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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.

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At least 577 records · Page 32
All sourcesNASA NTRSDOE OSTI
NASA NTRS2010

Next Generation Respiratory Viral Vaccine System: Advanced and Emerging Bioengineered Human Lung Epithelia Model (HLEM) Organoid Technology

Acute respiratory infections, including pneumonia and influenza, are the S t" leading cause of United States and worldwide deaths. Newly emerging pathogens signaled the need for an advanced generation of vaccine technology.. Human bronchial-tracheal epithelial tissue was bioengineered to detect, identify, host and study the pathogenesis of acute respiratory viral disease. The 3-dimensional (3D) human lung epithelio-mesechymal tissue-like assemblies (HLEM TLAs) share characteristics with human respiratory epithelium: tight junctions, desmosomes, microvilli, functional markers villin, keratins and production of tissue mucin. Respiratory Syntial Virus (RSV) studies demonstrate viral growth kinetics and membrane bound glycoproteins up to day 20 post infection in the human lung-orgainoid infected cell system. Peak replication of RSV occurred on day 10 at 7 log10 particles forming units per ml/day. HLEM is an advanced virus vaccine model and biosentinel system for emergent viral infectious diseases to support DoD global surveillance and military readiness.

Goodwin, Thomas J.↗
DOE OSTI2021

Tumor Control Probability Modeling and Systematic Review of the Literature of Stereotactic Body Radiation Therapy for Prostate Cancer

Dose escalation improves localized prostate cancer disease control, and moderately hypofractionated external beam radiation is noninferior to conventional fractionation. The evolving treatment approach of ultrahypofractionation with stereotactic body radiation therapy (SBRT) allows possible further biological dose escalation (biologically equivalent dose [BED]) and shortened treatment time.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2023

Transmission risk of Oropouche fever across the Americas

Abstract Background Vector-borne diseases (VBDs) are important contributors to the global burden of infectious diseases due to their epidemic potential, which can result in significant population and economic impacts. Oropouche fever, caused by Oropouche virus (OROV), is an understudied zoonotic VBD febrile illness reported in Central and South America. The epidemic potential and areas of likely OROV spread remain unexplored, limiting capacities to improve epidemiological surveillance. Methods To better understand the capacity for spread of OROV, we developed spatial epidemiology models using human outbreaks as OROV transmission-locality data, coupled with high-resolution satellite-derived vegetation phenology. Data were integrated using hypervolume modeling to infer likely areas of OROV transmission and emergence across the Americas. Results Models based on one-support vector machine hypervolumes consistently predicted risk areas for OROV transmission across the tropics of Latin America despite the inclusion of different parameters such as different study areas and environmental predictors. Models estimate that up to 5 million people are at risk of exposure to OROV. Nevertheless, the limited epidemiological data available generates uncertainty in projections. For example, some outbreaks have occurred under climatic conditions outside those where most transmission events occur. The distribution models also revealed that landscape variation, expressed as vegetation loss, is linked to OROV outbreaks. Conclusions Hotspots of OROV transmission risk were detected along the tropics of South America. Vegetation loss might be a driver of Oropouche fever emergence. Modeling based on hypervolumes in spatial epidemiology might be considered an exploratory tool for analyzing data-limited emerging infectious diseases for which little understanding exists on their sylvatic cycles. OROV transmission risk maps can be used to improve surveillance, investigate OROV ecology and epidemiology, and inform early detection.

60 APPLIED LIFE SCIENCES↗
NASA NTRS2010

Placental Induced Growth Factor (PIGf) in Coronary Artery Disease

Our previous studies on normal human lymphocytes have shown a five-fold increase (p less than 0.001) in angiogenic inducers such as Placental Induced Growth Factor (PIGf) in physiologically stressful environments such as modeled microgravity, a space analog. This suggests de-regulation of cardiovascular signalling pathways indicated by upregulation of PIGf. In the current study, we measured PIGf in the plasma of 33 patients with and without coronary artery disease (CAD) to investigate whether such disease is associated with increased levels of PIGf. A control consisting of 31 sex matched apparently healthy subjects was also included in the study. We observed that the levels of PIGf in CAD patients were significantly increased compared to those in healthy control subjects (p less than 0.001) and usually increased beyond the clinical threshold level (greater than 27ng/L). The mechanisms leading to up-regulation of angiogenic factors and the adaptation of organisms to stressful environments such as isolation, high altitude, hypoxia, ischemia, microgravity, increased radiation, etc are presently unknown and require further investigation in spaceflight and these other physiologically stressed environments.

Sundaresan, Alamelu↗
DOE OSTI2023

In-silico quantification of intersubject variability on aerosol deposition in the oral airway

The extrathoracic oral airway I s not only a major mechanical barrier for pharmaceutical aerosols to reach the lung but also a major source of variability in lung deposition. Using computational fluid dynamics, deposition of 1-30 µm particles was predicted in 11 CT-based models of the oral airways of adults (7 healthy subjects and 4 patients with chronic obstructive pulmonary disease). Simulations were performed for mouth breathing during both inspiration and expiration at two steady-state flow rates representative of resting/light activity (18 L/min) and of that typically used during aerosol inhalation from a dry powder inhaler (45 L/min). For each subject, oral deposition increased with increasing particle size and flow rate. When averaged over all subjects, there was no significant difference in oral deposition efficiency between inspiration and expiration, unlike subregional deposition that show significantly different patterns between the two breathing phases. Consistent with previous in-vitro inspiratory studies, our data showed a large intersubject variability in oral deposition. These results highlight the need for incorporating morphological variation of the upper airway in predictive models of aerosol deposition for accurate predictions of particle dosimetry in the intrathoracic region of the lung.

Computational fluid dynamics (CFD), Inertial impac↗
DOE OSTI2026

Beyond microbial abundance: metadata integration enhances disease prediction in human microbiome studies

Multiple studies have highlighted the interaction of the human microbiome with physiological systems such as the gut, immune, liver, and skin, via key axes. Advances in sequencing technologies and high-performance computing have enabled the analysis of large-scale metagenomic data, facilitating the use of machine learning to predict disease likelihood from microbiome profiles. However, challenges such as compositionality, high dimensionality, sparsity, and limited sample sizes have hindered the development of actionable models. One strategy to improve these models is by incorporating key metadata from both the human host and sample collection/processing protocols. This remains challenging due to sparsity and inconsistency in metadata annotation and availability. In this paper, we introduce a machine learning-based pipeline for predicting human disease states by integrating host and protocol metadata with microbiome abundance profiles from 68 different studies, processed through a consistent pipeline. Our findings indicate that metadata can enhance machine learning predictions, particularly at higher taxonomic ranks like Kingdom and Phylum, though this effect diminishes at lower ranks. Our study leverages a large collection of microbiome datasets comprising 11,208 samples, therefore enhancing the robustness and statistical confidence of our findings. This work is a critical step toward utilizing microbiome and metadata for predicting diseases such as gastrointestinal infections, diabetes, cancer, and neurological disorders.

Mathematics and Computing↗
DOE OSTI2023

Structure-informed clustering for population stratification in association studies

Background: Identifying variants associated with complex traits is a challenging task in genetic association studies due to linkage disequilibrium (LD) between genetic variants and population stratification, unrelated to the disease risk. Existing methods of population structure correction use principal component analysis or linear mixed models with a random effect when modeling associations between a trait of interest and genetic markers. However, due to stringent significance thresholds and latent interactions between the markers, these methods often fail to detect genuinely associated variants. Results: To overcome this, we propose CluStrat, which corrects for complex arbitrarily structured populations while leveraging the linkage disequilibrium induced distances between genetic markers. It performs an agglomerative hierarchical clustering using the Mahalanobis distance covariance matrix of the markers. In simulation studies, we show that our method outperforms existing methods in detecting true causal variants. Applying CluStrat on WTCCC2 and UK Biobank cohorts, we found biologically relevant associations in Schizophrenia and Myocardial Infarction. CluStrat was also able to correct for population structure in polygenic adaptation of height in Europeans. Conclusions: CluStrat highlights the advantages of biologically relevant distance metrics, such as the Mahalanobis distance, which captures the cryptic interactions within populations in the presence of LD better than the Euclidean distance.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Using intrahost single nucleotide variant data to predict SARS-CoV-2 detection cycle threshold values

Over the last four years, each successive wave of the COVID-19 pandemic has been caused by variants with mutations that improve the transmissibility of the virus. Despite this, we still lack tools for predicting clinically important features of the virus. In this study, we show that it is possible to predict the PCR cycle threshold (Ct) values from clinical detection assays using sequence data. Ct values often correspond with patient viral load and the epidemiological trajectory of the pandemic. Using a collection of 36,335 high quality genomes, we built models from SARS-CoV-2 intrahost single nucleotide variant (iSNV) data, computing XGBoost models from the frequencies of A, T, G, C, insertions, and deletions at each position relative to the Wuhan-Hu-1 reference genome. Our best model had an R 2 of 0.604 [0.593–0.616, 95% confidence interval] and a Root Mean Square Error (RMSE) of 5.247 [5.156–5.337], demonstrating modest predictive power. Overall, we show that the results are stable relative to an external holdout set of genomes selected from SRA and are robust to patient status and the detection instruments that were used. This study highlights the importance of developing modeling strategies that can be applied to publicly available genome sequence data for use in disease prevention and control.

COVID19↗
NASA NTRS2003

Cell Science and Cell Biology Research at MSFC: Summary

The common theme of these research programs is that they investigate regulation of gene expression in cells, and ultimately gene expression is controlled by the macromolecular interactions between regulatory proteins and DNA. The NASA Critical Path Roadmap identifies Muscle Alterations and Atrophy and Radiation Effects as Very Serious Risks and Severe Risks, respectively, in long term space flights. The specific problem addressed by Dr. Young's research ("Skeletal Muscle Atrophy and Muscle Cell Signaling") is that skeletal muscle loss in space cannot be prevented by vigorous exercise. Aerobic skeletal muscles (i.e., red muscles) undergo the most extensive atrophy during long-term space flight. Of the many different potential avenues for preventing muscle atrophy, Dr. Young has chosen to study the beta-adrenergic receptor (betaAR) pathway. The reason for this choice is that a family of compounds called betaAR agonists will preferentially cause an increase in muscle mass of aerobic muscles (i.e., red muscle) in animals, potentially providing a specific pharmacological solution to muscle loss in microgravity. In addition, muscle atrophy is a widespread medical problem in neuromuscular diseases, spinal cord injury, lack of exercise, aging, and any disease requiring prolonged bedridden status. Skeletal muscle cells in cell culture are utilized as a model system to study this problem. Dr. Richmond's research ("Radiation & Cancer Biology of Mammary Cells in Culture") is directed toward developing a laboratory model for use in risk assessment of cancer caused by space radiation. This research is unique because a human model will be developed utilizing human mammary cells that are highly susceptible to tumor development. This approach is preferential over using animal cells because of problems in comparing radiation-induced cancers between humans and animals.

Source record↗
NASA NTRS2016

The Effect of Future Ambient Air Pollution on Human Premature Mortality to 2100 Using Output from the ACCMIP Model Ensemble

Ambient air pollution from ground-level ozone and fine particulate matter (PM(sub 2.5)) is associated with premature mortality. Future concentrations of these air pollutants will be driven by natural and anthropogenic emissions and by climate change. Using anthropogenic and biomass burning emissions projected in the four Representative Concentration Pathway scenarios (RCPs), the ACCMIP ensemble of chemistry climate models simulated future concentrations of ozone and PM(sub 2.5) at selected decades between 2000 and 2100. We use output from the ACCMIP ensemble, together with projections of future population and baseline mortality rates, to quantify the human premature mortality impacts of future ambient air pollution. Future air-pollution-related premature mortality in 2030, 2050 and 2100 is estimated for each scenario and for each model using a health impact function based on changes in concentrations of ozone and PM(sub 2.5) relative to 2000 and projected future population and baseline mortality rates. Additionally, the global mortality burden of ozone and PM(sub 2.5) in 2000 and each future period is estimated relative to 1850 concentrations, using present-day and future population and baseline mortality rates. The change in future ozone concentrations relative to 2000 is associated with excess global premature mortality in some scenarios/periods, particularly in RCP8.5 in 2100 (316 thousand deaths per year), likely driven by the large increase in methane emissions and by the net effect of climate change projected in this scenario, but it leads to considerable avoided premature mortality for the three other RCPs. However, the global mortality burden of ozone markedly increases from 382000 (121000 to 728000) deaths per year in 2000 to between 1.09 and 2.36 million deaths per year in 2100, across RCPs, mostly due to the effect of increases in population and baseline mortality rates. PM(sub 2.5) concentrations decrease relative to 2000 in all scenarios, due to projected reductions in emissions, and are associated with avoided premature mortality, particularly in 2100: between 2.39 and 1.31 million deaths per year for the four RCPs. The global mortality burden of PM(sub 2.5) is estimated to decrease from 1.70 (1.30 to 2.10) million deaths per year in 2000 to between 0.95 and 1.55 million deaths per year in 2100 for the four RCPs due to the combined effect of decreases in PM(sub 2.5) concentrations and changes in population and baseline mortality rates. Trends in future air-pollution-related mortality vary regionally across scenarios, reflecting assumptions for economic growth and air pollution control specific to each RCP and region. Mortality estimates differ among chemistry climate models due to differences in simulated pollutant concentrations, which is the greatest contributor to overall mortality uncertainty for most cases assessed here, supporting the use of model ensembles to characterize uncertainty. Increases in exposed population and baseline mortality rates of respiratory diseases magnify the impact on premature mortality of changes in future air pollutant concentrations and explain why the future global mortality burden of air pollution can exceed the current burden, even where air pollutant concentrations decrease.

Silva, Raquel A.↗
DOE OSTI2022

A Double-Pronged Sword: XJB-5-131 Is a Suppressor of Somatic Instability and Toxicity in Huntington’s Disease

Due to large increases in the elderly populations across the world, age-related diseases are expected to expand dramatically in the coming years. Among these, neurodegenerative diseases will be among the most devastating in terms of their emotional and economic impact on patients, their families, and associated subsidized health costs. There is no currently available cure or rescue for dying brain cells. Viable therapeutics for any of these disorders would be a breakthrough and provide relief for the large number of affected patients and their families. Neurodegeneration is accompanied by elevated oxidative damage and inflammation. While natural antioxidants have largely failed in clinical trials, preclinical phenotyping of the unnatural, mitochondrial targeted nitroxide, XJB-5-131, bodes well for further translational development in advanced animal models or in humans. Here we consider the usefulness of synthetic antioxidants for the treatment of Huntington’s disease. The mitochondrial targeting properties of XJB-5-131 have great promise. It is both an electron scavenger and an antioxidant, reducing both somatic expansion and toxicity simultaneously through the same redox mechanism. By quenching reactive oxygen species, XJB-5-131 breaks the cycle between the rise in oxidative damage during disease progression and the somatic growth of the CAG repeat which depends on oxidation.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Modeling hepatitis C micro-elimination among people who inject drugs with direct-acting antivirals in metropolitan Chicago

Hepatitis C virus (HCV) infection is a leading cause of chronic liver disease and mortality worldwide. Direct-acting antiviral (DAA) therapy leads to high cure rates. However, persons who inject drugs (PWID) are at risk for reinfection after cure and may require multiple DAA treatments to reach the World Health Organization’s (WHO) goal of HCV elimination by 2030. Using an agent-based model (ABM) that accounts for the complex interplay of demographic factors, risk behaviors, social networks, and geographic location for HCV transmission among PWID, we examined the combination(s) of DAA enrollment (2.5%, 5%, 7.5%, 10%), adherence (60%, 70%, 80%, 90%) and frequency of DAA treatment courses needed to achieve the WHO’s goal of reducing incident chronic infections by 90% by 2030 among a large population of PWID from Chicago, IL and surrounding suburbs. We also estimated the economic DAA costs associated with each scenario. Our results indicate that a DAA treatment rate of >7.5% per year with 90% adherence results in 75% of enrolled PWID requiring only a single DAA course; however 19% would require 2 courses, 5%, 3 courses and <2%, 4 courses, with an overall DAA cost of $325 million to achieve the WHO goal in metropolitan Chicago. We estimate a 28% increase in the overall DAA cost under low adherence (70%) compared to high adherence (90%). Our modeling results have important public health implications for HCV elimination among U.S. PWID. Using a range of feasible treatment enrollment and adherence rates, we report robust findings supporting the need to address re-exposure and reinfection among PWID to reduce HCV incidence.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

Mathematical model of broadly reactive plasma cell production

Strain-specific plasma cells are capable of producing neutralizing antibodies that are essential for clearance of challenging pathogens. These neutralizing antibodies also function as a main defense against disease establishment in a host. However, when a rapidly mutating pathogen infects a host, successful control of the invasion requires shifting the production of plasma cells from strain-specific to broadly reactive. In this study, we develop a mathematical model of germinal center dynamics and use it to predict the events that lead to improved breadth of the plasma cell response. We examine scenarios that lead to germinal centers that are composed of B-cells that come from a single strain-specific clone, a single broadly reactive clone or both clones. We find that the initial B-cell clonal composition, T-follicular helper cell signaling, increased rounds of productive somatic hypermutation, and B-cell selection strength are among the mechanisms differentiating between strain-specific and broadly reactive plasma cell production during infections. Understanding the contribution of these factors to emergence of breadth may assist in boosting broadly reactive plasma cells production.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Accelerating massively parallel hemodynamic models of coarctation of the aorta using neural networks

Comorbidities such as anemia or hypertension and physiological factors related to exertion can influence a patient’s hemodynamics and increase the severity of many cardiovascular diseases. Observing and quantifying associations between these factors and hemodynamics can be difficult due to the multitude of co-existing conditions and blood flow parameters in real patient data. Machine learning-driven, physics-based simulations provide a means to understand how potentially correlated conditions may affect a particular patient. Here, we use a combination of machine learning and massively parallel computing to predict the effects of physiological factors on hemodynamics in patients with coarctation of the aorta. We first validated blood flow simulations against in vitro measurements in 3D-printed phantoms representing the patient’s vasculature. We then investigated the effects of varying the degree of stenosis, blood flow rate, and viscosity on two diagnostic metrics – pressure gradient across the stenosis (ΔP) and wall shear stress (WSS) - by performing the largest simulation study to date of coarctation of the aorta (over 70 million compute hours). Using machine learning models trained on data from the simulations and validated on two independent datasets, we developed a framework to identify the minimal training set required to build a predictive model on a per-patient basis. We then used this model to accurately predict ΔP (mean absolute error within 1.18 mmHg) and WSS (mean absolute error within 0.99 Pa) for patients with this disease.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Evaluation of two inoculation routes of an adenovirus-mediated viral protein inhibitor in a Crimean-Congo hemorrhagic fever mouse model

Crimean-Congo hemorrhagic fever virus (CCHFV) is a tick-borne nairovirus with a wide geographic spread that can cause severe and lethal disease. No specific medical countermeasures are approved to combat this illness. The CCHFV L protein contains an ovarian tumor (OTU) domain with a cysteine protease thought to modulate cellular immune responses by removing ubiquitin and ISG15 post-translational modifications from host and viral proteins. Viral deubiquitinases like CCHFV OTU are attractive drug targets, as blocking their activity may enhance cellular immune responses to infection, and potentially inhibit viral replication itself. We previously demonstrated that the engineered ubiquitin variant CC4 is a potent inhibitor of CCHFV replication in vitro. A major challenge of the therapeutic use of small protein inhibitors such as CC4 is their requirement for intracellular delivery, e.g., by viral vectors. In this study, we examined the feasibility of in vivo CC4 delivery by a replication-deficient recombinant adenovirus (Ad-CC4) in a lethal CCHFV mouse model. Since the liver is a primary target of CCHFV infection, we aimed to optimize delivery to this organ by comparing intravenous (tail vein) and intraperitoneal injection of Ad-CC4. While tail vein injection is a traditional route for adenovirus delivery, in our hands intraperitoneal injection resulted in higher and more widespread levels of adenovirus genome in tissues, including, as intended, the liver. However, despite promising in vitro results, neither route of in vivo CC4 treatment resulted in protection from a lethal CCHFV infection.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS2007

Spaceflight Alters Bacterial Gene Expression and Virulence and Reveals Role for Global Regulator Hfq

A comprehensive analysis of both the molecular genetic and phenotypic responses of any organism to the spaceflight environment has never been accomplished due to significant technological and logistical hurdles. Moreover, the effects of spaceflight on microbial pathogenicity and associated infectious disease risks have not been studied. The bacterial pathogen Salmonella typhimurium was grown aboard Space Shuttle mission STS-115 and compared to identical ground control cultures. Global microarray and proteomic analyses revealed 167 transcripts and 73 proteins changed expression with the conserved RNA-binding protein Hfq identified as a likely global regulator involved in the response to this environment. Hfq involvement was confirmed with a ground based microgravity culture model. Spaceflight samples exhibited enhanced virulence in a murine infection model and extracellular matrix accumulation consistent with a biofilm. Strategies to target Hfq and related regulators could potentially decrease infectious disease risks during spaceflight missions and provide novel therapeutic options on Earth.

Wilson, J. W.↗
NASA NTRS2014

Development of an Autonomous, Dual Chamber Bioreactor for the Growth of 3-Dimensional Epithelial-Stromal Tissues in Microgravity

We are developing a novel, autonomous bioreactor that can provide for the growth and maintenance in microgravity of 3‐D organotypic epithelial‐stromal cultures that require an air‐liquid interface. These complex 3‐D tissue models accurately represent the morphological features, differentiation markers, and growth characteristics observed in normal human epithelial tissues, including the skin, esophagus, lung, breast, pancreas, and colon. However, because of their precise and complex culture requirements, including that of an air‐liquid interface, these 3‐D models have yet to be utilized for life sciences research aboard the International Space Station. The development of a bioreactor for these cultures will provide the capability to perform biological research on the ISS using these realistic, tissue‐like human epithelial‐stromal cell models and will contribute significantly to advances in fundamental space biology research on questions regarding microgravity effects on normal tissue development, aging, cancer, and other disease processes. It will also allow for the study of how combined stressors, such as microgravity with radiation and nutritional deficiencies, affect multiple biological processes and will provide a platform for conducting countermeasure investigations on the ISS without the use of animal models. The technology will be autonomous and consist of a cell culture chamber that provides for air‐liquid, liquid‐liquid, and liquid‐air exchanges within the chambers while maintaining the growth and development of the biological samples. The bioreactor will support multiple tissue types and its modular design will provide for incorporation of add‐on capabilities such as microfluidics drug delivery, media sampling, and in situ biomarker analysis. Preliminary flight testing of the hardware will be conducted on a parabolic platform through NASA's Flight Opportunities Program.

Patel, Zarana S.↗