scholarly journals The Impact of Multiple Sclerosis Disease Status and Subtype on Hematological Profile

2021 ◽  
Vol 18 (6) ◽  
pp. 3318
Author(s):  
Jacob M. Miller ◽  
Jeremy T. Beales ◽  
Matthew D. Montierth ◽  
Farren B. Briggs ◽  
Scott F. Frodsham ◽  
...  
Keyword(s):  
Multiple Sclerosis ◽  
Random Forest ◽  
Demyelinating Disease ◽  
Complete Blood Count ◽  
Disease Status ◽  
Multiple Sclerosis Disease ◽  
Forest Models ◽  
Random Forest Models ◽  
The Central Nervous System ◽  
The Impact

Multiple sclerosis (MS) is an immune-mediated, demyelinating disease of the central nervous system. In this study, an MS cohort and healthy controls were stratified into Caucasian and African American groups. Patient hematological profiles—composed of complete blood count (CBC) and complete metabolic panel (CMP) test values—were analyzed to identify differences between MS cases and controls and between patients with different MS subtypes. Additionally, random forest models were used to determine the aggregate utility of common hematological tests in determining MS disease status and subtype. The most significant and relevant results were increased bilirubin and creatinine in MS cases. The random forest models achieved some success in differentiating between MS cases and controls (AUC values: 0.725 and 0.710, respectively) but were not successful in differentiating between subtypes. However, larger samples that adjust for possible confounding variables, such as treatment status, may reveal the value of these tests in differentiating between MS subtypes.

scholarly journals A multi-step machine learning approach to assess the impact of COVID-19 lockdown on NO2 attributable deaths in Milan and Rome, Italy

2022 ◽  
Vol 21 (1) ◽  
Author(s):  
Luca Boniardi ◽  
Federica Nobile ◽  
Massimo Stafoggia ◽  
Paola Michelozzi ◽  
Carla Ancona
Keyword(s):  
Machine Learning ◽  
Land Use ◽  
Air Pollution ◽  
Random Forest ◽  
Citizen Science ◽  
Learning Approach ◽  
Machine Learning Approach ◽  
Forest Models ◽  
Random Forest Models ◽  
The Impact

Abstract Background Air pollution is one of the main concerns for the health of European citizens, and cities are currently striving to accomplish EU air pollution regulation. The 2020 COVID-19 lockdown measures can be seen as an unintended but effective experiment to assess the impact of traffic restriction policies on air pollution. Our objective was to estimate the impact of the lockdown measures on NO2 concentrations and health in the two largest Italian cities. Methods NO2 concentration datasets were built using data deriving from a 1-month citizen science monitoring campaign that took place in Milan and Rome just before the Italian lockdown period. Annual mean NO2 concentrations were estimated for a lockdown scenario (Scenario 1) and a scenario without lockdown (Scenario 2), by applying city-specific annual adjustment factors to the 1-month data. The latter were estimated deriving data from Air Quality Network stations and by applying a machine learning approach. NO2 spatial distribution was estimated at a neighbourhood scale by applying Land Use Random Forest models for the two scenarios. Finally, the impact of lockdown on health was estimated by subtracting attributable deaths for Scenario 1 and those for Scenario 2, both estimated by applying literature-based dose–response function on the counterfactual concentrations of 10 μg/m3. Results The Land Use Random Forest models were able to capture 41–42% of the total NO2 variability. Passing from Scenario 2 (annual NO2 without lockdown) to Scenario 1 (annual NO2 with lockdown), the population-weighted exposure to NO2 for Milan and Rome decreased by 15.1% and 15.3% on an annual basis. Considering the 10 μg/m3 counterfactual, prevented deaths were respectively 213 and 604. Conclusions Our results show that the lockdown had a beneficial impact on air quality and human health. However, compliance with the current EU legal limit is not enough to avoid a high number of NO2 attributable deaths. This contribution reaffirms the potentiality of the citizen science approach and calls for more ambitious traffic calming policies and a re-evaluation of the legal annual limit value for NO2 for the protection of human health.

scholarly journals Treatment-Specific Composition of the Gut Microbiota Is Associated With Disease Remission in a Pediatric Crohn’s Disease Cohort

2019 ◽  
Vol 25 (12) ◽  
pp. 1927-1938 ◽  
Author(s):  
Daniel Sprockett ◽  
Natalie Fischer ◽  
Rotem Sigall Boneh ◽  
Dan Turner ◽  
Jarek Kierkus ◽  
...  
Keyword(s):  
Crohn’S Disease ◽  
Random Forest ◽  
Crohn's Disease ◽  
Gut Microbiota ◽  
Area Under The Curve ◽  
Disease Remission ◽  
Beneficial Effects ◽  
Forest Models ◽  
Random Forest Models ◽  
The Impact

Abstract Background The beneficial effects of antibiotics in Crohn’s disease (CD) depend in part on the gut microbiota but are inadequately understood. We investigated the impact of metronidazole (MET) and metronidazole plus azithromycin (MET+AZ) on the microbiota in pediatric CD and the use of microbiota features as classifiers or predictors of disease remission. Methods 16S rRNA-based microbiota profiling was performed on stool samples from 67 patients in a multinational, randomized, controlled, longitudinal, 12-week trial of MET vs MET+AZ in children with mild to moderate CD. Profiles were analyzed together with disease activity, and then used to construct random forest models to classify remission or predict treatment response. Results Both MET and MET+AZ significantly decreased diversity of the microbiota and caused large treatment-specific shifts in microbiota structure at week 4. Disease remission was associated with a treatment-specific microbiota configuration. Random forest models constructed from microbiota profiles before and during antibiotic treatment with metronidazole accurately classified disease remission in this treatment group (area under the curve [AUC], 0.879; 95% confidence interval, 0.683–0.9877; sensitivity, 0.7778; specificity, 1.000; P < 0.001). A random forest model trained on pre-antibiotic microbiota profiles predicted disease remission at week 4 with modest accuracy (AUC, 0.8; P = 0.24). Conclusions MET and MET+AZ antibiotic regimens in pediatric CD lead to distinct gut microbiota structures at remission. It may be possible to classify and predict remission based in part on microbiota profiles, but larger cohorts will be needed to realize this goal.

scholarly journals Treatment-Specific Composition of Gut Microbiota Is Associated with Disease Remission in a Pediatric Crohn’s Disease Cohort

2018 ◽  
Author(s):  
Daniel Sprockett ◽  
Natalie Fischer ◽  
Rotem Sigall Boneh ◽  
Dan Turner ◽  
Jarek Kierkus ◽  
...  
Keyword(s):  
Crohn’S Disease ◽  
Random Forest ◽  
Crohn's Disease ◽  
Gut Microbiota ◽  
Disease Remission ◽  
Beneficial Effects ◽  
Forest Models ◽  
Random Forest Models ◽  
Stool Samples ◽  
The Impact

AbstractBackgroundThe beneficial effects of antibiotics in Crohn’s disease (CD) depend in part on the gut microbiota but are inadequately understood. We investigated the impact of metronidazole (MET) and metronidazole plus azithromycin (MET+AZ) on the microbiota in pediatric CD, and the use of microbiota features as classifiers or predictors of disease remission.Methods16S rRNA-based microbiota profiling was performed on stool samples from 67 patients in a multinational, randomized, controlled, longitudinal, 12-week trial of MET vs. MET+AZ in children with mild to moderate CD. Profiles were analyzed together with disease activity, and then used to construct Random Forest models to classify remission or predict treatment response.ResultsBoth MET and MET+AZ significantly decreased diversity of the microbiota and caused large treatment-specific shifts in microbiota structure at week 4. Disease remission was associated with a treatment-specific microbiota configuration. Random Forest models constructed from microbiota profiles pre- and during antibiotic treatment with metronidazole accurately classified disease remission in this treatment group (AUC of 0.879, 95% CI 0.683, 0.9877; sensitivity 0.7778; specificity 1.000, P < 0.001). A Random Forest model trained on preantibiotic microbiota profiles predicted disease remission at week 4 with modest accuracy (AUC of 0.8, P = 0.24).ConclusionsMET and MET+AZ antibiotic regimens in pediatric CD lead to distinct gut microbiota structures at remission. It may be possible to classify and predict remission based in part on microbiota profiles, but larger cohorts will be needed to realize this goal.SummaryWe investigated the impact of metronidazole and metronidazole plus azithromycin on the gut microbiota in pediatric Crohn’s disease. Disease remission was associated with a treatment-specific microbiota configuration, and could be predicted based on pre-antibiotic microbiota profiles.

Genetics and Epidemiology of Multiple Sclerosis

2016 ◽  
Author(s):  
Gabriele C. Deluca ◽  
Richard L. Yates ◽  
A. Dessa Sadovnick
Keyword(s):  
Risk Factors ◽  
Multiple Sclerosis ◽  
Genetic Factors ◽  
Demyelinating Disease ◽  
Disease Risk ◽  
Multiple Sclerosis Pathogenesis ◽  
Multiple Sclerosis Disease ◽  
The Central Nervous System ◽  
Epidemiologic Features ◽  
Shed Light

Multiple sclerosis is a chronic inflammatory demyelinating disease of the central nervous system. Despite decades of research, its etiology remains unknown. It has been established that environmental and genetic factors contribute to multiple sclerosis disease risk. Although the identification of risk factors for multiple sclerosis has not yet radically advanced understanding of the pathophysiology, it has shed light on the complex epidemiology of this disease. This chapter highlights key epidemiologic features of multiple sclerosis, with a focus on environmental and genetic factors known to influence disease risk. The interplay between environmental and genetic factors in multiple sclerosis pathogenesis is discussed.

scholarly journals Effects of aquatic exercises in a rat model of brainstem demyelination with ethidium bromide on the beam walking test

2009 ◽  
Vol 67 (3a) ◽  
pp. 652-656 ◽  
Author(s):  
Cíntia Cristina Souza Nassar ◽  
Eduardo Fernandes Bondan ◽  
Sandra Regina Alouche
Keyword(s):  
Multiple Sclerosis ◽  
Central Nervous System ◽  
Experimental Model ◽  
Ethidium Bromide ◽  
Motor Performance ◽  
Demyelinating Disease ◽  
Control Group ◽  
Walking Test ◽  
The Central Nervous System ◽  
The Impact

Multiple sclerosis is a demyelinating disease of the central nervous system associated with varied levels of disability. The impact of early physiotherapeutic interventions in the disease progression is unknown. We used an experimental model of demyelination with the gliotoxic agent ethidium bromide and early aquatic exercises to evaluate the motor performance of the animals. We quantified the number of footsteps and errors during the beam walking test. The demyelinated animals walked fewer steps with a greater number of errors than the control group. The demyelinated animals that performed aquatic exercises presented a better motor performance than those that did not exercise. Therefore aquatic exercising was beneficial to the motor performance of rats in this experimental model of demyelination.

scholarly journals Data-Driven Wildfire Risk Prediction in Northern California

Atmosphere ◽  
2021 ◽  
Vol 12 (1) ◽  
pp. 109
Author(s):  
Ashima Malik ◽  
Megha Rajam Rao ◽  
Nandini Puppala ◽  
Prathusha Koouri ◽  
Venkata Anil Kumar Thota ◽  
...  
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Learning Curves ◽  
Data Driven ◽  
Northern California ◽  
Combined Model ◽  
Wildfire Risk ◽  
Study Results ◽  
Forest Models ◽  
Random Forest Models

Over the years, rampant wildfires have plagued the state of California, creating economic and environmental loss. In 2018, wildfires cost nearly 800 million dollars in economic loss and claimed more than 100 lives in California. Over 1.6 million acres of land has burned and caused large sums of environmental damage. Although, recently, researchers have introduced machine learning models and algorithms in predicting the wildfire risks, these results focused on special perspectives and were restricted to a limited number of data parameters. In this paper, we have proposed two data-driven machine learning approaches based on random forest models to predict the wildfire risk at areas near Monticello and Winters, California. This study demonstrated how the models were developed and applied with comprehensive data parameters such as powerlines, terrain, and vegetation in different perspectives that improved the spatial and temporal accuracy in predicting the risk of wildfire including fire ignition. The combined model uses the spatial and the temporal parameters as a single combined dataset to train and predict the fire risk, whereas the ensemble model was fed separate parameters that were later stacked to work as a single model. Our experiment shows that the combined model produced better results compared to the ensemble of random forest models on separate spatial data in terms of accuracy. The models were validated with Receiver Operating Characteristic (ROC) curves, learning curves, and evaluation metrics such as: accuracy, confusion matrices, and classification report. The study results showed and achieved cutting-edge accuracy of 92% in predicting the wildfire risks, including ignition by utilizing the regional spatial and temporal data along with standard data parameters in Northern California.

scholarly journals Regulation of CTLA-4 and PD-L1 Expression in Relapsing-Remitting Multiple Sclerosis Patients after Treatment with Fingolimod, IFNβ-1α, Glatiramer Acetate, and Dimethyl Fumarate Drugs

2021 ◽  
Vol 11 (8) ◽  
pp. 721
Author(s):  
Afshin Derakhshani ◽  
Zahra Asadzadeh ◽  
Hossein Safarpour ◽  
Patrizia Leone ◽  
Mahdi Abdoli Shadbad ◽  
...  
Keyword(s):  
Multiple Sclerosis ◽  
Mononuclear Cells ◽  
Demyelinating Disease ◽  
Immune Checkpoints ◽  
Peripheral Blood Mononuclear ◽  
Relapsing Remitting ◽  
Remitting Multiple Sclerosis ◽  
Multiple Sclerosis Patients ◽  
Relapsing Remitting Multiple Sclerosis ◽  
The Impact

Multiple sclerosis (MS) is a chronic demyelinating disease of the central nervous system (CNS) that is characterized by inflammation which typically results in significant impairment in most patients. Immune checkpoints act as co-stimulatory and co-inhibitory molecules and play a fundamental role in keeping the equilibrium of the immune system. Cytotoxic T-lymphocyte antigen-4 (CTLA-4) and Programmed death-ligand 1 (PD-L1), as inhibitory immune checkpoints, participate in terminating the development of numerous autoimmune diseases, including MS. We assessed the CTLA-4 and PD-L1 gene expression in the different cell types of peripheral blood mononuclear cells of MS patients using single-cell RNA-seq data. Additionally, this study outlines how CTLA-4 and PD-L1 expression was altered in the PBMC samples of relapsing-remitting multiple sclerosis (RRMS) patients compared to the healthy group. Finally, it investigates the impact of various MS-related treatments in the CTLA-4 and PD-L1 expression to restrain autoreactive T cells and stop the development of MS autoimmunity.

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