The Use of Kappa Free Light Chains to Diagnose Multiple Sclerosis.

Background: The positive implications of using free light chains in diagnosing multiple sclerosis have increasingly gained considerable interest in medical research and the scientific community. It is often presumed that free light chains, particularly kappa and lambda free light chains, are of practical use and are associated with a higher probability of obtaining positive results compared to oligoclonal bands. The primary purpose of the current paper was to conduct a systematic review to assess the up-to-date methods for diagnosing multiple sclerosis using kappa and lambda free light chains. Method: An organized literature search was performed across four electronic sources, including Google Scholar, Web of Science, Embase, and MEDLINE. The sources analyzed in this systematic review and meta-analysis comprise randomized clinical trials, prospective cohort studies, retrospective studies, controlled clinical trials, and systematic reviews. Results: The review contains 116 reports that includes 1204 participants. The final selection includes a vast array of preexisting literature concerning the study topic: 35 randomized clinical trials, 21 prospective cohort studies, 19 retrospective studies, 22 controlled clinical trials, and 13 systematic reviews. Discussion: The incorporated literature sources provided integral insights into the benefits of free light chain diagnostics for multiple sclerosis. It was also evident that the use of free light chains in the diagnosis of clinically isolated syndrome (CIS) and multiple sclerosis is relatively fast and inexpensive in comparison to other conventional state-of-the-art diagnostic methods, e.g., using oligoclonal bands (OCBs).

Emerging deep learning techniques using magnetic resonance imaging data applied in multiple sclerosis and clinical isolated syndrome patients (Review).

Computer-aided diagnosis systems aim to assist clinicians in the early identification of abnormal signs in order to optimize the interpretation of medical images and increase diagnostic precision. Multiple sclerosis (MS) and clinically isolated syndrome (CIS) are chronic inflammatory, demyelinating diseases affecting the central nervous system. Recent advances in deep learning (DL) techniques have led to novel computational paradigms in MS and CIS imaging designed for automatic segmentation and detection of areas of interest and automatic classification of anatomic structures, as well as optimization of neuroimaging protocols. To this end, there are several publications presenting artificial intelligence-based predictive models aiming to increase diagnostic accuracy and to facilitate optimal clinical management in patients diagnosed with MS and/or CIS. The current study presents a thorough review covering DL techniques that have been applied in MS and CIS during recent years, shedding light on their current advances and limitations.

Th1 and Th17 Cells and Associated Cytokines Discriminate among Clinically Isolated Syndrome and Multiple Sclerosis Phenotypes.

Multiple sclerosis (MS) is a chronic, inflammatory, and demyelinating disease of the central nervous system. It is a heterogeneous pathology that can follow different clinical courses, and the mechanisms that underlie the progression of the immune response across MS subtypes remain incompletely understood. Here, we aimed to determine differences in the immunological status among different MS clinical subtypes. Blood samples from untreated patients diagnosed with clinically isolated syndrome (CIS) (n = 21), different clinical forms of MS (n = 62) [relapsing-remitting (RRMS), secondary progressive, and primary progressive], and healthy controls (HCs) (n = 17) were tested for plasma levels of interferon (IFN)-γ, IL-10, TGF-ß, IL-17A, and IL-17F by immunoanalysis. Th1 and Th17 lymphocyte frequencies were determined by flow cytometry. Our results showed that IFN-γ levels and the IFN-γ/IL-10 ratio were higher in CIS patients than in RRMS patients and HC. Th1 cell frequencies were higher in CIS and RRMS than in progressive MS, and RRMS had a higher Th17 frequency than CIS. The Th1/Th17 cell ratio was skewed toward Th1 in CIS compared to MS phenotypes and HC. Receiver operating characteristic statistical analysis determined that IFN-γ, the IFN-γ/IL-10 ratio, Th1 cell frequency, and the Th1/Th17 cell ratio discriminated among CIS and MS subtypes. A subanalysis among patients expressing high IL-17F levels showed that IL-17F and the IFN-γ/IL-17F ratio discriminated between disease subtypes. Overall, our data showed that CIS and MS phenotypes displayed distinct Th1- and Th17-related cytokines and cell profiles and that these immune parameters discriminated between clinical forms. Upon validation, these parameters might be useful as biomarkers to predict disease progression.

Intervening to reduce the risk of future disability from multiple sclerosis: are we there yet?

Disease-modifying therapies (DMTs) delay or may prevent the progression of patients with high-risk clinically isolated syndrome (CIS) to clinically definite multiple sclerosis (MS), and from relapsing-remitting MS to secondary progressive MS. Current evidence on the effects of DMT on disability in MS is supported by the use of the Expanded Disability Status Scale (EDSS), which is dominated by ambulation, and usually used as a secondary outcome measure. Less is known about the long-term effects of DMTs on other aspects of functional status, particularly cognition, which is a key determinant of ability to work. The time scale for measurements of disability is at most a few years, with scant data from more than 10 years of observation. Longer prospective follow-up of large numbers of patients with CIS is needed to determine whether early intervention with a DMT influences long-term disease progression. Finally, the emergence of the radiologically isolated syndrome (RIS) as a clinical entity has shifted the debate about when to intervene to an even earlier time frame. Balancing the significant side-effects associated with DMT in general and the expected outcome of pharmacologic intervention is increasingly problematic for managing patients with uncertain prognosis, as many patients may have low-risk CIS, benign MS or patients with RIS only. Preventing long-term disability in MS should be recognised more clearly as an important outcome in its own right, with disability measured more consistently with more sensitive instruments beyond the use of the EDSS.

Cerebrospinal fluid analysis in the context of CNS demyelinating diseases / Analise do liquido cefalorraquiano no contexto das doencas desmielinizantes do SNC

The central nervous system demyelinating diseases are a group of disorders with different etiologies, characterized by inflammatory lesions that are associated with loss of myelin and eventually axonal damage. In this group the most studied ones are multiple sclerosis (MS), neuromyelitis optic (NMO) and acute disseminated encephalomyelitis (ADEM). The cerebrospinal fluid is essential to differentiate between these different syndromes and to define multiple sclerosis, helping to assess the probability of Clinical Isolated Syndrome turn into multiple sclerosis.

As doenças desmielinizantes do sistema nervoso central são um grupo de desordens de diferentes etiologias, caracterizadas por lesões inflamatórias associadas a perda da mielina e eventualmente dano axonal. Neste grupo de doenças, as mais estudadas são a esclerose múltipla (EM), a neuromielite óptica e a encefalomielite aguda disseminada. O estudo de liquido cefalorraquiano é essencial para o diagnóstico diferencial entre as diferentes síndromes e para a definição de EM, ajudando a estimar a probabilidade da transformação da síndrome clínica isolada em EM.