Design of Hospital Operation Management System Based on Business-Finance Integration.

In order to meet the requirements of the medical reform on the informatization construction of hospital operation management, a new and refined management system is established for the hospital, which integrates and analyzes the daily management data, realizes the unification of "capital flow and business flow," and enhances the management's understanding of people, management of the planning use, and coordination and control of various comprehensive resources of finance and materials. In this study, by creating a new type of comprehensive operation management platform for the hospital, and by establishing a whole new set of operation management decision-making system, the hospital can effectively and unifiedly manage the business related to people, finance, and materials, so that the multifaceted business data can be effectively managed. Interoperability and sharing, with cost accounting as the center, enhance the management's intuitive understanding of the operation of various aspects of the hospital and provide strong support for the management of planning, coordination, resource allocation, comprehensive analysis, etc., forming financial management categories and logistics. There are four management categories: management category, human resource management category, business analysis, and decision-making category; the ultimate realization of the optimization of hospital resources and operation mode, the decision-making system aiming at the comprehensive management of hospital personnel, finance, and materials, that is, the comprehensive operation management of the hospital system. The system can provide a strong guarantee for the stable, safe, and healthy economic operation of the hospital and improve the information construction and sustainable development of the hospital. Ultimately, the purpose of improving the comprehensive operation management of the hospital, realizing the refined management, enhancing the core competitiveness of the hospital, and finally realizing the integration of business and finance is achieved.

Evaluation and comparison of multi-omics data integration methods for cancer subtyping.

Computational integrative analysis has become a significant approach in the data-driven exploration of biological problems. Many integration methods for cancer subtyping have been proposed, but evaluating these methods has become a complicated problem due to the lack of gold standards. Moreover, questions of practical importance remain to be addressed regarding the impact of selecting appropriate data types and combinations on the performance of integrative studies. Here, we constructed three classes of benchmarking datasets of nine cancers in TCGA by considering all the eleven combinations of four multi-omics data types. Using these datasets, we conducted a comprehensive evaluation of ten representative integration methods for cancer subtyping in terms of accuracy measured by combining both clustering accuracy and clinical significance, robustness, and computational efficiency. We subsequently investigated the influence of different omics data on cancer subtyping and the effectiveness of their combinations. Refuting the widely held intuition that incorporating more types of omics data always produces better results, our analyses showed that there are situations where integrating more omics data negatively impacts the performance of integration methods. Our analyses also suggested several effective combinations for most cancers under our studies, which may be of particular interest to researchers in omics data analysis.

CeNet Omnibus: an R/Shiny application to the construction and analysis of competing endogenous RNA network.

BACKGROUND: The competing endogenous RNA (ceRNA) regulation is a newly discovered post-transcriptional regulation mechanism and plays significant roles in physiological and pathological progress. CeRNA networks provide global views to help understand the regulation of ceRNAs. CeRNA networks have been widely used to detect survival biomarkers, select candidate regulators of disease genes, and predict long noncoding RNA functions. However, there is no software platform to provide overall functions from the construction to analysis of ceRNA networks. RESULTS: To fill this gap, we introduce CeNet Omnibus, an R/Shiny application, which provides a unified framework for the construction and analysis of ceRNA network. CeNet Omnibus enables users to select multiple measurements, such as Pearson correlation coefficient (PCC), mutual information (MI), and liquid association (LA), to identify ceRNA pairs and construct ceRNA networks. Furthermore, CeNet Omnibus provides a one-stop solution to analyze the topological properties of ceRNA networks, detect modules, and perform gene enrichment analysis and survival analysis. CeNet Omnibus intends to cover comprehensiveness, high efficiency, high expandability, and user customizability, and it also offers a web-based user-friendly interface to users to obtain the output intuitionally. CONCLUSION: CeNet Omnibus is a comprehensive platform for the construction and analysis of ceRNA networks. It is highly customizable and outputs the results in intuitive and interactive. We expect that CeNet Omnibus will assist researchers to understand the property of ceRNA networks and associated biological phenomena. CeNet Omnibus is an R/Shiny application based on the Shiny framework developed by RStudio. The R package and detailed tutorial are available on our GitHub page with the URL https://github.com/GaoLabXDU/CeNetOmnibus .

Automatic Facial Paralysis Assessment via Computational Image Analysis.

Facial paralysis (FP) is a loss of facial movement due to nerve damage. Most existing diagnosis systems of FP are subjective, e.g., the House-Brackmann (HB) grading system, which highly depends on the skilled clinicians and lacks an automatic quantitative assessment. In this paper, we propose an efficient yet objective facial paralysis assessment approach via automatic computational image analysis. First, the facial blood flow of FP patients is measured by the technique of laser speckle contrast imaging to generate both RGB color images and blood flow images. Second, with an improved segmentation approach, the patient's face is divided into concerned regions to extract facial blood flow distribution characteristics. Finally, three HB score classifiers are employed to quantify the severity of FP patients. The proposed method has been validated on 80 FP patients, and quantitative results demonstrate that our method, achieving an accuracy of 97.14%, outperforms the state-of-the-art systems. Experimental evaluations also show that the proposed approach could yield objective and quantitative FP diagnosis results, which agree with those obtained by an experienced clinician.

A 532 nm fiber-optic displacement interferometer for low-velocity impact experiments.

Conventional fiber-optic displacement interferometers operated at 1550 nm suffer from low temporal or velocity resolution for lower velocity measurements. To overcome this drawback, a fiber-optic Doppler pin system operated at 532 nm is developed, and its capability is demonstrated with low-velocity plate impact experiments. The new instrument would be an important supplemental to the existed systems.