Activation of Interleukin-1β Release by the Classical Swine Fever Virus Is Dependent on the NLRP3 Inflammasome, Which Affects Virus Growth in Monocytes

Classical swine fever virus (CSFV) is a classic Flavivirus that causes the acute, febrile, and highly contagious disease known as classical swine fever (CSF). Inflammasomes are molecular platforms that trigger the maturation of proinflammatory cytokines to engage innate immune defenses that are induced upon cellular infection or stress. However, the relationship between the inflammasome and CSFV infection has not been thoroughly characterized. To understand the function of the inflammasome response to CSFV infection, we infected porcine peripheral blood monocytes (PBMCs) with CSFV. Our results indicated that CSFV infection induced both the generation of pro-interleukin-1β (pro-IL-1β) and its processing in monocytes, leading to the maturation and secretion of IL-1β through the activation of caspase 1. Moreover, CSFV infection in PBMCs induced the production and cleavage of gasdermin D (GSDMD), which is an inducer of pyroptosis. Additional studies showed that CSFV-induced IL-1β secretion was mediated by NLRP3 and that CSFV infection could sufficiently activate the assembly of the NLRP3 inflammasome in monocytes. These results revealed that CSFV infection inhibited the expression of NLRP3, and knockdown of NLRP3 enhanced the replication of CSFV. In conclusion, these findings demonstrate that the NLRP3 inflammasome plays an important role in the innate immune response to CSFV infection

Extreme multistability in memristive hyper-jerk system and stability mechanism analysis using dimensionality reduction model

This paper presents a memristive hyper-jerk system with smooth hyperbolic tangent memductance nonlinearity. Such a smooth memductance nonlinearity can cause the system to possess a line equilibrium therein, leading to the emergence of extreme multistability with coexisting infinitely many attractors due to the existence of a zero eigenvalue. To illustrate the stability mechanism, the dimensionality reduction model of the memristive hyper-jerk system is obtained using state variable mapping (SVM) method and several isolated equilibria are yielded from the dimensionality reduction model. As a consequence, the initial-dependent extreme multistability in the memristive hyper-jerk system is converted into the initial-related parameter-dependent dynamics in the dimensionality reduction model and the stability mechanism analysis is thereby executed. Furthermore, PSIM circuit simulations based on a physical circuit are performed to confirm the coexisting infinitely many attractors

Comparison of Machine Learning Methods and Conventional Logistic Regressions for Predicting Gestational Diabetes Using Routine Clinical Data: A Retrospective Cohort Study

Background. Gestational diabetes mellitus (GDM) contributes to adverse pregnancy and birth outcomes. In recent decades, extensive research has been devoted to the early prediction of GDM by various methods. Machine learning methods are flexible prediction algorithms with potential advantages over conventional regression. Objective. The purpose of this study was to use machine learning methods to predict GDM and compare their performance with that of logistic regressions. Methods. We performed a retrospective, observational study including women who attended their routine first hospital visits during early pregnancy and had Down’s syndrome screening at 16-20 gestational weeks in a tertiary maternity hospital in China from 2013.1.1 to 2017.12.31. A total of 22,242 singleton pregnancies were included, and 3182 (14.31%) women developed GDM. Candidate predictors included maternal demographic characteristics and medical history (maternal factors) and laboratory values at early pregnancy. The models were derived from the first 70% of the data and then validated with the next 30%. Variables were trained in different machine learning models and traditional logistic regression models. Eight common machine learning methods (GDBT, AdaBoost, LGB, Logistic, Vote, XGB, Decision Tree, and Random Forest) and two common regressions (stepwise logistic regression and logistic regression with RCS) were implemented to predict the occurrence of GDM. Models were compared on discrimination and calibration metrics. Results. In the validation dataset, the machine learning and logistic regression models performed moderately (AUC 0.59-0.74). Overall, the GBDT model performed best (AUC 0.74, 95% CI 0.71-0.76) among the machine learning methods, with negligible differences between them. Fasting blood glucose, HbA1c, triglycerides, and BMI strongly contributed to GDM. A cutoff point for the predictive value at 0.3 in the GBDT model had a negative predictive value of 74.1% (95% CI 69.5%-78.2%) and a sensitivity of 90% (95% CI 88.0%-91.7%), and the cutoff point at 0.7 had a positive predictive value of 93.2% (95% CI 88.2%-96.1%) and a specificity of 99% (95% CI 98.2%-99.4%). Conclusion. In this study, we found that several machine learning methods did not outperform logistic regression in predicting GDM. We developed a model with cutoff points for risk stratification of GDM

Label-Free SERS Analysis of Serum Using Ag NPs/Cellulose Nanocrystal/Graphene Oxide Nanocomposite Film Substrate in Screening Colon Cancer

Label-free surface-enhanced Raman scattering (SERS) analysis shows tremendous potential for the early diagnosis and screening of colon cancer, owing to the advantage of being noninvasive and sensitive. As a clinical diagnostic tool, however, the reproducibility of analytical methods is a priority. Herein, we successfully fabricated Ag NPs/cellulose nanocrystals/graphene oxide (Ag NPs/CNC/GO) nanocomposite film as a uniform SERS active substrate for label-free SERS analysis of clinical serum. The Ag NPs/CNC/GO suspensions by self-assembling GO into CNC solution through in-situ reduction method. Furthermore, we spin-coated the prepared suspensions on the bacterial cellulose membrane (BCM) to form Ag NPs/CNC/GO nanocomposite film. The nanofilm showed excellent sensitivity (LOD = 30 nM) and uniformity (RSD = 14.2%) for Nile Blue A detection. With a proof-of-concept demonstration for the label-free analysis of serum, the nanofilm combined with the principal component analysis-linear discriminant analysis (PCA-LDA) model can be effectively employed for colon cancer screening. The results showed that our model had an overall prediction accuracy of 84.1% for colon cancer (n = 28) and the normal (n = 28), and the specificity and sensitivity were 89.3% and 71.4%, respectively. This study indicated that label-free serum SERS analysis based on Ag NPs/CNC/GO nanocomposite film combined with machine learning holds promise for the early diagnosis of colon cancer

Graphene Dots Embedded Phosphide Nanosheet-Assembled Tubular Arrays for Efficient and Stable Overall Water Splitting

Bifunctional electrocatalysts are highly desired for overall water splitting. Herein, the design and fabrication of three-dimensional (3D) hierarchical earth-abundant transition bimetallic phosphide arrays constructed by one-dimensional tubular array that was derived from assembling two-dimensional nanosheet framework has been reported by tailoring the Co/Ni ratio and tunable morphologies, and zero-dimensional (0D) graphene dots were embedded on Co–Ni phosphide matrix to construct 0D/2D tubular array as a highly efficient electrode in the hydrogen evolution reaction (HER) and oxygen evolution reaction (OER). On the basis of advanced merits, such as the high surface-active sites, well-dispersed graphene dots, and enhanced electron transfer capacity as well as the confinement effect of the graphene dots on the nanosheets, the integrated GDs/Co_0.8Ni_0.2P tubular arrays as anode and cathode exhibit excellent OER and HER performance. By use of GDs/Co_0.8Ni_0.2P arrays in the two-electrode setup of the device, a remarkable electrocatalytic performance for full water splitting has been achieved with a high current density of 10 mA cm^–2 at 1.54 V and outstanding long-term operation stability in an alkaline environment, indicating a promising system based on nonprecious-metal electrocatalysts toward potential practical devices of overall water splitting

Fabrication of Cu-Doped CeO2 Catalysts with Different Dimension Pore Structures for CO Catalytic Oxidation

Transition metal oxides (TMOs) applied as catalysts whose catalytic activities are directly affected by their pores size and pores distributions. Herein, two-dimensional Cu-doped CeO2 (2D@Cu-CeO2) and three-dimensional Cu-doped CeO2 (3D@Cu-CeO2) were prepared by adopting the mesoporous silica SBA-15 and KIT-6 as templates, respectively. Nanometer Cu-doped CeO2 (nano@Cu-CeO2) was synthesized by the method of precipitation. All catalysts were evaluated for the catalytic oxidation of CO, and the 3D@Cu-CeO2 catalyst exhibited the highest catalytic activity (complete conversion temperature T-100 = 50 A degrees C), which can be ascribed to the three-dimensional porous channel structure, larger specific surface area and abundant active surface oxygen species. In addition, complete conversion of CO had remained the same after 3D@Cu-CeO2 was observed for 12 h, indicating it has the best catalytic stability for CO

Graphene Dots Embedded Phosphide Nanosheet-Assembled Tubular Arrays for Efficient and Stable Overall Water Splitting

Bifunctional electrocatalysts are highly desired for overall water splitting. Herein, the design and fabrication of three-dimensional (3D) hierarchical earth-abundant transition bimetallic phosphide arrays constructed by one-dimensional tubular array that was derived from assembling two-dimensional nanosheet framework has been reported by tailoring the Co/Ni ratio and tunable morphologies, and zero-dimensional (0D) graphene dots were embedded on Co–Ni phosphide matrix to construct 0D/2D tubular array as a highly efficient electrode in the hydrogen evolution reaction (HER) and oxygen evolution reaction (OER). On the basis of advanced merits, such as the high surface-active sites, well-dispersed graphene dots, and enhanced electron transfer capacity as well as the confinement effect of the graphene dots on the nanosheets, the integrated GDs/Co_0.8Ni_0.2P tubular arrays as anode and cathode exhibit excellent OER and HER performance. By use of GDs/Co_0.8Ni_0.2P arrays in the two-electrode setup of the device, a remarkable electrocatalytic performance for full water splitting has been achieved with a high current density of 10 mA cm^–2 at 1.54 V and outstanding long-term operation stability in an alkaline environment, indicating a promising system based on nonprecious-metal electrocatalysts toward potential practical devices of overall water splitting