Quercetin regulates dendritic cell activation by targeting STAT4 in the treatment of experimental autoimmune encephalomyelitis.

Multiple sclerosis (MS) is a class of autoimmune diseases mainly caused by the immune system attacking the myelin sheath of the axons in the nervous system. Although the pathogenesis of MS is complex, studies have shown that dendritic cells (DCs) play a vital role in the pathogenesis of MS. Quercetin (QU) has a unique advantage in clinical application, especially for treating autoimmune diseases. However, the mechanism of QU in the treatment of experimental autoimmune encephalomyelitis (EAE) remains unclear. In this study, we explore the potential role of QU in EAE. Finally, we find that QU has anti-inflammatory activities and neural protective effects in EAE. The experimental results suggest that the cellular basis for QU's function is to inhibit the activation of DCs while modulating the Th17 cell differentiation in the co-culture system. Further, QU may target STAT4 to inhibit its activation in DCs. This work will be of great significance for the future development and utilization of QU.

Improving DNA 6mA Site Prediction via Integrating Bidirectional Long Short-Term Memory, Convolutional Neural Network, and Self-Attention Mechanism.

Identifying DNA N6-methyladenine (6mA) sites is significantly important to understanding the function of DNA. Many deep learning-based methods have been developed to improve the performance of 6mA site prediction. In this study, to further improve the performance of 6mA site prediction, we propose a new meta method, called Co6mA, to integrate bidirectional long short-term memory (BiLSTM), convolutional neural networks (CNNs), and self-attention mechanisms (SAM) via assembling two different deep learning-based models. The first model developed in this study is called CBi6mA, which is composed of CNN, BiLSTM, and fully connected modules. The second model is borrowed from LA6mA, which is an existing 6mA prediction method based on BiLSTM and SAM modules. Experimental results on two independent testing sets of different model organisms, i.e., Arabidopsis thaliana and Drosophila melanogaster, demonstrate that Co6mA can achieve an average accuracy of 91.8%, covering 89% of all 6mA samples while achieving an average Matthews correlation coefficient value (0.839), which is higher than the second-best method DeepM6A.

Effects of the Extraction Solvents on Dissolution Rate and Antioxidant Capacity of Auricularia auricula (Agaricomycetes) Polysaccharides In Vitro and In Vivo.

This paper reports the effects of solvents on the dissolution rate and antioxidant capacity of Auricularia auricula polysaccharides (AAPs). The ultra-low temperature combined with microwave extraction (UME) was used to compare the dissolution rates and molecular weights of AAPs using deionized water and deep eutectic solvents (DES) as solvents, respectively. Scanning electron microscope (SEM) was used to observe the effects of water extract (AAPs-FW) and DES extract (AAPs-FD) on the cell wall of A. auricula. The antioxidant capacity of polysaccharide extracts in vitro was assessed by using various methods (DPPH, ABTS, and hydroxyl radicals). In addition, in vivo oxidative stress was assessed using Caenorhabditis elegans models. The extract yield of AAPs varied among the extracts and was 19.58% ± 0.56% in AAPs-FW. Whereas DES-UME increased the yield of polysaccharides (AAPs-FD) by 9.81% in the extraction medium containing triethylene glycol-choline chloride, under the optimum conditions of 60 min freezing time, 350 W, and 90 s microwave time. The microstructure of the cell wall shown by SEM was consistent with the results of polysaccharide yields. The molecular weights of AAPs-FW and AAPs-FD were found to be 398.107 kDa and 89.099 kDa, respectively. The results demonstrated that AAPs-FD exhibited potent radical scavenging activity against DPPH and a weaker scavenging ability for ABTS and OH radicals compared to AAPs-FW. In addition, both polysaccharide extracts increased the survival rate of C. elegans under methyl viologen induced oxidative stress at specific concentrations (p < 0.05), and the antioxidant capacity of AAPs-FD was higher than that of AAPs-FW at low concentrations (0.125 mg/mL). This indicated that both polysaccharides had a protective effect against damage induced by intracellular free radical generators (methyl viologen).

Improving protein-protein interaction site prediction using deep residual neural network.

Accurate identification of protein-protein interaction (PPI) sites is significantly important for understanding the mechanism of life and developing new drugs. However, it is expensive and time-consuming to identify PPI sites using wet-lab experiments. Developing computational methods is a new road to identify PPI sites, which can accelerate the procedure of PPI-related research. In this study, we propose a novel deep learning-based method (called D-PPIsite) to improve the accuracy of sequence-based PPI site prediction. In D-PPIsite, four discriminative sequence-driven features, i.e., position specific scoring matrix, relative solvent accessibility, position information and physical properties, are employed to feed into a well-designed deep learning module, consisting of convolutional, squeeze and excitation, and fully connected layers, to learn a prediction model. To reduce the risk of a single prediction model getting stuck in local optima, multiple prediction models with different initialization parameters are selected and integrated into one final model using the mean ensemble strategy. Experimental results on five independent testing data sets demonstrate that the proposed D-PPIsite can achieve an average accuracy of 80.2% and precision of 36.9%, covering 53.5% of all PPI sites while achieving the average Matthews correlation coefficient value (0.330) that is significantly higher than most of existing state-of-the-art prediction methods. We implement a new standalone-version predictor for predicting PPI sites, which is freely available at https://github.com/MingDongup/D-PPIsite for academic use.

Predicting RNA solvent accessibility from multi-scale context feature via multi-shot neural network.

Knowledge of RNA solvent accessibility has recently become attractive due to the increasing awareness of its importance for key biological process. Accurately predicting the solvent accessibility of RNA is crucial for understanding its 3D structure and biological function. In this study, we develop a novel computational method, termed M2pred, for accurately predicting the solvent accessibility of RNA from sequence-based multi-scale context feature. In M2pred, three single-view features, i.e., base-pairing probabilities, position-specific frequency matrix, and a binary one-hot encoding, are first generated as three feature sources, and immediately concatenated to engender a super feature. Secondly, for the super feature, the matrix-format features of each nucleotide are extracted using an initialized sliding window technique, and regularly stacked into a cube-format feature. Then, using multi-scale context feature extraction strategy, a pyramid feature constructed of contextual feature of four scales related to target nucleotides is extracted from the cube-format feature. Finally, a customized multi-shot neural network framework, which is equipped with four different scales of receptive fields mainly integrating several residual attention blocks, is designed to dig discrimination information from the contextual pyramid feature. Experimental results demonstrate that the proposed M2pred achieve a high prediction performance and outperforms existing state-of-the-art prediction methods of RNA solvent accessibility.

Dietary intake of monounsaturated and polyunsaturated fatty acids is related to the reduced risk of esophageal squamous cell carcinoma.

BACKGROUND: The relationship of consumption of dietary fat and fatty acids with esophageal squamous cell carcinoma (ESCC) risk remains unclear. This study aimed to explore the relationship of dietary fat and fatty acids intake with ESCC risk. METHODS: This case-control study included 879 incident cases and 892 community-based controls recruited from Southwest China. A food frequency questionnaire was adopted to collect information about dietary information, and intake of fat, saturated fatty acid (SFA), monounsaturated fatty acid (MUFA), polyunsaturated fatty acid (PUFA), and total fatty acid (TFA) was calculated. Odds ratios (ORs) with 95% confidence intervals (95% CIs) were estimated using the logistic regression model. RESULTS: When comparing the highest with lowest intake quintiles, MUFA (OR: 0.33, 95% CI: 0.21-0.51), PUFA (OR: 0.32, 95% CI: 0.20-0.51), and TFA (OR: 0.44, 95% CI: 0.28-0.70) were related to a reduced risk of ESCC after adjusting for confounders; for non-drinkers rather than drinkers, the intake of SFA was significantly related to a 61% (OR: 0.39, 95% CI: 0.19-0.81) reduced risk of ESCC when comparing the highest with the lowest intake quintiles. Dietary fat was not related to the risk of ESCC. CONCLUSIONS: This study suggested that the more intake of MUFA and PUFA, the lower risk of ESCC, whereas the protective effect of TFA was only observed among non-drinkers. Strategic nutritional programs should consider food rich in unsaturated fatty acids to mitigate the occurrence of ESCC.

Constructing Sensitive and Fast Lead-Free Single-Crystalline Perovskite Photodetectors.

We developed a high-performance photodetector based on (CH3NH3)3Sb2I9 (MA3Sb2I9) microsingle crystals (MSCs). The MA3Sb2I9 single crystals exhibit a low-trap state density of ∼1010 cm-3 and a long carrier diffusion length reaching 3.0 µm, suggesting its great potential for optoelectronic applications. However, the centimeter single crystal (CSC)-based photodetector exhibits low responsivity (10-6 A/W under 1 sun illumination) due to low charge-carrier collection efficiency. By constructing the MSC photodetector with efficient charge-carrier collection, the responsivity can be improved by three orders of magnitude (under 1 sun illumination) and reach 40 A/W with monochromatic light (460 nm). Furthermore, the MSC photodetectors exhibit fast response speed of <1 ms, resulting in a high gain of 108 and a gain-bandwidth product of 105 Hz. These numbers are comparable to the lead-perovskite single-crystal-based photodetectors.