Optimized Deep Learning-Inspired Model for the Diagnosis and Prediction of COVID-19

Detecting COVID-19 cases as early as possible became a critical issue that must be addressed to avoid the pandemic’s additional spread and early provide the appropriate treatment to the affected patients.This study aimed to develop a COVID-19 diagnosis and prediction(AIMDP)model that could identify patients with COVID-19 and distinguish it from other viral pneumonia signs detected in chest computed tomography(CT)scans.The proposed system uses convolutional neural networks(CNNs)as a deep learning technology to process hundreds of CT chest scan images and speeds up COVID-19 case prediction to facilitate its containment.We employed the whale optimization algorithm(WOA)to select the most relevant patient signs.A set of experiments validated AIMDP performance.It demonstrated the superiority of AIMDP in terms of the area under the curve-receiver operating characteristic(AUC-ROC)curve,positive predictive value(PPV),negative predictive rate(NPR)and negative predictive value(NPV).AIMDP was applied to a dataset of hundreds of real data and CT images,and it was found to achieve 96%AUC for diagnosing COVID-19 and 98%for overall accuracy.The results showed the promising performance of AIMDP for diagnosing COVID-19 when compared to other recent diagnosing and predicting models.

A pilot study on Paxlovid therapy for hemodialysis patients with severe acute respiratory syndrome coronavirus 2 infections

We aimed to investigate the safety and efficacy of nirmatrelvir/ritonavir(Paxlovid)therapy for hemodialysis-dependent patients with severe acute respiratory syndrome coronavirus 2(SARS-CoV-2)infection.Thirteen hemodialysis patients infected with the Omicron variant of SARS-CoV-2 from April 3 to May 30,2022,were recruited.Laboratory parameters and chest CT(computed tomography)imaging were analyzed.The treatment group included six patients who received 150 mg/100 mg of Paxlovid orally once daily for 5 days,whereas the control group included seven patients who received basic treatment.No serious adverse reactions or safety events were recorded.Four control patients progressed to moderate disease,and none in the treatment group showed progression of chest CT findings(P<0.05).Paxlovid therapy tended toward early viral clearance and low viral load on Day 8.Moreover,83.3%of the patients in the treatment group and 57.1%of the patients in the control group turned negative within 22 days.In the Paxlovid treatment group,we found significantly increased levels of lymphocytes(P=0.03)and eosinophils(P=0.02)and decreased levels of D-dimer on Day 8 compared with those on Day 1.Paxlovid therapy showed a potential therapeutic effect with good tolerance in hemodialysis patients.The optimal dose and effectiveness evaluation must be further investigated in a largeer cohort.