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40, p < .001; standard direct effect = 0.31, p < .001). Resilience affected turnover intention indirectly and significantly via the whole mediating effects of work engagement and compassion fatigue (standardized indirect effect = -0.36, p < .001).

Higher resilience leads to lower turnover intention via enhancing work engagement and reducing compassion fatigue in dialysis nurses. Nursing policies should be established to promote resilience training, enhance work engagement and reduce compassion fatigue in order to alleviate turnover intention in dialysis nurses.

Higher resilience leads to lower turnover intention via enhancing work engagement and reducing compassion fatigue in dialysis nurses. Nursing policies should be established to promote resilience training, enhance work engagement and reduce compassion fatigue in order to alleviate turnover intention in dialysis nurses.

It is feasible to use magnetic resonance (MR)-based radiomics to distinguish high-grade from low-grade prostate cancer (PCa), but radiomics model performance based on fully automated segmentation remains unknown.

To develop and test radiomics models based on manually or automatically gained masks on apparent diffusion coefficient (ADC) maps to predict high-grade (Gleason score ≥ 4 + 3) PCa at radical prostatectomy (RP).

Retrospective.

A total of 176 patients (94 high-grade PCa and 82 low-grade PCa) with complete RP, preoperative biopsy, and multiparametric magnetic resonance imaging (mpMRI) were retrospectively recruited and randomly divided into training (N = 123) and test (N = 53) cohorts.

Using a 3.0-T MR scanner, ADC maps were calculated from diffusion-weighted imaging (b values = 0, 1400 s/mm

, echo planar imaging).

Two radiologists segmented the whole prostate gland and the most index prostate lesion. Automatic segmentation of the prostate and the lesion were performed. Four radiomics modegmentation and biopsy, highlighting the possibility of a fully automatic workflow combining automated segmentation with radiomics analysis.

4 TECHNICAL EFFICACY Stage 2.

4 TECHNICAL EFFICACY Stage 2.

This study aims to explore the effect of integrated traditional Chinese and Western medicine management mode on nursing care of patients with coronavirus disease 2019 (COVID-19).

The study included 142 COVID-19 patients hospitalized in Huoshenshan Hospital from February to March 2020. Nursing measures in the treatment (n = 111) and control (n = 31) groups included integrated management care and usual care. The integrated management model had eight components WeChat group management, early Traditional Chinese Medicine (TCM) treatment, emotional nursing, music therapy, observation of tongue coating and pulse, vital Qi strengthening, preventing recurrence after recovery, and an individualized follow-up scheme. The endpoints included clinical outcome, recovery time of clinical symptoms and signs, days of hospital stay, and survival without intensive care unit (ICU) admission.

Recovery time from clinical symptoms such as fever, cough, shortness of breath, muscle aches, and fatigue in the treatment group was shorter than that in the control group. Less time was required to show negative chest computed tomography findings in the treatment group than in the control group (13.0 ± 2.8 vs. 16.7 ± 3.9, p < .025). click here The lengths of hospital stay in the two groups were 16.4 ± 7.3 days (treatment group) and 24.4 ± 4.8 days (control group) (p < .001). Kaplan-Meier curves for survival without ICU transfer showed statistical difference between the two groups (χ

= 5.427, p = .020).

Our study shows that the integrated management mode can relieve the symptoms of patients faster, shorten hospital stay and disease course, and reduce the rate of ICU transfers.

Our study shows that the integrated management mode can relieve the symptoms of patients faster, shorten hospital stay and disease course, and reduce the rate of ICU transfers.There is considerable interest in the development of libraries of scaffold-diverse macrocycles as a source of ligands for difficult targets, such as protein-protein interaction surfaces. A classic problem in the synthesis of high-quality macrocyclic libraries is that some linear precursors will cyclize efficiently while some will not, depending on their conformational preferences. We report here a powerful quality control method that can be employed to readily distinguish between scaffolds that do and do not cyclize efficiently during solid-phase synthesis of thioether macrocycles without the need for tedious liquid chromatography/mass spectrometry analysis. We demonstrate that this assay can be employed to identify linear impurities in a DNA-encoded library of macrocycles. We also use the method to establish a useful quality control protocol for re-synthesis of putative macrocyclic screening hits.Metal-organic frameworks (MOFs) are known for their versatile combination of inorganic building units and organic linkers, which offers immense opportunities in a wide range of applications. However, many MOFs are typically synthesized as multiphasic polycrystalline powders, which are challenging for studies by X-ray diffraction. Therefore, developing new structural characterization techniques is highly desired in order to accelerate discoveries of new materials. Here, we report a high-throughput approach for structural analysis of MOF nano- and sub-microcrystals by three-dimensional electron diffraction (3DED). A new zeolitic-imidazolate framework (ZIF), denoted ZIF-EC1, was first discovered in a trace amount during the study of a known ZIF-CO3 -1 material by 3DED. The structures of both ZIFs were solved and refined using 3DED data. ZIF-EC1 has a dense 3D framework structure, which is built by linking mono- and bi-nuclear Zn clusters and 2-methylimidazolates (mIm- ). With a composition of Zn3 (mIm)5 (OH), ZIF-EC1 exhibits high N and Zn densities. We show that the N-doped carbon material derived from ZIF-EC1 is a promising electrocatalyst for oxygen reduction reaction (ORR). The discovery of this new MOF and its conversion to an efficient electrocatalyst highlights the power of 3DED in developing new materials and their applications.