Search Results (323)

Multiple sclerosis (MS) and allergic diseases, traditionally considered immunologically opposing entities, may share pathogenic mechanisms rooted in immune dysregulation. While MS is predominantly mediated by Th1 and Th17 responses and allergies by Th2 responses, emerging evidence suggests overlapping immunological pathways, including the involvement of histamine, regulatory T cells, and innate lymphoid cells. This review synthesizes current knowledge on the epidemiological and immunopathological associations between MS and allergies. Epidemiological studies have yielded inconsistent results, with some suggesting a protective role for respiratory and food allergies against MS onset, while others find no significant correlation. Clinical studies indicate that food allergies in adults may be associated with increased MS inflammatory activity, whereas childhood atopy might exert a protective effect. In addition, we review hypersensitivity reactions to disease-modifying treatments for MS, detailing their immunological mechanisms, clinical presentation, and management, including desensitization protocols where applicable. Finally, we explore how treatments for allergic diseases—such as clemastine, allergen immunotherapy, montelukast, and omalizumab—may modulate MS pathophysiology, offering potential therapeutic synergies. Understanding the interplay between allergic and autoimmune processes is critical for optimizing care and developing innovative treatment approaches in MS. Full article

S100 proteins, a family of Ca²⁺-binding proteins, play numerous roles in cellular processes such as proliferation, differentiation, and apoptosis. Recent evidence has highlighted their critical involvement in neuroinflammation, a pathological hallmark of various neurodegenerative disorders including Alzheimer’s disease, multiple sclerosis, and Parkinson’s disease. Among these proteins, S100B and S100A8/A9 are particularly implicated in modulating inflammatory responses in the CNS. Acting as DAMPs, they interact with pattern recognition receptors like RAGE and TLRs, triggering pro-inflammatory signaling cascades and glial activation. While low concentrations of S100 proteins may support neuroprotective functions, increased levels are often associated with exacerbated inflammation and neuronal damage. This review explores the dualistic nature of S100 proteins in neuroinflammatory processes, their molecular interactions, and their potential as biomarkers and therapeutic targets in neurodegenerative disease management. Full article

This study aims to characterize short-wave infrared (SWIR) reflectance spectra at cranial (at the scalp overlying the frontal cortex and the temporal bone window) and extracranial (biceps and triceps) sites in patients with multiple sclerosis (MS) and age-/sex-matched controls. We sought to identify the diagnostic accuracy of wavelength-specific patterns in distinguishing MS from normal controls and spectral markers associated with disability (e.g., Expanded Disability Status Scale scores). To achieve these objectives, we employed a multi-site SWIR spectroscopy acquisition protocol that included measurements from traditional cranial locations as well as extracranial reference sites. Advanced spectral analysis techniques, including wavelength-dependent absorption modeling and machine learning-based classification, were applied to differentiate MS-related hemodynamic changes from normal physiological variability. Classification models achieved perfect performance (accuracy = 1.00), and cortical site regression models showed strong predictive power (EDSS: R²_CV = 0.980; FSS: R²_CV = 0.939). Variable Importance in Projection (VIP) analysis highlighted key wavelengths as potential spectral biomarkers. This approach allowed us to explore novel biomarkers of neural and systemic impairment in MS, paving the way for potential clinical applications of SWIR spectroscopy in disease monitoring and management. In conclusion, spectral analysis revealed distinct wavelength-specific patterns collected from cranial and extracranial sites reflecting biochemical and structural differences between patients with MS and normal subjects. These differences are driven by underlying physiological changes, including myelin integrity, neuronal density, oxidative stress, and water content fluctuations in the brain or muscles. This study shows that portable spectral devices may contribute to bedside individuation and monitoring of neural diseases, offering a cost-effective alternative to repeated imaging. Full article

Neurodegenerative diseases such as Alzheimer’s disease, Parkinson’s disease, Huntington’s disease, and amyotrophic lateral sclerosis remain incurable. Current therapeutic strategies primarily focus on slowing disease progression, alleviating symptoms, and improving patients’ quality of life, including the management of comorbid conditions. Over the past few decades, the incidence of diagnosed neurodegenerative disorders has risen significantly. As the number of affected individuals continues to grow, so does the urgent need for effective treatments that can halt or mitigate the progression of these diseases. Among the most promising therapeutic resources are bioactive compounds derived from fungi. The high quality of proteins, polysaccharides, unsaturated fatty acids, triterpenoids, sterols, and secondary metabolites found in fungi have attracted growing interest from researchers across multiple disciplines. One intensively studied direction involves the use of naturally occurring fungi-derived nutraceuticals in the treatment of various diseases, including neurodegenerative conditions. This article provides an overview of recent findings on fungal compounds—such as phenolic compounds, carbohydrates, peptides and proteins, and lipids—that may have potential applications in the treatment of neurodegenerative diseases and the alleviation of their symptoms. Full article

Background/Objectives: Fatigue and cognitive impairment are common issues for People with Multiple Sclerosis (PwMS), affecting over 80% and 40–65%, respectively. The relationship between these two debilitating conditions is complex, with cognitive deficits exacerbating fatigue and vice versa. This study investigates the effects of a multimodal intervention combining cognitive rehabilitation and neuromodulation to alleviate fatigue and enhance cognitive performance in PwMS. Methods: The research employed multiple baselines across the subjects in a Single-Case Experimental Design (mbSCED) with a cohort of three PwMS diagnosed with Relapsing–Remitting MS. The intervention protocol consisted of a baseline phase followed by a four-week treatment involving transcranial direct current stimulation (tDCS) and cognitive training using RehaCom^® software (version 6.9.0). Fatigue levels were measured using the modified Fatigue Impact Scale (mFIS), while cognitive performance was evaluated through standardized neuropsychological assessments. Results: The multimodal protocol exhibited high feasibility and acceptability, with no dropouts. Individual responsiveness outcomes varied, with two PwMS showing significant decreases in fatigue and improvements in cognitive performance, particularly in the trained domains. Their motor performance and quality of life also improved, suggesting that the treatment had indirect beneficial effects. Conclusions: This study provides preliminary evidence for the potential benefits of integrating neuromodulation and cognitive rehabilitation as a personalized therapeutic strategy for managing fatigue and cognitive impairments in MS. Further research is needed to delineate the specific contributions of each intervention component and establish standardized protocols for clinical implementation. The insights gained may lead to more effective, tailored treatment options for PwMS. Full article

Background/Objectives: The objective of this review is to document the modalities to enhance the neuromuscular effects of botulinum toxin (BoNT) injection in spastic patients with multiple sclerosis (MS). Methods: We conducted a literature review focusing on studies involving BoNT administration for MS-related spasticity and the use of adjunctive therapies aimed at reducing dosage and increasing injection intervals. Results: The findings revealed a limited number of studies specific to MS patients, addressing only a few adjunct techniques, including electrical stimulation, vibration therapy, physical exercise, and extracorporeal shock wave therapy. Conclusions: These preliminary findings highlight the need for further research into integrative therapeutic strategies tailored specifically to the MS population. Full article

On a worldwide scale, neurodegenerative diseases, including multiple sclerosis, Parkinson’s, and Alzheimer’s, face considerable healthcare challenges demanding the development of novel approaches to early detection and efficient treatment. With its ability to provide real-time patient monitoring, customized medical care, and advanced predictive analytics, artificial intelligence (AI) is fundamentally transforming the way healthcare is provided. Through the integration of wearable physiological sensors, motion sensors, and neurological assessment tools, the NeuroPredict platform harnesses AI and smart sensor technologies to enhance the management of specific neurodegenerative diseases. Machine learning algorithms process these data flows to find patterns that point out disease evolution. This paper covers the design and architecture of the NeuroPredict platform, stressing the ethical and regulatory requirements that guide its development. Initial development of AI algorithms for disease monitoring, technical achievements, and constant enhancements driven by early user feedback are addressed in the discussion section. To ascertain the platform’s trustworthiness and data security, it also points towards risk analysis and mitigation approaches. The NeuroPredict platform’s capability for achieving AI-driven smart healthcare solutions is highlighted, even though it is currently in the development stage. Subsequent research is expected to focus on boosting data integration, expanding AI models, and providing regulatory compliance for clinical application. The current results are based on incremental laboratory tests using simulated user roles, with no clinical patient data involved so far. This study reports an experimental technology evaluation of modular components of the NeuroPredict platform, integrating multimodal sensors and machine learning pipelines in a laboratory-based setting, with future co-design and clinical validation foreseen for a later project phase. Full article

The gut–brain axis (GBA) represents a complex bidirectional communication network that links the gut microbiota (GM) and the central nervous system (CNS). Recent research has revealed that neurosteroids (NSs) play crucial roles in modulating neuroinflammatory responses and promoting neuroprotection. Meanwhile, GM alterations have been associated with various neuroinflammatory and neurodegenerative conditions, such as multiple sclerosis, Alzheimer’s disease, and amyotrophic lateral sclerosis. This review aims to provide a comprehensive overview of the intricate interactions between NS, GM, and neuroinflammation. We discuss how NS and metabolites can influence neuroinflammatory pathways through immune, metabolic, and neuronal mechanisms. Additionally, we explore how GM modulation can impact neurosteroidogenesis, highlighting potential therapeutic strategies that include probiotics, neuroactive metabolites, and targeted interventions. Understanding these interactions may pave the way for innovative treatment approaches for neuroinflammatory and neurodegenerative diseases, promoting a more integrated view of brain health and disease management. Full article

Background: Cognitive impairment (CI) is a common and disabling symptom in patients with relapsing–remitting multiple sclerosis (RRMS), potentially emerging at any stage, including preclinical phases. Despite its impact on quality of life, CI often goes unrecognized, as clinical follow-up typically focuses on motor and sensory symptoms. Validated tools, such as the Brief International Cognitive Assessment for Multiple Sclerosis (BICAMS) and patient-reported outcomes (PROs), should be integrated into routine evaluations beyond the Expanded Disability Status Scale (EDSS). Objective: The objective of this study was to evaluate cognitive impairment and quality of life in patients with RRMS using the BICAMS and PROs. Methods: This cross-sectional, descriptive study included patients with RRMS under follow-up at a tertiary hospital in San Luis Potosí, Mexico. Participants underwent cognitive screening with the BICAMS battery and completed the MSQoL-54 (quality of life), FSMC (fatigue), and MSIS-29 (functional impact) scales. Statistical analyses included ANOVA, the Kruskal–Wallis test, and Pearson correlations. Results: Nineteen patients were evaluated (73.7% female, mean age 36.5 ± 8.9 years). BICAMS results showed variable cognitive performance, with no significant differences across treatment groups for processing speed (p = 0.222), verbal memory (p = 0.082), or visuospatial memory (p = 0.311). A significant correlation was found between verbal and visuospatial memory (r = 0.668, p = 0.002). Total quality of life differed significantly across treatments (F = 8.007, p = 0.029), with a strong correlation between overall quality of life and general health perception (r = 0.793, p < 0.001). Fatigue and MSIS scores showed no association with treatment. Conclusions: Cognitive impairment is common in RRMS and can be detected using brief assessment tools, such as the BICAMS. Incorporating cognitive screening and PROs into clinical practice is essential to guide comprehensive management. Full article

The combined use of serum and CSF biomarkers for prognostic stratification in multiple sclerosis (MS) remains underexplored. This multicenter observational study investigated associations between serum neurofilament light chain (sNfL), glial fibrillary acidic protein (sGFAP), and CSF lipid-specific IgM oligoclonal bands (LS-OCMB) with different forms of disability worsening, such as relapse-associated worsening (RAW), active progression independent of relapse activity (aPIRA), and non-active PIRA (naPIRA). A total of 535 patients with MS were included, all sampled within one year of disease onset. Biomarkers were quantified using single-molecule array and immunoblotting techniques, and CSF cell subsets were analyzed by flow cytometry. High sNfL z-scores and LS-OCMB positivity were independently associated with increased risk of RAW and aPIRA, collectively termed inflammatory-associated worsening (IAW), while elevated sGFAP levels predicted naPIRA. Patients with both high sNfL and LS-OCMB positivity had the highest risk of IAW. Among LS-OCMB–positive patients, higher regulatory T cell percentages were associated with lower sNfL levels, suggesting a protective role. Conversely, in LS-OCMB–negative patients, sNfL levels correlated with CSF C3 concentrations. These findings support the complementary role of sNfL, sGFAP, and LS-OCMB in identifying distinct mechanisms of disease worsening and may inform early personalized management strategies in MS. Full article

Lung cancer is one of the most common and deadly forms of cancer worldwide, despite sustained efforts to encourage smoking cessation and raise awareness of the risk factors. In Romania, lung cancer is a significant health challenge, being the leading cause of death caused by cancer, especially amongst men. The incidence of lung cancer in connective tissue disease (CTD) varies in different studies from 4.5% in rheumatoid arthritis (RA), to 4.4% in polymyositis or dermatomyositis, and up to 11.1% in systemic sclerosis. However, older studies have shown an increased risk of cancer in patients with rheumatoid arthritis (RA), ranging from 10% to 30% compared to the general population, particularly in those undergoing methotrexate therapy. Rheumatoid arthritis affects approximately 40 per 100,000 people annually worldwide, with a three- to four-fold higher incidence in women. Non-small cell lung cancer (NSCLC), the most common lung cancer subtype, has been linked to RA, yet the association remains poorly defined, with limited insight into the underlying molecular mechanisms. We present the case of a 61-year-old male with a 49-pack-year smoking history and a known diagnosis of rheumatoid arthritis, currently managed with methotrexate therapy. He was admitted for evaluation due to a progressive decline in general condition, characterized by worsening dyspnea and chest pain, symptoms that had been longstanding but had markedly exacerbated over the past two weeks. Based on a chest CT performed prior to the patient’s admission to our clinic, subsequent diagnostic investigations established the diagnosis of pulmonary adenocarcinoma. The diagnostic process proved to be particularly challenging due to the presence of multiple comorbidities, which significantly impacted both the diagnostic approach and the overall clinical trajectory. Full article

The discovery of effective therapeutics against Alzheimer’s disease (AD) and other neurological disorders remains a significant challenge. Artificial intelligence (AI) tools are of considerable interest in modern drug discovery processes and, by exploiting machine learning (ML) algorithms and deep learning (DL) tools, as well as data analytics, can expedite the identification of new drug targets and potential lead molecules. The current study was aimed at assessing the role of AI-based tools in the discovery of new drug targets against AD and other related neurodegenerative diseases and their efficacy in the discovery of new drugs against these diseases. AD represents a multifactorial neurological disease with limited therapeutics available for management and limited efficacy. The discovery of more effective medications is limited by the complicated pathophysiology of the disease, involving amyloid beta (Aβ), neurofibrillary tangles (NFTs), oxidative stress, and inflammation-induced damage in the brain. The integration of AI tools into the traditional drug discovery process against AD can help to find more effective, safe, highly potent compounds, identify new targets of the disease, and help in the optimization of lead molecules. A detailed literature review was performed to gather evidence regarding the most recent AI tools for drug discovery against AD, Parkinson’s disease (PD), multiple sclerosis (MLS), and epilepsy, focusing on biological markers, early diagnoses, and drug discovery using various databases like PubMed, Web of Science, Google Scholar, Scopus, and ScienceDirect to collect relevant literature. We evaluated the role of AI in analyzing multifaceted biological data and the properties of potential drug candidates and in streamlining the design of clinical trials. By exploring the intersection of AI and neuroscience, this review focused on providing insights into the future of AD treatment and the potential of AI to revolutionize the field of drug discovery. Our findings conclude that AI-based tools are not only cost-effective, but the success rate is extremely high compared to traditional drug discovery methods in identifying new therapeutic targets and in the screening of the majority of molecules for clinical trial purposes. Full article

Pulmonary arterial hypertension (PAH) is a severe complication associated with connective tissue diseases (CTDs), which is characterized by a significant influence on the patient’s prognosis and mortality. The prevalence of PAH varies depending on the type of CTD. Still, it is highly prevalent in patients with systemic sclerosis (SSc), systemic lupus erythematosus (SLE), mixed connective tissue disease (MCTD), and primary Sjögren’s syndrome (pSS). Identifying rheumatic disease-specific risk factors is crucial for early diagnosis and intervention. Risk factors for PAH development include specific sociological factors (related to race, gender, and age), clinical features (particularly severe Raynaud’s phenomenon and multiple telangiectasias), cardiological factors (pericarditis and left heart disease), biochemical factors (elevated NT-proBNP and decreased HDL-cholesterol), serological factors (presence of ANA, e.g., anti-U1-RNP or SSA, and antiphospholipid antibodies), and pulmonary factors (interstitial lung disease and decreased DLCO or DLCO/alveolar volume ratio < 70%, FVC/DLCO > 1.6). The analysis of risk factors can be the most useful during the selection of patients at high risk of PAH development. The initial diagnosis of PAH is usually based on transthoracic echocardiography (TTE) and is finally confirmed by right heart catheterization (RHC). Targeted therapies can improve outcomes and include endothelin receptor antagonists, prostacyclin analogs, phosphodiesterase inhibitors, and tailored immunosuppressive treatments. Effective management strategies require a multidisciplinary approach involving rheumatologists, cardiologists, and pulmonologists. The risk stratification and individualized treatment strategies can enhance survival and quality of life in patients with PAH-CTD. Full article

Background and Objectives: The Epstein-Barr virus (EBV) belongs to the gamma herpesviruses family. Evidence from the literature suggests that EBV initiates immune responses and the production of antibodies. Chronic or recurrent EBV infections may be associated with autoimmune diseases such as systemic lupus erythematosus, Sjögren’s syndrome, rheumatoid arthritis, multiple sclerosis, or inflammatory bowel diseases. This review aims to establish the role of EBV in the development and progression of autoimmune diseases. Materials and Methods: A literature search was conducted using PubMed, PMC, Google Scholar, and SCOPUS. Relevant studies, including meta-analyses, case-control studies, literature reviews, cross-sectional studies, and longitudinal studies, were identified through titles and abstracts screening for a comprehensive analysis. Results: Our study revealed a strong association between EBV infection and several autoimmune diseases, including multiple sclerosis, systemic lupus erythematosus, rheumatoid arthritis, and inflammatory bowel disease. Epstein-Barr virus seropositivity was significantly higher in affected individuals. Elevated EBV-specific antibodies correlated with disease onset and severity. EBV DNA and latency proteins were detected in diseased tissues, alongside immune dysregulation and molecular mimicry mechanisms. Conclusions: Our findings highlight that EBV may be an important factor in autoimmune disease pathogenesis, contributing to immune activation and tissue damage. Further research is needed to explore EBV-targeted therapies and their potential in preventing or managing autoimmune diseases. Full article

Background: Pediatric-onset multiple sclerosis (POMS) is a rare and heterogeneous condition, with clinical features, progression, and therapeutic response varying significantly according to age at onset. Early-onset MS (<10 years) presents particular diagnostic and management challenges due to atypical presentations and more active inflammatory profiles. Objectives: To identify age-related clinical, radiological, and therapeutic characteristics of pediatric MS, with a specific focus on early-onset cases, and to compare them with intermediate (10–12 years) and late-onset (>12 years) forms. Methods: We conducted a retrospective analysis of medical records from 120 pediatric patients diagnosed with MS at a tertiary neurology center between 2018 and 2024. Patients were grouped by age at onset and assessed for clinical presentation, number and timing of relapses, EDSS scores, imaging findings, and treatment patterns. Results: Early-onset MS was associated with atypical symptoms, delayed diagnosis, more frequent relapses, and multifocal brainstem and cerebellar involvement. The diagnosis was significantly delayed in younger children compared to adolescents. EDSS scores tended to remain stable in the first 2–3 years, but early-onset patients showed a notable decline after the fourth year. While most patients received disease-modifying therapies, high-efficacy agents were underused due to age-related restrictions. Intermediate-onset patients presented overlapping features of both early and late-onset MS and had the highest proportion of fully preserved motor function (EDSS 0) at the end of follow-up. MRI findings revealed more extensive and confluent lesions in younger patients, particularly in the first two years after onset. Conclusions: Age at disease onset is a key determinant of clinical course and treatment response in pediatric MS. Early recognition and timely initiation of appropriate therapy—especially high-efficacy agents—may improve outcomes and reduce long-term disability. Further multicenter studies with standardized imaging and cognitive assessment protocols are needed to optimize care for this vulnerable population. Full article