Background: During the coronavirus disease (COVID-19) pandemic, harsh social distancing measures were taken in China to contain viral spread. We examined their impact on the lives of medical students. Methods: A nation-wide cross-sectional survey of college students was conducted from 4–12 February 2020. We enrolled medical students studying public health in Beijing and Wuhan to assess their COVID-19 awareness and to evaluate their mental health status/behaviors using a self-administered questionnaire. We used the Patient Generalized Anxiety Disorder-7 and Health Questionnaire-9 to measure anxiety disorders and depression. We used multivariable logistic regression and path analysis to assess the associations between covariates and anxiety disorder/depression. Results: Of 933 students, 898 (96.2%) reported wearing masks frequently when going out, 723 (77.5%) reported daily handwashing with soap, 676 (72.5%) washed hands immediately after arriving home, and 914 (98.0%) reported staying home as much as possible. Prevalence of anxiety disorder was 17.1% and depression was 25.3%. Multivariable logistic regression showed anxiety to be associated with graduate student status (odds ratio (aOR) = 2.0; 95% confidence interval (CI): 1.2–3.5), negative thoughts or actions (aOR = 1.6; 95% CI: 1.4–1.7), and feeling depressed (aOR = 6.8; 95% CI: 4.0–11.7). Beijing students were significantly less likely to have anxiety than those in the Wuhan epicenter (aOR = 0.9; 95% CI: 0.8–1.0), but depression did not differ. Depression was associated with female students (aOR = 2.0; 95% CI: 1.2–3.3), negative thoughts or actions (aOR = 1.7; 95% CI: 1.5–1.9), and anxiety disorder (aOR = 5.8; 95% CI: 3.4–9.9). Path analysis validated these same predictors. Conclusions: Despite medical students’ knowledge of disease control and prevention, their lives were greatly affected by social distancing, especially in the Wuhan epicenter. Even well-informed students needed psychological support during these extraordinarily stressful times.

Observed choices between risky lotteries are difficult to reconcile with expected utility maximization, both because subjects appear to be too risk averse with regard to small gambles for this to be explained by diminishing marginal utility of wealth, as stressed by Rabin (2000), and because subjects’ responses involve a random element. We propose a unified explanation for both anomalies, similar to the explanation given for related phenomena in the case of perceptual judgments: they result from judgments based on imprecise (and noisy) mental representations of the decision situation. In this model, risk aversion results from a sort of perceptual bias — but one that represents an optimal decision rule, given the limitations of the mental representation of the situation. We propose a quantitative model of the noisy mental representation of simple lotteries, based on other evidence regarding numerical cognition, and test its ability to explain the choice frequencies that we observe in a laboratory experiment.

Background Coronavirus disease 2019 (COVID-19) is an emerging infectious disease that first appeared in Wuhan, China, and quickly spread throughout the world. We aimed to understand the relationship between diabetes mellitus and the prognosis of COVID-19. Methods Demographic, clinical, laboratory, radiologic, treatments, complications, and clinical outcomes data were extracted from electronic medical records and compared between diabetes (n = 84) and nondiabetes (n = 500) groups. Kaplan-Meier method and multivariate Cox analysis were applied to determine the risk factors for the prognosis of COVID-19. Results Compared with nondiabetic patients, diabetic patients had higher levels of neutrophils ( P = .014), C-reactive protein ( P = .008), procalcitonin ( P < .01), and D-dimer ( P = .033), and lower levels of lymphocytes ( P = .032) and albumin ( P = .035). Furthermore, diabetic patients had a significantly higher incidence of bilateral pneumonia (86.9%, P = .020). In terms of complications and clinical outcomes, the incidence of respiratory failure (36.9% vs 24.2%, P = .022), acute cardiac injury (47.4% vs 21.2%, P < .01), and death (20.2% vs 8.0%, P = .001) in the diabetes group was significantly higher than that in the nondiabetes group. Kaplan-Meier survival curve showed that COVID-19 patients with diabetes had a shorter overall survival time. Multivariate Cox analysis indicated that diabetes (hazard ratio 2.180, P = .031) was an independent risk factor for COVID-19 prognosis. In subgroup analysis, we divided diabetic patients into insulin-required and non-insulin-required groups according to whether they needed insulin, and found that diabetic patients requiring insulin may have a higher risk of disease progression and worse prognosis after the infection of severe acute respiratory syndrome coronavirus 2. Conclusions Diabetes is an independent risk factor for the prognosis of COVID-19. More attention should be paid to the prevention and treatment for diabetic patients, especially those who require insulin therapy.

Mechanically robust polymer electrolyte membranes (PEMs) exhibiting high ionic conductivity at ambient temperature are a prerequisite for next-generation electrochemical devices. We utilized a polymerization-induced microphase separation (PIMS) strategy to prepare nanostructured materials comprising continuous conducting nanochannels intertwined with a mechanically and thermally robust cross-linked polymeric framework. Addition of succinonitrile (SN) rendered the poly(ethylene oxide)/lithium (Li) salt conducting domains completely amorphous, resulting in outstanding conductivities (∼0.35 mS/cm) at 30 °C. Concurrently, a densely cross-linked polystyrene framework provided mechanical robustness (modulus E' ≈ 0.3 GPa at 30 °C) to the hybrid material. This work highlights a facile, single-pot strategy involving a homogeneous liquid reaction precursor that yields a high-performance ion-conducting membrane attractive for lithium-battery applications.

Nitrogen (N) is an extremely important macronutrient for plant growth and development. It is the main limiting factor in most agricultural production. However, it is well known that the nitrogen use efficiency (NUE) of rice gradually decreases with the increase of the nitrogen application rate. In order to clarify the underlying metabolic and molecular mechanisms of this phenomenon, we performed an integrated analysis of the rice transcriptome and metabolome. Both differentially expressed genes (DEGs) and metabolite Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis indicated that carbon and nitrogen metabolism is significantly affected by nitrogen availability. Further analysis of carbon and nitrogen metabolism changes in rice under different nitrogen availability showed that high N inhibits nitrogen assimilation and aromatic metabolism pathways by regulating carbon metabolism pathways such as the tricarboxylic acid (TCA) cycle and the pentose phosphate pathway (PPP). Under low nitrogen, the TCA cycle is promoted to produce more energy and α-ketoglutarate, thereby enhancing nitrogen transport and assimilation. PPP is also inhibited by low N, which may be consistent with the lower NADPH demand under low nitrogen. Additionally, we performed a co-expression network analysis of genes and metabolites related to carbon and nitrogen metabolism. In total, 15 genes were identified as hub genes. In summary, this study reveals the influence of nitrogen levels on the regulation mechanisms for carbon and nitrogen metabolism in rice and provides new insights into coordinating carbon and nitrogen metabolism and improving nitrogen use efficiency in rice.

Blends of hydroxypropyl methylcellulose acetate succinate (HPMCAS) and dodecyl (C)-tailed poly(N-isopropylacrylamide) (PNIPAm) were systematically explored as a model system to dispense the active ingredient phenytoin by rapid dissolution, followed by the suppression of drug crystallization for an extended period. Dynamic and static light scattering revealed that C-PNIPAm polymers, synthesized by reversible addition-fragmentation chain-transfer polymerization, self-assembled into micelles with dodecyl cores in phosphate-buffered saline (PBS, pH 6.5). A synergistic effect on drug supersaturation was documented during in vitro dissolution tests by varying the blending ratio, with HPMACS primarily aiding in rapid dissolution and PNIPAm maintaining supersaturation. Polarized light and cryogenic transmission electron microscopy experiments revealed that C-PNIPAm micelles maintain drug supersaturation by inhibiting both crystal nucleation and growth. Cross-peaks between the phenyl group of phenytoin and the isopropyl group of C-PNIPAm in 2D H nuclear Overhauser effect (NOESY) spectra confirmed the existence of drug-polymer intermolecular interactions in solution. Phenytoin and polymer diffusion coefficients, measured by diffusion-ordered NMR spectroscopy (DOSY), demonstrated that the drug-polymer association constant increased with increasing local density of the corona chains, coincident with a reduction in C-PNIPAm molecular weight. These findings demonstrate a new strategy for exploiting the versatility of polymer blends through the use of self-assembled micelles in the design of advanced excipients.

Solubility-enhancing amorphous solid dispersions are used in the oral delivery of hydrophobic, crystallizable drugs. Effective solid dispersion excipients enable high supersaturation drug concentrations and limit crystallization of the dissolved drug over extended times. We prepared poly­(N-isopropyl­acrylamide)-based excipients of varying molar mass and with various end group identities, and examined their ability to improve the aqueous solubility of the Biopharmaceutical Class System Class II drug, phenytoin. Solid dispersions of these excipients and phenytoin were prepared at 10 wt % drug loading. Performance depended largely on the tendency of the polymer excipient to form micellar aggregates in aqueous buffer. We present several systems that achieved significant improvement of phenytoin solubility, with no indication of drug crystallization over 6 h. This is among the highest enhancement factors seen for phenytoin to date, and the success of these systems is ascribed to the added stability of these “self-micellizing” solid dispersions.

Two poly(N-isopropylacrylamide-co-N,N-dimethylacrylamide) (PND) statistical copolymers and a series of three poly(N-isopropylacrylamide-co-N,N-dimethylacrylamide)-b-polystyrene (PND-b-PS-C 12 ) diblock polymers were synthesized by reversible addition−fragmentation chain transfer (RAFT) polymerization, in which the molecular weight of the thermoresponsive PND corona block was held constant while the polystyrene core block length was varied. The corona thickness and density of the micelles in phosphate-buffered saline (PBS, pH = 6.5) were quantified by a combination of dynamic light scattering (DLS) and small-angle X-ray scattering (SAXS). Two hydrophobic model drugs, phenytoin and nilutamide, were used to examine the drug−polymer interactions in aqueous solution. Intermolecular interactions between the diblock polymer micelle corona and both drugs were revealed by 2D 1 H nuclear Overhauser effect spectroscopy (NOESY). The drug−polymer "binding" strength, quantified by diffusion ordered NMR spectroscopy (DOSY), increased as corona density of the diblock polymer micelle increased for both drugs. The in vitro dissolution of the amorphous solid dispersions was systematically investigated as a function of drug type, drug loading, and the solution-state assembly of the polymers by using either a selective or nonselective spray drying solvent. Forming micelles prior to spray drying significantly enhanced phenytoin dissolution and supersaturation maintenance for the diblock polymers by storing the drug molecules in the corona.