Principles of artificial ventilation

The application of intermittent positive pressure ventilation (IPPV) during the 1952 Copenhagen polio epidemic led to the development of the world’s first intensive care unit. The requirement for ventilatory support is the most common indication for intensive therapy unit (ITU) admission and is a defining feature of the specialty. Ventilator technology continues to develop and there are many ways to deliver IPPV. The variety of modes of ventilation is increasingly complex and expanding, without evidence that any one mode is associated with improved outcome. Ventilatory support is part of the treatment for a range of conditions including acute respiratory failure, raised intracranial pressure (ICP) and circulatory shock. Ventilator-associated lung injury is reduced by using low tidal volumes and limiting plateau airway pressure to less than 30 cmH₂O. Prolonged artificial ventilation has an associated morbidity and mortality and thus should be reviewed by an expert clinician on a daily basis. Weaning aims to identify those patients who will be able to breathe spontaneously. Protocols exist to facilitate timely extubation without the need for re-intubation.     

Winter is over: The use of Artificial Intelligence to individualise radiation therapy for breast cancer

Artificial intelligence demonstrated its value for automated contouring of organs at risk and target volumes as well as for auto-planning of radiation dose distributions in terms of saving time, increasing consistency, and improving dose-volumes parameters. Future developments include incorporating dose/outcome data to optimise dose distributions with optimal coverage of the high-risk areas, while at the same time limiting doses to low-risk areas. An infinite gradient of volumes and doses to deliver spatially-adjusted radiation can be generated, allowing to avoid unnecessary radiation to organs at risk. Therefore, data about patient-, tumour-, and treatment-related factors have to be combined with dose distributions and outcome-containing databases.     

Effect of saccharin,a non-nutritive sweeteners,on insulin and blood glucose levels in healthy young men: A crossover trial

Background and aimsArtificial sweeteners used instead of sugar were considered as the best alternatives that have no endocrine effects. However, it has recently been suggested that artificial sweeteners may cause impaired metabolic parameters. The hypothesis of the study was there is an association between acute saccharin consumption and both glycemia and insulin response in young healthy adult men.Methods9 healthy adult males were included in this study. This study randomly provided participants with preloads as (a)300 ml of water, or 300 ml of water sweetened with (b)75 g of sucrose, (c)240 mg of saccharine (adjusted to the sweetness of 75 g of sucrose) 1 h before a standard breakfast.ResultsCompared to mean blood glucose and serum insulin after test drinks consumption, there was only one difference between sucrose and saccharin trials in the 15th minute (117.0 ± 18.70, 95.4 ± 5.64 mg/dl respectively, p < 0.05). At the 60th minute, insulin secretion (0.80 ± 0.27 pg/dl) after the sucrose trial was found significantly higher than the saccharin trial (0.53 ± 0.09 pg/dl) and water (0.49 ± 0.06 pg/dl) (p < 0.05). Although at all intervals (except 90th minute), the mean insulin is higher after the saccharin trial compared to the water trial, these were non-statistically significant differences (p > 0.05).ConclusionsConsequently, it was determined that saccharin had no glycemic effect. However, for the effect on serum insulin to be clarified, the long-term effects should be investigated.     

人工智能在中医药领域的应用进展及现状思考

人工智能（Artificial Intelligence，AI）的蓬勃兴起为现代社会带来了前所未有的机遇，中医药是中华民族传承千年的文化瑰宝。随着人工智能技术不断在中医药领域的科技创新中崭露头角，二者的融合不断加深，人工智能在中医药领域的发展前景、争议挑战也引发了诸多思考。本文将从人工智能在中医药领域的应用入手，对人工智能辅助中医诊断、智能决策与数据挖掘、健康管理及中草药现代化研究等方面，就近年来国内外研究进展进行总结与分析，以期为AI视域下实现中医药现代化、智能化赋能。     

Imaging of COVID-19: An update of current evidences

Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has been reported as a global emergency. As respiratory dysfunction is a major clinical presentation of COVID-19, chest computed tomography (CT) plays a central role in the diagnosis and management of patients with COVID-19. Recent advances in imaging approaches using artificial intelligence have been essential as a quantification and diagnostic tool to differentiate COVID-19 from other respiratory infectious diseases. Furthermore, cardiovascular involvement in patients with COVID-19 is not negligible and may result in rapid worsening of the disease and sudden death. Cardiac magnetic resonance imaging can accurately depict myocardial involvement in SARS-CoV-2 infection. This review summarizes the role of the radiology department in the management and the diagnosis of COVID-19, with a special emphasis on ultra-high-resolution CT findings, cardiovascular complications and the potential of artificial intelligence.