BACKGROUND: On January 20, 2020, a new coronavirus epidemic with human-to-human transmission was officially declared by the Chinese government, which caused significant public panic in China. In light of the coronavirus disease 2019 outbreak, pregnant women may be particularly vulnerable and in special need for preventive mental health strategies. Thus far, no reports exist to investigate the mental health response of pregnant women to the coronavirus disease 2019 outbreak. OBJECTIVE: This study aimed to examine the impact of coronavirus disease 2019 outbreak on the prevalence of depressive and anxiety symptoms and the corresponding risk factors among pregnant women across China. STUDY DESIGN: A multicenter, cross-sectional study was initiated in early December 2019 to identify mental health concerns in pregnancy using the Edinburgh Postnatal Depression Scale. This study provided a unique opportunity to compare the mental status of pregnant women before and after the declaration of the coronavirus disease 2019 epidemic. A total of 4124 pregnant women during their third trimester from 25 hospitals in 10 provinces across China were examined in this crosssectional study from January 1, 2020, to February 9, 2020. Of these women, 1285 were assessed after January 20, 2020, when the coronavirus epidemic was publicly declared and 2839 were assessed before this pivotal time point. The internationally recommended Edinburgh Postnatal Depression Scale was used to assess maternal depression and anxiety symptoms. Prevalence rates and risk factors were compared between the pre-and poststudy groups. RESULTS: Pregnant women assessed after the declaration of coronavirus disease 2019 epidemic had significantly higher rates of depressive symptoms (26.0% vs 29.6%, P¼.02) than women assessed before the epidemic declaration. These women were also more likely to have thoughts of self-harm (P¼.005). The depressive rates were positively associated with the number of newly confirmed cases of coronavirus disease 2019 (P¼.003), suspected infections (P¼.004), and deaths per day (P¼.001). Pregnant women who were underweight before pregnancy, primiparous, younger than 35 years, employed full time, in middle income category, and had appropriate living space were at increased risk for developing depressive and anxiety symptoms during the outbreak. CONCLUSION: Major life-threatening public health events such as the coronavirus disease 2019 outbreak may increase the risk for mental illness among pregnant women, including thoughts of self-harm. Strategies targeting maternal stress and isolation such as effective risk communication and the provision of psychological first aid may be particularly useful to prevent negative outcomes for women and their fetuses.
Nanoparticle clusters with molecular-like configurations are an emerging class of colloidal materials. Particles decorated with attractive surface patches acting as analogs of functional groups are used to assemble colloidal molecules (CMs); however, high-yield generation of patchy nanoparticles remains a challenge. We show that for nanoparticles capped with complementary reactive polymers, a stoichiometric reaction leads to reorganization of the uniform ligand shell and self-limiting nanoparticle bonding, whereas electrostatic repulsion between colloidal bonds governs CM symmetry. This mechanism enables high-yield CM generation and their programmable organization in hierarchical nanostructures. Our work bridges the gap between covalent bonding taking place at an atomic level and colloidal bonding occurring at the length scale two orders of magnitude larger and broadens the methods for nanomaterial fabrication.
GALAMOST [graphics processing unit (GPU)-accelerated large-scale molecular simulation toolkit] is a molecular simulation package designed to utilize the computational power of GPUs. Besides the common features of molecular dynamics (MD) packages, it is developed specially for the studies of self-assembly, phase transition, and other properties of polymeric systems at mesoscopic scale by using some lately developed simulation techniques. To accelerate the simulations, GALAMOST contains a hybrid particle-field MD technique where particle–particle interactions are replaced by interactions of particles with density fields. Moreover, the numerical potential obtained by bottom-up coarse-graining methods can be implemented in simulations with GALAMOST. By combining these force fields and particle-density coupling method in GALAMOST, the simulations for polymers can be performed with very large system sizes over long simulation time. In addition, GALAMOST encompasses two specific models, that is, a soft anisotropic particle model and a chain-growth polymerization model, by which the hierarchical self-assembly of soft anisotropic particles and the problems related to polymerization can be studied, respectively. The optimized algorithms implemented on the GPU, package characteristics, and benchmarks of GALAMOST are reported in detail.
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