Bench to bedside: recent advances in lung cancer pathological research with implications for diagnostic clinical practice

After decades of relative stagnation lung cancer is emerging as a disease type where rapid progress is being made in diagnosis and therapy, as well as in our understanding of disease biology. Much of this progress is of immediate impact to diagnosticians, and more is likely to affect diagnostic practice in the near future. In this review we seek to briefly summarize several key areas of active research of immediate or probable imminent value to trainee and consultant pulmonary pathologists alike. We cover some major changes in tumour classification, grading, and patient stratification, as well as considering the state of the art in machine-assisted interpretation of lung cancer histology, and the use of genetically modified lung cancer models.     

Systematic Review of Health Economic Evaluations Focused on Artificial Intelligence in Healthcare: The Tortoise and the Cheetah

ObjectivesThis study aimed to systematically review recent health economic evaluations (HEEs) of artificial intelligence (AI) applications in healthcare. The aim was to discuss pertinent methods, reporting quality and challenges for future implementation of AI in healthcare, and additionally advise future HEEs.MethodsA systematic literature review was conducted in 2 databases (PubMed and Scopus) for articles published in the last 5 years. Two reviewers performed independent screening, full-text inclusion, data extraction, and appraisal. The Consolidated Health Economic Evaluation Reporting Standards and Philips checklist were used for the quality assessment of included studies.ResultsA total of 884 unique studies were identified; 20 were included for full-text review, covering a wide range of medical specialties and care pathway phases. The most commonly evaluated type of AI was automated medical image analysis models (n = 9, 45%). The prevailing health economic analysis was cost minimization (n = 8, 40%) with the costs saved per case as preferred outcome measure. A total of 9 studies (45%) reported model-based HEEs, 4 of which applied a time horizon >1 year. The evidence supporting the chosen analytical methods, assessment of uncertainty, and model structures was underreported. The reporting quality of the articles was moderate as on average studies reported on 66% of Consolidated Health Economic Evaluation Reporting Standards items.ConclusionsHEEs of AI in healthcare are limited and often focus on costs rather than health impact. Surprisingly, model-based long-term evaluations are just as uncommon as model-based short-term evaluations. Consequently, insight into the actual benefits offered by AI is lagging behind current technological developments.     

近端胃切除人工三角瓣成形抗反流术的力学机理仿真分析

目的:对食管胃结合部腺癌近端胃切除术（PG）人工三角瓣成形后残胃食糜进行流体动力学数值模拟，并计算不同性质胃内食糜的流动特征。方法:构建常规PG和人工三角瓣成形术术后胃仿真模型，运用Fluent软件对不同粘度胃内食糜反流问题进行数值模拟。结果:站立位姿态时，相对常规PG方案，人工三角瓣成形手术方案表现出较好的抗反流作用；卧位姿态时，当胃内食糜粘度大于0.145 2 Pa[?s，且胃内食糜不超过人工三角瓣情况下，人工三角瓣成形抗反流手术表现出较好的抗反流效果；人工三角瓣抗反流成形手术方案数值模拟结果与临床上患者表现一致。结论:本研究仿真分析为人工三角瓣成形抗反流手术方案的有效性机理分析、临床患者术后饮食及手术方案的进一步改进提供理论及数值依据。 【关键词】食管胃结合部腺癌；抗反流；人工三角瓣；计算流体力学     

浅谈人工智能对中医诊断的影响＊

中医药是中华民族的瑰宝，随着人工智能（Artificial Intelligence，AI）的迅猛发展，中医药与AI的结合将可以为中医药的传承和现代化提供助力。本研究将从搭建AI中医专家系统入手，阐述AI对提高中医诊断的作用，物联网搭建AI中医药服务，存在的问题和对策，以及中医药人工智能化的前景，以图为中医药的发展提供新的思路和方法。     

The risk of fractures,acute myocardial infarction,atrial fibrillation and ventricular arrhythmia in geriatric patients exposed to promethazine

ABSTRACT

Objectives: This study aimed to compare the risk of fractures, acute myocardial infarction, atrial fibrillation, and ventricular arrhythmia among Danish citizens aged ≥ 65 which were new users of promethazine or domperidone, triazolam, loratadine, and betahistine. Secondly, the study aimed to perform a risk stratification to identify the most relevant predictors for the study outcomes.

Methods: The study period was 01/01/2015 to 31/12/2016. The data sources were the Danish registers. Each patient was followed for 90 days. A logistic regression model was used to compute the unadjusted and adjusted odds ratios (OR), and a conditional inference tree was used to identify the most relevant predictors for the study outcomes.

Results: Promethazine had a higher risk of hospitalization for atrial fibrillation than loratadine and betahistine (OR 1.58; 95% CI 1.07–2.63 and OR 3.22; 95% CI 1.69–7.14, respectively). For fractures, acute myocardial infarction, and ventricular arrhythmia hospitalizations, no statistically significant differences were found among drugs under investigation. The medical history of cardiac arrhythmia (OR 4.14; 95% CI 2.94–5.78, p < 0.0001) was the most relevant predictor for atrial fibrillation hospitalizations.

Conclusion: This study found an increased risk of atrial fibrillation hospitalization among promethazine users, and the risk was higher among patients with prior cardiac arrhythmia.