Neural Pairwise Ranking Factorization Machine for Item Recommendation

The factorization machine models attract significant attention from academia and industry because they can model the context information and improve the performance of recommendation. However, traditional factorization machine models generally adopt the point-wise learning method to learn the model parameters as well as only model the linear interactions between features. They fail to capture the complex interactions among features, which degrades the performance of factorization machine models. In this paper, we propose a neural pairwise ranking factorization machine for item recommendation, which integrates the multi-layer perceptual neural networks into the pairwise ranking factorization machine model. Specifically, to capture the high-order and nonlinear interactions among features, we stack a multi-layer perceptual neural network over the bi-interaction layer, which encodes the second-order interactions between features. Moreover, the pair-wise ranking model is adopted to learn the relative preferences of users rather than predict the absolute scores. Experimental results on real world datasets show that our proposed neural pairwise ranking factorization machine outperforms the traditional factorization machine models

Trends in hepatocellular carcinoma research from 2008 to 2017: a bibliometric analysis

Objectives To comprehensively analyse the global scientific outputs of hepatocellular carcinoma (HCC) research. Methods Data of publications were downloaded from the Web of Science Core Collection. We used CiteSpace IV and Excel 2016 to analyse literature information, including journals, countries/regions, institutes, authors, citation reports and research frontiers. Results Until March 31, 2018, a total of 24,331 papers in HCC research were identified as published between 2008 and 2017. Oncotarget published the most papers. China contributed the most publications and the United States occupied leading positions in H-index value and the number of ESI top papers. Llovet JM owned the highest co-citations. The keyword “transarterial chemoembolization” ranked first in the research front-line. Conclusions The amount of papers published in HCC research has kept increasing since 2008. China showed vast progress in HCC research, but the United States was still the dominant country. Transarterial chemoembolization, epithelial-mesenchymal transition, and cancer stem cell were the latest research frontiers and should be paid more attention

Case Report: Isolated facial and trigeminal nerve palsy without ataxia in anti-GQ1b antibody syndrome secondary to Mycoplasma pneumonia

The presence of anti-GQ1b antibodies in serum or cerebrospinal fluid is a diagnostic indicator of the Miller–Fisher variant of Guillain–Barré syndrome (GBS), whereas anti-GQ1b antibody syndrome is rarely presented as acute bilateral pain in the cheeks and masticatory muscle fatigue without ophthalmoplegia, ataxia, or limb weakness. Here, we report a case of a female patient diagnosed with GBS characterized only by the involvement of the facial and trigeminal nerves who was positive for serum anti-GQ1b antibodies secondary to Mycoplasma pneumoniae infection. The patient was treated with macrolide antibiotics and neurotrophic drugs, and her symptoms were significantly alleviated after 1 month. This case indicates a new clinical presentation of GBS and anti-GQ1b antibody syndrome with a differential diagnosis of multiple cranial nerve damage of which neurological physicians should be aware. Positive anti-GQ1b antibodies secondary to infection were observed in this case, and antibiotic treatment resulted in a favorable prognosis. The specific underlying mechanism requires further investigation

Inhibition of Glucose-6-Phosphate Dehydrogenase Could Enhance 1,4-Benzoquinone-Induced Oxidative Damage in K562 Cells

Benzene is a chemical contaminant widespread in industrial and living environments. The oxidative metabolites of benzene induce toxicity involving oxidative damage. Protecting cells and cell membranes from oxidative damage, glucose-6-phosphate dehydrogenase (G6PD) maintains the reduced state of glutathione (GSH). This study aims to investigate whether the downregulation of G6PD in K562 cell line can influence the oxidative toxicity induced by 1,4-benzoquinone (BQ). G6PD was inhibited in K562 cell line transfected with the specific siRNA of G6PD gene. An empty vector was transfected in the control group. Results revealed that G6PD was significantly upregulated in the control cells and in the cells with inhibited G6PD after they were exposed to BQ. The NADPH/NADP and GSH/GSSG ratio were significantly lower in the cells with inhibited G6PD than in the control cells at the same BQ concentration. The relative reactive oxygen species (ROS) level and DNA oxidative damage were significantly increased in the cell line with inhibited G6PD. The apoptotic rate and G2 phase arrest were also significantly higher in the cells with inhibited G6PD and exposed to BQ than in the control cells. Our results suggested that G6PD inhibition could reduce GSH activity and alleviate oxidative damage. G6PD deficiency is also a possible susceptible risk factor of benzene exposure

Decreased Triple Network Connectivity in Patients with Recent Onset Post-Traumatic Stress Disorder after a Single Prolonged Trauma Exposure

The triple network model provides a common framework for understanding affective and neurocognitive dysfunctions across multiple disorders, including central executive network (CEN), default mode network (DMN), and salience network (SN). Considering the effect of traumatic experience on post-traumatic stress disorder (PTSD), this study aims to explore the alteration of triple network connectivity in a specific PTSD induced by a single prolonged trauma exposure. With an arterial spin labeling sequence, three networks were first identified using independent component analysis among 10 PTSD patients and 10 healthy survivors, who experienced the same coal mining flood disaster. Then, the triple network connectivity was analyzed and compared between PTSD and non-PTSD groups. In PTSD patients, decreased connectivity was identified in left middle frontal gyrus of CEN, left precuneus and bilateral superior frontal gyrus of DMN, and right anterior insula of SN. The decreased connectivity in left middle frontal gyrus of CEN was associated with clinical severity. Furthermore, no significant connection of SN with CEN and DMN was found in PTSD patients. The decreased triple network connectivity was found in this study, which not only supports the triple network model, but also suggests a possible neurobiological mechanism for cognitive dysfunction of this type of PTSD

Transcriptomic-metabolomic reprogramming in EGFR-mutant NSCLC early adaptive drug escape linking TGFβ2-bioenergetics-mitochondrial priming.

The impact of EGFR-mutant NSCLC precision therapy is limited by acquired resistance despite initial excellent response. Classic studies of EGFR-mutant clinical resistance to precision therapy were based on tumor rebiopsies late during clinical tumor progression on therapy. Here, we characterized a novel non-mutational early adaptive drug-escape in EGFR-mutant lung tumor cells only days after therapy initiation, that is MET-independent. The drug-escape cell states were analyzed by integrated transcriptomic and metabolomics profiling uncovering a central role for autocrine TGFβ2 in mediating cellular plasticity through profound cellular adaptive Omics reprogramming, with common mechanistic link to prosurvival mitochondrial priming. Cells undergoing early adaptive drug escape are in proliferative-metabolic quiescent, with enhanced EMT-ness and stem cell signaling, exhibiting global bioenergetics suppression including reverse Warburg, and are susceptible to glutamine deprivation and TGFβ2 inhibition. Our study further supports a preemptive therapeutic targeting of bioenergetics and mitochondrial priming to impact early drug-escape emergence using EGFR precision inhibitor combined with broad BH3-mimetic to interrupt BCL-2/BCL-xL together, but not BCL-2 alone

Enhanced factorization machine via neural pairwise ranking and attention networks

The factorization machine models attract significant attention nowadays since they improve recommendation performance by incorporating context information into recommendation modeling. However, traditional factorization machine models often adopt the point-wise learning method for model parameter learning, as well as only model the linear interactions between features. They substantially fail to capture the complex interactions among features, which degrades the performance of factorization machine models. In this research, we propose a neural pairwise ranking factorization machine for item recommendation, namely NPRFM, which integrates the multi-layer perceptual neural networks into the pairwise ranking factorization machine model. Specifically, to capture the high-order and nonlinear interactions among features, we stack a multi-layer perceptual neural network over the bi-interaction layer, which encodes the second-order interactions between features. Moreover, instead of the prediction of the absolute scores, the pair-wise ranking model is adopted to learn the relative preferences of users. Since NPRFM does not take into account the importance of feature interactions, we propose a new variant of NPRFM, which learns the importance of feature interactions by introducing the attention mechanism. The empirical results on real-world datasets indicate that the proposed neural pairwise ranking factorization machine outperforms the traditional factorization machine models

Anti-Pseudomonas aeruginosa activity of natural antimicrobial peptides when used alone or in combination with antibiotics

The World Health Organization has recently published a list of 12 drug-resistant bacteria that posed a significant threat to human health, and Pseudomonas aeruginosa (P. aeruginosa) was among them. In China, P. aeruginosa is a common pathogen in hospital acquired pneumonia, accounting for 16.9–22.0%. It is a ubiquitous opportunistic pathogen that can infect individuals with weakened immune systems, leading to hospital-acquired acute and systemic infections. The excessive use of antibiotics has led to the development of various mechanisms in P. aeruginosa to resist conventional drugs. Thus, there is an emergence of multidrug-resistant strains, posing a major challenge to conventional antibiotics and therapeutic approaches. Antimicrobial peptides are an integral component of host defense and have been found in many living organisms. Most antimicrobial peptides are characterized by negligible host toxicity and low resistance rates, making them become promising for use as antimicrobial products. This review particularly focuses on summarizing the inhibitory activity of natural antimicrobial peptides against P. aeruginosa planktonic cells and biofilms, as well as the drug interactions when these peptides used in combination with conventional antibiotics. Moreover, the underlying mechanism of these antimicrobial peptides against P. aeruginosa strains was mainly related to destroy the membrane structure through interacting with LPS or increasing ROS levels, or targeting cellular components, leaded to cell lysis. Hopefully, this analysis will provide valuable experimental data on developing novel compounds to combat P. aeruginosa