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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.

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]

Structural topology and symmetry of a two-dimensional (2D) network play pivotal roles in defining its electrical properties and functionalities. Here, a binary buckled honeycomb lattice with C 3v symmetry, which naturally hosts topological Dirac fermions and out-of-plane polarity, is proposed. It is successfully achieved in a group IV-V compound, namely monolayer SiP epitaxially grown on Ag(111) surface. Combining first-principles calculations with angle-resolved photoemission spectroscopy, the degeneration of the Dirac nodal lines to points due to the broken horizonal mirror symmetry is elucidated. More interesting, the SiP monolayer manifests metallic nature, which is mutually exclusive with polarity in conventional materials. It is further found that the out-of-plane polarity is strongly suppressed by the metallic substrate. This study not only represents a breakthrough of realizing intrinsic polarity in 2D metallic material via ingenious design but also provides a comprehensive understanding of the intricate interplay of many exotic low-dimensional quantum phenomena., (© 2024 Wiley‐VCH GmbH.)

Background:  Provider burnout due to workload is a significant concern in primary care settings. Workload for primary care providers encompasses both scheduled visit care and non-visit care interactions. These interactions are highly influenced by patients' health conditions or acuity, which can be measured by the Adjusted Clinical Group (ACG) score. However, new patients typically have minimal health information beyond social determinants of health (SDOH) to determine ACG score., Objectives:  This study aims to assess new patient workload by first predicting the ACG score using SDOH, age, and gender and then using this information to estimate the number of appointments (scheduled visit care) and non-visit care interactions., Methods:  Two years of appointment data were collected for patients who had initial appointment requests in the first year and had the ACG score, appointment, and non-visit care counts in the subsequent year. State-of-art machine learning algorithms were employed to predict ACG scores and compared with current baseline estimation. Linear regression models were then used to predict appointments and non-visit care interactions, integrating demographic data, SDOH, and predicted ACG scores., Results:  The machine learning methods showed promising results in predicting ACG scores. Besides the decision tree, all other methods performed at least 9% better in accuracy than the baseline approach which had an accuracy of 78%. Incorporating SDOH and predicted ACG scores also significantly improved the prediction for both appointments and non-visit care interactions. The R 2 values increased by 95.2 and 93.8%, respectively. Furthermore, age, smoking tobacco, family history, gender, usage of injection birth control, and ACG were significant factors for determining appointments. SDOH factors such as tobacco usage, physical exercise, education level, and group activities were strongly correlated with non-visit care interactions., Conclusion:  The study highlights the importance of SDOH and predicted ACG scores in predicting provider workload in primary care settings., Competing Interests: None declared., (Thieme. All rights reserved.)