Establishment and application of triple PCR method for CPV, CCoV and CRV

Canine parvovirus (CPV), canine coronavirus (CCoV) and canine rotavirus (CRV) are the three main causative viruses of diarrhea in dogs with similar clinical symptoms;thereby it is necessary to establish a high effective molecular detection method for differentiating the above pathogens. By optimizing the primer concentration and annealing temperature, a triple PCR method was established for simultaneous detection of CPV, CCoV and CRV, and then the specificity, sensitivity and repeatability of the method were tested. The results showed that the target fragments of CPV VP2 gene (253 bp), CCoV ORF-1b gene (379 bp) and CRV VP6 gene (852 bp) could be accurately amplified by the triple PCR method with high specificity, the detection limits of CPV, CCOV and CRV were 6.44x10-1 pg/L, 8.72x10-1 pg/L and 8.35x10-1 pg/L respectively with high sensitivity, and the method had good stability. Using this triple PCR method, 135 canine diarrhea fecal samples collected in Chengdu region from 2019 to 2020 were detected, and compared with those of single PCR method. The detection rates of CPV, CCoV and CRV were 16.30%, 20.74% and 4.44%, respectively, and the total infection rate was 51.11% (65/135) with 20.00% (13/65) co-infection rate. The detection results were consistent with three single PCR methods. In conclusion, CPV/CCoV/CRV triple PCR method successfully established in this paper can be applied as an effective molecular method to detection of related pathogens and to the epidemiological investigation.

Colchicine prophylaxis is associated with fewer gout flares after COVID-19 vaccination.

OBJECTIVES: COVID-19 vaccination often triggers a constellation of transitory inflammatory symptoms. Gout is associated with several comorbidities linked to poor outcomes in COVID-19, and gout flares can be triggered by some vaccinations. We analysed the risk of gout flares in the first 3 months after COVID-19 vaccination with inactivated virus, and whether colchicine can prevent gout flares following post-COVID-19 vaccination. METHODS: A clinical delivery population-based cross-sectional study was conducted in the Gout Clinic at the Affiliated Hospital of Qingdao University between February and October 2021. Study participants were selected using a systematic random sampling technique among follow-up patients with gout. We collected data, including vaccinations and potential risk factors, using a combination of interviews, health QR codes and medical records. Logistic regression was used to adjust for covariates. RESULTS: We enrolled 549 gout participants (median age 39 years, 84.2% vaccinated). For the 462 patients who received COVID-19 vaccine, 203 (43.9%) developed at least one gout flare in the 3 months after vaccination. Most of these flares were experienced within 1 month after the first (99/119 (83.2%)) or second (70/115 (60.9%)) dose of vaccine. Compared with unvaccinated participants, COVID-19 vaccination was associated with higher odds of gout flare within 3 months (adjusted OR 6.02; 95% CI 3.00 to 12.08). Colchicine use was associated with 47% less likelihood of postvaccine gout flare. CONCLUSION: COVID-19 vaccination was associated with increased odds of gout flare, which developed mainly in month 1 after each vaccine dose, and was negatively associated with colchicine prophylaxis.

Artificial intelligence for stepwise diagnosis and monitoring of COVID-19.

BACKGROUND: Main challenges for COVID-19 include the lack of a rapid diagnostic test, a suitable tool to monitor and predict a patient's clinical course and an efficient way for data sharing among multicenters. We thus developed a novel artificial intelligence system based on deep learning (DL) and federated learning (FL) for the diagnosis, monitoring, and prediction of a patient's clinical course. METHODS: CT imaging derived from 6 different multicenter cohorts were used for stepwise diagnostic algorithm to diagnose COVID-19, with or without clinical data. Patients with more than 3 consecutive CT images were trained for the monitoring algorithm. FL has been applied for decentralized refinement of independently built DL models. RESULTS: A total of 1,552,988 CT slices from 4804 patients were used. The model can diagnose COVID-19 based on CT alone with the AUC being 0.98 (95% CI 0.97-0.99), and outperforms the radiologist's assessment. We have also successfully tested the incorporation of the DL diagnostic model with the FL framework. Its auto-segmentation analyses co-related well with those by radiologists and achieved a high Dice's coefficient of 0.77. It can produce a predictive curve of a patient's clinical course if serial CT assessments are available. INTERPRETATION: The system has high consistency in diagnosing COVID-19 based on CT, with or without clinical data. Alternatively, it can be implemented on a FL platform, which would potentially encourage the data sharing in the future. It also can produce an objective predictive curve of a patient's clinical course for visualization. KEY POINTS: • CoviDet could diagnose COVID-19 based on chest CT with high consistency; this outperformed the radiologist's assessment. Its auto-segmentation analyses co-related well with those by radiologists and could potentially monitor and predict a patient's clinical course if serial CT assessments are available. It can be integrated into the federated learning framework. • CoviDet can be used as an adjunct to aid clinicians with the CT diagnosis of COVID-19 and can potentially be used for disease monitoring; federated learning can potentially open opportunities for global collaboration.