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ABSTRACT Neuraminidase (NA) is a pivotal surface enzyme and a key therapeutic target in combating the influenza A virus. Its evolution can lead to potential zoonotic transmission, seasonal epidemics, and the emergence of drug-resistant mutants. To gain comprehensive insights into the mutational effects and drug resistance profiles of NA, we employed a high-throughput profiling system to quantify the replication capacity of NA mutants at the single-nucleotide level in mouse lung tissues. The fitness of NA mutants is generally correlated with natural mutation occurrence and is constrained by both the requirement to maintain protein stability and NA function. Leveraging this system, we profiled the drug resistance to the three most commonly used neuraminidase inhibitors (NAIs): zanamivir, oseltamivir, and peramivir. In addition to identifying previously reported drug resistance mutations, we validated novel mutants. Notably, we identified an allosteric mutation that confers resistance to all three drugs, which may affect drug binding by interfering with the tetramerization of NA. Moreover, the fitness cost associated with drug-resistant mutations may limit their widespread dissemination. In summary, we provided a parallel characterization of NA’s fitness and drug resistance landscape in an in vivo context, which may guide the rational selection of antiviral drugs for optimal therapeutic efficacy and second-generation NAI development. IMPORTANCE NA is a crucial surface antigen and drug target of influenza A virus. A comprehensive understanding of NA’s mutational effect and drug resistance profiles in vivo is essential for comprehending the evolutionary constraints and making informed choices regarding drug selection to combat resistance in clinical settings. In the current study, we established an efficient deep mutational screening system in mouse lung tissues and systematically evaluated the fitness effect and drug resistance to three neuraminidase inhibitors of NA single-nucleotide mutations. The fitness of NA mutants is generally correlated with a natural mutation in the database. The fitness of NA mutants is influenced by biophysical factors such as protein stability, complex formation, and the immune response triggered by viral infection. In addition to confirming previously reported drug-resistant mutations, novel mutations were identified. Interestingly, we identified an allosteric drug-resistance mutation that is not located within the drug-binding pocket but potentially affects drug binding by interfering with NA tetramerization. The dual assessments performed in this study provide a more accurate assessment of the evolutionary potential of drug-resistant mutations and offer guidance for the rational selection of antiviral drugs.

Abstract Background Whether a sequential or concurrent regimen of anthracyclines and taxanes is superior for breast cancer is controversial. We compared the efficacy of two regimens in patients with operable breast cancer based on all relevant published data of phase III randomized controlled trials. Methods A comprehensive literature search on PubMed, Web of Science, Embase, ScienceDirect, Google Scholar, and ClinicalTrials.gov databases was performed up to May 2020. Meta-analysis was performed to evaluate the different efficacy on disease-free survival (DFS) and overall survival (OS) for the two chemotherapy regimens. Subgroup analyses were further carried out in terms of node status and anthracycline selection. Results Compared to the concurrent regimen, the sequential regimen did not improve the DFS or OS in the population studied. Subgroup analysis showed that in node-positive patients, the sequential regimen had better DFS, but not OS, than the concurrent regimen. In sequential regimen, patients who received doxorubicin and taxanes had improved DFS and OS than patients who were administered epirubicin and taxanes. Furthermore, for patients who received doxorubicin and taxanes, compared to the sequential regimen, fewer cycles (4 cycles) of concurrent treatment resulted in a worse DFS and OS, which can be rescued by more cycles (6 cycles). Conclusions The sequential regimen of anthracyclines and taxanes for patients with operable breast cancer did not yield a significant benefit in DFS or OS over the concurrent regimen. The sequential regimen, however, provided a better DFS than concurrent regimen for node-positive patients. Interestingly, further subgroup analysis showed that for node-positive patients who were given doxorubicin and taxanes, more cycles (6 cycles) of the concurrent regimen may rescue the efficacy for fewer cycles (4 cycles).

Identification of low abundance proteins interacting with viral proteins can be challenging. Here, Du et al. develop an mRNA display approach with a library of even distribution, identify host proteins interacting with NS1 protein of influenza A virus, and show that one interactor provides a means to regulate cellular fatty acids synthesis.

A state-of-the-art deep domain decomposition method (D3M) based on the variational principle is proposed for partial differential equations (PDEs). The solution of PDEs can be formulated as the solution of a constrained optimization problem, and we design a hierarchical neural network framework to solve this optimization problem. Through decomposing a PDE system into components parts, our D3M builds local neural networks on physical subdomains independently (which can be implemented in parallel), so as to obtain efficient neural network approximations for complex problems. Our analysis shows that the D3M approximation solution converges to the exact solution of the underlying PDEs. The accuracy and the efficiency of D3M are validated and demonstrated with numerical experiments.

Background Marital status has been reported as an independent prognostic factor in various types of malignancies. However, the association between marital status and outcomes of patients with adenocarcinoma of the esophagogastric junction (AEG) has not been fully explored. To this end, we aimed to investigate the effect of marital status on survival of AGE patients. Methods The Surveillance Epidemiology and End Results (SEER) database (2010–2015) was used to extract eligible patients with Siewert type II AEG. Meanwhile, propensity score matching was performed to match 1576 unmarried patients with 1576 married patients. Kaplan–Meier method with log-rank test was used to plot survival curves, univariate and multivariate Cox regression analyses were adopted to investigate the association of marital status with overall survival (OS) and cancer-specific survival (CSS) in AEG patients before and after matching. Results Multivariate analysis in the unmatched cohort revealed that marital status was an independent prognostic factor in patients with Siewert type II AEG. Unmarried patients had poorer OS (hazard ratio [HR]: 1.22, 95% confidence interval [CI]: 1.12–1.29, P < .001) and poorer CSS (HR: 1.19, 95% CI: 1.10–1.29, P < .001) than married patients before matching. Additionally, widowed patients had the poorest OS (HR: 1.26, 95% CI: 1.11–1.44, P < .001) and CSS (HR: 1.29, 95% CI: 1.12–1.48, P < .001) compared with married patients. Furthermore, unmarried status remained as an independent prognostic for both OS (HR: 1.20, 95% CI: 1.10–1.31, P < .001) and CSS (HR: 1.18, 95% CI: 1.08–1.30, P < .001) in 1:1 propensity score-matched analysis. Subgroup analysis further revealed that OS and CSS rates were significantly higher in married patients than unmarried ones in most subgroups stratified by different variables. Conclusions This population-based study identified that marital status was an independent prognostic indicator for AEG patients. Married AEG patients had better prognosis than their unmarried counterparts.

Martynoside (MAR) is a bioactive glycoside of Rehmannia glutinosa, a traditional Chinese herb frequently prescribed for treating chemotherapy-induced pancytopenia. Despite its clinical usage in China for thousands of years, the mechanism of MAR’s hematopoietic activity and its impact on chemotherapy-induced antitumor activity are still unclear. Here, we showed that MAR protected ex vivo bone marrow cells from 5-fluorouracil (5-FU)-induced cell death and inflammation response by down-regulating the TNF signaling pathway, in which II1b was the most regulatory gene. Besides, using mouse models with melanoma and colon cancer, we further demonstrated that MAR had protective effects against 5-FU-induced myelosuppression in mice without compromising its antitumor activity. Our results showed that MAR increased the number of bone marrow nucleated cells (BMNCs) and the percentage of leukocyte and granulocytic populations in 5-FU-induced myelosuppressive mice, accompanied by an increase in numbers of circulating white blood cells and platelets. The transcriptome profile of BMNCs further showed that the mode of action of MAR might be associated with the increased survival of BMNCs and the improvement of the bone marrow microenvironment. In summary, we revealed the potential molecular mechanism of MAR to counteract 5-FU-induced bone marrow cytotoxicity both ex vivo and in vivo, and highlighted its potential clinical usage in cancer patients experiencing chemotherapy-induced multi-lineage myelosuppression.

Background: Endoscopic resection has been introduced as an alternative treatment for superficial adenocarcinoma of the esophagogastric junction (AEG), but is limited by positive nodal status. We aimed to investigate the predictors of lymph node metastasis (LNM) in patients with Siewert type II T1 AEG. Methods: The Surveillance, Epidemiology, and End Results (SEER) database was used to identify eligible patients with Siewert type II T1 AEG. The prevalence of LNM was assessed. Logistic regression analysis with multivariable adjustment was used to determine predictors of LNM. We also performed Cox regression analysis to examine the prognostic value of LNM, which was further confirmed by competing risk analysis and cumulative incidence function (CIF). Results: In total, 2651 patients with T1 AEG were included, with a median age of 69 years and a median follow-up of 28 months. The overall prevalence of LNM was 17.2% in T1 AEG. When stratified by tumor invasion depth, the prevalence of LNM was 8.5% for intramucosal tumors and 22.6% for submucosal tumors. Adjusted logistic regression analysis showed that age, sex, tumor grade, tumor size and tumor infiltration depth were independent predictors of LNM in T1 AEG. Multivariate Cox regression analysis revealed that positive nodal status was significantly associated with worse overall survival and cancer-specific survival (CSS). Subgroup analysis consistently demonstrated that patients with LNM had significantly poorer CSS than those without LNM in most subgroups. Finally, the CIF was calculated, showing that patients with LNM had a significantly higher cancer-specific death rate than those without LNM. Conclusions: This population-based study identified age, sex, tumor grade, tumor infiltration depth and tumor size as independent predictors of LNM in T1 AEG. Considering the high prevalence of LNM in T1 AEG, endoscopic resection for curative aims may only be introduced in patients without high risks of LNM.

Failure probability evaluation for complex physical and engineering systems governed by partial differential equations (PDEs) are computationally intensive, especially when high-dimensional random parameters are involved. Since standard numerical schemes for solving these complex PDEs are expensive, traditional Monte Carlo methods which require repeatedly solving PDEs are infeasible. Alternative approaches which are typically the surrogate based methods suffer from the so-called “curse of dimensionality”, which limits their application to problems with high-dimensional parameters. For this purpose, we develop a novel hierarchical neural hybrid (HNH) method to efficiently compute failure probabilities of these challenging high-dimensional problems. Especially, multifidelity surrogates are constructed based on neural networks with different levels of layers, such that expensive highfidelity surrogates are adapted only when the parameters are in the suspicious domain. The efficiency of our new HNH method is theoretically analyzed and is demonstrated with numerical experiments. From numerical results, we show that to achieve an accuracy in estimating the rare failure probability (e.g., 10-5), the traditional Monte Carlo method needs to solve PDEs more than a million times, while our HNH only requires solving them a few thousand times.

Background Because of the increasing dysplasia rate in the lifelong course of inflammatory bowel disease (IBD) patients, it is imperative to characterize the crosstalk between IBD and colorectal cancer (CRC). However, there have been no reports revealing the occurrence of the ceRNA network in IBD-related CRC. Methods In this study, we conducted gene expression profile studies of databases and performed an integrated analysis to detect the potential of lncRNA-miRNA-mRNA ceRNA in regulating disease transformation. R packages were used to screen differentially expressed mRNA, lncRNA and miRNA among CRC, IBD and normal tissue. The lncRNA-miRNA-mRNA network was constructed based on predicted miRNA-targeted lncRNAs and miRNA-targeted mRNAs. Functional analyses were then conducted to identify genes involved in the ceRNA network, and key lncRNAs were evaluated based on several clinical outcomes. Results A total of three lncRNAs, 15 miRNAs, and 138 mRNAs were identified as potential mediators in the pathophysiological processes of IBD-related CRC. Gene Ontology annotation enrichment analysis confirmed that the dysplasia process was strongly associated with immune response, response to lipopolysaccharide, and inflammatory response. Survival analysis showed that LINC01106 (HR = 1.7; p

Parametric optimal control problems governed by partial differential equations (PDEs) are widely found in scientific and engineering applications. Traditional grid-based numerical methods for such problems generally require repeated solutions of PDEs with different parameter settings, which is computationally prohibitive, especially for problems with high-dimensional parameter spaces. Although recently proposed neural network methods make it possible to obtain the optimal solutions simultaneously for different parameters, challenges still remain when dealing with problems with complex constraints. In this paper, we propose the AONN, an adjoint-oriented neural network method, to overcome the limitations of existing approaches in solving parametric optimal control problems. In an AONN, the neural networks are served as parametric surrogate models for the control, adjoint, and state functions to get the optimal solutions all at once. In order to reduce the training difficulty and handle complex constraints, we introduce an iterative training framework inspired by the classical direct-adjoint looping method so that penalty terms arising from the Karush--Kuhn--Tucker system can be avoided. Once the training is done, parameter-specific optimal solutions can be quickly computed through the forward propagation of the neural networks, which may be further used for analyzing the parametric properties of the optimal solutions. The validity and efficiency of the AONN is demonstrated through a series of numerical experiments with problems involving various types of parameters. [ABSTRACT FROM AUTHOR]

This work proposes a Tensor Train Random Projection (TTRP) method for dimension reduction, where pairwise distances can be approximately preserved. Our TTRP is systematically constructed through a Tensor Train (TT) representation with TT-ranks equal to one. Based on the tensor train format, this random projection method can speed up the dimension reduction procedure for high-dimensional datasets and requires fewer storage costs with little loss in accuracy, comparedwith existingmethods. We provide a theoretical analysis of the bias and the variance of TTRP, which shows that this approach is an expected isometric projectionwith bounded variance, and we show that the scaling Rademacher variable is an optimal choice for generating the corresponding TT-cores. Detailed numerical experiments with synthetic datasets and theMNIST dataset are conducted to demonstrate the efficiency of TTRP. [ABSTRACT FROM AUTHOR]