Homocysteine as a Predictor Tool in Multiple Sclerosis

Multiple sclerosis (MS) is a progressive and irreversible disease which affects the central nervous system (CNS) with still unknown etiology. Our study aimes to establish the homocysteine pattern that can predict the MS diseases progression and to identify a potential disease progression marker that can be easy to perform and non-invasive, in order to predict the diseases outcome. In order to achieve this goal, we included 10 adult RRMS subjects, 10 adult SPMS subjects and 10 age-matched healthy subjects. The homocysteine plasma level was measured using automated latex enhanced immunoassay and the cobalamin and folate measurements were performed using automated chemiluminescence immunoassay (CLIA). HCR was calculated by dividing the homocysteine plasma level by cobalamin plasma level. We found that the homocysteine level in plasma of both RRMS patients and SPMS group are significantly increased compared with the control group. There is a significantly higher concentration of homocysteine in SPMS group compared with the RRMS group. In addition, the HCR is significantly increased in SPMS compared with the RRMS group and is a very good index of disease severity.

Cognitive Decline in Parkinson’s Disease: A Subgroup of Extreme Decliners Revealed by a Data-Driven Analysis of Longitudinal Progression

Cognitive impairment is an important symptom of Parkinson’s disease (PD) and predicting future cognitive decline is crucial for clinical practice. Here, we aim to identify latent sub-groups of longitudinal trajectories of cognitive change in PD patients, and explore predictors of differences in cognitive change. Longitudinal cognitive performance data from 349 newly diagnosed PD patients and 145 healthy controls from the Parkinson Progression Marker Initiative were modeled using a multivariate latent class linear mixed model. Resultant latent classes were compared on a number of baseline demographics and clinical variables, as well as cerebrospinal fluid (CSF) biomarkers and striatal dopamine transporter (DAT) density markers of neuropathology. Trajectories of cognitive change in PD were best described by two latent classes. A large subgroup (90%), which showed a subtle impairment in cognitive performance compared to controls but remained stable over the course of the study, and a small subgroup (10%) which rapidly declined in all cognitive performance measures. Rapid decliners did not differ significantly from the larger group in terms of disease duration, severity, or motor symptoms at baseline. However, rapid decliners had lower CSF amyloidß42 levels, a higher prevalence of sleep disorder and pronounced loss of caudate DAT density at baseline. These data suggest the existence of a distinct minority sub-type of PD in which rapid cognitive change in PD can occur uncoupled from motor symptoms or disease severity, likely reflecting early pathological change that extends from motor areas of the striatum into associative compartments and cortex.

Computer-assisted photoreceptor assessment on Heidelberg Engineering Spectralis™ High Magnification Module™ images

Purpose To evaluate reliability and repeatability of computer-assisted measurements of cone photoreceptor metrics on Heidelberg Engineering Spectralis™ High Magnification Module (HMM™) Automatic Real-time Tracking (ART™) images. Methods We analyzed HMM™ images in three separate study arms. Computer-assisted cone identification software was validated using an open-access adaptive optics (AO) dataset. We compared results of the first arm to data from AO and histology. We evaluated intersession repeatability of our computer-assisted cone analysis in the second arm. We assessed the capability of HMM™ to visualize cones in the presence of pathology in the third arm. Results We included 10 healthy subjects in the first arm of our study, 5 additional healthy participants in the second arm and 5 patients in the third arm. In total, we analyzed 225 regions of interest on HMM™ images. We were able to automatically identify cone photoreceptors and assess corresponding metrics at all eccentricities between 2 and 9° from the fovea. Cone density significantly declined with increasing eccentricity (p = 4.890E-26, Friedman test). With increasing eccentricity, we found a significant increase in intercell distance (p = 2.196E-25, Friedman test) and nearest neighbor distance (p = 1.997E-25, Friedman test). Cone hexagonality ranged between 71 and 85%. We found excellent automated intersession repeatability of cone density counts and spacing measurements. In pathology, we were also able to repeatedly visualize photoreceptors. Conclusion Computer-assisted cone photoreceptor analysis on Spectralis™ HMM™ images is feasible, and most cone metrics show excellent repeatability. HMM™ imaging may be useful for photoreceptor analysis as progression marker in outer retinal disease.

Artificial Intelligence-Based Assistance in Clinical 123I-FP-CIT SPECT Scan Interpretation

Purpose: Dopamine transporter (DAT) imaging with 123I-FP-CIT SPECT is used to support the diagnosis of Parkinson’s disease (PD) in clinically uncertain cases. Previous studies showed that automatic classification of 123I‑FP‑CIT SPECT images (marketed as DaTSCAN) is feasible by using machine learning algorithms. However, these studies lacked sizable use of data from routine clinical practice. This study aims to contribute to the discussion whether artificial intelligence (AI) can be applied in clinical practice. Moreover, we investigated the need for hospital specific training data.Methods: A convolutional neural network (CNN) named DaTNet-3 was designed and trained to classify DaTSCAN images as either normal or supportive of a dopaminergic deficit. Both a multi-site data set (n = 2412) from the Parkinson’s Progression Marker Initiative (PPMI) and an in-house data set containing clinical images (n = 932) obtained in routine practice at the St Antonius hospital (STA) were used for training and testing. STA images were labeled based on interpretation by nuclear medicine physicians. To investigate whether indeterminate scans effects classification accuracy, a threshold was applied on the output probability.Results: DaTNet-3 trained with STA data reached an accuracy of 89.0% in correctly identifying images of the clinical STA test set as either normal or with decreased striatal DAT binding (98.5% on the PPMI test set). When thresholded, accuracy increased to 95.7%. This increase was not observed when trained with PPMI data, indicating the incorrect images were confidently classified as the incorrect class.Conclusion: Based on results of DaTNet-3 we conclude that automatic interpretation of DaTSCAN images with AI is feasible and robust. Further, we conclude DaTNet-3 performs slightly better when it is trained with hospital specific data. This difference increased when output probability was thresholded. Therefore we conclude that the usability of a data set increases if it contains indeterminate images.

A Phase II study to evaluate the safety and efficacy of prasinezumab in early Parkinson's disease (PASADENA): rationale, design and baseline data

Background Currently available treatments for Parkinson's disease (PD) do not slow clinical progression nor target alpha-synuclein, the main pathology associated with the disease. Objective The study objective was to evaluate the efficacy and safety of prasinezumab, a humanized monoclonal antibody that binds aggregated alpha-synuclein, in individuals with early PD. The study rationale, design, and baseline characteristics of enrolled subjects are presented here. Methods The PASADENA study is a multicenter, randomized, double-blind, placebo-controlled treatment study. Individuals with early PD, recruited across the US and Europe, received monthly intravenous doses of prasinezumab (1500 mg or 4500 mg) or placebo for a 52-week period (Part 1), followed by a 52-week extension (Part 2) in which all participants received active treatment. Key inclusion criteria were: aged 40-80 years; Hoehn & Yahr (H&Y) Stage I or II; time from diagnosis ≤2 years; having bradykinesia plus one other cardinal sign of PD (e.g. resting tremor, rigidity); DaT-SPECT imaging consistent with PD; and either treatment naive or on a stable monoamine oxidase B (MAO-B) inhibitor dose. Study design assumptions for sample size and study duration were built using a patient cohort from the Parkinson1s Progression Marker Initiative (PPMI). In this report, baseline characteristics are compared between the treatment-naive and MAO-B inhibitor-treated PASADENA cohorts and between the PASADENA and PPMI populations. Results Of the 443 patients screened, 316 were enrolled into the PASADENA study between June 2017 and November 2018, with an average age of 59.9 years and 67.4% being male. The mean time from diagnosis at baseline was 10.11 months, with 75.3% in H&Y Stage II. Baseline motor and non-motor symptoms (assessed using Movement Disorder Society - Unified Parkinson's Disease Rating Scale [MDS-UPDRS]) were similar in severity between the MAO-B inhibitor-treated and treatment-naive PASADENA cohorts (MDS-UPDRS Total score [standard deviation (SD)]; 30.21 [11.96], 32.10 [13.20], respectively). The overall PASADENA population (63.6% treatment naive and 36.4% on MAO-B inhibitor) also showed a similar severity in MDS-UPDRS scores (e.g. MDS-UPDRS Total score [SD]; 31.41 [12.78], 32.63 [13.04], respectively) to the PPMI cohort (all treatment naive). Conclusions The PASADENA study population is suitable to investigate the potential of prasinezumab to slow disease progression in individuals with early PD.

Step Length Is a Promising Progression Marker in Parkinson’s Disease

Current research on Parkinson’s disease (PD) is increasingly concerned with the identification of objective and specific markers to make reliable statements about the effect of therapy and disease progression. Parameters from inertial measurement units (IMUs) are objective and accurate, and thus an interesting option to be included in the regular assessment of these patients. In this study, 68 patients with PD (PwP) in Hoehn and Yahr (H&Y) stages 1–4 were assessed with two gait tasks—20 m straight walk and circular walk—using IMUs. In an ANCOVA model, we found a significant and large effect of the H&Y scores on step length in both tasks, and only a minor effect on step time. This study provides evidence that from the two potentially most important gait parameters currently accessible with wearable technology under supervised assessment strategies, step length changes substantially over the course of PD, while step time shows surprisingly little change in the progression of PD. These results show the importance of carefully evaluating quantitative gait parameters to make assumptions about disease progression, and the potential of the granular evaluation of symptoms such as gait deficits when monitoring chronic progressive diseases such as PD.

Serum Extracellular Vesicle-Derived miRNAs in Patients with Non-Small Cell Lung Cancer—Search for Non-Invasive Diagnostic Biomarkers

The aim of the study was a search for diagnostic and/or prognostic biomarkers in patients with non-small cell lung cancer (NSCLC) patients, based on circulating microRNAs (miRs: miR-23a, miR-361, miR-1228 and miR-let7i) in extracellular vesicles (EVs). Serum EVs were isolated from NSCLC patients (n = 31) and control subjects (n = 21). RNA was isolated from EVs and reverse transcription reaction was performed. Relative levels of miR-23a, miR-361, miR-1228 and miR-let7i were assessed in real-time qPCR using TaqMan probes. Analysis was based on the 2-ΔΔCT method. Statistically significant lower levels of miR-23a and miR-let7i were observed among NSCLC patients vs. control group: miR-23a, 0.054 vs. 0.107; miR-let7i, 0.193 vs. 0.369 (p = 0.003, p = 0.005, respectively). A receiver operating characteristic (ROC) curve analysis demonstrated the diagnostic potential of each individual serum EV-derived miRNA with an area under the curve AUC = 0.744 for miR-23a (p = 0.0003), 0.733 for miR-let7i (p = 0.0007). The decreased level of miR-23a in patients correlated with metastasis to lymph nodes and with AJCC tumor staging system. The results demonstrate that miR-23a and miR-let7i may prove clinically useful as significant, non-invasive markers in NSCLC diagnosis. Additionally, changing profile level of miR-23a that correlates with cancer development may be considered as an NSCLC progression marker.