Post-COVID-19 arthritis: is it hyperinflammation or autoimmunity?

BACKGROUND:  Various musculoskeletal and autoimmune manifestations have been described in patients with coronavirus disease 2019 (COVID-19). Objectives: This study aims to investigate the prevalence and etiology of arthritis in post-COVID Egyptian patients. Methods: We included 100 post-COVID Egyptian patients who recovered 6 months ago and assessed several inflammatory and autoimmune markers. Results: The prevalence of post-COVID arthritis was 37%. Ankle, knee, and wrist were the most commonly affected joints. Old age (P = 0.010), smoking (P = 0.001), and arthralgia (P = 0.049) were all linked with post-COVID arthritis. Levels of pretreatment (baseline) interleukin (IL)-6 (46.41 ± 3.67 vs. 24.03 ± 2.46; P = 0.001), as well as 6-month post-COVID C-reactive protein (CRP; 98.49 ± 67.55 vs. 54.32 ± 65.73; P = 0.002), and erythrocyte sedimentation rate (ESR; 109.08 ± 174.91 vs. 58.35 ± 37.87; P = 0.029) were significantly higher in patients with arthritis compared to those without. On the other hand, complement C3 (P = 0.558) and C4 (P = 0.192), anti-nuclear antibodies (P = 0.709), and anti-cyclic citrullinated peptides (anti-CCP; P = 0.855) did not show significant differences. Only pretreatment IL-6 level was the significant single predictor of post-COVID arthritis with an odds ratio (95% confidence interval) of 3.988 (1.460-10.892) and a P-value of 0.007. CONCLUSION:  The strong association observed with inflammatory markers (ESR and CRP) and the insignificant association with serologic markers of autoimmunity (ANA and anti-CCP) in our study support the notion that the underlying mechanism of post-COVID-19 arthritis is primarily due to the hyperinflammatory process associated with COVID-19 infection, and not the result of an autoimmune reaction. IL-6 levels before therapy can predict post-COVID arthritis allowing for early management.

Clinical characteristics and outcomes of post-COVID-19 pulmonary fibrosis: A case-control study.

ABSTRACT: The development of pulmonary fibrosis is a rare complication of the novel coronavirus disease 2019 (COVID-19). Limited information is available in the literature about that, and the present study aimed to address this gap.This case-control study included 64 patients with post-COVID-19 pulmonary fibrosis who were hospitalized for COVID-19.The percentage of patients aged ≥65 years (44%) who demised was higher than those who survived (25%). Male patients (62%) had higher mortality than female patients (37%). The most frequently reported clinical symptoms were shortness of breath (98%), cough (91%), and fever (70%). Most COVID-19 patients with pulmonary fibrosis (81%) were admitted to an intensive care unit (ICU), and 63% required mechanical ventilation. Bilateral lung infiltrates (94%), "ground glass" opacity (91%), "honeycomb" lung (25%), and pulmonary consolidation (9%) were commonly identified in COVID-19 patients with pulmonary fibrosis who survived. The findings for computed tomography and dyspnea scale were significantly higher in severe cases admitted to the ICU who required mechanical ventilation. A higher computerized tomography score also correlated significantly with a longer duration of stay in hospital and a higher degree of dyspnea. Half of the COVID-19 patients with pulmonary fibrosis (50%) who survived required oxygen therapy, and those with "honeycomb" lung required long-term oxygen therapy to a far greater extent than others. Cox regression revealed that smoking and asthma were significantly associated with ICU admission and the risk of mortality.Post-COVID-19 pulmonary fibrosis is a severe complication that leads to permanent lung damage or death.

Automatic clustering method to segment COVID-19 CT images.

Coronavirus pandemic (COVID-19) has infected more than ten million persons worldwide. Therefore, researchers are trying to address various aspects that may help in diagnosis this pneumonia. Image segmentation is a necessary pr-processing step that implemented in image analysis and classification applications. Therefore, in this study, our goal is to present an efficient image segmentation method for COVID-19 Computed Tomography (CT) images. The proposed image segmentation method depends on improving the density peaks clustering (DPC) using generalized extreme value (GEV) distribution. The DPC is faster than other clustering methods, and it provides more stable results. However, it is difficult to determine the optimal number of clustering centers automatically without visualization. So, GEV is used to determine the suitable threshold value to find the optimal number of clustering centers that lead to improving the segmentation process. The proposed model is applied for a set of twelve COVID-19 CT images. Also, it was compared with traditional k-means and DPC algorithms, and it has better performance using several measures, such as PSNR, SSIM, and Entropy.

Efficient artificial intelligence forecasting models for COVID-19 outbreak in Russia and Brazil.

COVID-19 is a new member of the Coronaviridae family that has serious effects on respiratory, gastrointestinal, and neurological systems. COVID-19 spreads quickly worldwide and affects more than 41.5 million persons (till 23 October 2020). It has a high hazard to the safety and health of people all over the world. COVID-19 has been declared as a global pandemic by the World Health Organization (WHO). Therefore, strict special policies and plans should be made to face this pandemic. Forecasting COVID-19 cases in hotspot regions is a critical issue, as it helps the policymakers to develop their future plans. In this paper, we propose a new short term forecasting model using an enhanced version of the adaptive neuro-fuzzy inference system (ANFIS). An improved marine predators algorithm (MPA), called chaotic MPA (CMPA), is applied to enhance the ANFIS and to avoid its shortcomings. More so, we compared the proposed CMPA with three artificial intelligence-based models include the original ANFIS, and two modified versions of ANFIS model using both of the original marine predators algorithm (MPA) and particle swarm optimization (PSO). The forecasting accuracy of the models was compared using different statistical assessment criteria. CMPA significantly outperformed all other investigated models.