Annals of translational medicine
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COVID-19 is currently rampant in China, causing unpredictable harm to humans. This study aimed to quantitatively and qualitatively investigate the research trends on coronaviruses using bibliometric analysis to identify new prevention strategies. ⋯ We considered the publication information regarding different countries, institutions, authors, journals, etc. by summarizing the literature on coronaviruses over the past 20 years. We analysed the studies on COVID-19 and the SARS and MERS coronaviruses. Notably, COVID-19 must become the research hotspot of coronavirus research, and clinical research on COVID-19 may be the key to defeating this epidemic.
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This study was conducted retrospectively to investigate the survival of patients undergoing gastric cancer surgery with epidural combined with general anesthesia (EGA) and general anesthesia alone (GA). ⋯ In summary, patients might benefit from EGA as a result of better analgesic and anti-inflammatory effects, fewer postoperative complications, higher safety, and a lower rate of metastasis and recurrence is conducive to postoperative recovery in patients with gastric cancer.
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Coronavirus disease 2019 (COVID-19), caused by a novel coronavirus (designated as SARS-CoV-2) has become a pandemic worldwide. Based on the current reports, hypertension may be associated with increased risk of sever condition in hospitalized COVID-19 patients. Angiotensin-converting enzyme 2 (ACE2) was recently identified to functional receptor of SARS-CoV-2. Previous experimental data revealed ACE2 level was increased following treatment with ACE inhibitors (ACEIs) and angiotensin receptor blockers (ARBs). Currently doctors concern whether these commonly used renin-angiotensin system (RAS) blockers-ACEIs/ARBs may increase the severity of COVID-19. ⋯ We observed there was no obvious difference in clinical characteristics between RAS blockers and non-RAS blockers groups. These data suggest ACEIs/ARBs may have few effects on increasing the clinical severe conditions of COVID-19.
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The coronavirus disease 2019 (COVID-19) was first identified in Wuhan, China on December 2019 in patients presenting with atypical pneumonia. Although 'city-lockdown' policy reduced the spatial spreading of the COVID-19, the city-level outbreaks within each city remain a major concern to be addressed. The local or regional level disease control mainly depends on individuals self-administered infection prevention actions. The contradiction between choice of taking infection prevention actions or not makes the elimination difficult under a voluntary acting scheme, and represents a clash between the optimal choice of action for the individual interest and group interests. ⋯ Through an imitating social learning process, individual-level behavioral change on taking infection prevention actions have the potentials to significantly reduce the COVID-19 outbreak in terms of size and timing at city-level. Timely and substantially resources and supports for improving the willingness-to-act and conducts of self-administered infection prevention actions are recommended to reduce to the COVID-19 associated risks.
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To evaluate the diagnostic efficacy of Densely Connected Convolutional Networks (DenseNet) for detection of COVID-19 features on high resolution computed tomography (HRCT). ⋯ Deep learning (DL) with DenseNet can accurately classify COVID-19 on HRCT with an AUC of 0.98, which can reduce the miss diagnosis rate (combined with radiologists' evaluation) and radiologists' workload.