Biomarkers of Multiple Sclerosis

The search for an ideal multiple sclerosis biomarker with good diagnostic value, prognostic reference and an impact on clinical outcome has yet to be realized and is still ongoing. The aim of this review is to establish an overview of the frequent biomarkers for multiple sclerosis that exist to date. The review summarizes the results obtained from electronic databases, as well as thorough manual searches. In this review the sources and methods of biomarkers extraction are described; in addition to the description of each biomarker, determination of the prognostic, diagnostic, disease monitoring and treatment response values besides clinical impact they might possess. We divided the biomarkers into three categories according to the achievement method: laboratory markers, genetic-immunogenetic markers and imaging markers. We have found two biomarkers at the time being considered the gold standard for MS diagnostics. Unfortunately, there does not exist a single solitary marker being able to present reliable diagnostic value, prognostic value, high sensitivity and specificity as well as clinical impact. We need more studies to find the best biomarker for MS.publishersversionPeer reviewe

Π10 classes, LR degrees and Turing degrees

We say that A≤LRB if every B-random set is A-random with respect to Martin–Löf randomness. We study this relation and its interactions with Turing reducibility, π10 classes, hyperimmunity and other recursion theoretic notions

The role of serum free light chain as biomarker of Myasthenia Gravis

Background and aim: Myasthenia gravis (MG) is a B lymphocyte\u2013mediated disease affecting neuromuscular transmission. The clinical course of MG is unpredictable due to the fluctuating nature and heterogeneity of the disease. Increased levels of free light chains (FLC), which reflect B cell activation, have been detected in different autoimmune disorders. In this study, we evaluated the potential role of FLC as diagnostic and prognostic biomarkers of MG. Materials and methods: 74 MG patients and 52 healthy individuals were included in the study. Serum FLC levels were measured by turbidimetric assay (Freelite, The Binding Site Group Ltd) on the Optilite Analyser System in both groups. In MG patients, anti-AChR and anti-MuSK autoantibodies were detected by enzyme-linked immunosorbent assay. Results: MG patients displayed significantly higher serum \u3ba and total FLC levels than controls, respectively for \u3baFLC 16.0 vs 13.8 mg/L and for total FLC 29.8 vs 25.9 mg/L. Moreover, increased \u3baFLC levels were observed in seropositive MG patients. No association was observed between serum FLC levels and clinical manifestations of disease as well as with severity, age at MG onset, thymoma and treatment. Conclusion: Increased levels of \u3baFLC and total FLC could serve as biomarkers to support the diagnosis of MG

Erratum to: Survival prediction in high-grade gliomas using CT perfusion imaging [J Neurooncol (2015) 123, 93-102, DOI 10.1007/s11060-015-1766-5]

Background: Clinical and molecular factors are essential to define the prognosis in patients with glioblastoma (GBM). O6-methylguanine-DNA methyltransferase (MGMT) methylation status, age, Karnofsky Performance Status (KPS), and extent of surgical resection are the most relevant prognostic factors. Our investigation of the role of gender in predicting prognosis shows a slight survival advantage for female patients. Methods: We performed a prospective evaluation of the Project of Emilia Romagna on Neuro-Oncology (PERNO) registry to identify prognostic factors in patients with GBM who received standard treatment. Results: A total of 169 patients (99 males [58.6%] and 70 females [41.4%]) were evaluated prospectively. MGMT methylation was evaluable in 140 patients. Among the male patients, 36 were MGMT methylated (25.7%) and 47 were unmethylated (33.6%); among the female patients, 32 were methylated (22.9%) and 25 were unmethylated (17.9%). Survival was longer in the methylated females compared with the methylated males (P = 0.028) but was not significantly different between the unmethylated females and the unmethylated males (P = 0.395). In multivariate analysis, gender and MGMT methylation status considered together (methylated females vs. methylated males; hazard ratio [HR], 0.459; 95% confidence interval [CI], 0.242-0.827; P = 0.017), age (HR, 1.025; 95% CI, 1.002-1.049; P = 0.032), and KPS (HR, 0.965; 95% CI, 0.948-0.982; P < 0.001) were significantly correlated with survival. Conclusions: Survival was consistently longer among MGMT methylated females compared with males. Gender can be considered as a further prognostic factor