While many deep-learning-based computer-aided detection systems (CAD) have been developed and commercialized for abnormality detection in chest radiographs (CXR), their ability to localize a target abnormality is rarely reported. Localization accuracy is important in terms of model interpretability, which is crucial in clinical settings. Moreover, diagnostic performances are likely to vary depending on thresholds which define an accurate localization. In a multi-center, stand-alone clinical trial using temporal and external validation datasets of 1,050 CXRs, we evaluated localization accuracy, localization-adjusted discrimination, and calibration of a commercially available deep-learning-based CAD for detecting consolidation and pneumothorax. The CAD achieved image-level AUROC (95% CI) of 0.960 (0.945, 0.975), sensitivity of 0.933 (0.899, 0.959), specificity of 0.948 (0.930, 0.963), dice of 0.691 (0.664, 0.718), moderate calibration for consolidation, and image-level AUROC of 0.978 (0.965, 0.991), sensitivity of 0.956 (0.923, 0.978), specificity of 0.996 (0.989, 0.999), dice of 0.798 (0.770, 0.826), moderate calibration for pneumothorax. Diagnostic performances varied substantially when localization accuracy was accounted for but remained high at the minimum threshold of clinical relevance. In a separate trial for diagnostic impact using 461 CXRs, the causal effect of the CAD assistance on clinicians’ diagnostic performances was estimated. After adjusting for age, sex, dataset, and abnormality type, the CAD improved clinicians’ diagnostic performances on average (OR [95% CI] = 1.73 [1.30, 2.32]; p < 0.001), although the effects varied substantially by clinical backgrounds. The CAD was found to have high stand-alone diagnostic performances and may beneficially impact clinicians’ diagnostic performances when used in clinical settings.

There have been consistent efforts to assess treatment effect heterogeneity (TEH) of Head Start using the data from the Head Start Impact Study (HSIS), a randomized controlled trial of a federally funded child development program for a nationally representative sample of low-income parents and their 3- and 4-year-old children in the United States. Including 28 studies on TEH of Head Start, this review found that multiple high-risk subgroups (e.g., children with lower cognitive abilities, Spanish-speaking dual language learners) experienced larger gains across a range of developmental and parental outcomes, but mixed results for several subgroups. Most studies focused on subgroup analyses, cognitive and social-emotional outcomes, and short-term effects. Further studies on distributional effects, health and parental outcomes, and long-term effects are warranted. Finally, suggestions for future research on TEH of Head Start are discussed, which are applicable to other child development programs and policy evaluations.

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The societal disruptions resulting from the coronavirus disease 2019 (COVID-19) pandemic may have caused changes in smoking and alcohol consumption. Using data from the Koreans’ Happiness Survey, a nationally representative survey in South Korea, we (1) described population-level smoking and drinking behaviors; (2) assessed changes in smoking and drinking behaviors during the COVID-19 pandemic; and (3) identified employment, economic, and sociodemographic factors associated with these changes using multinomial logistic regression. The overall amount of smoking and drinking decreased during the pandemic, but the changes were heterogeneous across subgroups. Male gender, receipt of the basic living allowance, self-employment, unemployment, and chronic disease status were associated with increased smoking, while higher household income, temporary worker status, living with someone (versus alone), and having fewer offline friends were associated with decreased smoking. Male gender, self-employment, living alone, having more offline friends, and chronic disease status were associated with increased drinking, while younger age, male gender, low and high household income (i.e., a U-shaped relationship), long-term rent with a deposit, temporary worker status, and chronic disease status were associated with decreased drinking. Our findings provide evidence on changes in smoking and drinking during the COVID-19 pandemic in South Korea and differential changes across subgroups.

Human and animal alcohol-induced hangovers are caused by adverse effects of acetaldehyde formed in vivo by the enzymatic oxidation of ethyl alcohol to acetaldehyde. This study aims to determine the effect of the combination of a bioprocessed black rice bran (BRB-F) and glutathione-enriched yeast extract (GEYE) on hangovers as tested in rats and mice. Because analysis by HPLC showed that the content of the biologically active rice bran compound γ-oryzanol as well as of the antioxidant reduced glutathione were unaffected during the preparation of tablets containing 100 mg/kg of the bran formulation, the tablets were then administered orally to rats. The results showed decreased blood concentrations of both alcohol and acetaldehyde compared to the control group. Additional behavior experiments using the Rota-rod and wire tests in mice confirmed that the food formulation relieved hangover behavior caused by alcohol. It seems that the combination of BRB-F and GEYE can effectively control hangovers in rodents caused by alcohol intake. Mechanistic aspects of the hangover and anti-hangover effects of alcohol-derived acetaldehyde are similar to browning-type reactions between the aldehyde group of glucose and proteins, the antibiotic effects of cinnamaldehyde against pathogenic bacteria, the adverse effects of the heat-induced food toxin acrylamide, and the alkali-induced formation of the unnatural amino acid lysinoalanine.