Effects of psychoeducation combined with transcranial direct current stimulation on reducing cigarette craving and consumption in male smokers.

Psychoeducation (PE) has been widely used in smoking interventions, but its long-term effects are limited. Recent studies have reported that, in some fields, a combination of transcranial direct current stimulation (tDCS) and cognitive training (e.g., working memory tasks) may improve cognitive outcomes; thus, we aimed to investigate whether such a combined intervention has a better effect than a PE intervention for reducing smoking cravings and cigarette consumption. In Exp. 1, 19 male smokers received four types of interventions at one-week intervals. In each session, participants were presented with audio PE (or control) while receiving 2-mA active (or sham) tDCS of the dorsolateral prefrontal cortex (DLPFC). In Exp. 2, 48 male smokers were randomized into four experimental groups (PE + Active, Control + Active, PE + Sham, or Control + Sham). Each participant received one type of five-day intervention (i.e., watching a five-minute PE/Control video twice while receiving 2-mA active/sham tDCS) and was followed up for one week. The results showed (a) an enhancement effect of tDCS on PE's ability to reduce cigarette consumption; (b) that repeated PE has a cumulative effect on reducing both craving and cigarette consumption during the intervention period; and (c) that, compared with PE alone, PE combined with tDCS is capable of helping participants maintain a low intake of cigarettes over one week. These findings suggest that repeated interventions of PE combined with tDCS may be effective in reducing smoking consumption and that further studies are warranted to confirm its application.

Connectome-based prediction of eating disorder-associated symptomatology.

BACKGROUND: Despite increasing knowledge on the neuroimaging patterns of eating disorder (ED) symptoms in non-clinical populations, studies using whole-brain machine learning to identify connectome-based neuromarkers of ED symptomatology are absent. This study examined the association of connectivity within and between large-scale functional networks with specific symptomatic behaviors and cognitions using connectome-based predictive modeling (CPM). METHODS: CPM with ten-fold cross-validation was carried out to probe functional networks that were predictive of ED-associated symptomatology, including body image concerns, binge eating, and compensatory behaviors, within the discovery sample of 660 participants. The predictive ability of the identified networks was validated using an independent sample of 821 participants. RESULTS: The connectivity predictive of body image concerns was identified within and between networks implicated in cognitive control (frontoparietal and medial frontal), reward sensitivity (subcortical), and visual perception (visual). Crucially, the set of connections in the positive network related to body image concerns identified in one sample was generalized to predict body image concerns in an independent sample, suggesting the replicability of this effect. CONCLUSIONS: These findings point to the feasibility of using the functional connectome to predict ED symptomatology in the general population and provide the first evidence that functional interplay among distributed networks predicts body shape/weight concerns.

Treatment of coronavirus disease 2019 in Shandong, China: a cost and affordability analysis.

BACKGROUND: Coronavirus disease 2019 (COVID-19) is now a global public threat. Given the pandemic of COVID-19, the economic impact of COVID-19 is essential to add value to the policy-making process. We retrospectively conducted a cost and affordability analysis to determine the medical costs of COVID-19 patients in China, and also assess the factors affecting their costs. METHODS: This analysis was retrospectively conducted in Shandong Provincial Chest Hospital between 24 January and 16 March 2020. The total direct medical expenditures were analyzed by cost factors. We also assessed affordability by comparing the simulated out-of-pocket expenditure of COVID-19 cases relative to the per capita disposable income. Differences between groups were tested by student t test and Mann-Whitney test when appropriate. A multiple logistic regression model was built to determine the risk factors associated with high cost. RESULTS: A total of 70 COVID-19 patients were included in the analysis. The overall mean cost was USD 6827 per treated episode. The highest mean cost was observed in drug acquisition, accounting for 45.1% of the overall cost. Total mean cost was significantly higher in patients with pre-existing diseases compared to those without pre-existing diseases. Pre-existing diseases and the advanced disease severity were strongly associated with higher cost. Around USD 0.49 billion were expected for clinical manage of COVID-19 in China. Among rural households, the proportions of health insurance coverage should be increased to 70% for severe cases, and 80% for critically ill cases to avoid catastrophic health expenditure. CONCLUSIONS: Our data demonstrate that clinical management of COVID-19 patients incurs a great financial burden to national health insurance. The cost for drug acquisition is the major contributor to the medical cost, whereas the risk factors for higher cost are pre-existing diseases and severity of COVID-19. Improvement of insurance coverage will need to address the barriers of rural patients to avoid the occurrence of catastrophic health expenditure.