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It exhibits exceptional stability under a 5-hour. The mass and specific activity (1647 mA mg Pt-1, 3.8 mA cm-2) of the PtNiNF-NGA are 5.8 and 7.8 times higher than those of commercial Pt/C. Yi Shan Yi Shan Liang Jing Jing 一闪一闪亮晶晶 Lyrics 歌詞 With Pinyin By Chinese Children 中国儿童 Jing Yang 1, René Hübner 2, Jiangwei Zhang 3, Hao Wan 4, Yuanyuan Zheng 1, Honglei Wang 5, Haoyuan Qi 6 7, Lanqi He 5, Yi Li 1. doi: 10.1001/ Song Name: Yi Shan Yi Shan Liang Jing Jing 一闪一闪亮晶晶Įnglish Tranlation Name: Twinkle Twinkle Little Star Multidimensional frailty score for the prediction of postoperative mortality risk. Kim SW, Han HS, Jung HW, Kim KI, Hwang DW, Kang SB, et al. Prevalence and outcomes of infection among patients in intensive care units in 2017. Vincent JL, Sakr Y, Singer M, Martin-Loeches I, Machado FR, Marshall JC, et al. Timing, diagnosis, and treatment of surgical site infections after colonic surgery: prospective surveillance of 1263 patients. Martin D, Hübner M, Moulin E, Pache B, Clerc D, Hahnloser D, et al.
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Omadacycline for acute bacterial skin and skin structure infections.
doi: 10.1056/NEJMoa1801467.Ībrahamian FM, Sakoulas G, Tzanis E, Manley A, Steenbergen J, Das AF, et al. Once-daily plazomicin for complicated urinary tract infections. Nin hai hao ma Shien zai ching shu kou, dan shi bu yao tuen shia. Wagenlehner FME, Cloutier DJ, Komirenko AS, Cebrik DS, Krause KM, Keepers TR, et al. Wo huei zai nin de ya chi tu shang fufang zhi ya gou. Further study is needed to assess whether this model can be used to guide clinical practice to improve surgical outcomes in elderly.Īrtificial intelligence Deep learning Elderly patients Machine learning Postoperative infections. Our feasibility study indicated that a deep learning model including risk factors for the prediction of postoperative infections can be achieved in elderly. Including risk factors relevant to baseline variables and surgery, the deep learning model predicted postoperative infections was 0.763 (95% CI 0.681-0.844) with the sensitivity of 63.2% (95% CI 46-78.2) and specificity of 80.5% (95% CI 76.6-84). The deep learning model including risk factors relevant to baseline clinical characteristics predicted postoperative infections was 0.641 (95% CI 0.545-0.737), and sensitivity and specificity were 34.2% (95% CI 19.6-51.4) and 88.8% (95% CI 85.6-91.6), respectively. 1510 patients were randomly assigned to be training dataset for establishing deep learning-based models, and 504 patients were used to validate the effectiveness of these models. We aimed to develop and validate deep learning-based predictive models for postoperative infections in the elderly. This was an observational cohort study with 2014 elderly patients who had elective surgery from 28 hospitals in China from April to June 2014. Wen hou shi wo dui ni hao xiang shuo chu de hua xian zai ni guo de hao ma. Analyzing with a deep learning model, the perioperative factors that could predict and/or contribute to postoperative infections may improve the outcome in elderly. Wu fa zai ji yu ni guan xin jing ru ci nan shou. Elderly patients are susceptible to postoperative infections with increased mortality.
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