Associations between mother's depressive symptoms during pregnancy and newborn's brain functional connectivity.

Depression during pregnancy is common and the prevalence further increased during the COVID pandemic. Recent findings have shown potential impact of antenatal depression on children's neurodevelopment and behavior, but the underlying mechanisms are unclear. Nor is it clear whether mild depressive symptoms among pregnant women would impact the developing brain. In this study, 40 healthy pregnant women had their depressive symptoms evaluated by the Beck Depression Inventory-II at ~12, ~24, and ~36 weeks of pregnancy, and their healthy full-term newborns underwent a brain MRI without sedation including resting-state fMRI for evaluation of functional connectivity development. The relationships between functional connectivities and maternal Beck Depression Inventory-II scores were evaluated by Spearman's rank partial correlation tests using appropriate multiple comparison correction with newborn's gender and gestational age at birth controlled. Significant negative correlations were identified between neonatal brain functional connectivity and mother's Beck Depression Inventory-II scores in the third trimester, but not in the first or second trimester. Higher depressive symptoms during the third trimester of pregnancy were associated with lower neonatal brain functional connectivity in the frontal lobe and between frontal/temporal lobe and occipital lobe, indicating a potential impact of maternal depressive symptoms on offspring brain development, even in the absence of clinical depression.

The impact of social shopping feature richness on buying intention: a product perspective

Purpose>Social shopping platforms have flourished by using multiple social shopping features, yet little is known about how the combination of these features affects purchase intention, particularly in terms of the product itself. The purpose of the paper is to draw on the concept of social shopping feature richness, adopting a formative approach on the survey used, and endeavors to reveal the concept's impact on consumers' buying intention from a product perspective.Design/methodology/approach>Building on mental accounting and signaling theories, a theoretical model is proposed and empirically evaluated with 356 samples collected using a questionnaire survey.Findings>The results suggest that social shopping feature richness promotes consumers' consumption by providing information signals to satisfy acquisition utility and transaction utility. Specifically, social shopping feature richness enhances perceived product quality, while decreasing negative perceptions regarding price. Moreover, perceived product quality and perceived price significantly influence buying intention through the mechanism of perceived value.Originality/value>The authors' study highlights the role of the combination of functionally diverse social shopping features on product sales for social shopping platforms.

Evaluation of favipiravir in the treatment of COVID-19 based on the real-world.

BACKGROUND: The role of favipiravir (FVP) as a COVID-19 treatment is recognized but not fully elucidated. We aimed to evaluate whether FVP has definite clinical efficacy and safety in the treatment of COVID-19. METHODS: International and Chinese databases were searched for randomized controlled clinical trials evaluating FVP for the treatment of COVID-19. A meta-analysis was performed and published literature was synthesized to evaluate the corresponding therapeutic effects. RESULTS: We included 13 studies (1430 patients in total). Meta-analysis showed that patients with mild-to-moderate disease treated with FVP had a significantly higher viral clearance rate than those in the control group 10 and 14 days after initiation of treatment [RR: 1.13 (95% CI: 1.00, 1.28), P = 0.04; I2 = 39% for day 10 and RR: 1.16 (95% CI: 1.04, 1.30), P = 0.008; I2 = 38% for day 14] and a significantly shorter hospital stay [MD: -1.52 (95% CI: -2.82, -0.23), P = 0.02; I2 = 0%]. CONCLUSIONS: FVP significantly promotes viral clearance and reduces the hospitalization duration in mild-to-moderate COVID-19 patients, which can reduce the risk of severe disease outcomes in patients. However, more importantly, the results showed no benefit of FVP in severe patients, and caution should be taken regarding the treatment options of FVP in severe patients.

PLAIN LANGUAGE SUMMARYThe urgent need to identify effective interventions to treat novel coronavirus infections is a major challenge. The role of favipiravir (FVP) as a COVID-19 treatment is recognized but not fully elucidated. Our study showed a significant correlation between viral clearance and the promotion of clinical improvement with FVP in mild-to-moderate patients, which is significant for reducing the length of hospital stay of patients, reducing the risk of patients progressing to severe disease, thereby reducing mortality. However, the results showed no benefit of FVP in severe patients and the conclusion of this study still needs to be further verified by clinical trials with large samples.

A Joint Optimization Model considering the Product User's Risk Preference for Supply System Disruption

Logistics distribution is the terminal link that connects the manufacturer and product user and determines the efficiency of the manufacturer’s service. Therefore, the disruption risk of the joint system is an essential factor affecting the product user experience. In this paper, while considering the product user’s supply disruption risk preference (PUSDRP), a biobjective integer nonlinear programming (INLP) model with subjective cost-utility is proposed to solve the manufacturer’s combined location routing inventory problem (CLRIP). According to the user’s time satisfaction requirement, a routing change selection framework (RCSF) is designed based on the bounded rational behavior of the user. Additionally, the Lagrange Relaxation and Modified Genetic Algorithm (LR-MGA) is proposed. The LR method relaxes the model, and the MGA finds a compromise solution. The experimental results show that the biobjective cost-utility model proposed in this paper is effective and efficient. The RCSF based on user behavior is superior to the traditional expected utility theory model. The compromise solution provides a better solution for the manufacturer order allocation delivery combinatorial optimization problem. The compromise solution not only reduces the manufacturer’s total operating cost but also improves the user's subjective utility. To improve the stability of cooperation between manufacturers and users, the behavior decision-making method urges manufacturers to consider product users’ supply disruption risk preferences (PUSDRPs) in attempting to optimize economic benefits for the long term. This paper uses behavior decision-making methods to expand the ideas of the CLRIP joint system.