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Predictive model of risk factors of High Flow Nasal Cannula using machine learning in COVID-19

Infectious Disease Modelling 2022年第3期7卷 526-534页

作者： Nobuaki Matsunaga keisuke kamata Yusuke Asai Shinya Tsuzuki Yasuaki Sakamoto Shinpei Ijichi Takayuki Akiyama a Jiefu Yu Gen Yamada Mari Terada Setsuko Suzuki Kumiko Suzuki Sho Saito Kayoko Hayakawa Norio Ohmagari AMR Clinical Reference Center National Center for Global Health and MedicineTokyoJapan DataRobot Inc. TokyoJapan Disease Control and Prevention Center National Center for Global Health and MedicineTokyoJapan Biostatistics Division Center for Research Administration and SupportNational Cancer CenterTokyoJapan

With the rapid increase in the number of COVID-19 patients in Japan,the number of patients receiving oxygen at home has also increased rapidly,and some of these patients have *** efficient approach to identify high-risk patients with slowly progressing and rapidly worsening COVID-19,and to avoid missing the timing of therapeutic intervention will improve patient prognosis and prevent medical *** admitted to medical institutions in Japan from November 14,2020 to April 11,2021 and registered in the COVID-19 Registry Japan were *** factors for patients with High Flow Nasal Cannula invasive respiratory management or higher were comprehensively explored using machine ***-specific cohorts were created,and severity prediction was performed for the patient surge *** were able to obtain a model that was able to predict severe disease with a sensitivity of 57%when the specificity was set at 90%for those aged 40e59 years,and with a specificity of 50%and 43%when the sensitivity was set at 90%for those aged 60e79 years and 80 years and older,*** were able to identify lactate dehydrogenase level(LDH)as an important factor in predicting the severity of illness in all age *** machine learning,we were able to identify risk factors with high accuracy,and predict the severity of the *** plan to develop a tool that will be useful in determining the indications for hospitalisation for patients undergoing home care and early hospitalisation.

关键词： COVID-19 Machine learning Severity Risk prediction Japan

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Differential Mechanisms of the Effect of Peroxisome Proliferator-Activated Receptor Gamma Agonists on Bleomycin-Induced Lung Fibrosis

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Open Journal of Respiratory Diseases 2013年第2期3卷 31-38页

作者： keisuke Miyamoto Sadatomo Tasaka Yasushi Nakano Hiromi Shinoda Hirofumi kamata Wakako Yamasawa Makoto Ishii Naoki Hasegawa Tomoko Betsuyaku Center for Infectious Diseases and Infection Control Keio University School of Medicine Tokyo Japan Department of Laboratory Medicine Keio University School of Medicine Tokyo Japan Division of Pulmonary Medicine Keio University School of Medicine Tokyo Japan

Background and Objectives: Peroxisome proliferator-activated receptor-g (PPAR-g) is a nuclear receptor whose activation regulates inflammation and fibrosis in various organs. We aimed to investigate the effect of two PPAR-g ligands, telmisartan and rosiglitazone, on lung injury and fibrosis induced by intratracheal bleomycin (BLM). Methods: Lung injury and fibrosis was induced in female C57Bl/6 mice by intratracheal instillation of 1.0 mg/kg of BLM. Some of the animals received rosiglitazone intraperitoneally or telmisartan in drinking water. Bronchoalveolar lavage (BAL) was performed 2, 7, 14 or 21 days after BLM instillation for cell counting and measurement of mediators in the lung. In a separate series, the lungs were sampled for collagen assay and histopathological evaluation. Results: Treatment with rosiglitazone or telmisartan significantly attenuated the BLM-induced increases in lung collagen content, pathological score, and inflammatory cells in BAL fluid. Rosiglitazone significantly suppressed BLM-induced elevation of TGF-b1, MCP-1, and IL-6 levels in the lung. In contrast, telmisartan made no changes in these cytokines, whereas it mitigated the BLM-induced increase in prostaglandin F2a in the lung. Higher concentrations of rosiglitazone and telmisartan attenuated proliferation of lung fibroblasts in vitro. Conclusions: Two PPAR-g ligands, rosiglitazone and telmisartan, exert protective effects on BLM-induced lung fibrosis through the suppression of different profibrotic mediators.

关键词： Peroxisome Proliferator-Activated Receptor-γ Bleomycin Pulmonary Fibrosis Transforming Growth Factor-β1 Prostaglandin F2α

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