Using the i-PARIHS theoretical framework to develop evidence implementation strategies for central venous catheter maintenance: a multi-site quality improvement project.

INTRODUCTION: Evidence-based nursing practice can reduce complications associated with central venous catheters (CVCs). In this project, the Integrated Promoting Action on Research Implementation in Health Services (i-PARIHS) framework was considered an ideal theoretical instrument to identify facilitators and barriers to implementing evidence-based practice. METHODS: The project was conducted in pediatric intensive care units in six Chinese tertiary children's hospitals. Twenty-two audit criteria were obtained from best practice recommendations, and a baseline audit was conducted to assess current practice against best practice. Next, the i-PARIHS framework was used to identify facilitators and barriers to best practice and develop improvement strategies. A follow-up audit was then conducted to measure changes in compliance with best practices. RESULTS: Facilitators and barriers were identified at the innovation, recipient, and context levels. A comprehensive CVC maintenance strategy was then developed to apply the best evidence to nurses' clinical work. Of the 22 audit criteria, 17 showed significant improvement compared with the baseline audit. CONCLUSIONS: The i-PARIHS framework is an effective tool for developing targeted, evidence-based improvement strategies and applying these to the clinical setting. The quality of the nurses' clinical practice improved during CVC maintenance. However, there is no certainty that these positive results can be maintained, and long-term data are needed to verify this. SPANISH ABSTRACT: http://links.lww.com/IJEBH/A185.

Rolling Bearing Fault Diagnosis Based on Support Vector Machine Optimized by Improved Grey Wolf Algorithm.

This study targets the low accuracy and efficiency of the support vector machine (SVM) algorithm in rolling bearing fault diagnosis. An improved grey wolf optimizer (IGWO) algorithm was proposed based on deep learning and a swarm intelligence optimization algorithm to optimize the structural parameters of SVM and improve the rolling bearing fault diagnosis. A nonlinear contraction factor update strategy was also proposed. The variable coefficient changes with the shrinkage factor α. Thus, the search ability was balanced at different early and late stages by controlling the dynamic changes of the variable coefficient. In the early stages of optimization, its speed is low to avoid falling into local optimization. In the later stages of optimization, the speed is higher, and finding the optimal solution is easier, balancing the two different global and local optimization capabilities to complete efficient convergence. The dynamic weight update strategy was adopted to perform position updates based on adaptive dynamic weights. First, the dataset of Case Western Reserve University was used for simulation, and the results showed that the diagnosis accuracy of IGWO-SVM was 98.75%. Then, the IGWO-SVM model was trained and tested using data obtained from the full-life-cycle test platform of mechanical transmission bearings independently researched and developed by Nanjing Agricultural University. The fault diagnosis accuracy and convergence value of the adaptation curve were compared with those of PSO-SVM (particle swarm optimization) and GWO-SVM diagnosis models. Results showed that the IGWO-SVM model had the highest rolling bearing fault diagnosis accuracy and the best diagnosis convergence.

Information technology and artificial intelligence support in management experiences of the pediatric designated hospital during the COVID-19 epidemic in 2022 in Shanghai.

Objective: To describe the information technology and artificial intelligence support in management experiences of the pediatric designated hospital in the wave of COVID-19 in Shanghai. Methods: We retrospectively concluded the management experiences at the largest pediatric designated hospital from March 1st to May 11th in 2022 in Shanghai. We summarized the application of Internet hospital, face recognition technology in outpatient department, critical illness warning system and remote consultation system in the ward and the structed electronic medical record in the inpatient system. We illustrated the role of the information system through the number and prognosis of patients treated. Results: The COVID-19 designated hospitals were built particularly for critical patients requiring high-level medical care, responded quickly and scientifically to prevent and control the epidemic situation. From March 1st to May 11th, 2022, we received and treated 768 children confirmed by positive RT-PCR and treated at our center. In our management, we use Internet Information on the Internet Hospital, face recognition technology in outpatient department, critical illness warning system and remote consultation system in the ward, structed electronic medical record in the inpatient system. No deaths or nosocomial infections occurred. The number of offline outpatient visits dropped, from March to May 2022, 146,106, 48,379, 57,686 respectively. But the outpatient volume on the internet hospital increased significantly (3,347 in March 2022 vs. 372 in March 2021; 4,465 in April 2022 vs. 409 in April 2021; 4,677 in May 2022 vs. 538 in May 2021). Conclusions: Information technology and artificial intelligence has provided significant supports in the management. The system might optimize the admission screening process, increases the communication inside and outside the ward, achieves early detection and diagnosis, timely isolates patients, and timely treatment of various types of children.

Biomass and terpenoids produced by mutant strains of Arthrospira under low temperature and light conditions.

The filamentous Cyanobacterium Arthrospira is commercially produced and is a functional, high-value, health food. We identified 5 low temperature and low light intensity tolerant strains of Arthrospira sp. (GMPA1, GMPA7, GMPB1, GMPC1, and GMPC3) using ethyl methanesulfonate mutagenesis and low temperature screening. The 5 Arthrospira strains grew rapidly below 14 °C, 43.75 µmol photons m-2 s-1 and performed breed conservation at 2.5 °C, 8.75 µmol photons m-2 s-1. We used morphological identification and molecular genetic analysis to identify GMPA1, GMPA7, GMPB1 and GMPC1 as Arthrospira platensis, while GMPC3 was identified as Arthrospira maxima. Growth at different culture temperatures was determined at regular intervals using dry biomass. At 16 °C and 43.75 µmol photons m-2 s-1, the maximum dry biomass production and the mean dry biomass productivity of GMPA1, GMPB1, and GMPC1 were 2057 ± 80 mg l-1, 68.7 ± 2.5 mg l-1 day-1, 1839 ± 44 mg l-1, 60.6 ± 1.8 mg l-1 day-1, and 2113 ± 64 mg l-1, 77.7 ± 2.5 mg l-1 day-1 respectively. GMPB1 was chosen for additional low temperature tolerance studies and growth temperature preference. In winter, GMPB1 grew well at mean temperatures <10 °C, achieving 3258 mg dry biomass from a starting 68 mg. In summer, GMPB1 grew rapidly at mean temperatures more than 28 °C, achieving 1140 mg l-1 dry biomass from a starting 240 mg. Phytonutrient analysis of GMPB1 showed high levels of C-phycocyanin and carotenoids. Arthrospira metabolism relates to terpenoids, and the methyl-D-erythritol 4-phosphate pathway is the only terpenoid biosynthetic pathway in Cyanobacteria. The 1-deoxy-D-xylulose 5-phosphate reductoisomerase (DXR) gene from GMPB1 was cloned and phylogenetic analysis showed that GMPB1 is closest to the Cyanobacterium Oscillatoria nigro-viridis PCC711. Low temperature tolerant Arthrospira strains could broaden the areas suitable for cultivation, extend the seasonal cultivation time, and lower production costs.