Psoriasis is a chronic recurrent inflammatory autoimmune pathology with a significant genetic component and several interferences of immunological cells and their cytokines. The complex orchestration of psoriasis pathogenesis is related to the synergic effect of immune cells, polygenic alterations, autoantigens, and several other external factors. The major act of the IL-23/IL-17 axis, strongly influencing the inflammatory pattern established during the disease activity, is visible as a continuous perpetuation of the pro-inflammatory response and keratinocyte activation and proliferation, leading to the development of psoriatic lesions. Genome-wide association studies (GWASs) offer a better view of psoriasis pathogenic pathways, with approximately one-third of psoriasis’s genetic impact on psoriasis development associated with the MHC region, with genetic loci located on chromosome 6. The most eloquent genetic factor of psoriasis, PSORS1, was identified in the MHC I site. Among the several factors involved in its complex etiology, dysbiosis, due to genetic or external stimulus, induces a burst of pro-inflammatory consequences; both the cutaneous and gut microbiome get involved in the psoriasis pathogenic process. Cutting-edge research studies and comprehensive insights into psoriasis pathogenesis, fostering novel genetic, epigenetic, and immunological factors, have generated a spectacular improvement over the past decades, securing the path toward a specific and targeted immunotherapeutic approach and delayed progression to inflammatory arthritis. This review aimed to offer insight into various domains that underline the pathogenesis of psoriasis and how they influence disease development and evolution. The pathogenesis mechanism of psoriasis is multifaceted and involves an interplay of cellular and humoral immunity, which affects susceptible microbiota and the genetic background. An in-depth understanding of the role of pathogenic factors forms the basis for developing novel and individualized therapeutic targets that can improve disease management.

The microbiota–gut–brain axis has garnered increasing attention in recent years for its role in various health conditions, including neuroinflammatory disorders like complex regional pain syndrome (CRPS). CRPS is a debilitating condition characterized by chronic neuropathic pain, and its etiology and pathophysiology remain elusive. Emerging research suggests that alterations in the gut microbiota composition and function could play a significant role in CRPS development and progression. Our paper explores the implications of microbiota in CRPS and the potential therapeutic role of boron (B). Studies have demonstrated that individuals with CRPS often exhibit dysbiosis, with imbalances in beneficial and pathogenic gut bacteria. Dysbiosis can lead to increased gut permeability and systemic inflammation, contributing to the chronic pain experienced in CRPS. B, an essential trace element, has shown promise in modulating the gut microbiome positively and exerting anti-inflammatory effects. Recent preclinical and clinical studies suggest that B supplementation may alleviate neuropathic pain and improve CRPS symptoms by restoring microbiota balance and reducing inflammation. Our review highlights the complex interplay between microbiota, inflammation, and neuropathic pain in CRPS and underscores the potential of B as a novel therapeutic approach to target the microbiota–gut–brain axis, offering hope for improved management of this challenging condition.

Background and Objectives: Rheumatoid arthritis (RA) is a severe autoimmune disease characterized by chronic inflammation of the joints accompanied by the progressive deformation and destruction of cartilage and joint bones. This study aims to gain insight into the outcomes related to adherence in patients with rheumatoid arthritis. Predicting the medication adherence in RA patients is a key point to improve the treatment outcome. Materials and Methods: A number of 119 Romanian patients with RA were included and divided into two groups: first group included 79 patients treated with conventional therapy and second group included 40 patients treated with biologic therapy. A CQR-9 (compliance questionnaire rheumatology with nine items) and PDSQ (psychiatric diagnostic screening questionnaire) were performed to assess correlations between medication adherence, patient sociodemographic variables, 11 psychiatric scales (major depressive disorder, posttraumatic stress disorder, obsessive-compulsive disorder, panic disorder, psychosis, agoraphobia, social phobia, drug abuse/dependence, generalized anxiety disorder, somatization disorder, hypochondriasis) and lifestyle (bulimia, alcohol intake). Results: Whilst modelling factors associated with adherence, it was found that women and patients with higher education are more adherent. From the psychiatric indicators, only major depressive disorder and post-traumatic stress disorder were found to be positively correlated with therapeutic adherence. None of the assessed lifestyle factors influenced the adherence of RA patients. Conclusion: The knowledge of factors that impact on treatment adherence can be useful for clinicians to guide patient-centred care.

Identifying certain serum biomarkers associated with the degree of rheumatoid arthritis (RA) activity can provide us with a more accurate view of the evolution, prognosis, and future quality of life for these patients. Our aim was to analyze the presence and clinical use of matrix metalloproteinase-13 (MMP-13), along with vascular endothelial growth factor (VEGF) and well-known cytokines such as tumor necrosis factor-alpha (TNF-α) and interleukin 6 (IL-6) for patients with RA. We also wanted to identify the possible correlations between MMP-13 and these serological markers, as well as their relationship with disease activity indices, quality of life, and ultrasonographic evaluation. For this purpose, we analyzed serum samples of 34 RA patients and 12 controls. In order to assess serum concentrations for MMP-13, VEGF, TNF-α, and IL-6, we used the enzyme-linked immunosorbent assay (ELISA) technique. Our results concluded that higher levels of MMP-13, VEGF, TNF-α, and IL-6 were present in the serum of RA patients compared to controls, with statistical significance. We furthermore identified moderately positive correlations between VEGF, MMP-13, and disease activity indices, as well as with the ultrasound findings. We also observed that VEGF had the best accuracy (97.80%), for differentiating patients with moderate disease activity. According to the data obtained in our study, that although MMP-13, TNF-α and C-reactive protein (CRP) have the same sensitivity (55.56%), MMP-13 has a better specificity (86.67%) in the diagnosis of patients with DAS28_(4v) CRP values corresponding to moderate disease activity. Thus, MMP-13 can be used as a biomarker that can differentiate patients with moderate or low disease activity. VEGF and MMP-13 can be used as additional parameters, along with TNF-α and IL-6, that can provide the clinician a better picture of the inflammatory process, disease activity, and structural damage in patients with RA. Our data can certainly constitute a start point for future research and extended studies with multicenter involvement, to support the selection of individualized and accurate therapeutic management strategies for our patients.

Early diagnosis in rheumatoid arthritis (RA) and axial spondyloarthritis (axSpA) is essential to initiate timely interventions, such as medication and lifestyle changes, preventing irreversible joint damage, reducing symptoms, and improving long-term outcomes for patients. Since magnetic resonance imaging (MRI) of the wrist and hand, in case of RA and MRI of the sacroiliac joints (SIJ) in case of axSpA can identify inflammation before it is clinically discernible, this modality may be crucial for early diagnosis. Artificial intelligence (AI) techniques, together with machine learning (ML) and deep learning (DL) have quickly evolved in the medical field, having an important role in improving diagnosis, prognosis, in evaluating the effectiveness of treatment and monitoring the activity of rheumatic diseases through MRI. The improvements of AI techniques in the last years regarding imaging interpretation have demonstrated that a computer-based analysis can equal and even exceed the human eye. The studies in the field of AI have investigated how specific algorithms could distinguish between tissues, diagnose rheumatic pathology and grade different signs of early inflammation, all of them being crucial for tracking disease activity. The aim of this paper is to highlight the implementation of AI models in MRI with focus on diagnosis of RA and axSpA through a literature review.

In recent decades, researchers have investigated the bidirectional links between periodontal disease and systemic diseases, and the results have allowed the development of the concept of periodontal medicine. This concept incorporates and analyzes the mutually influential interactions that can occur between periodontitis and systemic diseases such as diabetes mellitus or cardiovascular diseases. Sjögren’s syndrome (SS) is a chronic autoimmune disorder that targets the exocrine glands of the body, such as the lacrimal and salivary glands. The amount of saliva produced may gradually decrease with the progression of the disease, which can have an impact on the structures within the oral cavity. Although the reduction in saliva flow produces negative effects in the oral cavity, a direct association between Sjögren’s syndrome and periodontal disease has not yet been demonstrated. Available studies on this topic have not identified significant differences in the periodontal status of patients with Sjögren’s syndrome and control groups at the clinical and bacteriological levels. On the other hand, other studies on this topic consider that patients with periodontitis have a higher risk of developing Sjögren’s syndrome than the general population. Therefore, the results remain inconclusive, highlighting the need for further complementary studies.