Neuro-axonal injury in COVID-19: the role of systemic inflammation and SARS-CoV-2 specific immune response

Background: In coronavirus disease-2019 (COVID-19) patients, there is increasing evidence of neuronal injury by the means of elevated serum neurofilament light chain (sNfL) levels. However, the role of systemic inflammation and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)–specific immune response with regard to neuronal injury has not yet been investigated. Methods: In a prospective cohort study, we recruited patients with mild–moderate (n = 39) and severe (n = 14) COVID-19 and measured sNfL levels, cytokine concentrations, SARS-CoV-2-specific antibodies including neutralizing antibody titers, and cell-mediated immune responses at enrollment and at 28(±7) days. We explored the association of neuro-axonal injury as by the means of sNfL measurements with disease severity, cytokine levels, and virus-specific immune responses. Results: sNfL levels, as an indicator for neuronal injury, were higher at enrollment and increased during follow-up in severely ill patients, whereas during mild–moderate COVID-19, sNfL levels remained unchanged. Severe COVID-19 was associated with increased concentrations of cytokines assessed [interleukin (IL)-6, IL-8, interleukin-1 beta (IL-1β), and tumor necrosis factor-alpha (TNF-α)], higher anti-spike IgG and anti-nucleocapsid IgG concentrations, and increased neutralizing antibody titers compared with mild–moderate disease. Patients with more severe disease had higher counts of defined SARS-CoV-2-specific T cells. Increases in sNfL concentrations from baseline to day 28(±7) positively correlated with anti-spike protein IgG antibody levels and with titers of neutralizing antibodies. Conclusion: Severe COVID-19 is associated with increased serum concentration of cytokines and subsequent neuronal injury as reflected by increased levels of sNfL. Patients with more severe disease developed higher neutralizing antibody titers and higher counts of SARS-CoV-2-specific T cells during the course of COVID-19 disease. Mounting a pronounced virus-specific humoral and cell-mediated immune response upon SARS-CoV-2 infection did not protect from neuro-axonal damage as by the means of sNfL levels

Kappa free light chains is a valid tool in the diagnostics of MS: A large multicenter study

To validate kappa free light chain (KFLC) and lambda free light chain (LFLC) indices as a diagnostic biomarker in multiple sclerosis (MS).We performed a multicenter study including 745 patients from 18 centers (219 controls and 526 clinically isolated syndrome (CIS)/MS patients) with a known oligoclonal IgG band (OCB) status. KFLC and LFLC were measured in paired cerebrospinal fluid (CSF) and serum samples. Gaussian mixture modeling was used to define a cut-off for KFLC and LFLC indexes.The cut-off for the KFLC index was 6.6 (95% confidence interval (CI) = 5.2-138.1). The cut-off for the LFLC index was 6.9 (95% CI = 4.5-22.2). For CIS/MS patients, sensitivity of the KFLC index (0.88; 95% CI = 0.85-0.90) was higher than OCB (0.82; 95%CI = 0.79-0.85; p < 0.001), but specificity (0.83; 95% CI = 0.78-0.88) was lower (OCB = 0.92; 95% CI = 0.89-0.96; p < 0.001). Both sensitivity and specificity for the LFLC index were lower than OCB.Compared with OCB, the KFLC index is more sensitive but less specific for diagnosing CIS/MS. Lacking an elevated KFLC index is more powerful for excluding MS compared with OCB but the latter is more important for ruling in a diagnosis of CIS/MS

Neurofilament light chain, a biomarker for polyneuropathy in systemic amyloidosis

OBJECTIVE: To study serum neurofilament light chain (sNfL) in amyloid light chain (AL) amyloidosis patients with and without polyneuropathy (PNP) and to corroborate previous observations that sNfL is increased in hereditary transthyretin-related (ATTRv) amyloidosis patients with PNP. METHODS: sNfL levels were assessed retrospectively in patients with AL amyloidosis with and without PNP (AL/PNP+ and AL/PNP-, respectively), patients with ATTRv amyloidosis and PNP (ATTRv/PNP+), asymptomatic transthyretin (TTR) gene mutation carriers (TTRv carriers) and healthy controls. Healthy controls (HC) were age- and sex-matched to both AL/PNP- (HC/AL) and TTRv carriers (HC/TTRv). The single-molecule array (Simoa) assay was used to assess sNfL levels. RESULTS: sNfL levels were increased both in 10 AL/PNP+ patients (p  I) had the highest sNfL levels compared to patients with early PNP (PND-score I) (p = .05). sNfL levels did not differ between TTRv carriers and HC/TTRv individuals. In the group comprising all healthy controls and in the group of TTRv carriers, sNfL levels correlated with age. CONCLUSION: sNfL levels are increased in patients with PNP in both AL and ATTRv amyloidosis and are related to severity of PNP in ATTRv amyloidosis. sNfL is a promising biomarker to detect PNP, not only in ATTRv but also in AL amyloidosis

GAMER MRI: Gated-attention mechanism ranking of multi-contrast MRI in brain pathology.

During the last decade, a multitude of novel quantitative and semiquantitative MRI techniques have provided new information about the pathophysiology of neurological diseases. Yet, selection of the most relevant contrasts for a given pathology remains challenging. In this work, we developed and validated a method, Gated-Attention MEchanism Ranking of multi-contrast MRI in brain pathology (GAMER MRI), to rank the relative importance of MR measures in the classification of well understood ischemic stroke lesions. Subsequently, we applied this method to the classification of multiple sclerosis (MS) lesions, where the relative importance of MR measures is less understood. GAMER MRI was developed based on the gated attention mechanism, which computes attention weights (AWs) as proxies of importance of hidden features in the classification. In the first two experiments, we used Trace-weighted (Trace), apparent diffusion coefficient (ADC), Fluid-Attenuated Inversion Recovery (FLAIR), and T1-weighted (T1w) images acquired in 904 acute/subacute ischemic stroke patients and in 6,230 healthy controls and patients with other brain pathologies to assess if GAMER MRI could produce clinically meaningful importance orders in two different classification scenarios. In the first experiment, GAMER MRI with a pretrained convolutional neural network (CNN) was used in conjunction with Trace, ADC, and FLAIR to distinguish patients with ischemic stroke from those with other pathologies and healthy controls. In the second experiment, GAMER MRI with a patch-based CNN used Trace, ADC and T1w to differentiate acute ischemic stroke lesions from healthy tissue. The last experiment explored the performance of patch-based CNN with GAMER MRI in ranking the importance of quantitative MRI measures to distinguish two groups of lesions with different pathological characteristics and unknown quantitative MR features. Specifically, GAMER MRI was applied to assess the relative importance of the myelin water fraction (MWF), quantitative susceptibility mapping (QSM), T1 relaxometry map (qT1), and neurite density index (NDI) in distinguishing 750 juxtacortical lesions from 242 periventricular lesions in 47 MS patients. Pair-wise permutation t-tests were used to evaluate the differences between the AWs obtained for each quantitative measure. In the first experiment, we achieved a mean test AUC of 0.881 and the obtained AWs of FLAIR and the sum of AWs of Trace and ADC were 0.11 and 0.89, respectively, as expected based on previous knowledge. In the second experiment, we achieved a mean test F1 score of 0.895 and a mean AW of Trace = 0.49, of ADC = 0.28, and of T1w = 0.23, thereby confirming the findings of the first experiment. In the third experiment, MS lesion classification achieved test balanced accuracy = 0.777, sensitivity = 0.739, and specificity = 0.814. The mean AWs of T1map, MWF, NDI, and QSM were 0.29, 0.26, 0.24, and 0.22 (p &lt; 0.001), respectively. This work demonstrates that the proposed GAMER MRI might be a useful method to assess the relative importance of MRI measures in neurological diseases with focal pathology. Moreover, the obtained AWs may in fact help to choose the best combination of MR contrasts for a specific classification problem

Anti-α-glucose-based glycan IgM antibodies predict relapse activity in multiple sclerosis after the first neurological event

Background There is no specific serum-based biomarker for the diagnosis or prognosis of relapsing-remitting multiple sclerosis (RRMS). Objective We investigated whether levels of IgM antibodies to Glc(alpha 1,4) Glc(alpha) (GAGA4) or to a panel of four glucose-based glycans could differentiate MS from other neurological diseases (OND) or predict risk of early relapse following first presentation (FP) of RRMS. Methods Retrospective analysis of 440 sera samples of three cohorts: A) FP-RRMS (n = 44), OND (n = 44); B) FP-RRMS (n = 167), OND (n = 85); and C) FP (n = 100). Anti-GAGA4 IgM levels were measured by enzyme immunoassay in cohort-A and cohort-B. Cohort-C IgM antibodies to glucose-based glycan panel were measured by immunofluorescence. Results FP-RRMS had higher levels of anti-GAGA4 IgM than OND patients (cohort-A, P = 0.01; cohort-B, P = 0.0001). Sensitivity and specificity were 27% and 97% for cohort-A; and 26% and 90% for cohort-B, respectively. In cohort-C, 58 patients experienced early relapse (= 24 months), and 11 did not experience second attack during follow-up. Kaplan-Meier curves demonstrated decrease in time to next relapse for patients positive for the antibody panel (P = 0.02, log rank). Conclusions Serum anti-GAGA4 IgM discerns FP-RRMS patients from OND patients. Higher levels of serum anti-alpha-glucose IgM in FP patients predict imminent early relapse. Multiple Sclerosis 2009; 15: 422-430. http://msj.sagepub.co

Longitudinal machine learning modeling of MS patient trajectories improves predictions of disability progression

Background and Objectives: Research in Multiple Sclerosis (MS) has recently focused on extracting knowledge from real-world clinical data sources. This type of data is more abundant than data produced during clinical trials and potentially more informative about real-world clinical practice. However, this comes at the cost of less curated and controlled data sets. In this work we aim to predict disability progression by optimally extracting information from longitudinal patient data in the real-world setting, with a special focus on the sporadic sampling problem. Methods: We use machine learning methods suited for patient trajectories modeling, such as recurrent neural networks and tensor factorization. A subset of 6682 patients from the MSBase registry is used. Results: We can predict disability progression of patients in a two-year horizon with an ROC-AUC of 0.85, which represents a 32% decrease in the ranking pair error (1-AUC) compared to reference methods using static clinical features. Conclusions: Compared to the models available in the literature, this work uses the most complete patient history for MS disease progression prediction and represents a step forward towards AI-assisted precision medicine in MS

Kappa free light chains is a valid tool in the diagnostics of MS: A large multicenter study

Objective: To validate kappa free light chain (KFLC) and lambda free light chain (LFLC) indices as a diagnostic biomarker in multiple sclerosis (MS). Methods: We performed a multicenter study including 745 patients from 18 centers (219 controls and 526 clinically isolated syndrome (CIS)/MS patients) with a known oligoclonal IgG band (OCB) status. KFLC and LFLC were measured in paired cerebrospinal fluid (CSF) and serum samples. Gaussian mix- ture modeling was used to define a cut-off for KFLC and LFLC indexes. Results: The cut-off for the KFLC index was 6.6 (95% confidence interval (CI) = 5.2-138.1). The cut-off for the LFLC index was 6.9 (95% CI=4.5-22.2). For CIS/MS patients, sensitivity of the KFLC index (0.88; 95% CI = 0.85-0.90) was higher than OCB (0.82; 95%CI = 0.79-0.85; p < 0.001), but specificity (0.83; 95% CI = 0.78-0.88) was lower (OCB = 0.92; 95% CI = 0.89-0.96; p < 0.001). Both sensitivity and specificity for the LFLC index were lower than OCB. Conclusion: Compared with OCB, the KFLC index is more sensitive but less specific for diagnosing CIS/MS. Lacking an elevated KFLC index is more powerful for excluding MS compared with OCB but the latter is more important for ruling in a diagnosis of CIS/MS

Systemic inflammatory response and neuromuscular involvement in amyotrophic lateral sclerosis

OBJECTIVE: To evaluate the combined blood expression of neuromuscular and inflammatory biomarkers as predictors of disease progression and prognosis in amyotrophic lateral sclerosis (ALS). METHODS: Logistic regression adjusted for markers of the systemic inflammatory state and principal component analysis were carried out on plasma levels of creatine kinase (CK), ferritin, and 11 cytokines measured in 95 patients with ALS and 88 healthy controls. Levels of circulating biomarkers were used to study survival by Cox regression analysis and correlated with disease progression and neurofilament light chain (NfL) levels available from a previous study. Cytokines expression was also tested in blood samples longitudinally collected for up to 4 years from 59 patients with ALS. RESULTS: Significantly higher levels of CK, ferritin, tumor necrosis factor (TNF)-α, and interleukin (IL)-1β, IL-2, IL-8, IL-12p70, IL-4, IL-5, IL-10, and IL-13 and lower levels of interferon (IFN)-γ were found in plasma samples from patients with ALS compared to controls. IL-6, TNF-α, and IFN-γ were the most highly regulated markers when all explanatory variables were jointly analyzed. High ferritin and IL-2 levels were predictors of poor survival. IL-5 levels were positively correlated with CK, as was TNF-α with NfL. IL-6 was strongly associated with CRP levels and was the only marker showing increasing expression towards end-stage disease in the longitudinal analysis. CONCLUSIONS: Neuromuscular pathology in ALS involves the systemic regulation of inflammatory markers mostly active on T-cell immune responses. Disease stratification based on the prognostic value of circulating inflammatory markers could improve clinical trials design in ALS

Testing the predictability and efficiency of securitized real estate markets

This paper conducts tests of the random walk hypothesis and market efficiency for 14 national public real estate markets. Random walk properties of equity prices influence the return dynamics and determine the trading strategies of investors. To examine the stochastic properties of local real estate index returns and to test the hypothesis that public real estate stock prices follow a random walk, the single variance ratio tests of Lo and MacKinlay (1988) as well as the multiple variance ratio test of Chow and Denning (1993) are employed. Weak-form market efficiency is tested directly using non-parametric runs tests. Empirical evidence shows that weekly stock prices in major securitized real estate markets do not follow a random walk. The empirical findings of return predictability suggest that investors might be able to develop trading strategies allowing them to earn excess returns compared to a buy-and-hold strategy