ABSTRACT
CD26/Dipeptidyl peptidase 4 (DPP4) is a type II transmembrane glycoprotein that is widely expressed in various organs and cells. It can also exist in body fluids in a soluble form. DPP4 participates in various physiological and pathological processes by regulating energy metabolism, inflammation, and immune function. DPP4 inhibitors have been approved by the Food and Drug Administration (FDA) for the treatment of type 2 diabetes mellitus. More evidence has shown the role of DPP4 in the pathogenesis of lung diseases, since it is highly expressed in the lung parenchyma and the surface of the epithelium, vascular endothelium, and fibroblasts of human bronchi. It is a potential biomarker and therapeutic target for various lung diseases. During the coronavirus disease-19 (COVID-19) global pandemic, DPP4 was found to be an important marker that may play a significant role in disease progression. Some clinical trials on DPP4 inhibitors in COVID-19 are ongoing. DPP4 also affects other infectious respiratory diseases such as Middle East respiratory syndrome and non-infectious lung diseases such as pulmonary fibrosis, lung cancer, chronic obstructive pulmonary disease (COPD), and asthma. This review aims to summarize the roles of DPP4 and its inhibitors in infectious lung diseases and non-infectious diseases to provide new insights for clinical physicians.
ABSTRACT
Background: Recently, Coronavirus Disease 2019 (COVID-19), caused by severe acute respiratory syndrome virus 2 (SARS-CoV-2), has affected more than 200 countries and lead to enormous losses. This study systematically reviews the application of Artificial Intelligence (AI) techniques in COVID-19, especially for diagnosis, estimation of epidemic trends, prognosis, and exploration of effective and safe drugs and vaccines; and discusses the potential limitations. Methods: We report this systematic review following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. We searched PubMed, Embase and the Cochrane Library from inception to 19 September 2020 for published studies of AI applications in COVID-19. We used PROBAST (prediction model risk of bias assessment tool) to assess the quality of literature related to the diagnosis and prognosis of COVID-19. We registered the protocol (PROSPERO CRD42020211555). Results: We included 78 studies: 46 articles discussed AI-assisted diagnosis for COVID-19 with total accuracy of 70.00 to 99.92%, sensitivity of 73.00 to 100.00%, specificity of 25 to 100.00%, and area under the curve of 0.732 to 1.000. Fourteen articles evaluated prognosis based on clinical characteristics at hospital admission, such as clinical, laboratory and radiological characteristics, reaching accuracy of 74.4 to 95.20%, sensitivity of 72.8 to 98.00%, specificity of 55 to 96.87% and AUC of 0.66 to 0.997 in predicting critical COVID-19. Nine articles used AI models to predict the epidemic of the COVID-19, such as epidemic peak, infection rate, number of infected cases, transmission laws, and development trend. Eight articles used AI to explore potential effective drugs, primarily through drug repurposing and drug development. Finally, 1 article predicted vaccine targets that have the potential to develop COVID-19 vaccines. Conclusions: In this review, we have shown that AI achieved high performance in diagnosis, prognosis evaluation, epidemic prediction and drug discovery for COVID-19. AI has the potential to enhance significantly existing medical and healthcare system efficiency during the COVID-19 pandemic.
ABSTRACT
OBJECTIVES: To assess the reporting quality of abstracts for published randomized controlled trials (RCTs) of interventions for coronavirus disease 2019 (COVID-19), including the use of spin strategies and the level of spin for RCTs with statistically non-significant primary outcomes, and to explore potential predictors for reporting quality and the severity of spin. STUDY DESIGN AND SETTING: PubMed was searched to find RCTs that tested interventions for COVID-19, and the reporting quality and spin in the abstracts were assessed. Linear regression analyses were used to identify potential predictors. RESULTS: Forty RCT abstracts were included in our assessment of reporting quality, and a higher word count in the abstract was significantly correlated with higher reporting scores (95% CI 0.044 to 0.658, P=0.026). Multiple spin strategies were identified. Our multivariate analyses showed that geographical origin was associated with severity of spin, with research from non-Asian regions containing fewer spin strategies (95% CI -0.760 to -0.099, P=0.013). CONCLUSIONS: The reporting quality of abstracts of RCTs of interventions for COVID-19 is far from satisfactory. A relatively high proportion of the abstracts contained spin, and the findings reported in the results and conclusion sections of these abstracts need to be interpreted with caution.