease due to Streptococcus agalactiae, a non-respiratory pathogen, were collected from nine laboratories for comparison. The stringency of COVID-19 containment measures was quantified using the Oxford COVID-19 Government Response Tracker.
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