Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “disease”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 253 records · Page 14
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Impacts of Vaccination and Severe Acute Respiratory Syndrome Coronavirus 2 Variants Alpha and Delta on Coronavirus Disease 2019 Transmission Dynamics in Four Metropolitan Areas of the United States

To characterize Coronavirus Disease 2019 (COVID-19) transmission dynamics in each of the metropolitan statistical areas (MSAs) surrounding Dallas, Houston, New York City, and Phoenix in 2020 and 2021, we extended a previously reported compartmental model accounting for effects of multiple distinct periods of non-pharmaceutical interventions by adding consideration of vaccination and Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) variants Alpha (lineage B.1.1.7) and Delta (lineage B.1.617.2). For each MSA, we found region-specific parameterizations of the model using daily reports of new COVID-19 cases available from January 21, 2020 to October 31, 2021. In the process, we obtained estimates of the relative infectiousness of Alpha and Delta as well as their takeoff times in each MSA (the times at which sustained transmission began). The estimated infectiousness of Alpha ranged from 1.1x to 1.4x that of viral strains circulating in 2020 and early 2021. The estimated relative infectiousness of Delta was higher in all cases, ranging from 1.6x to 2.1x. The estimated Alpha takeoff times ranged from February 1 to February 28, 2021. The estimated Delta takeoff times ranged from June 2 to June 26, 2021. In conclusion, estimated takeoff times are consistent with genomic surveillance data.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

Verifying infectious disease scenario planning for geographically diverse populations

In the face of the COVID-19 pandemic, the literature saw a spike in publications for epidemic models, and a renewed interest in capturing contact networks and geographic movement of populations. There remains a general lack of consensus in the modeling community around best practices for spatiotemporal epi-modeling, specifically as it pertains to the infection rate formulation and the underlying contact or mixing model. We mathematically verify several common modeling assumptions in the literature, to prove when certain choices can provide consistent results across different geographic resolutions, population densities and patterns, and mixing assumptions. The most common infection rate formulation, a computationally low cost per capita infection rate assumption, fails the consistency tests for heterogeneous populations and gravity-weighting assumptions. Future modeling efforts in spatiotemporal disease modeling should be wary of this limitation, particularly when working with more heterogeneous or sparse populations. Our results provide guidance for testing that a model preserves desirable properties even when model inputs mask potential problems due to symmetry or homogeneity. We also provide a recipe for performing this type of verification, strengthening decision support tools.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Modulation of Neuroinflammation by Low-Dose Radiation Therapy in an Animal Model of Alzheimer's Disease

Recently, several studies have reported that low-dose radiation therapy (RT) suppresses the release of proinflammatory cytokines in inflammatory-degenerative disorders, including Alzheimer disease (AD). AD is the most common cause of dementia, and neuroinflammation is one of the major contributing factors in AD pathogenesis. Therefore, low-dose RT may be used clinically for treating AD. However, the appropriate doses, effects, and underlying mechanisms of RT in AD have not been determined. In this study, we aimed to determine the appropriate RT dose and schedule for AD treatment and to investigate the therapeutic effects and mechanisms of low-dose RT in AD.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2021

Radiation Toxicity in Patients With Collagen Vascular Disease: A Meta-Analysis of Case-Control Studies

Several retrospective series have reported that patients with collagen vascular disease (CVD) are at increased risk of radiation (RT) toxicity. However, the evidence is mixed, and many series lack control groups. We performed a meta-analysis including only case-cohort or randomized studies that examined the risk of RT toxicity for patients with CVD compared with controls.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2025

Lipid and lipoprotein metabolism in microglia: Alzheimer’s disease mechanisms and interventions

Alzheimer's disease (AD) presents a significant challenge owing to its widespread prevalence and complex neuropathogenesis, affecting millions worldwide. Current therapeutic strategies that predominantly target amyloid-beta accumulation are insufficient, particularly for ApoE4 carriers. Alterations in lipid composition are well documented in AD, characterized by reductions in phospholipids and sulfatides, along with increases in cholesterol, cholesteryl esters, and triglycerides (TGs). Microglia, the brain's resident immune cells, link dysfunctional lipid processing to AD neuropathogenesis. For example, genetic studies have pointed to microglial lipid and lipoprotein processing gene variants as some of the strongest risk factors for AD. In addition, microglial dysfunction, characterized by lipid droplet accumulation, increased cholesterol and TG levels, and altered lipid transport, may exacerbate the pathological hallmarks of AD, such as amyloid-beta and tau accumulation. Conversely, emerging studies have shown that strategies aimed at inhibiting lipid droplet accumulation in microglia, reducing TG synthesis, and promoting the activity of lipoprotein receptors expressed by microglia can improve cell functions and markers of AD pathology. This review dissects the interplay between microglial lipid metabolism and AD, highlighting the significance of lipid transport and trafficking within the CNS. Given the intrinsic link between microglial metabolism and AD progression, emerging and potential therapeutic strategies aimed at restoring lipid handling and improving microglial function are explored. This review provides a comprehensive examination of the emerging literature, detailing the current state of knowledge on microglial lipid metabolism, its genetic underpinnings, and the potential for novel interventions targeting these mechanisms to ameliorate AD pathology.

Alzheimer's disease↗
DOE OSTI2025

Discovery of Proteoforms Associated With Alzheimer's Disease Through Quantitative Top-Down Proteomics

The complex nature of Alzheimer's disease (AD) and its heterogenous clinical presentation has prompted numerous large-scale - omic analyses aimed at providing a global understanding of the pathophysiological processes involved. AD involves isoforms, proteolytic products, and posttranslationally modified proteins such as amyloid beta (Aβ) and microtubule-associated protein tau. Top-down proteomics directly measures these species and thus, offers a comprehensive view of pathologically relevant proteoforms that are difficult to analyze using traditional proteomic techniques. Here, we broadly explored associations between proteoforms and clinicopathological traits of AD by deploying a quantitative top-down proteomics approach across frontal cortex of 103 subjects selected from the ROS and MAP cohorts. The approach identified 1213 proteins and 11,782 proteoforms, of which 154 proteoforms had at least one significant association with a clinicopathological phenotype. One important finding included identifying Aβ C-terminal truncation state as the key property for differential association between amyloid plaques and cerebral amyloid angiopathy. Furthermore, various N-terminally truncated forms of Aβ had noticeably stronger association with amyloid plaques and global cognitive function. Additionally, we discovered six VGF neuropeptides that were positively associated with cognitive function independent of pathological burden. The database of brain cortex proteoforms provides a valuable context for functional characterization of the proteins involved in AD and other late-onset brain pathologies.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Bivalent hemagglutinin and neuraminidase influenza replicon particle vaccines protect pigs against influenza a virus without causing vaccine associated enhanced respiratory disease

Alphavirus-derived RNA replicon particle (RP) vaccines represent the next generation of swine influenza A virus (IAV) vaccines, as they were shown to be safe, effective, and offer advantages over traditional vaccine platforms. IAV is a significant respiratory pathogen of swine and there is a critical need to improve current commercial swine IAV vaccine platforms. Adjuvanted whole inactivated virus (WIV) IAV swine vaccines provide limited heterologous protection and may lead to vaccine-associated enhanced respiratory disease (VAERD). This study investigated the ability of RP IAV hemagglutinin (HA) vaccines to avoid VAERD and evaluated experimental multivalent HA and neuraminidase (NA) RP vaccines. RP vaccines were formulated with HA or NA heterologous or homologous to the challenge virus in monovalent HA or HA and NA bivalent combinations (HA/NA bivalent). Pigs were vaccinated with an HA RP, HA/NA bivalent RP, or heterologous HA WIV, followed by IAV challenge and necropsy 5 days post infection. RP vaccines provided homologous protection from challenge and induced robust peripheral and local antibody responses. The RP vaccine did not induce VAERD after challenge with a virus containing the heterologous HA, in contrast to the traditional WIV vaccine. The HA monovalent and HA/NA bivalent RP vaccines showed superior protection compared to traditional WIV. Additionally, the RP platform allows greater flexibility to adjust HA and NA content to reflect circulating IAV in swine antigenic diversity.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Human umbilical cord mesenchymal stem cells regulate CD54 and CD105 in vascular endothelial cells and suppress inflammation in Kawasaki disease

Highlights: • CD31{sup +}CD54{sup +} and CD31{sup +}CD105{sup +} EMPs change inconsistently at different stages of KD, potentially reflect endothelial function. • HucMSCs suppress inflammation and regulate the expression levels of CD54 and CD105 in vascular endothelial cells in KD. • HucMSCs inhibit coronary artery inflammation while also promoting the repair of damaged tissues in KD mice. The objective was to evaluate the expression levels of CD31{sup +}CD54{sup +} and CD31{sup +}CD105{sup +} endothelial microparticles (EMPs) before and after intravenous immunoglobulin (IVIG) treatment of Kawasaki disease (KD). To explore the role of human umbilical cord mesenchymal stem cells (hucMSCs) in inhibiting endothelial inflammation in KD, the effects of hucMSCs on the expression of CD54 and CD105 in endothelial cells in KD were analyzed in vivo and in vitro.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

A Copper-Binding Peptide with Therapeutic Potential against Alzheimer′s Disease: From the Blood–Brain Barrier to Metal Competition

Alzheimer’s disease (AD) is the most common form of dementia worldwide. AD brains are characterized by the accumulation of amyloid-β peptides (Aβ) that bind Cu 2+ and have been associated with several neurotoxic mechanisms. Although the use of copper chelators to prevent the formation of Cu 2+ -Aβ complexes has been proposed as a therapeutic strategy, recent studies show that copper is an important neuromodulator that is essential for a neuroprotective mechanism mediated by Cu 2+ binding to the cellular prion protein (PrPC). Therefore, in addition to metal selectivity and blood–brain barrier (BBB) permeability, an emerging challenge for copper chelators is to prevent the formation of neurotoxic Cu 2+ -Aβ species without perturbing the neuroprotective Cu 2+ -PrPC interaction. Previously, we reported the design of a tetrapeptide (TP) that withdraws Cu 2+ from Aβ(1–16) and impacts the Cu 2+ -induced aggregation of Aβ(1–40). In this study, we improved the drug-like properties of TP in a BBB model, evaluated the metal selectivity of the optimized peptide (TP*), and tested its effect on Cu 2+ coordination to PrPC and proteins involved in copper trafficking, such as copper transporter 1 and albumin. Our results show that changing the stereochemistry of the first residue prevents TP degradation in the BBB model and coadministration of TP with a peptide that increases BBB permeability allows its passage through the BBB model. TP* is highly selective toward Cu 2+ in the presence of Zn 2+ ions, transfers Cu 2+ to copper-trafficking proteins, and forms a ternary TP*-Cu 2+ -PrP species that does not perturb the physiological conformation of PrP and displays only a minor impact in the neuroprotective Cu 2+ -dependent interaction of PrPC with the N-methyl-d-aspartate receptor. Overall, these results show that TP* displays desirable features for a copper chelator with therapeutic potential against AD. Moreover, this is the first study that explores the effect of a Cu 2+ chelator with therapeutic potential for AD on Cu 2+ coordination to PrPC (an emerging key player in AD pathology), integrating recent knowledge about metalloproteins involved in AD with the design of copper chelators against AD.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

A simple model of COVID-19 explains disease severity and the effect of treatments

Abstract Considerable effort has been made to better understand why some people suffer from severe COVID-19 while others remain asymptomatic. This has led to important clinical findings; people with severe COVID-19 generally experience persistently high levels of inflammation, slower viral load decay, display a dysregulated type-I interferon response, have less active natural killer cells and increased levels of neutrophil extracellular traps. How these findings are connected to the pathogenesis of COVID-19 remains unclear. We propose a mathematical model that sheds light on this issue by focusing on cells that trigger inflammation through molecular patterns: infected cells carrying pathogen-associated molecular patterns (PAMPs) and damaged cells producing damage-associated molecular patterns (DAMPs). The former signals the presence of pathogens while the latter signals danger such as hypoxia or lack of nutrients. Analyses show that SARS-CoV-2 infections can lead to a self-perpetuating feedback loop between DAMP expressing cells and inflammation, identifying the inability to quickly clear PAMPs and DAMPs as the main contributor to hyperinflammation. The model explains clinical findings and reveal conditions that can increase the likelihood of desired clinical outcome from treatment administration. In particular, the analysis suggest that antivirals need to be administered early during infection to have an impact on disease severity. The simplicity of the model and its high level of consistency with clinical findings motivate its use for the formulation of new treatment strategies.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

Machine learning prediction of incidence of Alzheimer’s disease using large-scale administrative health data

Nationwide population-based cohort provides a new opportunity to build an automated risk prediction model based on individuals’ history of health and healthcare beyond existing risk prediction models. We tested the possibility of machine learning models to predict future incidence of Alzheimer’s disease (AD) using large-scale administrative health data. From the Korean National Health Insurance Service database between 2002 and 2010, we obtained de-identified health data in elders above 65 years (N = 40,736) containing 4,894 unique clinical features including ICD-10 codes, medication codes, laboratory values, history of personal and family illness and socio-demographics. To define incident AD we considered two operational definitions: “definite AD” with diagnostic codes and dementia medication (n = 614) and “probable AD” with only diagnosis (n = 2026). We trained and validated random forest, support vector machine and logistic regression to predict incident AD in 1, 2, 3, and 4 subsequent years. For predicting future incidence of AD in balanced samples (bootstrapping), the machine learning models showed reasonable performance in 1-year prediction with AUC of 0.775 and 0.759, based on “definite AD” and “probable AD” outcomes, respectively; in 2-year, 0.730 and 0.693; in 3-year, 0.677 and 0.644; in 4-year, 0.725 and 0.683. The results were similar when the entire (unbalanced) samples were used. Important clinical features selected in logistic regression included hemoglobin level, age and urine protein level. This study may shed a light on the utility of the data-driven machine learning model based on large-scale administrative health data in AD risk prediction, which may enable better selection of individuals at risk for AD in clinical trials or early detection in clinical settings.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Uncovering heterogeneous intercommunity disease transmission from neutral allele frequency time series

The COVID-19 pandemic has underscored the need for accurate epidemic forecasting to predict pathogen spread, evolution, and evaluate intervention strategies. Forecast reliability hinges on detailed knowledge of disease transmission across population segments, which may be inferred from contact surveys or mobility data. However, these indirect approaches make it difficult to estimate rare transmissions between socially or geographically distant communities. We show that the steep ramp-up of genome sequencing surveillance during the pandemic can be leveraged to directly identify transmission patterns between geographically defined communities. Our approach uses a hidden Markov model to infer the fraction of infections a community imports from others based on how rapidly allele frequencies in the focal community converge to those in the donor communities. Applying this method to SARS-CoV-2 sequencing data from England and the United States, we uncover networks of intercommunity transmission that reflect geographical relationships while exposing significant long-range interactions. The scaling of importation rate with distance is consistent across both countries, yet weaker than expected based on mobility data, highlighting limitations of indirect inference. We show that transmission patterns can change between waves of variants of concern and analyze how the inferred heterogeneity in intercommunity transmission impacts evolutionary forecasts. While applied here to geographically defined communities, our approach could be applied to those defined by other traits (e.g., age, socioeconomic status), provided time-series data can be stratified accordingly. Overall, our study highlights population genomic time series data as a crucial record of epidemiological interactions, which can be deciphered using tree-free inference methods.

Okada, Takashi [Department of Physics; University ↗
DOE OSTI2023

Characterization of Humoral Responses to Nipah Virus Infection in the Syrian Hamster Model of Disease

Abstract Nipah virus (NiV) is a highly pathogenic paramyxovirus. The Syrian hamster model recapitulates key features of human NiV disease and is a critical tool for evaluating antivirals and vaccines. Here we describe longitudinal humoral immune responses in NiV-infected Syrian hamsters. Samples were obtained 1–28 days after infection and analyzed by ELISA, neutralization, and Fc-mediated effector function assays. NiV infection elicited robust antibody responses against the nucleoprotein and attachment glycoprotein. Levels of neutralizing antibodies were modest and only detectable in surviving animals. Fc-mediated effector functions were mostly observed in nucleoprotein-targeting antibodies. Antibody levels and activities positively correlated with challenge dose.

Immunology↗
DOE OSTI2023

NREM sleep as a novel protective cognitive reserve factor in the face of Alzheimer's disease pathology

Alzheimer’s disease (AD) pathology impairs cognitive function. Yet some individuals with high amounts of AD pathology suffer marked memory impairment, while others with the same degree of pathology burden show little impairment. Why is this? One proposed explanation is cognitive reserve i.e., factors that confer resilience against, or compensation for the effects of AD pathology. Deep NREM slow wave sleep (SWS) is recognized to enhance functions of learning and memory in healthy older adults. However, that the quality of NREM SWS (NREM slow wave activity, SWA) represents a novel cognitive reserve factor in older adults with AD pathology, thereby providing compensation against memory dysfunction otherwise caused by high AD pathology burden, remains unknown. Here, we tested this hypothesis in cognitively normal older adults (N = 62) by combining 11 C-PiB (Pittsburgh compound B) positron emission tomography (PET) scanning for the quantification of β-amyloid (Aβ) with sleep electroencephalography (EEG) recordings to quantify NREM SWA and a hippocampal-dependent face-name learning task. We demonstrated that NREM SWA significantly moderates the effect of Aβ status on memory function. Specifically, NREM SWA selectively supported superior memory function in individuals suffering high Aβ burden, i.e., those most in need of cognitive reserve (B = 2.694, p = 0.019). In contrast, those without significant Aβ pathological burden, and thus without the same need for cognitive reserve, did not similarly benefit from the presence of NREM SWA (B = -0.115, p = 0.876). This interaction between NREM SWA and Aβ status predicting memory function was significant after correcting for age, sex, Body Mass Index, gray matter atrophy, and previously identified cognitive reserve factors, such as education and physical activity (p = 0.042). These findings indicate that NREM SWA is a novel cognitive reserve factor providing resilience against the memory impairment otherwise caused by high AD pathology burden. Furthermore, this cognitive reserve function of NREM SWA remained significant when accounting both for covariates, and factors previously linked to resilience, suggesting that sleep might be an independent cognitive reserve resource. Beyond such mechanistic insights are potential therapeutic implications. Unlike many other cognitive reserve factors (e.g., years of education, prior job complexity), sleep is a modifiable factor. As such, it represents an intervention possibility that may aid the preservation of cognitive function in the face of AD pathology, both present moment and longitudinally.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

Investigating the clinico-anatomical dissociation in the behavioral variant of Alzheimer disease

We previously found temporoparietal-predominant atrophy patterns in the behavioral variant of Alzheimer’s disease (bvAD), with relative sparing of frontal regions. Here, we aimed to understand the clinico-anatomical dissociation in bvAD based on alternative neuroimaging markers. We retrospectively included 150 participants, including 29 bvAD, 28 “typical” amnestic-predominant AD (tAD), 28 behavioral variant of frontotemporal dementia (bvFTD), and 65 cognitively normal participants. Patients with bvAD were compared with other diagnostic groups on glucose metabolism and metabolic connectivity measured by [ 18 F]FDG-PET, and on subcortical gray matter and white matter hyperintensity (WMH) volumes measured by MRI. A receiver-operating-characteristic-analysis was performed to determine the neuroimaging measures with highest diagnostic accuracy. bvAD and tAD showed predominant temporoparietal hypometabolism compared to controls, and did not differ in direct contrasts. However, overlaying statistical maps from contrasts between patients and controls revealed broader frontoinsular hypometabolism in bvAD than tAD, partially overlapping with bvFTD. bvAD showed greater anterior default mode network (DMN) involvement than tAD, mimicking bvFTD, and reduced connectivity of the posterior cingulate cortex with prefrontal regions. Analyses of WMH and subcortical volume showed closer resemblance of bvAD to tAD than to bvFTD, and larger amygdalar volumes in bvAD than tAD respectively. The top-3 discriminators for bvAD vs. bvFTD were FDG posterior-DMN-ratios (bvAD bvFTD, area under the curve [AUC] range 0.85–0.91, all p < 0.001). The top-3 for bvAD vs. tAD were amygdalar volume (bvAD>tAD), MRI anterior-DMN-ratios (bvAD<tAD), FDG anterior-DMN-ratios (bvAD<tAD, AUC range 0.71–0.84, all p < 0.05). Subtle frontoinsular hypometabolism and anterior DMN involvement may underlie the prominent behavioral phenotype in bvAD.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Optimizing Patient-Specific Medication Regimen Policies Using Wearable Sensors in Parkinson’s Disease

Effective treatment of Parkinson’s disease (PD) is a continual challenge for healthcare providers, and providers can benefit from leveraging emerging technologies to supplement traditional clinic care. We develop a data-driven reinforcement learning (RL) framework to optimize PD medication regimens through wearable sensors. We leverage a data set of n = 26 PD patients who wore wrist-mounted movement trackers for two separate six-day periods. Using these data, we first build and validate a simulation model of how individual patients’ movement symptoms respond to medication administration. We then pair this simulation model with an on-policy RL algorithm that recommends optimal medication types, timing, and dosages during the day while incorporating human-in-the-loop considerations on medication administration. The results show that the RL-prescribed medication regimens outperform physicians’ medication regimens, despite physicians having access to the same data as the RL agent. To validate our results, we assess our wearable-based RL medication regimens using n = 399 PD patients from the Parkinson’s Progression Markers Initiative data set. We show that the wearable-based RL medication regimens would lead to significant symptom improvement for these patients, even more so than training RL policies directly from this data set. In doing so, we show that RL models from even small data sets of wearable data can offer novel, generalizable clinical insights and medication strategies, which may outperform those derived from larger data sets without wearable data.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Interventions to Disrupt Coronavirus Disease Transmission at a University, Wisconsin, USA, August–October 2020

University settings have demonstrated potential for coronavirus disease (COVID-19) outbreaks; they combine congregate living, substantial social activity, and a young population predisposed to mild illness. Using genomic and epidemiologic data, we describe a COVID-19 outbreak at the University of Wisconsin-Madison, Madison, Wisconsin, USA. During August-October 2020, a total of 3,485 students, including 856/6,162 students living in dormitories, tested positive. Case counts began rising during move-in week, August 25–31, 2020, then rose rapidly during September 1–11, 2020. The university initiated multiple prevention efforts, including quarantining 2 dormitories; a subsequent decline in cases was observed. Genomic surveillance of cases from Dane County, in which the university is located, did not find evidence of transmission from a large cluster of cases in the 2 quarantined dorms during the outbreak. Coordinated implementation of prevention measures can reduce COVID-19 spread in university settings and may limit spillover to the surrounding community.

60 APPLIED LIFE SCIENCES↗