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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.

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]

Structural design simultaneously governed by earthquakes and environmental vibrations has received a lot of attention in recent years. Base-isolated composite structures are typically used in the above-mentioned structural design. The corresponding analysis involves validating structural safety under earthquakes and human comfort under environmental vibrations through a time-history analysis. Thus, a reasonable damping model is essential. In this work, the representatives of viscous damping model and rate-independent damping model, namely the Rayleigh damping model and uniform damping model, were adopted to investigate the influence of damping models on the time-history analysis of such structural designs. The energy dissipation characteristics of the above-mentioned damping models were illustrated via a dynamic test of recycled aggregate concrete specimens. A case study was performed on a base-isolated steelconcrete composite structure. The dynamic responses under the excitation of earthquakes and environmental vibrations were compared using different damping models. The uniform damping model was found to be more flexible than the Rayleigh damping model in dealing with excitations with different frequency components. The uniform damping model is both theoretically advantageous and easy to use, demonstrating its potential in dynamic analysis of structures designed simultaneously governed by earthquakes and environmental vibrations. [ABSTRACT FROM AUTHOR]

In this article, we address a two-stage scheduling problem that requires coordination between clinical and surgical appointments for specialized surgeries. First, patients have a clinical appointment with a surgeon to determine whether they are an appropriate candidate for the surgical procedure. Subsequently, if the decision to pursue the surgery is made the patient undergoes the procedure on a later date. However, the scheduling process aims to book both the clinical and surgical appointments for a patient at the time of the initial appointment request. Two sources of uncertainty make this scheduling process challenging: (i) the patient may or may not need surgery after the clinical appointment and (ii) the surgery duration for each patient and procedure is unknown. We present a Distributionally Robust Optimization (DRO) approach for coordinating clinical and surgical appointments under these uncertainties. A case study of the Transcatheter Aortic Valve Replacement procedure at Mayo Clinic, Rochester, MN is presented. Numerical results include comparisons with the current practice and four heuristic scheduling policies from the literature. Results show that the DRO-based scheduling policies lead to lower total surgeon idle-time and overtime per day. The proposed policies also restrict the under and over utilization of clinical capacity. [ABSTRACT FROM AUTHOR]

Phosphorus is an important non‐metal element with many allotropes and tunable electronic properties. As one of the most important allotropes of phosphorus, two‐dimensional (2D) black phosphorus (BP) possesses many extraordinary properties, such as high charge carrier mobility with high on/off ratio, thickness‐dependent direct bandgap varying from 0.3 to 2 eV, and anisotropic electrical, optical and thermal properties. Inspiring by the success of 2D BP, other 2D phosphorus allotropes with intriguing properties for next‐generation electronic and optoelectronic devices have also attracted much attention. However, large‐scale growth of high‐quality 2D single/few‐layer phosphorus remains a great challenge. In this review, we highlight recent progress achieved on 2D phosphorus, with special focus on the epitaxial growth of 2D BP films, blue phosphorus (BlueP) monolayers as well as phosphorus‐metal porous networks. The remaining challenges and future perspectives on the development of monolayer phosphorus or phosphorus‐metal alloys are also provided. [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.)