Prediction of IDH1 gene mutation by a nomogram based on multiparametric and multiregional MR images

Objective: To investigate the value of a nomogram based on multiparametric and multiregional MR images to predict Isocitrate Dehydrogenase-1 (IDH1) gene mutations in glioma. Data and methods: The authors performed a retrospective analysis of 110 MR images of surgically confirmed pathological gliomas; 33 patients with IDH1 gene Mutation (IDH1-M) and 77 patients with Wild-type IDH1 (IDH1-W) were divided into training and validation sets in a 7:3 ratio. The clinical features were statistically analyzed using SPSS and R software. Three glioma regions (rCET, rE, rNEC) were outlined using ITK-SNAP software and projected to four conventional sequences (T1, T2, Flair, T1C) for feature extraction using AI-Kit software. The extracted features were screened using R software. A logistic regression model was established, and a nomogram was generated using the selected clinical features. Eight models were developed based on different sequences and ROIs, and Receiver Operating Characteristic (ROC) curves were used to evaluate the predictive efficacy. Decision curve analysis was performed to assess the clinical usefulness. Results: Age was selected with Radscore to construct the nomogram. The Model 1 AUC values based on four sequences and three ROIs were the highest in these models, at 0.93 and 0.89, respectively. Decision curve analysis indicated that the net benefit of model 1 was higher than that of the other models for most Pt-values. Conclusion: A nomogram based on multiparametric and multiregional MR images can predict the mutation status of the IDH1 gene accurately

Importance measures for inspections in binary networks

Many infrastructure systems can be modeled as networks of components with binary states (intact, damaged). Information about components conditions is crucial for the maintenance process of the system. However, it is often impossible to collect information of all components due to budget constraints. Several metrics have been developed to assess the importance of the components in relation to maintenance actions: an important component is one that should receive high maintenance priority. Instead, in this paper we focus on the priority to be assigned for component inspections and information collection. We investigate metrics based on system level (global) and component level (local) decision making after inspection for networks with different topology, and compare these results with traditional ones. We then discuss the computational challenges of these metrics and provide possible approximation approaches.We acknowledge the support of NSF project CMMI #1653716, titled CAREER: Infrastructure Management under Model Uncertainty: Adaptive Sequential Learning and Decision Making

Strengthening dendrite suppression in lithium metal anode by in-situ construction of Li–Zn alloy layer

Abstract(#br)The lithium metal anode is one of the most attractive candidates for high-energy lithium rechargeable batteries because it has an ultrahigh theoretical specific capacity and the lowest electrode potential. Unfortunately, uncontrollable growth of dendritic Li leads to problems such as safety hazards and low cycling reversibility, which greatly hinder its commercial application. Here, a Li–Zn alloy layer is constructed in situ on Li metal foil by a simple chemical reaction of zinc trifluoromethanesulfonate with Li metal. The modified Li metal anode forms an interface with fast charge transfer kinetics and high chemical resistance to the electrolyte, which enables deposition of Li with a smooth, dense morphology without the growth of dendritic Li. In symmetrical cells, the Li metal anode with the Li–Zn alloy layer can reach a cycling lifetime of more than 500 h under a current density of 2 mA cm −2 . This work provides a simple and effective strategy to suppress the formation of Li dendrites

Gut microbiota mediated the effects of high relative humidity on lupus in female MRL/lpr mice

Abstract Introduction The relationship between humidity and systemic lupus erythematosus (SLE) has yielded inconsistent results in prior research, while the effects of humidity on lupus in animal experiments and its underlying mechanism remain inadequately explored. Methods The present study aimed to investigate the impact of high humidity (80 ± 5%) on lupus using female and male MRL/lpr mice, with a particular focus on elucidating the role of gut microbiota in this process. To this end, fecal microbiota transplantation (FMT) was employed to transfer the gut microbiota of MRL/lpr mice under high humidity to blank MRL/lpr mice under normal humidity (50 ± 5%), allowing for an assessment of the effect of FMT on lupus. Results The study revealed that high humidity exacerbated lupus indices (serum anti-dsDNA, ANA, IL-6, and IFN- g, and renal pathology) in female MRL/lpr mice but had no significant effect on male MRL/lpr mice. The aggravation of lupus caused by high humidity may be attributed to the increased abundances of the Rikenella, Romboutsia, Turicibacter, and Escherichia-Shigella genera in female MRL/lpr mice. Furthermore, FMT also exacerbated lupus in female MRL/lpr mice but not in male MRL/lpr mice. Conclusion In summary, this study has demonstrated that high humidity exacerbated lupus by modulating gut microbiota in female MRL/lpr mice. The findings underscore the importance of considering environmental factors and gut microbiota in the development and progression of lupus, particularly among female patients

Hydroxychloroquine ameliorates immune functionality and intestinal flora disorders of IgA nephropathy by inhibition of C1GALT1/Cosmc pathway

Hydroxychloroquine (HCQ) has emerged as a potential and secure antiproteinuric agent in IgA nephropathy (IgAN). This study endeavored to explore the impact of HCQ on the immune functionality and intestinal flora disorders in IgAN rats, as well as to elucidate the underlying mechanisms through in vivo and in vitro experiments. IgAN model was established in Sprague-Dawley rats through the administration of BSA, LPS, and CCl4, and the IgAN rats received a continuous 8-week treatment with HCQ. Moreover, the human glomerular mesangial cells (HMCs) were incubated with IgA1 to establish an in vitro cellular model of IgAN. At the end of experimental period, samples were collected for further analysis. HCQ ameliorated the elevated levels of 24hUTP, SCr, BUN, the number of urinary RBC, and the activation of inflammation-related proteins within the TLR4/NF-κB signaling pathway. In the IgAN rat group, there was a pronounced escalation in IgA deposition, mesangial matrix hyperplasia, and glomerular inflammatory cell infiltration, while the administration of HCQ effectively mitigated these pathological changes. In addition, the reduced production of CD4+CD25+Foxp3+ Treg in the IgAN group was effectively reversed by HCQ. Furthermore, HCQ has the capacity to restore the compromised state of the intestinal mucosal barrier induced by IgAN and mitigate the circumstances of intestinal permeability and disruption in the intestinal flora. HCQ diminishes IgA aberrant glycosylation levels, ameliorates renal and intestinal histopathological damage, and attenuates intestinal flora disorders and immune dysfunction in IgAN rats by means of activating the C1GALT1/Cosmc pathway.</p

Context-aware collaborative topic regression with social matrix factorization for recommender systems

Online social networking sites have become popular platforms on which users can link with each other and share information, not only basic rating information but also information such as contexts, social relationships, and item contents. However, as far as we know, no existing works systematically combine diverse types of information to build more accurate recommender systems. In this paper, we propose a novel context-aware hierarchical Bayesian method. First, we propose the use of spectral clustering for user-item subgrouping, so that users and items in similar contexts are grouped. We then propose a novel hierarchical Bayesian model that can make predictions for each user-item subgroup, our model incorporate not only topic modeling to mine item content but also social matrix factorization to handle ratings and social relationships. Experiments on an Epinions dataset show that our method significantly improves recommendation performance compared with six categories of state-of-the-art recommendation methods in terms of both prediction accuracy and recall. We have also conducted experiments to study the extent to which ratings, contexts, social relationships, and item contents contribute to recommendation performance in terms of prediction accuracy and recall.7 page(s