Precision health: A pragmatic approach to understanding and addressing key factors in autoimmune diseases

Artificial intelligence for predicting treatment responses in autoimmune rheumatic diseases: advancements, challenges, and future perspectives

Autoimmune rheumatic diseases (ARD) present a significant global health challenge characterized by a rising prevalence. These highly heterogeneous diseases involve complex pathophysiological mechanisms, leading to variable treatment efficacies across individuals. This variability underscores the need for personalized and precise treatment strategies. Traditionally, clinical practices have depended on empirical treatment selection, which often results in delays in effective disease management and can cause irreversible damage to multiple organs. Such delays significantly affect patient quality of life and prognosis. Artificial intelligence (AI) has recently emerged as a transformative tool in rheumatology, offering new insights and methodologies. Current research explores AI's capabilities in diagnosing diseases, stratifying risks, assessing prognoses, and predicting treatment responses in ARD. These developments in AI offer the potential for more precise and targeted treatment strategies, fostering optimism for enhanced patient outcomes. This paper critically reviews the latest AI advancements for predicting treatment responses in ARD, highlights the current state of the art, identifies ongoing challenges, and proposes directions for future research. By capitalizing on AI's capabilities, researchers and clinicians are poised to develop more personalized and effective interventions, improving care and outcomes for patients with ARD.

Precision prevention in occupational health: a conceptual analysis and development of a unified understanding and an integrative framework

Introduction

Precision prevention implements highly precise, tailored health interventions for individuals by directly addressing personal and environmental determinants of health. However, precision prevention does not yet appear to be fully established in occupational health. There are numerous understandings and conceptual approaches, but these have not yet been systematically presented or synthesized. Therefore, this conceptual analysis aims to propose a unified understanding and develop an integrative conceptual framework for precision prevention in occupational health.

Methods

Firstly, to systematically present definitions and frameworks of precision prevention in occupational health, six international databases were searched for studies published between January 2010 and January 2024 that used the term precision prevention or its synonyms in the context of occupational health. Secondly, a qualitative content analysis was conducted to analyze the existing definitions and propose a unified understanding. Thirdly, based on the identified frameworks, a multi-stage exploratory development process was applied to develop and propose an integrative conceptual framework for precision prevention in occupational health.

Results

After screening 3,681 articles, 154 publications were reviewed, wherein 29 definitions of precision prevention and 64 different frameworks were found, which can be summarized in eight higher-order categories. The qualitative content analysis revealed seven themes and illustrated many different wordings. The proposed unified understanding of precision prevention in occupational health takes up the identified themes. It includes, among other things, a contrast to a "one-size-fits-all approach" with a risk- and resource-oriented data collection and innovative data analytics with profiling to provide and improve tailored interventions. The developed and proposed integrative conceptual framework comprises three overarching stages: (1) data generation, (2) data management lifecycle and (3) interventions (development, implementation and adaptation).

Discussion

Although there are already numerous studies on precision prevention in occupational health, this conceptual analysis offers, for the first time, a proposal for a unified understanding and an integrative conceptual framework. However, the proposed unified understanding and the developed integrative conceptual framework should only be seen as an initial proposal that should be critically discussed and further developed to expand and strengthen both research on precision prevention in occupational health and its practical application in the workplace.

Autoantibodies in COVID-19 and Other Viral Diseases: Molecular, Cellular, and Clinical Perspectives

Autoantibodies are immune system-produced antibodies that wrongly target the body's cells and tissues for attack. The COVID-19 pandemic has made it possible to link autoantibodies to both the severity of pathogenic infection and the emergence of several autoimmune diseases after recovery from the infection. An overview of autoimmune disorders and the function of autoantibodies in COVID-19 and other infectious diseases are discussed in this review article. We also investigated the different categories of autoantibodies found in COVID-19 and other infectious diseases including the potential pathways by which they contribute to the severity of the illness. Additionally, it also highlights the probable connection between vaccine-induced autoantibodies and their adverse outcomes. The review also discusses the therapeutic perspectives of autoantibodies. This paper advances our knowledge about the intricate interaction between autoantibodies and COVID-19 by thoroughly assessing the most recent findings.

Serum IgE anti-dsDNA autoantibodies in patients with proliferative lupus nephritis are associated with tubulointerstitial inflammation

Systemic lupus erythematosus (SLE) is an autoimmune disease characterized by the overproduction of multiple autoantibodies. Lupus nephritis (LN), the most common cause of morbidity and mortality, requires early detection. However, only a limited number of serum biomarkers have been associated with the disease activity of LN. Serum IgE anti-dsDNA autoantibodies are prevalent in patients with SLE and may be associated with the pathogenesis of LN. In this study, serum samples from 88 patients with biopsy-proven proliferative LN were collected along with complete clinical and pathological data to investigate the clinical and pathological associations of anti-dsDNA IgE autoantibodies using ELISA. This study found that the prevalence of IgE anti-dsDNA autoantibodies in patients with proliferative LN was 38.6% (34/88). Patients with anti-dsDNA IgE autoantibodies were more prone to acute kidney injury (17/34 vs. 14/54; p = .025). Levels of anti-dsDNA IgE autoantibodies were associated with interstitial inflammation (r = 0.962, p = .017). Therefore, anti-dsDNA IgE autoantibody levels are associated with tubulointerstitial inflammation in patients with proliferative LN.

Machine learning, a new tool for the detection of immunodeficiency patterns in systemic lupus erythematosus

Systemic lupus erythematosus (SLE) is a complex autoimmune disease that affects several organs and causes variable clinical symptoms. Early diagnosis is currently the most effective way to save the lives of patients with SLE. But it is very difficult to detect in the early stages of the disease. Because of this, this study proposes a machine learning system to help diagnose patients with SLE. To carry out the research, the extreme gradient boosting method has been implemented due to its performance characteristics, as it allows high performance, scalability, accuracy, and low computational load. From this method we try to recognize patterns in the data obtained from patients, which allow the classification of SLE patients with high accuracy and differentiate these patients from controls. Several machine learning methods have been analyzed in this study. The proposed method achieves a higher prediction value of patients who may suffer from SLE than the rest of the compared systems. The proposed algorithm achieved an improvement in accuracy of 4.49% over k-Nearest Neighbors. As for the Support Vector Machine and Gaussian Naive Bayes (GNB) methods, they achieved a lower performance than the proposed one, reaching values of 83% and 81%, respectively. It should be noted that the proposed system showed a higher area under the curve (90%) and a balanced accuracy (90%) than the other machine learning methods. This study shows the usefulness of ML techniques for identifying and predicting SLE patients. These results demonstrate the possibility of developing automatic diagnostic support systems for SLE patients based on machine learning techniques.

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

OBJECTIVE

To assess the long-term outcome in patients with Inflammatory Idiopathic 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, 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.

JAK inhibitors and autoimmune rheumatic diseases

The four Janus kinase (JAK) proteins and the seven Signal Transducers of Activated Transcription (STAT) mediate intracellular signal transduction downstream of cytokine receptors, which are involved in the pathology of allergic, autoimmune, and inflammatory diseases. The development of targeted small-molecule treatments with diverse selective inhibitory profiles, such as JAK inhibitors (JAKi), has supported an important change in the treatment of multiple disorders. Indeed, JAKi inhibit intracellular signalling controlled by numerous cytokines implicated in the disease process of rheumatoid arthritis and several other inflammatory and immune diseases. Therefore, JAKi have the capacity to target multiple pathways of those diseases. Other autoimmune diseases treated with JAKi include systemic sclerosis, systemic lupus erythematosus, dermatomyositis, primary Sjogren's syndrome, and vasculitis. In all of these cases, innate immunity stimulation activates adaptive immunity, resulting in the production of autoreactive T cells as well as the stimulation and differentiation of B cells. Mechanism-based treatments that target JAK-STAT pathways have the possibility of improving outcomes by reducing the consumption of glucocorticoids and/or non-specific immunosuppressive drugs in the management of systemic immune-mediated inflammatory diseases.

Current state and completeness of reporting clinical prediction models using machine learning in systemic lupus erythematosus: A systematic review

OBJECTIVE

We carried out a systematic review (SR) of adherence in diagnostic and prognostic applications of ML in SLE using the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) Statement.

METHODS

A SR employing five databases was conducted from its inception until December 2021. We identified articles that evaluated the utilization of ML for prognostic and/or diagnostic purposes. This SR was reported based on the PRISMA guidelines. The TRIPOD statement assessed adherence to reporting standards. Assessment for risk of bias was done using PROBAST tool.

RESULTS

We included 45 studies: 29 (64.4%) diagnostic and 16 (35.5%) prognostic prediction- model studies. Overall, articles adhered by between 17% and 67% (median 43%, IQR 37-49%) to TRIPOD items. Only few articles reported the model's predictive performance (2.3%, 95% CI 0.06-12.0), testing of interaction terms (2.3%, 95% CI 0.06-12.0), flow of participants (50%, 95% CI; 34.6-65.4), blinding of predictors (2.3%, 95% CI 0.06-12.0), handling of missing data (36.4%, 95% CI 22.4-52.2), and appropriate title (20.5%, 95% CI 9.8-35.3). Some items were almost completely reported: the source of data (88.6%, 95% CI 75.4-96.2), eligibility criteria (86.4%, 95% CI 76.2-96.5), and interpretation of findings (88.6%, 95% CI 75.4-96.2). In addition, most of model studies had high risk of bias.

CONCLUSIONS

The reporting adherence of ML-based model developed for SLE, is currently inadequate. Several items deemed crucial for transparent reporting were not fully reported in studies on ML-based prediction models.

REVIEW REGISTRATION

PROSPERO ID# CRD42021284881. (Amended to limit the scope).

Interferon in systemic lupus erythematosus-A halfway between monogenic autoinflammatory and autoimmune disease

Although perceived as an adaptative immune disorder, mainly related to Lymphocyte B and T, last years focus on Systemic Lupus Erythematosus (SLE) pathogeny emphasised the important role of innate immunity. This should not take us by surprise since the lupus cell described by Hargraves and colleagues in 1948 was a neutrophil or macrophage with specific aspect after coloration with haematoxylin related to cell detritus engulfment (Hargraves et al., 1948) [1] (Presentation of two bone marrow elements; the tart. Hargraves M, Ricmond H, Morton R. 1948, Proc Staff Meet Mayo Clinic, pp. 23:25-28). Normal immune system maintains homeostasis through innate and adaptative response that are working together to prevent both infection and autoimmunity. Failure of the immune mechanisms to preserve the balance between these two will initiate and propagate autoinflammation and/or autoimmunity. It is well known now that autoinflammation and autoimmunity are the two extremes of different pathologic conditions marked with multiple overlaps in many diseases. Recent findings in SLE demonstrated that innate immune system initiates the abnormal autoimmunity and starts the continuous inflammatory reaction after that, interferon being one of the key cytokines in innate immunity and SLE. Understanding this mechanism might offer a better clue for an efficient treatment in SLE patients. The purpose of this review is to highlight the enormous impact of innate immunity and mostly interferons in SLE.

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.

CIDP: Current Treatments and Identification of Targets for Future Specific Therapeutic Intervention

Chronic inflammatory demyelinating polyneuropathy (CIDP) is an acquired immune-mediated inflammatory disorder of the peripheral nervous system. This clinically heterogeneous neurological disorder is closely related to Guillain–Barré syndrome and is considered the chronic counterpart of that acute disease. Currently available treatments are mostly empirical; they include corticosteroids, intravenous immunoglobulins, plasma exchange and chronic immunosuppressive agents, either alone or in combination. Recent advances in the understanding of the underlying pathogenic mechanisms in CIDP have brought a number of novel ways of possible intervention for use in CIDP. This review summarizes selected pre-clinical and clinical findings, highlights the importance of using adapted animal models to evaluate the efficacy of novel treatments, and proposes the outlines of future directions to ameliorate the conditions of patients with CIDP.

Autoinflammatory Diseases and Cytokine Storms-Imbalances of Innate and Adaptative Immunity

Innate and adaptive immune responses have a well-known link and represent the distinctive origins of several diseases, many of which may be the consequence of the loss of balance between these two responses. Indeed, autoinflammation and autoimmunity represent the two extremes of a continuous spectrum of pathologic conditions with numerous overlaps in different pathologies. A common characteristic of these dysregulations is represented by hyperinflammation, which is an exaggerated response of the immune system, especially involving white blood cells, macrophages, and inflammasome activation with the hyperproduction of cytokines in response to various triggering stimuli. Moreover, hyperinflammation is of great interest, as it is one of the main manifestations of COVID-19 infection, and the cytokine storm and its most important components are the targets of the pharmacological treatments used to combat COVID-19 damage. In this context, the purpose of our review is to provide a focus on the pathogenesis of autoinflammation and, in particular, of hyperinflammation in order to generate insights for the identification of new therapeutic targets and strategies.

The mosaic of autoimmunity - A taste for more. The 12th international congress of autoimmunity 2021 (AUTO12) virtual

Notwithstanding the fact that the 12th international congress of autoimmunity (AUTO12) was held virtual this year, the number of the abstracts submitted and those presented crossed the thousand marks. Leading investigators and researchers from all over the world presented the latest developments of their research in the domain of autoimmunity and its correlation with various diseases. In terms of mechanisms of autoimmunity, an update on the mechanisms behind the association of autoimmunity with systemic diseases focusing on hyperstimulation was presented during AUTO12. In addition, a new mechanism of ASIA syndrome caused by an intrauterine contraceptive device was revealed demonstrating a complete resolution of symptoms following device removal. In regard to the correlation between autoimmunity and neurogenerative diseases, the loss of structural protein integrity as the trigger of immunological response was shown. Schizophrenia as well, and its correlation to pro-inflammatory cytokines was also addressed. Furthermore, and as it was said AUTO12 virtual due to COVID-19 pandemic, various works were dedicated to SARS-CoV-2 infection and COVID-19 in terms of autoimmune mechanisms involved in the pathogenesis, treatment and complications of COVID-19. For instance, the correlation between autoimmunity and the severity of COVID-19 was viewed. Moreover, the presence and association of autoantibodies in COVID-19 was also demonstrated, as well as the clinical outcomes of COVID-19 in patients with rheumatic diseases. Finally, immune-mediated reactions and processes secondary to SARS-CoV-2 vaccination was displayed. Due to the immense importance of all of the topics addressed and while several hundreds of works were presented which cannot be summed up in one paper, we aimed hereby to highlight some of the outstanding abstracts and presentations during AUTO12.

Hygiene hypothesis and autoimmune diseases: A narrative review of clinical evidences and mechanisms

Since the start of the "modern era", characterized by the increase in urbanization, a progressive attention to hygiene and autoimmune conditions has considerably grown. Although these diseases are often multifactorial, it was demonstrated that environment factors such as pollution, diet and lifestyles may play a crucial role together with genetic signature. Our research, based on the newest and most significant literature of this topic, highlights that the progressive depletion of microbes and parasites due to increased socioeconomic improvement, may lead to a derangement of immunoregulatory mechanisms. Moreover, special attention was given to the complex interplay between microbial agents, as gut microbiome, diet and vitamin D supplementation with the aim of identifying promising future therapeutic options. In conclusion, autoimmunity cannot be limited to hygiene-hypothesis, but from the point of view of precision medicine, this theory represents a fundamental element together with the study of genomics, the microbiome and proteomics, in order to understand the complex functioning of the immune system.

Digital health, big data and smart technologies for the care of patients with systemic autoimmune diseases: Where do we stand?

The past decade has seen tremendous development in digital health, including in innovative new technologies such as Electronic Health Records, telemedicine, virtual visits, wearable technology and sophisticated analytical tools such as artificial intelligence (AI) and machine learning for the deep-integration of big data. In the field of rare connective tissue diseases (rCTDs), these opportunities include increased access to scarce and remote expertise, improved patient monitoring, increased participation and therapeutic adherence, better patient outcomes and patient empowerment. In this review, we discuss opportunities and key-barriers to improve application of digital health technologies in the field of autoimmune diseases. We also describe what could be the fully digital pathway of rCTD patients. Smart technologies can be used to provide real-world evidence about the natural history of rCTDs, to determine real-life drug utilization, advanced efficacy and safety data for rare diseases and highlight significant unmet needs. Yet, digitalization remains one of the most challenging issues faced by rCTD patients, their physicians and healthcare systems. Digital health technologies offer enormous potential to improve autoimmune rCTD care but this potential has so far been largely unrealized due to those significant obstacles. The need for robust assessments of the efficacy, affordability and scalability of AI in the context of digital health is crucial to improve the care of patients with rare autoimmune diseases.

Autoantibody Discovery, Assay Development and Adoption: Death Valley, the Sea of Survival and Beyond

Dating to the discovery of the Lupus Erythematosus (LE) cell in 1948, there has been a dramatic growth in the discovery of unique autoantibodies and their cognate targets, all of which has led to the availability and use of autoantibody testing for a broad spectrum of autoimmune diseases. Most studies of the sensitivity, specificity, commutability, and harmonization of autoantibody testing have focused on widely available, commercially developed and agency-certified autoantibody kits. However, this is only a small part of the spectrum of autoantibody tests that are provided through laboratories world-wide. This manuscript will review the wider spectrum of testing by exploring the innovation pathway that begins with autoantibody discovery followed by assessment of clinical relevance, accuracy, validation, and then consideration of regulatory requirements as an approved diagnostic test. Some tests are offered as "Research Use Only (RUO)", some as "Laboratory Developed Tests (LDT)", some enter Health Technology Assessment (HTA) pathways, while others are relegated to a "death valley" of autoantibody discovery and become "orphan" autoantibodies. Those that achieve regulatory approval are further threatened by the business world's "Darwinian Sea of Survival". As one example of the trappings of autoantibody progression or failure, it is reported that more than 200 different autoantibodies have been described in systemic lupus erythematosus (SLE), a small handful (~10%) of these have achieved regulatory approval and are widely available as commercial diagnostic kits, while a few others may be available as RUO or LDT assays. However, the vast majority (90%) are orphaned and languish in an autoantibody 'death valley'. This review proposes that it is important to keep an inventory of these "orphan autoantibodies" in 'death valley' because, with the increasing availability of multi-analyte arrays and artificial intelligence (MAAI), some can be rescued to achieve a useful role in clinical diagnostic especially in light of patient stratification and precision medicine.

Digital Twins for Multiple Sclerosis

An individualized innovative disease management is of great importance for people with multiple sclerosis (pwMS) to cope with the complexity of this chronic, multidimensional disease. However, an individual state of the art strategy, with precise adjustment to the patient's characteristics, is still far from being part of the everyday care of pwMS. The development of digital twins could decisively advance the necessary implementation of an individualized innovative management of MS. Through artificial intelligence-based analysis of several disease parameters - including clinical and para-clinical outcomes, multi-omics, biomarkers, patient-related data, information about the patient's life circumstances and plans, and medical procedures - a digital twin paired to the patient's characteristic can be created, enabling healthcare professionals to handle large amounts of patient data. This can contribute to a more personalized and effective care by integrating data from multiple sources in a standardized manner, implementing individualized clinical pathways, supporting physician-patient communication and facilitating a shared decision-making. With a clear display of pre-analyzed patient data on a dashboard, patient participation and individualized clinical decisions as well as the prediction of disease progression and treatment simulation could become possible. In this review, we focus on the advantages, challenges and practical aspects of digital twins in the management of MS. We discuss the use of digital twins for MS as a revolutionary tool to improve diagnosis, monitoring and therapy refining patients' well-being, saving economic costs, and enabling prevention of disease progression. Digital twins will help make precision medicine and patient-centered care a reality in everyday life.

Sialylated immunoglobulin G: a promising diagnostic and therapeutic strategy for autoimmune diseases

Human immunoglobulin G (IgG), especially autoantibodies, has major implications for the diagnosis and management of a wide range of autoimmune diseases. However, some healthy individuals also have autoantibodies, while a portion of patients with autoimmune diseases test negative for serologic autoantibodies. Recent advances in glycomics have shown that IgG Fc N-glycosylations are more reliable diagnostic and monitoring biomarkers than total IgG autoantibodies in a wide variety of autoimmune diseases. Furthermore, these N-glycosylations of IgG Fc, particularly sialylation, have been reported to exert significant anti-inflammatory effects by upregulating inhibitory FcγRIIb on effector macrophages and reducing the affinity of IgG for either complement protein or activating Fc gamma receptors. Therefore, sialylated IgG is a potential therapeutic strategy for attenuating pathogenic autoimmunity. IgG sialylation-based therapies for autoimmune diseases generated through genetic, metabolic or chemoenzymatic modifications have made some advances in both preclinical studies and clinical trials.

Systemic autoinflammatory disease in adults

Systemic autoinflammatory disorders comprise an expanding group of rare conditions. They are mediated by dysfunction of the innate immune system and share a core of phenotypic manifestations including recurrent attacks of fever, cutaneous signs, chest or abdominal pain, lymphadenopathy, vasculopathy, and musculoskeletal symptoms. Diagnosis is often established in childhood, but a growing number of adult patients are being recognized with systemic autoinflammatory disorders, including adult-onset disease. In this review, we provide a concise update on the pathophysiology, clinical presentation, and diagnostic approach of systemic autoinflammatory disorders with an emphasis on the adult patient population. Despite the recent advances in genetic testing, the diagnosis of autoinflammatory disease in adult patients is often based on a thorough knowledge of the clinical phenotype. Becoming acquainted with the clinical features of these rare disorders may assist in developing a high index of suspicion for autoinflammatory disease in patients presenting with unexplained episodes of fever or inflammation.

Beyond the joints, the extra-articular manifestations in rheumatoid arthritis

Rheumatoid arthritis (RA) is an inflammatory disease typically affecting the joints, but the systemic inflammatory process may involve other tissues and organs. Many extra-articular manifestations are recognized, which are related to worse long outcomes. Rheumatoid nodules are the most common extra-articular feature, found in about 30% of patients. Secondary Sjögren's syndrome and pulmonary manifestations are observed in almost 10% of patients, also in the early disease. Active RA with high disease activity has been associated with an increased risk of such features. Male gender, smoking habit, severe joint disease, worse function, high pro-inflammatory markers levels, high titer of rheumatoid factor, and HLA-related shared epitope have been reported as clinical predictors of occurrence of these rheumatoid complications. In addition, there is a little evidence deriving from randomized controlled trials in this field, thus the therapeutic strategy is mainly empiric and based on small case series and retrospective studies. However, considering that these extra-articular manifestations are usually related to the more active and severe RA, an aggressive therapeutic strategy is usually employed in view of the poor outcomes of these patients. The extra-articular features of RA remain, despite the improvement of joint damage, a major diagnostic and therapeutic challenge, since these are associated with a poor prognosis and need to be early recognized and promptly managed.

The growing role of precision medicine for the treatment of autoimmune diseases; results of a systematic review of literature and Experts' Consensus

Autoimmune diseases (AIDs) share similar serological, clinical, and radiological findings, but, behind these common features, there are different pathogenic mechanisms, immune cells dysfunctions, and targeted organs. In this context, multiple lines of evidence suggest the application of precision medicine principles to AIDs to reduce the treatment failure. Precision medicine refers to the tailoring of therapeutic strategies to the individual characteristics of each patient, thus it could be a new approach for management of AIDS which considers individual variability in genes, environmental exposure, and lifestyle. Precision medicine would also assist physicians in choosing the right treatment, the best timing of administration, consequently trying to maximize drug efficacy, and, possibly, reducing adverse events. In this work, the growing body of evidence is summarized regarding the predictive factors for drug response in patients with AIDs, applying the precision medicine principles to provide high-quality evidence for therapeutic opportunities in improving the management of these patients.

An updated advance of autoantibodies in autoimmune diseases

Autoantibodies are abnormal antibodies which are generated by pathogenic B cells when targeting an individual's own tissue. Autoantibodies have been identified as a symbol of autoimmune disorders and are frequently considered a clinical marker of these disorders. Autoimmune diseases, including system lupus erythematosus and rheumatoid arthritis, consist of a series of disorders that share some similarities and differences. They are characterized by chronic, systemic, excessive immune activation and inflammation and involve in almost all body tissues. Autoimmune diseases occur more frequently in women than men due to hormonal impacts. In this review we systemically introduce and summarize the latest advances of various autoantibodies in multiple autoimmune diseases.

Phenotyping of Adaptive Immune Responses in Inflammatory Diseases

Immunophenotyping on the molecular and cellular level is a central aspect for characterization of patients with inflammatory diseases, both to better understand disease etiopathogenesis and based on this to develop diagnostic and prognostic biomarkers which allow patient stratification and tailor-made treatment strategies. Technology-driven developments have considerably expanded the range of analysis tools. Especially the analysis of adaptive immune responses, often regarded as central though mostly poorly characterized disease drivers, is a major focus of personalized medicine. The identification of the disease-relevant antigens and characterization of corresponding antigen-specific lymphocytes in individual patients benefits significantly from recent developments in cytometry by sequencing and proteomics. The aim of this workshop was to identify the important developments for state-of-the-art immunophenotyping for clinical application and precision medicine. We focused here on recent key developments in analysis of antigen-specific lymphocytes, sequencing, and proteomics approaches, their relevance in precision medicine and the discussion of the major challenges and opportunities for the future.