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At least 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2015

CFD Modeling and Image Analysis of Exhaled Aerosols due to a Growing Bronchial Tumor: towards Non-Invasive Diagnosis and Treatment of Respiratory Obstructive Diseases

Diagnosis and prognosis of tumorigenesis are generally performed with CT, PET, or biopsy. Such methods are accurate, but have the limitations of high cost and posing additional health risks to patients. In this study, we introduce an alternative computer aided diagnostic tool that can locate malignant sites caused by tumorigenesis in a non-invasive and low-cost way. Our hypothesis is that exhaled aerosol distribution is unique to lung structure and is sensitive to airway structure vari-ations. With appropriate approaches, it is possible to locate the disease site, determine the disease severity, and subsequently formulate a targeted drug delivery plan to treat the disease. This study numerically evaluated the feasibility of the proposed breath test in an image-based lung model with varying pathological stages of a bronchial squamous tumor. Large eddy simulations and a Lagran-gian tracking approach were used to model respiratory airflows and aerosol dynamics. Respira-tions of tracer aerosols of 1 µm at a flow rate of 20 L/min were simulated, with the distributions of exhaled aerosols recorded on a filter at the mouth exit. Aerosol patterns were quantified with multiple analytical techniques such as concentration disparity, spatial scanning and fractal analysis. We demonstrated that a growing bronchial tumor induced notable variations in both the airflow and exhaled aerosol distribution. These variations became more apparent with increasing tumor severity. The exhaled aerosols exhibited distinctive pattern parameters such as spatial probability, fractal dimension, and multifractal spectrum. Results of this study show that morphometric measures of the exhaled aerosol pattern can be used to detect and monitor the pathological states of respiratory diseases in the upper airway. The proposed breath test also has the potential to locate the site of the disease, which is critical in developing a personalized, site-specific drug de-livery protocol.

Aerosol breath test↗
DOE OSTI2014

Disease Ontology 2015 update: An expanded and updated database of human diseases for linking biomedical knowledge through disease data

The current version of the Human Disease Ontology (DO) (http://www.disease-ontology.org) database expands the utility of the ontology for the examination and comparison of genetic variation, phenotype, protein, drug and epitope data through the lens of human disease. DO is a biomedical resource of standardized common and rare disease concepts with stable identifiers organized by disease etiology. The content of DO has had 192 revisions since 2012, including the addition of 760 terms. Thirty-two percent of all terms now include definitions. DO has expanded the number and diversity of research communities and community members by 50+ during the past two years. These community members actively submit term requests, coordinate biomedical resource disease representation and provide expert curation guidance. Since the DO 2012 NAR paper, there have been hundreds of term requests and a steady increase in the number of DO listserv members, twitter followers and DO website usage. DO is moving to a multi-editor model utilizing Protégé to curate DO in web ontology language. In conclusion, this will enable closer collaboration with the Human Phenotype Ontology, EBI's Ontology Working Group, Mouse Genome Informatics and the Monarch Initiative among others, and enhance DO's current asserted view and multiple inferred views through reasoning.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2019

Analytics for Investigation of Disease Outbreaks: Web-Based Analytics Facilitating Situational Awareness in Unfolding Disease Outbreaks

Background: Information from historical infectious disease outbreaks provides real-world data about outbreaks and their impacts on affected populations. These data can be used to develop a picture of an unfolding outbreak in its early stages, when incoming information is sparse and isolated, to identify effective control measures and guide their implementation. Objective: This study aimed to develop a publicly accessible Web-based visual analytic called Analytics for the Investigation of Disease Outbreaks (AIDO) that uses historical disease outbreak information for decision support and situational awareness of an unfolding outbreak. Methods: We developed an algorithm to allow the matching of unfolding outbreak data to a representative library of historical outbreaks. This process provides epidemiological clues that facilitate a user’s understanding of an unfolding outbreak and facilitates informed decisions about mitigation actions. Disease-specific properties to build a complete picture of the unfolding event were identified through a data-driven approach. A method of analogs approach was used to develop a short-term forecasting feature in the analytic. The 4 major steps involved in developing this tool were (1) collection of historic outbreak data and preparation of the representative library, (2) development of AIDO algorithms, (3) development of user interface and associated visuals, and (4) verification and validation. Results: The tool currently includes representative historical outbreaks for 39 infectious diseases with over 600 diverse outbreaks. We identified 27 different properties categorized into 3 broad domains (population, location, and disease) that were used to evaluate outbreaks across all diseases for their effect on case count and duration of an outbreak. Statistical analyses revealed disease-specific properties from this set that were included in the disease-specific similarity algorithm. Although there were some similarities across diseases, we found that statistically important properties tend to vary, even between similar diseases. This may be because of our emphasis on including diverse representative outbreak presentations in our libraries. AIDO algorithm evaluations (similarity algorithm and short-term forecasting) were conducted using 4 case studies and we have shown details for the Q fever outbreak in Bilbao, Spain (2014), using data from the early stages of the outbreak. Using data from only the initial 2 weeks, AIDO identified historical outbreaks that were very similar in terms of their epidemiological picture (case count, duration, source of exposure, and urban setting). The short-term forecasting algorithm accurately predicted case count and duration for the unfolding outbreak. Conclusions: AIDO is a decision support tool that facilitates increased situational awareness during an unfolding outbreak and enables informed decisions on mitigation strategies. AIDO analytics are available to epidemiologists across the globe with access to internet, at no cost. In this study, we presented a new approach to applying historical outbreak data to provide actionable information during the early stages of an unfolding infectious disease outbreak.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

A Comparison of Infectious Disease Forecasting Methods across Locations, Diseases, and Time

Accurate infectious disease forecasting can inform efforts to prevent outbreaks and mitigate adverse impacts. This study compares the performance of statistical, machine learning (ML), and deep learning (DL) approaches in forecasting infectious disease incidences across different countries and time intervals. We forecasted three diverse diseases: campylobacteriosis, typhoid, and Q-fever, using a wide variety of features (n = 46) from public datasets, e.g., landscape, climate, and socioeconomic factors. We compared autoregressive statistical models to two tree-based ML models (extreme gradient boosted trees [XGB] and random forest [RF]) and two DL models (multi-layer perceptron and encoder–decoder model). The disease models were trained on data from seven different countries at the region-level between 2009–2017. Forecasting performance of all models was assessed using mean absolute error, root mean square error, and Poisson deviance across Australia, Israel, and the United States for the months of January through August of 2018. The overall model results were compared across diseases as well as various data splits, including country, regions with highest and lowest cases, and the forecasted months out (i.e., nowcasting, short-term, and long-term forecasting). Overall, the XGB models performed the best for all diseases and, in general, tree-based ML models performed the best when looking at data splits. There were a few instances where the statistical or DL models had minutely smaller error metrics for specific subsets of typhoid, which is a disease with very low case counts. Feature importance per disease was measured by using four tree-based ML models (i.e., XGB and RF with and without region name as a feature). The most important feature groups included previous case counts, region name, population counts and density, mortality causes of neonatal to under 5 years of age, sanitation factors, and elevation. This study demonstrates the power of ML approaches to incorporate a wide range of factors to forecast various diseases, regardless of location, more accurately than traditional statistical approaches.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Advocating for Coccidioidomycosis to Be a Reportable Disease Nationwide in the United States and Encouraging Disease Surveillance across North and South America

Coccidioidomycosis (Valley fever) has been a known health threat in the United States (US) since the 1930s, though not all states are currently required to report disease cases. Texas, one of the non-reporting states, is an example of where both historical and contemporary scientific evidence define the region as endemic, but we don’t know disease incidence in the state. Mandating coccidioidomycosis as a reportable disease across more US states would increase disease awareness, improve clinical outcomes, and help antifungal drug and vaccine development. It would also increase our understanding of where the disease is endemic and the relationships between environmental conditions and disease cases. This is true for other nations in North and South America that are also likely endemic for coccidioidomycosis, especially Mexico. This commentary advocates for US state and territory epidemiologists to define coccidioidomycosis as a reportable disease and encourages disease surveillance in other endemic regions across North and South America in order to protect human health and reduce disease burden.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2017

Established patterns of animal study design undermine translation of disease-modifying therapies for Parkinson’s disease

Translation of disease-modifying therapies in neurodegenerative disease has been disappointing. Parkinson’s disease (PD) was used to compare patterns of preclinical study design for symptomatic and potentially disease-modifying interventions. We examined the relationship of model, intervention type and timing, outcomes and outcome measures in 543 animal and human studies (1973–2015) across a contemporary cohort of animal and human interventional studies (n = 445), animal studies for approved interventions (n = 28), animal and human studies for those that failed to translate (n = 70). Detailed study design data were collected for 216 studies in non-human primate (NHP) and rodent toxin-induced models. Species-specific patterns of study design prevailed regardless of whether interventions were symptomatic or potentially disease-modifying. In humans and NHPs, interventions were typically given to both sexes well after the PD phenotype was established, and clinical outcome measures were collected at single (symptomatic) or multiple (disease-modifying) time-points. In rodents, interventions often preceded induction of the model, acute toxic protocols were common, usually given to young males, clinical outcome measures were used less commonly, and outcomes were less commonly assessed at multiple time points. These patterns were more prevalent in mice than rats. In contrast, study design factors such as randomization and blinding did not differ appreciably across symptomatic and disease-modifying intervention categories. The translational gap for potentially disease-modifying interventions in PD in part results from study designs, particularly in mice, that fail to model the progressive nature and relatively late intervention characteristic of PD, or that anchor mechanistic and neuropathologic data to longitudinal clinical outcomes. Even if measures to improve reproducibility are broadly adopted, perpetuation of these norms will continue to impede effective translation.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Analysis of the first ten years of FDA’s rare pediatric disease priority review voucher program: designations, diseases, and drug development

Abstract Background The Rare Pediatric Disease (RPD) Priority Review Voucher (PRV) Program was enacted in 2012 to support the development of new products for children. Prior to requesting a voucher, applicants can request RPD designation, which confirms their product treats or prevents a rare disease in which the serious manifestations primarily affect children. This study describes the trends and characteristics of these designations. Details of RPD designations are not publicly disclosable; this research represents the first analysis of the RPD designation component of the program. Results We used an internal US Food and Drug Administration database to analyze all RPD designations between 2013 and 2022. Multiple characteristics were analyzed, including the diseases targeted by RPD designation, whether the product targeted a neonatal disease, product type (drug/biologic), and the level of evidence (preclinical/clinical) to support designation. There were 569 RPD designations during the study period. The top therapeutic areas were neurology (26%, n = 149), metabolism (23%, n = 131), oncology (18%, n = 105). The top diseases targeted by RPD designation were Duchenne muscular dystrophy, neuroblastoma, and sickle cell disease. Neonatology products represented 6% (n = 33), over half were for drug products and 38% were supported by clinical data. Conclusions The RPD PRV program was created to encourage development of new products for children. The results of this study establish that a wide range of diseases have seen development—from rare pediatric cancers to rare genetic disorders. Continued support of product development for children with rare diseases is needed to find treatments for all children with unmet needs.

Mease, Catherine (ORCID:000900050156198X)↗
DOE OSTI2012

Analysis of Dose Response for Circulatory Disease After Radiotherapy for Benign Disease

Purpose: To assess the shape of the dose-response for various circulatory disease endpoints, and modifiers by age and time since exposure. Methods and Materials: This was an analysis of the US peptic ulcer data testing for heterogeneity of radiogenic risk by circulatory disease endpoint (ischemic heart, cerebrovascular, other circulatory disease). Results: There were significant excess risks for all circulatory disease, with an excess relative risk Gy{sup -1} of 0.082 (95% CI 0.031-0.140), and ischemic heart disease, with an excess relative risk Gy{sup -1} of 0.102 (95% CI 0.039-0.174) (both p = 0.01), and indications of excess risk for stroke. There were no statistically significant (p > 0.2) differences between risks by endpoint, and few indications of curvature in the dose-response. There were significant (p < 0.001) modifications of relative risk by time since exposure, the magnitude of which did not vary between endpoints (p > 0.2). Risk modifications were similar if analysis was restricted to patients receiving radiation, although the relative risks were slightly larger and the risk of stroke failed to be significant. The slopes of the dose-response were generally consistent with those observed in the Japanese atomic bomb survivors and in occupationally and medically exposed groups. Conclusions: There were excess risks for a variety of circulatory diseases in this dataset, with significant modification of risk by time since exposure. The consistency of the dose-response slopes with those observed in radiotherapeutically treated groups at much higher dose, as well as in lower dose-exposed cohorts such as the Japanese atomic bomb survivors and nuclear workers, implies that there may be little sparing effect of fractionation of dose or low-dose-rate exposure.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2013

Anal Carcinoma: Impact of TN Category of Disease on Survival, Disease Relapse, and Colostomy Failure in US Gastrointestinal Intergroup RTOG 98-11 Phase 3 Trial

Purpose: The long-term update of US GI Intergroup RTOG 98-11 anal cancer trial found that concurrent chemoradiation (CCRT) with fluorouracil (5-FU) plus mitomycin had a significant impact on disease-free survival (DFS) and overall survival (OS) compared with induction plus concurrent 5-FU plus cisplatin. The intent of the current analysis was to determine the impact of tumor node (TN) category of disease on survival (DFS and OS), colostomy failure (CF), and relapse (local-regional failure [LRF] and distant metastases [DM]) in this patient group. Methods and Materials: DFS and OS were estimated univariately by using the Kaplan-Meier method, and 6 TN categories were compared by the log–rank test (T2N0, T3N0, T4N0, T2N1-3, T3N1-3, and T4N1-3). Time to relapse and colostomy were estimated by the cumulative incidence method, and TN categories were compared using Gray's test. Results: Of 682 patients, 620 were analyzable for outcomes by TN category. All endpoints showed statistically significant differences among the TN categories of disease (OS, P<.0001; DFS, P<.0001; LRF, P<.0001; DM, P=.0011; CF, P=.01). Patients with the poorest OS, DFS, and LRF outcomes were those with T3-4N-positive (+) disease. CF was lowest for T2N0 and T2N+ (11%, 11%, respectively) and worst for the T4N0, T3N+, and T4N+ categories (26%, 27%, 24%, respectively). Conclusions: TN category of disease has a statistically significant impact on OS, DFS, LRF, DM, and CF in patients treated with CCRT and provides excellent prognostic information for outcomes in patients with anal carcinoma. Significant challenges remain for patients with T4N0 and T3-4N+ categories of disease with regard to survival, relapse, and CF and lesser challenges for T2-3N0/T2N+ categories.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2025

Specific Bacterial Taxa and Their Metabolite, DHPS, May Be Linked to Gut Dyshomeostasis in Patients with Alzheimer’s Disease, Parkinson’s Disease, and Amyotrophic Lateral Sclerosis

Background: Neurodegenerative diseases (NDDs) are multifactorial disorders frequently associated with gut dysbiosis, oxidative stress, and inflammation; however, the pathophysiological mechanisms remain poorly understood. Methods: Using untargeted mass spectrometry-based metabolomics and 16S sequencing of human stool, we investigated bacterial and metabolic dyshomeostasis in the gut microbiome associated with early disease stages across three NDDs—amyotrophic lateral sclerosis (ALS), Alzheimer’s disease (AD), Parkinson’s disease (PD)—and healthy controls (HC). Results: We discovered a previously unrecognized link between a microbial-derived metabolite with an unknown role in human physiology, 2,3-dihydroxypropane-1-sulfonate (DHPS), and gut dysbiosis in NDDs. DHPS was downregulated in AD, ALS, and PD, while bacteria involved in DHPS metabolism, Eubacterium and Desulfovibrio, were increased in all disease cohorts. Additionally, select taxa within the Clostridia class had strong negative correlations to DHPS, suggesting a potential role in DHPS metabolism. A catabolic product of DHPS is hydrogen sulfide, and when in excess, it is known to promote inflammation, oxidative stress, mitochondrial damage, and gut dysbiosis, known hallmarks of NDDs. Conclusions: These findings suggest that cryptic sulfur metabolism via DHPS is a potential missing link in our current understanding of gut dysbiosis associated with NDD onset and progression. As this was a hypothesis generating study, more work is needed to elucidate the role of DHPS in gut dysbiosis and neurodegenerative diseases.

Nutrition & Dietetics↗
DOE OSTI2014

Disease Prediction Models and Operational Readiness

INTRODUCTION: The objective of this manuscript is to present a systematic review of biosurveillance models that operate on select agents and can forecast the occurrence of a disease event. One of the primary goals of this research was to characterize the viability of biosurveillance models to provide operationally relevant information for decision makers to identify areas for future research. Two critical characteristics differentiate this work from other infectious disease modeling reviews. First, we reviewed models that attempted to predict the disease event, not merely its transmission dynamics. Second, we considered models involving pathogens of concern as determined by the US National Select Agent Registry (as of June 2011). Methods: We searched dozens of commercial and government databases and harvested Google search results for eligible models utilizing terms and phrases provided by public health analysts relating to biosurveillance, remote sensing, risk assessments, spatial epidemiology, and ecological niche-modeling, The publication date of search results returned are bound by the dates of coverage of each database and the date in which the search was performed, however all searching was completed by December 31, 2010. This returned 13,767 webpages and 12,152 citations. After de-duplication and removal of extraneous material, a core collection of 6,503 items was established and these publications along with their abstracts are presented in a semantic wiki at http://BioCat.pnnl.gov. Next, PNNL’s IN-SPIRE visual analytics software was used to cross-correlate these publications with the definition for a biosurveillance model resulting in the selection of 54 documents that matched the criteria resulting Ten of these documents, However, dealt purely with disease spread models, inactivation of bacteria, or the modeling of human immune system responses to pathogens rather than predicting disease events. As a result, we systematically reviewed 44 papers and the results are presented in this analysis.

biosecurity↗
DOE OSTI2014

Differential Network Analyses of Alzheimer’s Disease Identify Early Events in Alzheimer’s Disease Pathology

In late-onset Alzheimer’s disease (AD), multiple brain regions are not affected simultaneously. Comparing the gene expression of the affected regions to identify the differences in the biological processes perturbed can lead to greater insight into AD pathogenesis and early characteristics. We identified differentially expressed (DE) genes from single cell microarray data of four AD affected brain regions: entorhinal cortex (EC), hippocampus (HIP), posterior cingulate cortex (PCC), and middle temporal gyrus (MTG). We organized the DE genes in the four brain regions into region-specific gene coexpression networks. Differential neighborhood analyses in the coexpression networks were performed to identify genes with low topological overlap (TO) of their direct neighbors. The low TO genes were used to characterize the biological differences between two regions. Our analyses show that increased oxidative stress, along with alterations in lipid metabolism in neurons, may be some of the very early events occurring in AD pathology. Cellular defense mechanisms try to intervene but fail, finally resulting in AD pathology as the disease progresses. Furthermore, disease annotation of the low TO genes in two independent protein interaction networks has resulted in association between cancer, diabetes, renal diseases, and cardiovascular diseases.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

COVID19 Disease Map, a computational knowledge repository of virus–host interaction mechanisms

We need to effectively combine the knowledge from surging literature with complex datasets to propose mechanistic models of SARS-CoV-2 infection, improving data interpretation and predicting key targets of intervention. Here, we describe a large-scale community effort to build an open access, interoperable and computable repository of COVID-19 molecular mechanisms. The COVID-19 Disease Map (C19DMap) is a graphical, interactive representation of disease-relevant molecular mechanisms linking many knowledge sources. Notably, it is a computational resource for graph-based analyses and disease modelling. To this end, we established a framework of tools, platforms and guidelines necessary for a multifaceted community of biocurators, domain experts, bioinformaticians and computational biologists. The diagrams of the C19DMap, curated from the literature, are integrated with relevant interaction and text mining databases. We demonstrate the application of network analysis and modelling approaches by concrete examples to highlight new testable hypotheses. This framework helps to find signatures of SARS-CoV-2 predisposition, treatment response or prioritisation of drug candidates. Such an approach may help deal with new waves of COVID-19 or similar pandemics in the long-term perspective.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2016

A customizable model for chronic disease coordination: Lessons learned from the coordinated chronic disease program

In 2012, the Centers for Disease Control and Prevention provided funding and technical assistance to all states and territories to implement the Coordinated Chronic Disease Program, marking the first time that all state health departments had federal resources to coordinate chronic disease prevention and control programs. This article describes lessons learned from this initiative and identifies key elements of a coordinated approach. We analyzed 80 programmatic documents from 21 states and conducted semistructured interviews with 7 chronic disease directors. Six overarching themes emerged: 1) focused agenda, 2) identification of functions, 3) comprehensive planning, 4) collaborative leadership and expertise, 5) managed resources, and 6) relationship building. Furthermore, these elements supported 4 essential activities: 1) evidence-based interventions, 2) strategic use of staff, 3) consistent communication, and 4) strong program infrastructure. On the basis of these elements and activities, we propose a conceptual model that frames overarching concepts, skills, and strategies needed to coordinate state chronic disease prevention and control programs.

60 APPLIED LIFE SCIENCES↗
NASA NTRS1995

Editing for an AMPA receptor subunit RNA in prefrontal cortex and striatum in Alzheimer's disease, Huntington's disease and schizophrenia

Animal studies and cell culture experiments demonstrated that posttranscriptional editing of the transcript of the GluR-2 gene, resulting in substitution of an arginine for glutamine in the second transmembrane region (TM II) of the expressed protein, is associated with a reduction in Ca2+ permeability of the receptor channel. Thus, disturbances in GluR-2 RNA editing with alteration of intracellular Ca2+ homeostasis could lead to neuronal dysfunction and even neuronal degeneration. The present study determined the proportions of edited and unedited GluR-2 RNA in the prefrontal cortex of brains from patients with Alzheimer's disease, in the striatum of brains from patients with Huntington's disease, and in the same areas of brains from age-matched schizophrenics and controls, by using reverse transcriptase-polymerase chain reaction, restriction endonuclease digestion, gel electrophoresis and scintillation radiometry. In the prefrontal cortex of controls, < 0.1% of all GluR-2 RNA molecules were unedited and > 99.9% were edited; in the prefrontal cortex both of schizophrenics and of Alzheimer's patients approximately 1.0% of all GluR-2 RNA molecules were unedited and 99% were edited. In the striatum of controls and of schizophrenics, approximately 0.5% of GluR-2 RNA molecules were unedited and 99.5% were edited; in the striatum of Huntington's patients nearly 5.0% of GluR-2 RNA was unedited. In the prefrontal white matter of controls, approximately 7.0% of GluR-2 RNA was unedited. In the normal human prefrontal cortex and striatum, the large majority of GluR-2 RNA molecules contains a CGG codon for arginine in the TMII coding region; this implies that the corresponding AMPA receptors have a low Ca2+ permeability, as previously demonstrated for the rat brain. The process of GluR-2 RNA editing is compromised in a region-specific manner in schizophrenia, in Alzheimer's disease and Huntington's Chorea although in each of these disorders there is still a large excess of edited GluR-2 RNA molecules. Disturbances of GluR-2 RNA editing leading to excessive Ca2+ permeability, may contribute to neuronal dysfunction in schizophrenia and to neuronal death in Alzheimer's disease and Huntington's disease.

NASA Discipline Neuroscience↗
DOE OSTI2017

An alpha-synuclein MRM assay with diagnostic potential for Parkinson's disease and monitoring disease progression

Aim: The alpha-synuclein (α-syn) level in human cerebrospinal fluid (CSF), as measured by immunoassays, is promising as a Parkinson’s disease (PD) biomarker. However, the levels of total α-syn are inconsistent among studies with large cohorts and different measurement platforms. Total α-syn level also does not correlate with disease severity or progression. Here, we developed a highly sensitive Multiple Reaction Monitoring (MRM) method to measure absolute CSF α-syn peptide concentrations without prior enrichment or fractionation, aiming to discover new candidate biomarkers. Results: Six peptides covering 73% of protein sequence were reliably identified, and two were consistently quantified in cross-sectional and longitudinal cohorts. Absolute concentration of α-syn in human CSF was determined to be 2.1ng/mL. A unique α-syn peptide, TVEGAGSIAAATGFVK (81-96), displayed excellent correlation with previous immunoassay results in two independent PD cohorts (p < 0.001), correlated with disease severity, and its changes significantly tracked the disease progression longitudinally. Conclusions: An MRM assay to quantify human CSF α-syn was developed and optimized. Sixty clinical samples from cross-sectional and longitudinal PD cohorts were analyzed with this approach. Although further larger-scale validation is needed, the results suggest that α-syn peptide could serve as a promising biomarker in PD diagnosis and progression.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2019

Amelioration of Alzheimer’s disease pathology and cognitive deficits by immunomodulatory agents in animal models of Alzheimer’s disease

The most common age-related neurodegenerative disease is Alzheimer’s disease (AD) characterized by aggregated amyloid-β (Aβ) peptides in extracellular plaques and aggregated hyperphosphorylated tau protein in intraneuronal neurofibrillary tangles, together with loss of cholinergic neurons, synaptic alterations, and chronic inflammation within the brain. These lead to progressive impairment of cognitive function. There is evidence of innate immune activation in AD with microgliosis. Classically-activated microglia (M1 state) secrete inflammatory and neurotoxic mediators, and peripheral immune cells are recruited to inflammation sites in the brain. The few drugs approved by the US FDA for the treatment of AD improve symptoms but do not change the course of disease progression and may cause some undesirable effects. Translation of active and passive immunotherapy targeting Aβ in AD animal model trials had limited success in clinical trials. Treatment with immunomodulatory/anti-inflammatory agents early in the disease process, while not preventive, is able to inhibit the inflammatory consequences of both Aβ and tau aggregation. The studies described in this review have identified several agents with immunomodulatory properties that alleviated AD pathology and cognitive impairment in animal models of AD. The majority of the animal studies reviewed had used transgenic models of early-onset AD. More effort needs to be given to create models of late-onset AD. The effects of a combinational therapy involving two or more of the tested pharmaceutical agents, or one of these agents given in conjunction with one of the cell-based therapies, in an aged animal model of AD would warrant investigation.

60 APPLIED LIFE SCIENCES↗