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Novel Strategies for Diagnosis and Treatment of Autoimmune Diseases

A special issue of Journal of Clinical Medicine (ISSN 2077-0383). This special issue belongs to the section "Immunology".

Deadline for manuscript submissions: closed (25 September 2024) | Viewed by 6585

Special Issue Editor

Special Issue Information

Dear Colleagues,

Autoimmune diseases are a group of complex disorders where the immune system mistakenly attacks healthy cells. Diagnosing and treating these conditions has long posed significant challenges. However, recent advances in medical research and technology are ushering in an era of novel strategies that promise to revolutionize patient care in this field. This Special Issue, titled "Novel Strategies for Diagnosis and Treatment of Autoimmune Diseases," is dedicated to exploring these groundbreaking developments. We delve into innovative diagnostic techniques, drawing on molecular biology, genetics, and bioinformatics that allow for early, accurate detection. This Special Issue also explores emerging therapeutic approaches, emphasizing precision medicine and personalized treatments tailored to individual genetic and molecular profiles. Finally, we examine new immunomodulatory treatments and advancements in biomarker discovery. Our goal is to foster dialogue, inspire further research, and inform clinical practice, moving us closer to effectively managing autoimmune diseases.

Dr. Kassem Sharif
Guest Editor

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Keywords

  • autoimmunity
  • diagnosis
  • therapy
  • immunomodulation
  • biomarkers
  • genetics
  • bioinformatics
  • personalized
  • precision
  • innovation

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Published Papers (3 papers)

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Research

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13 pages, 845 KiB  
Article
Clinical Phenotype of HLA B*44 Patients in a Rheumatology Outpatient Clinic Favors Peripheral Arthropathies
by Jure Aljinović, Daniela Šošo, Marin Petrić, Dijana Perković, Daniela Marasović Krstulović, Darko Kero and Ivanka Marinović
J. Clin. Med. 2024, 13(18), 5440; https://doi.org/10.3390/jcm13185440 - 13 Sep 2024
Cited by 1 | Viewed by 1974
Abstract
Objective: The genetic background of HLA-B*27 in spondyloarthritis is known, and the search for another gene with similar role is ongoing. We wanted to investigate clinical presentations of HLA-B*44 patients in rheumatology practice. Methods: A cross-sectional retrospective study of 303 HLA-B*44 adult patients [...] Read more.
Objective: The genetic background of HLA-B*27 in spondyloarthritis is known, and the search for another gene with similar role is ongoing. We wanted to investigate clinical presentations of HLA-B*44 patients in rheumatology practice. Methods: A cross-sectional retrospective study of 303 HLA-B*44 adult patients from the outpatient rheumatology clinic from 5/2018-5/2024. Clinical phenotype, confirmed or excluded rheumatic diagnosis, therapy used, and data on HLA A, B, and DR alleles inherited with B*44 were analyzed. Results: A female predominance of 2.79:1 was noted. A total of 150 [49.5%] patients were referred due to peripheral joint pain, 77 [25.4%] due to combined spine and peripheral joint pain or spine alone (57 [18.8%]). A total of 19 [6.3%] patients had no symptoms of the musculoskeletal system. Statistically significant peripheral joint affection was proved in females but not in males (p = 0.04). A total of 121 [40%] patients from B*44 group had established rheumatic disease, with the rest being excluded or under observation. The most common working diagnoses were polyarthritis (32 [10.5%]) and mono-oligoarthritis (14 [4.6%]). A second allele in addition to HLA B*44 showed a similar frequency to the general population. Patients with HLA B*44/44 and B*27/44 genotypes were at the most risk for having definitive rheumatic disease (>60%). Conventional synthetic disease-modifying anti-rheumatic drugs (DMARDs) were used in 38.6% of patients, non-steroidal anti-inflammatory drugs were used in 31.6% of patients, biologic DMARDs were used in 8.9% of patients, and corticosteroids were used in 7.3% of patients. Conclusions: The most common presentation in HLA-B*44 patients is peripheral joint affection. Most patients with HLA-B*27/44 and B*44/44 genotypes had definitive rheumatic disease. B*44 homozygosity or B*27/44 might be risk factors for arthritis development. Full article
(This article belongs to the Special Issue Novel Strategies for Diagnosis and Treatment of Autoimmune Diseases)
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11 pages, 617 KiB  
Article
Sensitization to Food and Aero-Allergens in Children with Coeliac Disease Assessed with the Use of a Multiplex Molecular Diagnostic Technique
by Izabela Knyziak-Mędrzycka, Bożena Cukrowska, Wojciech Nazar, Joanna Beata Bierła, Kamil Janeczek, Paulina Krawiec, Weronika Gromek, Mariusz Wysokiński, Ewa Konopka, Ilona Trojanowska, Sylwia Smolińska and Emilia Majsiak
J. Clin. Med. 2024, 13(10), 2992; https://doi.org/10.3390/jcm13102992 - 19 May 2024
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Abstract
(1) Background. Coeliac disease (CD) often co-occurs with autoimmune conditions or genetic syndromes, but there are few studies on the co-existence of CD and immunoglobulin E (IgE)-mediated allergies. The purpose of this study was to assess sensitization to food and aero-allergens in pediatric [...] Read more.
(1) Background. Coeliac disease (CD) often co-occurs with autoimmune conditions or genetic syndromes, but there are few studies on the co-existence of CD and immunoglobulin E (IgE)-mediated allergies. The purpose of this study was to assess sensitization to food and aero-allergens in pediatric patients with CD. (2) Methods. A multiplex ALEX®2 test was used to determine specific IgEs (sIgEs). (3) Results. The study included 108 children newly diagnosed with CD. Allergen extract- and/or allergen molecule-sIgEs were detected in 49.1% of children. Most children (41.5%) were sensitized to both inhalant and food allergens. The three most common aero-allergens (timothy pollen, ryegrass, silver birch) were molecules Phl p 1, Lol p 1, and Bet v 1. The most common food allergens (hazelnut, apple, and peanut) were Cor a 1, Mal d 1, and Ara h 8 molecules of the PR-10 subfamily. Patients were not sensitized to cereal allergens containing gluten. Spearman’s rank correlation analysis of sensitized patients showed a significant positive relationship (r = 0.31) between the patients’ age and the occurrence of positive sIgEs (≥0.3 kUA/L) for inhalant allergen molecules (p = 0.045). In sensitized patients, mainly symptoms of inhalant allergy were observed, such as hay fever, conjunctivitis, and bronchial asthma. (4) Conclusions. The current study indicates the co-occurrence of IgE sensitization to food and inhalant allergens in children with CD. The study highlights the need to take a closer look at the diagnosis of IgE-mediated allergy in patients with CD, which may help in their care and lead to a better understanding of the relationship between CD and IgE-mediated allergy. Full article
(This article belongs to the Special Issue Novel Strategies for Diagnosis and Treatment of Autoimmune Diseases)
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13 pages, 1096 KiB  
Systematic Review
Deep Learning in Coeliac Disease: A Systematic Review on Novel Diagnostic Approaches to Disease Diagnosis
by Kassem Sharif, Paula David, Mahmud Omar, Yousra Sharif, Yonatan Shneor Patt, Eyal Klang and Adi Lahat
J. Clin. Med. 2023, 12(23), 7386; https://doi.org/10.3390/jcm12237386 - 29 Nov 2023
Cited by 2 | Viewed by 1904
Abstract
Background: Coeliac disease affects approximately 1% of the global population with the diagnosis often relying on invasive and time-demanding methods. Deep learning, a powerful tool in medical science, shows potential for non-invasive, accurate coeliac disease diagnosis, though challenges remain. Objective: This systematic review [...] Read more.
Background: Coeliac disease affects approximately 1% of the global population with the diagnosis often relying on invasive and time-demanding methods. Deep learning, a powerful tool in medical science, shows potential for non-invasive, accurate coeliac disease diagnosis, though challenges remain. Objective: This systematic review aimed to evaluate the current state of deep-learning applications in coeliac disease diagnosis and identify potential areas for future research that could enhance diagnostic accuracy, sensitivity, and specificity. Methods: A systematic review was conducted using the following databases: PubMed, Embase, Web of Science, and Scopus. PRISMA guidelines were applied. Two independent reviewers identified research articles using deep learning for coeliac disease diagnosis and severity assessment. Only original research articles with performance metrics data were included. The quality of the diagnostic accuracy studies was assessed using the QUADAS-2 tool, categorizing studies based on risk of bias and concerns about applicability. Due to heterogeneity, a narrative synthesis was conducted to describe the applications and efficacy of the deep-learning techniques (DLT) in coeliac disease diagnosis. Results: The initial search across four databases yielded 417 studies with 195 being removed due to duplicity. Finally, eight studies were found to be suitable for inclusion after rigorous evaluation. They were all published between 2017 and 2023 and focused on using DLT for coeliac disease diagnosis or assessing disease severity. Different deep-learning architectures were applied. Accuracy levels ranged from 84% to 95.94% with the GoogLeNet model achieving 100% sensitivity and specificity for video capsule endoscopy images. Conclusions: DLT hold substantial potential in coeliac disease diagnosis. They offer improved accuracy and the prospect of mitigating clinician bias. However, key challenges persist, notably the requirement for more extensive and diverse datasets, especially to detect milder forms of coeliac disease. These methods are in their nascent stages, underscoring the need of integrating multiple data sources to achieve comprehensive coeliac disease diagnosis. Full article
(This article belongs to the Special Issue Novel Strategies for Diagnosis and Treatment of Autoimmune Diseases)
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