Intravenous immunoglobulin as an important adjunct in the prevention and therapy of coronavirus 2019 disease.

The coronavirus disease-19 (COVID-19) pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) challenged globally with its morbidity and mortality. A small percentage of affected patients (20%) progress into the second stage of the disease clinically presenting with severe or fatal involvement of lung, heart and vascular system, all contributing to multiple-organ failure. The so-called 'cytokines storm' is considered the pathogenic basis of severe disease and it is a target for treatment with corticosteroids, immunotherapies and intravenous immunoglobulin (IVIg). We provide an overview of the role of IVIg in the therapy of adult patients with COVID-19 disease. After discussing the possible underlying mechanisms of IVIg immunomodulation in COVID-19 disease, we review the studies in which IVIg was employed. Considering the latest evidence that show a link between new coronavirus and autoimmunity, we also discuss the use of IVIg in COVID-19 and anti-SARS-CoV-2 vaccination related autoimmune diseases and the post-COVID-19 syndrome. The benefit of high-dose IVIg is evident in almost all studies with a rapid response, a reduction in mortality and improved pulmonary function in critically ill COVID-19 patients. It seems that an early administration of IVIg is crucial for a successful outcome. Studies' limitations are represented by the small number of patients, the lack of control groups in some and the heterogeneity of included patients. IVIg treatment can reduce the stay in ICU and the demand for mechanical ventilation, thus contributing to attenuate the burden of the disease.

The role of microbiota and oxidative stress axis and the impact of intravenous immunoglobulin in systemic lupus erythematosus.

Systemic lupus erythematosus (SLE) is a complex autoimmune disease characterized by widespread inflammation affecting various organs. This review discusses the role of oxidative stress and gut microbiota in the pathogenesis of SLE and evaluates the therapeutic potential of intravenous immunoglobulins (IVIg). Oxidative stress contributes to SLE by causing impairment in the function of mitochondria, resulting in reactive oxygen species production, which triggers autoantigenicity and proinflammatory cytokines. Gut microbiota also plays a significant role in SLE. Dysbiosis has been associated to disease's onset and progression. Moreover, dysbiosis exacerbates SLE symptoms and influences systemic immunity, leading to a breakdown in bacterial tolerance and an increase in inflammatory responses. High-dose IVIg has emerged as a promising treatment for refractory cases of SLE. The beneficial effects of IVIg are partly due to its antioxidant property, reducing oxidative stress markers and modulating the immune responses. Additionally, IVIg can normalize the gut flora, as demonstrated in a case of severe intestinal pseudo-obstruction. In summary, both oxidative stress and dysregulation of microbiota are pivotal in the pathogenesis of SLE. The use of IVIg may improve the disease's outcome. Future research should be directed to elucidating the precise mechanisms by which oxidative stress and microbiota are linked with autoimmunity in SLE in developing targeted therapies.

A machine learning analysis to evaluate the outcome measures in inflammatory myopathies.

OBJECTIVE: To assess the long-term outcome in patients with Idiopathic Inflammatory Myopathies (IIM), focusing on damage and activity disease indexes using artificial intelligence (AI). BACKGROUND: IIM are a group of rare diseases characterized by involvement of different organs in addition to the musculoskeletal. Machine Learning analyses large amounts of information, using different algorithms, decision-making processes and self-learning neural networks. METHODS: We evaluate the long-term outcome of 103 patients with IIM, diagnosed on 2017 EULAR/ACR criteria. We considered different parameters, including clinical manifestations and organ involvement, number and type of treatments, serum creatine kinase levels, muscle strength (MMT8 score), disease activity (MITAX score), disability (HAQ-DI score), disease damage (MDI score), and physician and patient global assessment (PGA). The data collected were analysed, applying, with R, supervised ML algorithms such as lasso, ridge, elastic net, classification, and regression trees (CART), random forest and support vector machines (SVM) to find the factors that best predict disease outcome. RESULTS AND CONCLUSION: Using artificial intelligence algorithms we identified the parameters that best correlate with the disease outcome in IIM. The best result was on MMT8 at follow-up, predicted by a CART regression tree algorithm. MITAX was predicted based on clinical features such as the presence of RP-ILD and skin involvement. A good predictive capacity was also demonstrated on damage scores: MDI and HAQ-DI. In the future Machine Learning will allow us to identify the strengths or weaknesses of the composite disease activity and damage scores, to validate new criteria or to implement classification criteria.

A machine learning analysis to predict the response to intravenous and subcutaneous immunoglobulin in inflammatory myopathies. A proposal for a future multi-omics approach in autoimmune diseases.

OBJECTIVE: To evaluate the response to treatment with intravenous (IVIg) and subcutaneous (20%SCIg) immunoglobulin in our series of patients with Inflammatory idiopathic myopathies (IIM) by the means of artificial intelligence. BACKGROUND: IIM are rare diseases mainly involving the skeletal muscle with particular clinical, laboratory and radiological characteristics. Artificial intelligence (AI) represents computer processes which allows to perform complex calculations and data analyses, with the least human intervention. Recently, the use an AI in medicine significantly expanded, especially through machine learning (ML) which analyses huge amounts of information and accordingly makes decisions, and deep learning (DL) which uses artificial neural networks to analyse data and automatically learn. METHODS: In this study, we employed AI in the evaluation of the response to treatment with IVIg and 20%SCIg in our series of patients with IIM. The diagnoses were determined on the established EULAR/ACR criteria. The treatment response was evaluated employing the following: serum creatine kinase levels, muscle strength (MMT8 score), disease activity (MITAX score) and disability (HAQ-DI score). We evaluated all the above parameters, applying, with R, different supervised ML algorithms, including Least Absolute Shrinkage and Selection Operator, Ridge, Elastic Net, Classification and Regression Trees and Random Forest to estimate the most important predictors for a good response to IVIg and 20%SCIg treatment. RESULTS AND CONCLUSION: By the means of AI we have been able to identify the scores that best predict a good response to IVIg and 20%SCIg treatment. The muscle strength as evaluated by MMT8 score at the follow-up is predicted by the presence of dysphagia and of skin disorders, and the myositis activity index (MITAX) at the beginning of the treatment. The relationship between muscle strength and MITAX indicates a better action of IVIg therapy in patients with more active systemic disease. Considering our results, Elastic Net and similar approaches were seen to be the most viable, efficient, and effective ML methods for predicting the clinical outcome (MMT8 and MITAX at most) in myositis.

Bisphenol A: A notorious player in the mosaic of autoimmunity.

The multifactorial etiology of autoimmune diseases has been studied at large. Genetic risk factors and environmental agents play an integral role in the pathogenesis of autoimmune processes. In recent decades, Bisphenol A (BPA), an exogenous compound found in polycarbonate plastic, has gained attention for its harmful multifocal effects on a diverse subset of systemic pathways, potentially contributing to disease onset and exacerbation. BPA is a xenoestrogen used globally in the manufacture of daily use products including plastic storage containers, water and infant bottles, and food and drink packaging. BPA exhibits immune stimulatory activity bringing into question the association between its greater global presence and the increased prevalence of autoimmune diseases. The purpose of this multi-study analysis is to assess recent research investigating the underlying role of BPA in autoreactive mechanisms. Although research at present does not directly link BPA exposure to the development of autoimmune diseases, a large body of evidence supports the pro-inflammatory effects of BPA on the immune system. Further studies are required to elucidate the role of BPA in autoimmune pathogenesis, however caution should be taken in the use of BPA containing products by those affected or genetically susceptible to developing autoimmune diseases.