Changes in the Fruit Quality, Phenolic Compounds, and Antioxidant Potential of Red-Fleshed Kiwifruit during Postharvest Ripening.

Kiwifruit is very popular for its unique flavor and nutritional value, and for its potential health benefits, which are closely related to its richness in a variety of natural antioxidant substances, in which polyphenolics play a non-negligible role. This study investigated changes in the fruit quality, phenolic compounds, and antioxidant potential of Chinese red-fleshed kiwifruit "Hongshi No. 2" during postharvest ripening at room temperature (20 ± 1 °C). Results showed that the weight loss rate slowly increased, the firmness rapidly decreased, and the soluble solid concentration gradually increased during the postharvest ripening of red-flesh kiwifruit. In addition, the total phenolic (TPC), total flavonoid (TFC), and total proanthocyanidin (TPAC) contents gradually increased during postharvest ripening. The most abundant phenolic compounds in kiwifruit throughout postharvest ripening were catechin (CC), proanthocyanidin B1 (PB1), and proanthocyanidin B2 (PB2). Furthermore, the methanolic extracts of red-flesh kiwifruit exhibited remarkable antioxidant activities throughout postharvest ripening stages. Indeed, some phenolic compounds showed good correlations with antioxidant activities; for instance, chlorogenic acid (CHL) showed a significantly positive correlation with ferric reducing antioxidant power (FRAP), and isoquercitrin (IS) showed a significantly negative correlation with DPPH free radical scavenging ability. The findings from this study are beneficial to better understanding the quality profile of red-flesh kiwifruit "Hongshi No. 2" during postharvest ripening.

An artificial intelligence-based risk prediction model of myocardial infarction.

BACKGROUND: Myocardial infarction can lead to malignant arrhythmia, heart failure, and sudden death. Clinical studies have shown that early identification of and timely intervention for acute MI can significantly reduce mortality. The traditional MI risk assessment models are subjective, and the data that go into them are difficult to obtain. Generally, the assessment is only conducted among high-risk patient groups. OBJECTIVE: To construct an artificial intelligence-based risk prediction model of myocardial infarction (MI) for continuous and active monitoring of inpatients, especially those in noncardiovascular departments, and early warning of MI. METHODS: The imbalanced data contain 59 features, which were constructed into a specific dataset through proportional division, upsampling, downsampling, easy ensemble, and w-easy ensemble. Then, the dataset was traversed using supervised machine learning, with recursive feature elimination as the top-layer algorithm and random forest, gradient boosting decision tree (GBDT), logistic regression, and support vector machine as the bottom-layer algorithms, to select the best model out of many through a variety of evaluation indices. RESULTS: GBDT was the best bottom-layer algorithm, and downsampling was the best dataset construction method. In the validation set, the F1 score and accuracy of the 24-feature downsampling GBDT model were both 0.84. In the test set, the F1 score and accuracy of the 24-feature downsampling GBDT model were both 0.83, and the area under the curve was 0.91. CONCLUSION: Compared with traditional models, artificial intelligence-based machine learning models have better accuracy and real-time performance and can reduce the occurrence of in-hospital MI from a data-driven perspective, thereby increasing the cure rate of patients and improving their prognosis.

Big Data Health Care Platform With Multisource Heterogeneous Data Integration and Massive High-Dimensional Data Governance for Large Hospitals: Design, Development, and Application.

BACKGROUND: With the advent of data-intensive science, a full integration of big data science and health care will bring a cross-field revolution to the medical community in China. The concept big data represents not only a technology but also a resource and a method. Big data are regarded as an important strategic resource both at the national level and at the medical institutional level, thus great importance has been attached to the construction of a big data platform for health care. OBJECTIVE: We aimed to develop and implement a big data platform for a large hospital, to overcome difficulties in integrating, calculating, storing, and governing multisource heterogeneous data in a standardized way, as well as to ensure health care data security. METHODS: The project to build a big data platform at West China Hospital of Sichuan University was launched in 2017. The West China Hospital of Sichuan University big data platform has extracted, integrated, and governed data from different departments and sections of the hospital since January 2008. A master-slave mode was implemented to realize the real-time integration of multisource heterogeneous massive data, and an environment that separates heterogeneous characteristic data storage and calculation processes was built. A business-based metadata model was improved for data quality control, and a standardized health care data governance system and scientific closed-loop data security ecology were established. RESULTS: After 3 years of design, development, and testing, the West China Hospital of Sichuan University big data platform was formally brought online in November 2020. It has formed a massive multidimensional data resource database, with more than 12.49 million patients, 75.67 million visits, and 8475 data variables. Along with hospital operations data, newly generated data are entered into the platform in real time. Since its launch, the platform has supported more than 20 major projects and provided data service, storage, and computing power support to many scientific teams, facilitating a shift in the data support model-from conventional manual extraction to self-service retrieval (which has reached 8561 retrievals per month). CONCLUSIONS: The platform can combine operation systems data from all departments and sections in a hospital to form a massive high-dimensional high-quality health care database that allows electronic medical records to be used effectively and taps into the value of data to fully support clinical services, scientific research, and operations management. The West China Hospital of Sichuan University big data platform can successfully generate multisource heterogeneous data storage and computing power. By effectively governing massive multidimensional data gathered from multiple sources, the West China Hospital of Sichuan University big data platform provides highly available data assets and thus has a high application value in the health care field. The West China Hospital of Sichuan University big data platform facilitates simpler and more efficient utilization of electronic medical record data for real-world research.

Bibliometric Analysis of Nursing Informatics Research.

This study aims to provide a bibliometric overview of research at nursing informatics and understand the state in nursing informatics in the last ten years. We used the Web of Science to extract relevant literature published from 2009 to 2018. A total of 455 articles were retrieved and analyzed. The total of the top 5 institutions, countries, journals was discussed. This study will help researchers to understand trends and the situation in nursing informatics research.

A Bibliometric Analysis of Alert Override in Clinical Decision Support.

The aim of this study was to understand the status and trend in alert override research over the past two decades (1999-2018). We used the Web of Science core collection (WoSCC) database to extract all papers of alert override in clinical decision support from 1999 to 2018. A total of 150 papers were identified, most (86.67%) being articles. This study presented the key bibliometric indicators such as annual publications, top 5 authors, institutions, countries, and co-occurrence of terms from the titles and abstracts. VOSviewer was used to visualize keywords knowledge maps. The results show that alert override research has a wide variety of research themes and a multidisciplinary character. This study provides a broad view of the current status and trends in alert override research. It may help researchers, clinicians and policymakers better understand alert override research field change and direction in the future.

Enriching the international clinical nomenclature with Chinese daily used synonyms and concept recognition in physician notes.

BACKGROUND: It has been shown that the entities in everyday clinical text are often expressed in a way that varies from how they are expressed in the nomenclature. Owing to lots of synonyms, abbreviations, medical jargons or even misspellings in the daily used physician notes in clinical information system (CIS), the terminology without enough synonyms may not be adequately suitable for the task of Chinese clinical term recognition. METHODS: This paper demonstrates a validated system to retrieve the Chinese term of clinical finding (CTCF) from CIS and map them to the corresponding concepts of international clinical nomenclature, such as SNOMED CT. The system focuses on the SNOMED CT with Chinese synonyms enrichment (SCCSE). The literal similarity and the diagnosis-related similarity metrics were used for concept mapping. Two CTCF recognition methods, the rule- and terminology-based approach (RTBA) and the conditional random field machine learner (CRF), were adopted to identify the concepts in physician notes. The system was validated against the history of present illness annotated by clinical experts. The RTBA and CRF could be combined to predict new CTCFs besides SCCSE persistently. RESULTS: Around 59,000 CTCF candidates were accepted as valid and 39,000 of them occurred at least once in the history of present illness. 3,729 of them were accordant with the description in referenced Chinese clinical nomenclature, which could cross map to other international nomenclature such as SNOMED CT. With the hybrid similarity metrics, another 7,454 valid CTCFs (synonyms) were succeeded in concept mapping. For CTCF recognition in physician notes, a series of experiments were performed to find out the best CRF feature set, which gained an F-score of 0.887. The RTBA achieved a better F-score of 0.919 by the CTCF dictionary created in this research. CONCLUSIONS: This research demonstrated that it is feasible to help the SNOMED CT with Chinese synonyms enrichment based on physician notes in CIS. With continuous maintenance of SCCSE, the CTCFs could be precisely retrieved from free text, and the CTCFs arranged in semantic hierarchy of SNOMED CT could greatly improve the meaningful use of electronic health record in China. The methodology is also useful for clinical synonyms enrichment in other languages.