All findings suggested that METTL3 depletion inhibits the degradation of NOD1 and RIPK2 mRNA mediated by YTHDF1 and YTHDF2, which upregulate the NOD1 pathway and subsequently promote the LPS-induced inflammatory response in macrophages.Despite the importance of metabolic reprogramming in cancer cells, the molecular mechanism regulating the tumor metabolic shift is still poorly understood. Deregulation of Jumonji-C domain-containing protein 5 (JMJD5) has been associated with multiple facets of biological processes in cancer cells. However, the role of JMJD5 in pancreatic cancer cells has seldom been discussed and requires further investigation. In the present study, by silencing or overexpressing JMJD5 in pancreatic cancer cells, we examined the impact of JMJD5 on cell proliferation and glucose metabolism. Using a dual luciferase assay, we assessed the effect of JMJD5 on the transcriptional activity of the c-Myc target gene. Analyzing The Cancer Genome Atlas and the Gene Expression Omnibus datasets revealed that low JMJD5 expression was associated with poor prognosis in patients with pancreatic cancer. JMJD5 loss promoted pancreatic cancer cell proliferation and induced a cellular metabolic shift from oxidative phosphorylation to glycolysis. In addition, in vivo experiments confirmed that ectopic JMJD5 expression inhibited cancer cell growth and the expression of glycolytic enzymes, such as lactate dehydrogenase and phosphoglycerate kinase 1. Moreover, JMJD5 negatively regulated c-Myc expression, the main regulator of cancer metabolism, leading to decreased c-Myc-targeted gene expression. Overall, the present study indicated that decreased JMJD5 expression promoted cell proliferation and glycolytic metabolism in pancreatic cancer cells in a c-Myc-dependent manner.Automatic medical event prediction (MEP), e.g. diagnosis prediction, medication prediction, using electronic health records (EHRs) is a popular research direction in health informatics. In many cases, MEP relies on the determinations from different types of medical events, which demonstrates the heterogeneous nature of EHRs. However, most existing methods for MEP fail to distinguishingly model the type of event that is highly associated with the prediction task, i.e. task-wise event, which usually plays a more significant role than other events. In this paper, we proposed a Long Short-Term Memory network (LSTM)-based method for MEP, named Multi-Channel Fusion LSTM (MCF-LSTM), which models the correlations between different types of medical events using multiple network channels. To this end, we designed a task-wise fusion module, in which a gated network is applied to select how much information can be transferred between events. Furthermore, the irregular temporal interval between adjacent medical visits is also modeled in an individual channel, which is combined with other events in a unified manner. We compared MCF-LSTM with state-of-the-art methods on four MEP tasks on two public datasets MIMIC-III and eICU. Experimental results show that MCF-LSTM achieves promising results on AUC(receiver operating characteristic curve), AUPR (area under the precision-recall curve), and top-k recall, and outperforms other methods with high stability.Versican is a large chondroitin sulfate/dermatan sulfate proteoglycan that plays a key role in the formation of the provisional matrix. Here, we generated dextran sulfate sodium-induced colitis in knockin-mice, R/R, expressing ADAMTS-resistant versican, and investigated the impact of accumulating versican and its turnover in the inflammatory colon mucosa. Histologically, R/R colon showed decreased levels of tissue destruction and an increased number of myofibroblasts and macrophages. Characterization of inflammatory cells revealed an increase in F4/80+ macrophages in R/R colon, compared with wildtype, without a clear shift between M1 and M2 populations. Intestinal stroma exhibited a higher number of myofibroblasts in R/R, suggesting increased levels of tissue regeneration. Coculture of macrophages and stromal fibroblasts obtained from inflammatory colon showed that wild-type macrophages inhibited myofibroblastic differentiation of R/R fibroblasts but not wild-type. This inhibitory effect was due to an increased level of versikine, a cleaved fragment of versican by ADAMTS proteinases. Taken together, our results demonstrate versikine as the direct regulator that inhibits repair of inflamed tissue.Human aromatase, also called CYP19A1, plays a major role in the conversion of androgens into estrogens. Inhibition of aromatase is an important target for estrogen receptor (ER)-responsive breast cancer therapy. Use of azole compounds as aromatase inhibitors is widespread despite their low selectivity. PLX4032 price A toxicological evaluation of commonly used azole-based drugs and agrochemicals with respect to CYP19A1 is currently requested by the European Union- Registration, Evaluation, Authorization and Restriction of Chemicals (EU-REACH) regulations due to their potential as endocrine disruptors. In this connection, identification of structural alerts (SAs) is an effective strategy for the toxicological assessment and safe drug design. The present study describes the identification of SAs of azole-based chemicals as guiding experts to predict the aromatase activity. Total 21 SAs associated with aromatase activity were extracted from dataset of 326 azole-based drugs/chemicals obtained from Tox21 library. A cross-validated classification model having high accuracy (error rate 5%) was proposed which can precisely classify azole chemicals into active/inactive toward aromatase. In addition, mechanistic details and toxicological properties (agonism/antagonism) of azoles with respect to aromatase were explored by comparing active and inactive chemicals using structure-activity relationships (SAR). Lastly, few structural alerts were applied to form chemical categories for read-across applications.Sepsis is a life-threatening organ dysfunction caused by a dysregulated host response to infection. Septic shock is a subset of sepsis with underlying circulatory cellular and metabolic abnormalities associated with higher mortality rates. However, a detailed understanding of sepsis is still limited. The present study reports the differences in the metabolic profile of serum samples of patients with sepsis compared to healthy controls using Nuclear Magnetic Resonance (NMR) spectroscopy. The study also compares the NMR metabolomics on day zero of admission among sepsis survivors (those who survived till day seven) and sepsis non-survivors (those who succumbed on day zero). Furthermore, the different metabolites in serum were analysed by univariate and multivariate analysis, ROC analysis, principal component analysis (PCA), partial least squares discriminant analysis (PLS-DA) and orthogonal partial least squares discriminant analysis (OPLS-DA) methods. Metabolites with VIP score (>1·0) were considered as potential biomarker/s to discriminate sepsis survivors from non- survivors at day zero. Data showed that phenylalanine was significantly higher in sepsis patients compared to healthy controls, whereas isoleucine, valine and histidine were significantly lower in sepsis patients compared to healthy controls. Also, non-survivors had higher serum levels of creatine, phosphocreatine, choline, betaine, tyrosine, histidine and phenylalanine concentrations than survivors. These findings suggest that the metabolic alterations at day zero may predict the survival of patients with sepsis. The significant differences seen in metabolites concentration of amino acids, phospholipids and creatine may be used as early prognostic markers to discriminate non-survivors from survivors of sepsis patients at day zero. Our findings indicate that the metabolite alterations are associated with the progression of the disease.Low-field MR scanners are more accessible in resource-constrained settings where skilled personnel are scarce. Images acquired in such scenarios are prone to artifacts such as wrap-around and Gibbs ringing. Such artifacts negatively affect the diagnostic quality and may be confused with pathology or reduce the region of interest visibility. As a first step solution, ArtifactID identifies wrap-around and Gibbs ringing in low-field brain MRI. We utilized two datasets 179 T1-weighted pathological brain images from a 0.36 T scanner and 581 publicly available T1-weighted brain images. Individual binary classification models were trained to identify through-plane wrap-around, in-plane wrap-around, and Gibbs ringing. Visual explanations obtained via the GradCAM method helped develop trust in the wrap-around model. The mean precision and recall metrics across the four implemented models were 97.6% and 92.83% respectively. Agreement analysis of the models and the radiologists‘ labels returned Cohen’s kappa values of 0.768 ± 0.062, 1.00 ± 0.000, 0.89 ± 0.085, and 0.878 ± 0.103 for the through-plane wrap-around, in-plane wrap-around, and Gibbs ringing models, respectively.To solve the problem of long sampling time for diffusion magnetic resonance imaging (dMRI), in this study we propose a dMRI super-resolution reconstruction network. This method not only uses a three-dimensional (3D) convolution kernel to reconstruct the dMRI data in the space and angle domains, but also introduces an adversarial learning and attention mechanism to solve the problem of the traditional loss function not fully quantifying the gap between high-dimensional data and not paying more attention to important feature maps. Experimental results from the comparison of peak signal-to-noise ratio, structural similarity, and orientation distribution function visualization show that these methods bring better results. They also prove the feasibility of using an attention mechanism in dMRI reconstruction and the use of adversarial learning in a 3D convolution kernel.The mitogen-activated protein kinase (MAPK) cascade plays a crucial role in regulating many important biological processes in plants. Here, we identified and characterized eight MAPKK and 49 MAPKKK genes in sorghum and analyzed their differential expression under drought treatment; we also characterized 16 sorghum MAPK genes. RNA-seq analysis revealed that 10 MAPK cascade genes were involved in drought stress response at the transcriptome level in sorghum. Overexpression of SbMPK14 in Arabidopsis and maize resulted in hypersensitivity to drought by promoting water loss, indicating that SbMPK14 functions as a negative regulator of the drought response. Subsequent transcriptome analysis and qRT-PCR verification of maize SbMPK14 overexpression lines revealed that SbMPK14 likely increases plant drought sensitivity by suppressing the activity of specific ERF and WRKY transcription factors. This comprehensive study provides valuable insight into the mechanistic basis of MAPK cascade gene function and their responses to drought in sorghum.High-fat diet (HFD) consumption leads to obesity and a chronic state of low-grade inflammation, named metainflammation. Notably, metainflammation contributes to neuroinflammation due to the increased levels of circulating free fatty acids and cytokines. It indicates a strict interplay between peripheral and central counterparts in the pathogenic mechanisms of obesity-related mood disorders. In this context, the impairment of internal hypothalamic circuitry runs in tandem with the alteration of other brain areas associated with emotional processing (i.e., hippocampus and amygdala). Palmitoylethanolamide (PEA), an endogenous lipid mediator belonging to the N-acylethanolamines family, has been extensively studied for its pleiotropic effects both at central and peripheral level. Our study aimed to elucidate PEA capability in limiting obesity-induced anxiety-like behavior and neuroinflammation-related features in an experimental model of HFD-fed obese mice. PEA treatment promoted an improvement in anxiety-like behavior of obese mice and the systemic inflammation, reducing serum pro-inflammatory mediators (i.