Background and purpose
Chronic inflammatory demyelinating polyneuropathy (CIDP) is an acquired immunomediated condition affecting the peripheral nervous system where probably macrophages are the ...primary effector cells for demyelination. Reactive oxygen species (ROS), catalyzed by the NOX family of nicotinamide adenine dinucleotide phosphate (NADPH) oxidase enzymes, can induce peroxidation and are potentially injurious to myelin. Our aim was to assess the activity of NOX2, an isoform of NOX, in a series of CIDP patients and to analyze the effect of intravenous immunoglobulin (IVIg) on NOX2.
Methods
Thirty CIDP patients treated with IVIg and 30 control subjects were enrolled. To evaluate NOX2 activity, neutrophil and monocyte oxidative burst was measured directly in fresh whole blood using the Phagoburst™ assay, a fluorescence‐activated cell sorting method. The mean fluorescence intensity, emitted in response to different stimuli, leads to the production of ROS and corresponds to the percentage of oxidizing cells and their enzymatic activity.
Results
Mean fluorescence intensity values for granulocyte and monocyte burst in patients (mean 633.3, SD 191; mean 111.8, SD 28.5) were different from those measured in healthy controls (granulocytes, mean 436.6, SD 137.0, P = 0.0003; monocytes, mean 78.2, SD 17.3, P = 0.000001). Moreover, IVIg administration increased both granulocyte (P = 0.005) and monocyte (P = 0.0009) burst.
Conclusion
Our findings demonstrate that oxidative burst is significantly increased in CIDP patients and that treatment with IVIg enhances oxidative values, thus representing a possible IVIg therapeutic effect linked to a regulatory effect of ROS. Based on this, the development of treatments targeting the specific activation of NOX may be beneficial in autoimmune disorders.
Objective
To employ Artificial Intelligence to model, predict and simulate the amyotrophic lateral sclerosis (ALS) progression over time in terms of variable interactions, functional impairments, and ...survival.
Methods
We employed demographic and clinical variables, including functional scores and the utilisation of support interventions, of 3940 ALS patients from four Italian and two Israeli registers to develop a new approach based on Dynamic Bayesian Networks (DBNs) that models the ALS evolution over time, in two distinct scenarios of variable availability. The method allows to simulate patients’ disease trajectories and predict the probability of functional impairment and survival at different time points.
Results
DBNs explicitly represent the relationships between the variables and the pathways along which they influence the disease progression. Several notable inter-dependencies were identified and validated by comparison with literature. Moreover, the implemented tool allows the assessment of the effect of different markers on the disease course, reproducing the probabilistically expected clinical progressions. The tool shows high concordance in terms of predicted and real prognosis, assessed as time to functional impairments and survival (integral of the AU-ROC in the first 36 months between 0.80–0.93 and 0.84–0.89 for the two scenarios, respectively).
Conclusions
Provided only with measurements commonly collected during the first visit, our models can predict time to the loss of independence in walking, breathing, swallowing, communicating, and survival and it can be used to generate in silico patient cohorts with specific characteristics. Our tool provides a comprehensive framework to support physicians in treatment planning and clinical decision-making.
IntroductionAmyotrophic lateral sclerosis (ALS) is a fatal progressive neurological disorder characterised by a selective degeneration of motor neurons (MNs). Stem cell transplantation is considered ...as a promising strategy in neurological disorders therapy and the possibility of inducing bone marrow cells (BMCs) to circulate in the peripheral blood is suggested to investigate stem cells migration in degenerated ALS nerve tissues where potentially repair MN damage. Granulocyte-colony stimulating factor (G-CSF) is a growth factor which stimulates haematopoietic progenitor cells, mobilises BMCs into injured brain and it is itself a neurotrophic factor for MN. G-CSF safety in humans has been demonstrated and many observations suggest that it may affect neural cells. Therefore, we decided to use G-CSF to mobilise BMCs into the peripheral circulation in patients with ALS, planning a clinical trial to evaluate the effect of G-CSF administration in ALS patients compared with placebo.Methods and analysisSTEMALS-II is a phase II multicentre, randomised double-blind, placebo-controlled, parallel group clinical trial on G-CSF (filgrastim) and mannitol in ALS patients. Specifically, we investigate safety, tolerability and efficacy of four repeated courses of intravenous G-CSF and mannitol administered in 76 ALS patients in comparison with placebo (indistinguishable glucose solution 5%). We determine increase of G-CSF levels in serum and cerebrospinal fluid as CD34+ cells and leucocyte count after treatment; reduction in ALS Functional Rating Scale-Revised Score, forced vital capacity, Scale for Testing Muscle Strength Score and quality of life; the adverse events/reactions during the treatment; changes in neuroinflammation biomarkers before and after treatment.Ethics and disseminationThe study protocol was approved by the Ethics Committee of Azienda Ospedaliera Universitaria ‘Città della Salute e della Scienza’, Torino, Italy. Results will be presented during scientific symposia or published in scientific journals.Trial registration numberEudract 2014-002228-28.
The proliferation of Big Data applications puts pressure on improving and optimizing the handling of diverse datasets across different domains. Among several challenges, major difficulties arise in ...data-sensitive domains like banking, telecommunications, etc., where strict regulations make very difficult to upload and experiment with real data on external cloud resources. In addition, most Big Data research and development efforts aim to address the needs of IT experts, while Big Data analytics tools remain unavailable to non-expert users to a large extent. In this paper, we report on the work-in-progress carried out in the context of the H2020 project I-BiDaaS (Industrial-Driven Big Data as a Self-service Solution) which aims to address the above challenges. The project will design and develop a novel architecture stack that can be easily configured and adjusted to address cross-sectoral needs, helping to resolve data privacy barriers in sensitive domains, and at the same time being usable by non-experts. This paper discusses and motivates the need for Big Data as a self-service, reviews the relevant literature, and identifies gaps with respect to the challenges described above. We then present the I-BiDaaS paradigm for Big Data as a self-service, position it in the context of existing references, and report on initial work towards the conceptual specification of the I-BiDaaS software architecture.
PDF
