Deep learning-based hybrid clinical decision support system algorithm for COVID-19 diagnosis via PCR graphics and Thorax CT images, preliminary data

Introduction: The limited sensitivity of microbiological testing, challenges in radiological differential diagnosis, and expectations of quick and accurate diagnosis required developing clinical decision support systems (CDSS). We propose a new deep learning-based hybrid CDSS that combines the advantageous aspects of thorax computed tomography(CT) and reverse transcriptase-polymerase chain reaction(PCR) to overcome the weakness of each one. Method(s): We retrospectively constructed a database that contains CT images of healthy subjects and patients with COVID-19 pneumonia(CP), bacterial/viral pneumonia(BVP), interstitial lung diseases(ILD), and PCR data of patients who were tested positive and negative for SARS-CoV-2. A new 3D-convolutional neural network (3D-CNN) and long short-term memory network(LSTM) based CDSS is developed to perform accurate and robust detection of COVID19 using CT images and PCR data. Result(s): Performance results of the proposed models (Fig1) provide highly reliable diagnosis of COVID-19 with 93.2% and 99.7% AUC for CT and PCR data, respectively. Conclusion(s): Proposed CDSS with state-of-the-art deep learning methods provides similar performance compared to both radiologists in CT evaluation and microbiologists in PCR evaluation and can be safely used. We plan to develop a hybrid CDSS algorithm further, combining laboratory data with CT and PCR models.

Repurposing Sitagliptin for COVID-19 in Adults: Clinical Benefits and An Approach for the Mechanism

Introduction: Dipeptidyl peptidase-4 (DPP4) has been shown to be a functional receptor for MERS-CoV. An interaction between the viral spike protein and DPP4 is thought to facilitate viral entry. We aimed to find out whether sitagliptin, a member of DPP4 inhibitors, would have beneficial effects in COVID-19 patients. Materials and Methods: In this single center retrospective study, we evaluated 58 patients of whom 16 were on sitagliptin treatment. Molecular docking studies were performed to identify possible interactions between ACE2 and sitagliptin. Results: Sitagliptin use shortened the time to clinical recovery about 3.5 and fastened viral clearance more than 5 days. Resolution of all symptoms was achieved on a mean±standard error (SE) of 2.50 ± 0.40 days in sitagliptin (+) group and 5.69 ± 0.61 days in sitagliptin (-) group (Log-rank test, p< 0.001). PCR tests for SARS-CoV-2 resulted negative in mean ± SE of 7.50 ± 0.98 days in sitagliptin (+) and 13.17 ± 1.07 days in sitagliptin (-) group (Log-rank test, p= 0.003). Compared to day 0, CRP, ferritin and D-dimer levels on days three, five, and seven were significantly lower whereas lymphocyte count was higher in sitagliptin (+) group. Conclusion: Our results suggest that sitagliptin seems to have a potential to be considered for the treatment of COVID-19.