Search

Abstract Background Obstructive sleep apnea (OSA) is associated with increased risk of lung cancer mortality. Nevertheless, little is known about the underlying molecular mechanisms. This research aimed to investigate differentially expressed genes (DEGs) and explore their function in Lewis lung carcinoma (LLC)-bearing mice exposed to chronic intermittent hypoxia (CIH) by transcriptome sequencing. Methods Lung cancer tissues in LLC-bearing mice exposed to CIH or normoxia were subjected for transcriptome sequencing to examine DEGs. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway analyses were employed to explore the function of DEGs. To evaluate the prognostic value of DEGs, the Kaplan–Meier survival analysis in combination with Cox proportional hazard model were applied based on The Cancer Genome Atlas. Results A total of 388 genes with 207 up-regulated and 181 down-regulated genes were differentially expressed between the CIH and normoxia control groups. Bioinformatics analysis revealed that the DEGs were related to various signaling pathways such as chemokine signaling pathway, IL-17 signaling pathway, TGF-β signaling pathway, transcriptional misregulation in cancer, natural killer cell mediated cytotoxicity, PPAR signaling pathway. In addition, the DEGs including APOL1, ETFB, KLK8, PPP1R3G, PRL, SPTA1, PLA2G3, PCP4L1, NINJ2, MIR186, and KLRG1 were proven to be significantly correlated with poorer overall survival in lung adenocarcinoma. Conclusions CIH caused a significant change of gene expression profiling in LLC-bearing mice. The DEGs were found to be involved in various physiological and pathological processes and correlated with poorer prognosis in lung cancer.

Monolayer VSe2 , featuring both charge density wave and magnetism phenomena, represents a unique van der Waals magnet in the family of metallic 2D transition-metal dichalcogenides (2D-TMDs). Herein, by means of in situ microscopy and spectroscopic techniques, including scanning tunneling microscopy/spectroscopy, synchrotron X-ray and angle-resolved photoemission, and X-ray absorption, direct spectroscopic signatures are established, that identify the metallic 1T-phase and vanadium 3d1 electronic configuration in monolayer VSe2 grown on graphite by molecular-beam epitaxy. Element-specific X-ray magnetic circular dichroism, complemented with magnetic susceptibility measurements, further reveals monolayer VSe2 as a frustrated magnet, with its spins exhibiting subtle correlations, albeit in the absence of a long-range magnetic order down to 2 K and up to a 7 T magnetic field. This observation is attributed to the relative stability of the ferromagnetic and antiferromagnetic ground states, arising from its atomic-scale structural features, such as rotational disorders and edges. The results of this study extend the current understanding of metallic 2D-TMDs in the search for exotic low-dimensional quantum phenomena, and stimulate further theoretical and experimental studies on van der Waals monolayer magnets.

In recent years,the topic sentiment model as an important research in the field of unsupervised learning,has been used in text topic mining and sentiment analysis.However,Weibo has brought some challenges to the topic sentiment model because of its short text and in complete structure.Therefore,the related research and improvement work of this paper will be carried out around the topic sentiment model of Weibo.We introduce the word vector technology to the popular model-TSMMF(topic sentiment model based on multi-feature fusion),use multivariate Gaussian distribution to sample neighboring words fast from the word embedding space,and replace the words generated by the Dirichlet multinomial distribution.Thus,the words with lowcooccurrence frequency and less information will be transformed into words with prominent topic and clear information.At the same time,the nearest neighbor search algorithm is used to further improve the running speed of the model when processing large-scale Weibo corpus,and then the GWE-TSMMF model is proposed.The experimental results show that the average F1 value of GWE-TSMMF model is about 0.718.The sentiment polarity analysis is better than the original model and the existing mainstream word embedding topic sentiment models (WS-TSWE and HST-SCW).

Healthcare call centers serve an important role in the healthcare system to support patients' access to care and satisfaction and optimize medical resource allocation. Limited research has provided sufficient evidence on how to increase healthcare call center's efficiency from managerial aspects. In this study, a simulation model is built based on real-life healthcare call center settings and call information, to identify the opportunities for improvement. This call center processes approximately 2638 calls for 25 clinic sites every day. The call abandon rate was at 16.7%, which was far from the organization's goal of 5%. Some improvement opportunities based on managerial interests such as cross-coverage, non-scheduling call transition, staff availability and staff allocation without increasing staff numbers, are identified and their operational impacts are evaluated by the simulation model. Moreover, a genetic algorithm is used to find the optimal shift for staff allocation. The simulation results demonstrate the validity of the proposed model, provide valuable insights for decision-making before the implementation and provide other healthcare call centers' alternatives to improve performance. The recommendations were communicated to call center staff and consentaneous opportunities were implemented. The results showed a significant reduction of 39.5% in the call abandon rate. [ABSTRACT FROM AUTHOR]

An algorithm for the two-stage robust optimization surgery-to-operating room allocation problem is presented. The second-stage problem is an integer linear program whose convex hull is approximated using three types of specialized valid inequalities and Chvátal-Gomory cuts. The resulting linear relaxation of the second-stage problem is then dualized and integrated into the first-stage problem. The resulting mixed integer linear program, which is an approximation of the original problem, is then solved using a commercial solver. If the solution of this model is not optimal for the second-stage problem, valid inequalities for the second-stage problem are generated, yielding a type of column-generation based approach that we refer to as the sequential follower refinement (SFR) algorithm. Data from an academic medical center are used to compare the computational performance of SFR with the constraint and column generation (C&CG) algorithm, which is the only exact approach that has been specifically applied for this problem in the literature. An extensive numerical study of SFR and its computational characteristics is presented that shows that SFR yields better-quality solutions compared with C&CG, even as the termination criterion of SFR is met much sooner, especially for problems involving higher number of surgeries. History: Accepted by Paul Brooks, Area Editor for Applications in Biology, Medicine, & Healthcare. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information (https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2022.0191) as well as from the IJOC GitHub software repository (https://github.com/INFORMSJoC/2022.0191). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/. [ABSTRACT FROM AUTHOR]