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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 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
NASA NTRS2019

Spatiotemporal Monitoring of Hydrilla [Hydrilla verticillata (L. f.) Royle] to Aid Management Actions

Hydrilla is an invasive aquatic plant that has rapidly spread through many inland water bodies across the globe by outcompeting native aquatic plants. Drinking water supply, power generation, and recreational use of lakes are threatened by the spread of this invasive species. Moreover, hydrilla serves as a host for an epiphytic, toxic cyanobacteria known to be the causative agent of the neurodegenerative disease called avian vacuolar myelinopathy, which affects waterfowl, raptors, and amphibians. The early detection of hydrilla infestation is very important to reduce the costs associated with control and removal efforts of this invasive species. Therefore, this study was aimed at developing a rapid, frequent, and large-scale tool for monitoring and predicting spatial extent of hydrilla habitat. This was achieved by integrating in situ and Landsat 8 OLI satellite data for Lake J. Strom Thurmond, the largest US Army Corps of Engineers Lake east of the Mississippi River located on the Georgia/South Caroline border. The predictive model incorporated remote sensing reflectance, Secchi disk depth (SDD), light attenuation coefficient (Kd), maximum depth of colonization (Zc), and percentage of light available through water column (PLW). The model predicted ideal habitat for hydrilla featuring high SDD, low values of Kd, high Zc and PLW. Final analysis showed that hydrilla starts growing in April, reaches peak around October, begins retreating in the following months, and disappears in February. Additionally, physical meteorological factors were analyzed and were found to be closely associated with hydrilla extent. Management agencies can use these results to not only to plan future removal efforts, but also evaluate and adapt their current mitigation efforts.

Percentage of light available through the water co↗
DOE OSTI2021

Recommended reporting items for epidemic forecasting and prediction research: The EPIFORGE 2020 guidelines

Background: The importance of infectious disease epidemic forecasting and prediction research is underscored by decades of communicable disease outbreaks, including COVID-19. Unlike other fields of medical research, such as clinical trials and systematic reviews, no reporting guidelines exist for reporting epidemic forecasting and prediction research despite their utility. We therefore developed the EPIFORGE checklist, a guideline for standardized reporting of epidemic forecasting research. Methods and findings: We developed this checklist using a best-practice process for development of reporting guidelines, involving a Delphi process and broad consultation with an international panel of infectious disease modelers and model end users. The objectives of these guidelines are to improve the consistency, reproducibility, comparability, and quality of epidemic forecasting reporting. The guidelines are not designed to advise scientists on how to perform epidemic forecasting and prediction research, but rather to serve as a standard for reporting critical methodological details of such studies. Conclusions: These guidelines have been submitted to the EQUATOR network, in addition to hosting by other dedicated webpages to facilitate feedback and journal endorsement.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

Sequence similarity between SARS-CoV-2 nucleocapsid and multiple sclerosis-associated proteins provides insight into viral neuropathogenesis following infection

Abstract The novel coronavirus SARS-CoV-2 continues to cause death and disease throughout the world, underscoring the necessity of understanding the virus and host immune response. From the start of the pandemic, a prominent pattern of central nervous system (CNS) pathologies, including demyelination, has emerged, suggesting an underlying mechanism of viral mimicry to CNS proteins. We hypothesized that immunodominant epitopes of SARS-CoV-2 share homology with proteins associated with multiple sclerosis (MS). Using PEPMatch, a newly developed bioinformatics package which predicts peptide similarity within specific amino acid mismatching parameters consistent with published MHC binding capacity, we discovered that nucleocapsid protein shares significant overlap with 22 MS-associated proteins, including myelin proteolipid protein (PLP). Further computational evaluation demonstrated that this overlap may have critical implications for T cell responses in MS patients and is likely unique to SARS-CoV-2 among the major human coronaviruses. Our findings substantiate the hypothesis of viral molecular mimicry in the pathogenesis of MS and warrant further experimental exploration.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

Torque Teno Sus Virus 1: A Potential Surrogate Pathogen to Study Pig-Transmitted Transboundary Animal Diseases

Understanding the epidemiology and transmission dynamics of transboundary animal diseases (TADs) among wild pigs (Sus scrofa) will aid in preventing the introduction or containment of TADs among wild populations. Given the challenges associated with studying TADs in free-ranging populations, a surrogate pathogen system may predict how pathogens may circulate and be maintained within wild free-ranging swine populations, how they may spill over into domestic populations, and how management actions may impact transmission. We assessed the suitability of Torque teno sus virus 1 (TTSuV1) to serve as a surrogate pathogen for molecular epidemiological studies in wild pigs by investigating the prevalence, persistence, correlation with host health status and genetic variability at two study areas: Archbold’s Buck Island Ranch in Florida and Savannah River Site in South Carolina. We then conducted a molecular epidemiological case study within Archbold’s Buck Island Ranch site to determine how analysis of this pathogen could inform transmission dynamics of a directly transmitted virus. Prevalence was high in both study areas (40%, n = 190), and phylogenetic analyses revealed high levels of genetic variability within and between study areas. Our case study showed that pairwise host relatedness and geographic distance were highly correlated to pairwise viral genetic similarity. Molecular epidemiological analyses revealed a distinct pattern of direct transmission from pig to pig occurring within and between family groups. Our results suggest that TTSuV1 is highly suitable for molecular epidemiological analyses and will be useful for future studies of transmission dynamics in wild free-ranging pigs.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Five key aspects of metaproteomics as a tool to understand functional interactions in host-associated microbiomes

Host-associated microbial communities (microbiomes) play critical roles in human, animal, and plant health and development. However, interactions between the host, members of the microbiome, and invading pathogens are in most cases still poorly understood. Such interactions are multidimensional and can alter the taxonomic composition and/or the functional metabolic activities of the microbiome in response to disease or treatment conditions. For example, after 2 days of antibiotic treatment, the mouse gut microbiome is altered and more susceptible to invasion by the pathogen Clostridioides difficile. Studies of these multidimensional interactions have been fueled by the ability to use high-throughput sequencing of phylogenetic marker genes to profile microbial community composition and shotgun metagenomics to profile functional potential. However, many protein-coding genes predicted from metagenomes are not necessarily expressed under a given condition, and thus, it is difficult to assess the activities and functional interactions in microbial communities based on DNA sequencing data alone. The physiological and pathological processes expressed in these communities under specific conditions are better reflected by the abundances of transcripts or proteins. In this Pearl, we provide a brief introduction to metaproteomics, which is a tool for the large-scale analysis of proteins in microbiomes that allows researchers to address a diversity of questions related to functions and interactions in microbiomes. The term “metaproteomics” was first used in 2004 for “the large-scale characterization of the entire protein complement of environmental microbiota at a given point in time”, and since then, a large array of metaproteomics approaches have been developed. Our objective in this Pearl is to highlight what we feel are 5 essential elements to be considered for a metaproteomics research campaign and to introduce nonexpert readers to the topic without going into too much technical detail.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Host cell and viral protease targets of human SERPINs identified by in silico docking

Serine protease inhibitors (SERPINs) are involved in various physiological processes and diseases, such as inflammation, cancer metastasis, and neurodegeneration. Their role in viral infections is poorly understood, as their expression patterns during infection and the range of proteases they target have yet to be fully characterized. Here, we show widespread expression of human SERPINs in response to respiratory virus infections, both in bronchioalveolar lavages from COVID-19 patients and in polarized human airway epithelial cultures. Using in silico docking of 10 SERPINs to 48 host proteases, we confirm known targets and predict new interactions. Protease activity assays validated selected interactions, confirming the newly predicted host targets for PAI-1 (SERPINE1) and PAI-2 (SERPINB2). PAI-1 inhibits cathepsin L, essential for SARS-CoV-2 maturation, and suppresses multi-cycle replication of both ancestral SARS-CoV-2 WA-1 and its variant Omicron BA.1. In addition, we identify PAI-2 as an antiviral SERPIN that reduces infectivity of human adenovirus 5 by directly inhibiting the adenoviral protease. Our study leverages in silico docking using full-length 3D protein structures to uncover new SERPIN targets, offering a range of candidate targets for therapeutic interventions.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS2019

Systemic Alterations with Spaceflight Associated Health Risks Originating from Both Circulating miRNAs and Mitochondrial Biology

The many known health risks currently associated with space travel include increased risk of cardiovascular disease, cancer, central nervous system related diseases, muscle degeneration, and changes with host-gut microbiome interactions that can have profound impact with these and other health risks. The majority of the risk from space travel stem of the two components of the space environment which are microgravity and radiation. Two specific systemic effects have been uncovered by us to impact the body as a whole due to the space environment. One factor is related from our earlier work (Beheshti et al, PLOS One, 2018), we predicted that there is a systemic component of the host that causes general increased health risks due to spaceflight driven by a circulating microRNA (miRNA) signature consisting of 13 miRNAs that directly regulates both p53 and TGF1. MiRNAs are small non-coding RNA molecules with a negative and post-transcriptional regulation on gene expression) are increasingly recognized as major systemic regulators of responses to stressors, including microgravity, oxidative stress, and DNA damage. In addition, due to the size and stability of miRNAs, it is known that miRNAs can circulate throughout the body and have been found in the majority of the bodily fluids including blood, urine, saliva, and tears. Here, we start to dissect the actual impact of this miRNA signature on both the radiation and microgravity components and prove that this miRNA signature actually exists in the circulation of a host. The other systemic factor we uncovered was the impact the mitochondria on the whole body due to spaceflight. We hypothesize that spaceflight may promote a physiologic response driven by systemic mitochondria pathways leading to metabolic disorder stemming from the liver and directly impacting other organs and tissues. A systems biology method was implemented utilizing GeneLab datasets that involved in vitro experiments performed at the low Earth orbit, in vivo experiments involving mice flown to space, and finally human physiological data from astronauts. A comprehensive multi-omics approach was implemented which involved correlating transcriptomic analysis with proteomics, metabolomics, and methylation analysis. This approach led us to confirm our hypothesis that a systemic mitochondrial driven response is responsible for increasing potential health risk and is conserved from the in vitro studies, to the in vivo studies, and finally confirmed in astronauts.

Radiation↗
DOE OSTI2023

Pressured cytotoxic T cell epitope strength among SARS-CoV-2 variants correlates with COVID-19 severity

Heterogeneity in susceptibility among individuals to COVID-19 has been evident through the pandemic worldwide. Cytotoxic T lymphocyte (CTL) responses generated against pathogens in certain individuals are known to impose selection pressure on the pathogen, thus driving emergence of new variants. In this study, we probe the role played by host genetic heterogeneity in terms of HLA-genotypes in determining differential COVID-19 severity in patients. We use bioinformatic tools for CTL epitope prediction to identify epitopes under immune pressure. Using HLA-genotype data of COVID-19 patients from a local cohort, we observe that the recognition of pressured epitopes from the parent strain Wuhan-Hu-1 correlates with COVID-19 severity. We also identify and rank list HLA-alleles and epitopes that offer protectivity against severe disease in infected individuals. Finally, we shortlist a set of 6 pressured and protective epitopes that represent regions in the viral proteome that are under high immune pressure across SARS-CoV-2 variants. In conclusion, identification of such epitopes, defined by the distribution of HLA-genotypes among members of a population, could potentially aid in prediction of indigenous variants of SARS-CoV-2 and other pathogens.

60 APPLIED LIFE SCIENCES↗
NASA NTRS2000

Forecasting disease risk for increased epidemic preparedness in public health

Emerging infectious diseases pose a growing threat to human populations. Many of the world's epidemic diseases (particularly those transmitted by intermediate hosts) are known to be highly sensitive to long-term changes in climate and short-term fluctuations in the weather. The application of environmental data to the study of disease offers the capability to demonstrate vector-environment relationships and potentially forecast the risk of disease outbreaks or epidemics. Accurate disease forecasting models would markedly improve epidemic prevention and control capabilities. This chapter examines the potential for epidemic forecasting and discusses the issues associated with the development of global networks for surveillance and prediction. Existing global systems for epidemic preparedness focus on disease surveillance using either expert knowledge or statistical modelling of disease activity and thresholds to identify times and areas of risk. Predictive health information systems would use monitored environmental variables, linked to a disease system, to be observed and provide prior information of outbreaks. The components and varieties of forecasting systems are discussed with selected examples, along with issues relating to further development.

Disease Outbreaks↗
DOE OSTI2024

The Exoproteome and Surfaceome of Toxigenic Corynebacterium diphtheriae 1737 and Its Response to Iron Restriction and Growth on Human Hemoglobin

Toxin-producing Corynebacterium diphtheriae strains are the etiological agents of the severe upper respiratory disease, diphtheria. A global phylogenetic analysis revealed that biotype gravis is particularly lethal as it produces diphtheria toxin and a range of other virulence factors, particularly when it encounters low levels of iron at sites of infection. Here, to gain insight into how it colonizes its host, we have identified iron-dependent changes in the exoproteome and surfaceome of C. diphtheriae strain 1737 using a combination of whole-cell fractionation, intact cell surface proteolysis, and quantitative proteomics. In total, we identified 1414 of the predicted 2265 proteins (62%) encoded by its reference genome. For each protein, we quantified its degree of secretion and surface exposure, revealing that exoproteases and hydrolases predominate in the exoproteome, while the surfaceome is enriched with adhesins, particularly DIP2093. Our analysis provides insight into how components in the heme-acquisition system are positioned, showing pronounced surface exposure of the strain-specific ChtA/ChtC paralogues and high secretion of the species-conserved heme-binding HtaA protein, suggesting it functions as a hemophore. Profiling the response of the exoproteome and surfaceome after microbial exposure to human hemoglobin and iron limitation reveals potential virulence factors that may be expressed at sites of infection. Data are available via ProteomeXchange with identifier PXD051674.

cell envelope↗
DOE OSTI2023

Deriving spatially explicit direct and indirect interaction networks from animal movement data

Abstract Quantifying spatiotemporally explicit interactions within animal populations facilitates the understanding of social structure and its relationship with ecological processes. Data from animal tracking technologies (Global Positioning Systems [“GPS”]) can circumvent longstanding challenges in the estimation of spatiotemporally explicit interactions, but the discrete nature and coarse temporal resolution of data mean that ephemeral interactions that occur between consecutive GPS locations go undetected. Here, we developed a method to quantify individual and spatial patterns of interaction using continuous‐time movement models (CTMMs) fit to GPS tracking data. We first applied CTMMs to infer the full movement trajectories at an arbitrarily fine temporal scale before estimating interactions, thus allowing inference of interactions occurring between observed GPS locations. Our framework then infers indirect interactions—individuals occurring at the same location, but at different times—while allowing the identification of indirect interactions to vary with ecological context based on CTMM outputs. We assessed the performance of our new method using simulations and illustrated its implementation by deriving disease‐relevant interaction networks for two behaviorally differentiated species, wild pigs ( Sus scrofa ) that can host African Swine Fever and mule deer ( Odocoileus hemionus ) that can host chronic wasting disease. Simulations showed that interactions derived from observed GPS data can be substantially underestimated when temporal resolution of movement data exceeds 30‐min intervals. Empirical application suggested that underestimation occurred in both interaction rates and their spatial distributions. CTMM‐Interaction method, which can introduce uncertainties, recovered majority of true interactions. Our method leverages advances in movement ecology to quantify fine‐scale spatiotemporal interactions between individuals from lower temporal resolution GPS data. It can be leveraged to infer dynamic social networks, transmission potential in disease systems, consumer–resource interactions, information sharing, and beyond. The method also sets the stage for future predictive models linking observed spatiotemporal interaction patterns to environmental drivers.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

A bacterial sensor taxonomy across earth ecosystems for machine learning applications

Microbial communities have evolved to colonize all ecosystems of the planet, from the deep sea to the human gut. Microbes survive by sensing, responding, and adapting to immediate environmental cues. This process is driven by signal transduction proteins such as histidine kinases, which use their sensing domains to bind or otherwise detect environmental cues and “transduce” signals to adjust internal processes. We hypothesized that an ecosystem’s unique stimuli leave a sensor “fingerprint,” able to identify and shed insight on ecosystem conditions. To test this, we collected 20,712 publicly available metagenomes from Host-associated, Environmental, and Engineered ecosystems across the globe. We extracted and clustered the collection’s nearly 18M unique sensory domains into 113,712 similar groupings with MMseqs2. We built gradient-boosted decision tree machine learning models and found we could classify the ecosystem type (accuracy: 87%) and predict the levels of different physical parameters (R2 score: 83%) using the sensor cluster abundance as features. Feature importance enables identification of the most predictive sensors to differentiate between ecosystems which can lead to mechanistic interpretations if the sensor domains are well annotated. To demonstrate this, a machine learning model was trained to predict patient’s disease state and used to identify domains related to oxygen sensing present in a healthy gut but missing in patients with abnormal conditions. Moreover, since 98.7% of identified sensor domains are uncharacterized, importance ranking can be used to prioritize sensors to determine what ecosystem function they may be sensing. Furthermore, these new predictive sensors can function as targets for novel sensor engineering with applications in biotechnology, ecosystem maintenance, and medicine.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Structure of the divergent human astrovirus MLB capsid spike

Despite their worldwide prevalence and association with human disease, the molecular bases of human astrovirus (HAstV) infection and evolution remain poorly characterized. Here, we report the structure of the capsid protein spike of the divergent HAstV MLB clade (HAstV MLB). While the structure shares a similar folding topology with that of classical-clade HAstV spikes, it is otherwise strikingly different. We find no evidence of a conserved receptor-binding site between the MLB and classical HAstV spikes, suggesting that MLB and classical HAstVs utilize different receptors for host-cell attachment. We provide evidence for this hypothesis using a novel HAstV infection competition assay. Comparisons of the HAstV MLB spike structure with structures predicted from its sequence reveal poor matches, but template-based predictions were surprisingly accurate relative to machine-learning-based predictions. Our data provide a foundation for understanding the mechanisms of infection by diverse HAstVs and can support structure determination in similarly unstudied systems.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRSDate not supplied

Murine Host-gut Microbiota Interactions are Modulated During Spaceflight

The rodent habitat on the International Space Station has provided critical insight into the impact of spaceflight on mammalian physiology. These effects include dysfunction of carbohydrate, steroid and lipid metabolism, and immune response, as well as induction of symptoms characteristic of liver disease, insulin resistance, osteopenia and myopathy, which are anticipated to intensify over long-duration spaceflight. Although these physiological responses can involve the microbiome, the host-microorganism interactions during spaceflight are still largely unknown. NASA GeneLab curates a wide range of space research data and the current work harnesses GeneLab multi’omic data from recent Rodent Research studies to explore changes to gut microbiota during spaceflight and their associations with host physiology when compared to ground controls. Using a hybrid analysis of DNA barcoding and whole genome shotgun data, an array of bacteria, fungi and nematodes could be identified at species level, and significant differences in relative abundances associated with spaceflight. Functional prediction based on differential abundance of species and metagenome gene inventories as well as metatranscriptomic gene expression at the host-gut microbiome interface implicate microbiota interactions could contribute to spaceflight pathology. Harnessing carefully curated publicly available data, such as from Genelab, to generate multi‘omic space science discoveries can help decipher the complex host-microbiome interactions that influence both health on Earth and the feasibility of long-duration spaceflight.

Microbiome↗
DOE OSTI2023

An accurate and interpretable model for antimicrobial resistance in pathogenic Escherichia coli from livestock and companion animal species

Understanding the microbial genomic contributors to antimicrobial resistance (AMR) is essential for early detection of emerging AMR infections, a pressing global health threat in human and veterinary medicine. Here we used whole genome sequencing and antibiotic susceptibility test data from 980 disease causing Escherichia coli isolated from companion and farm animals to model AMR genotypes and phenotypes for 24 antibiotics. We determined the strength of genotype-to-phenotype relationships for 197 AMR genes with elastic net logistic regression. Model predictors were designed to evaluate different potential modes of AMR genotype translation into resistance phenotypes. Our results show a model that considers the presence of individual AMR genes and total number of AMR genes present from a set of genes known to confer resistance was able to accurately predict isolate resistance on average (mean F 1 score = 98.0%, SD = 2.3%, mean accuracy = 98.2%, SD = 2.7%). However, fitted models sometimes varied for antibiotics in the same class and for the same antibiotic across animal hosts, suggesting heterogeneity in the genetic determinants of AMR resistance. We conclude that an interpretable AMR prediction model can be used to accurately predict resistance phenotypes across multiple host species and reveal testable hypotheses about how the mechanism of resistance may vary across antibiotics within the same class and across animal hosts for the same antibiotic.

Chung, Henri C.↗
DOE OSTI2021

Gut microbiome partially mediates and coordinates the effects of genetics on anxiety-like behavior in Collaborative Cross mice

Abstract Growing evidence suggests that the gut microbiome (GM) plays a critical role in health and disease. However, the contribution of GM to psychiatric disorders, especially anxiety, remains unclear. We used the Collaborative Cross (CC) mouse population-based model to identify anxiety associated host genetic and GM factors. Anxiety-like behavior of 445 mice across 30 CC strains was measured using the light/dark box assay and documented by video. A custom tracking system was developed to quantify seven anxiety-related phenotypes based on video. Mice were assigned to a low or high anxiety group by consensus clustering using seven anxiety-related phenotypes. Genome-wide association analysis (GWAS) identified 141 genes (264 SNPs) significantly enriched for anxiety and depression related functions. In the same CC cohort, we measured GM composition and identified five families that differ between high and low anxiety mice. Anxiety level was predicted with 79% accuracy and an AUC of 0.81. Mediation analyses revealed that the genetic contribution to anxiety was partially mediated by the GM. Our findings indicate that GM partially mediates and coordinates the effects of genetics on anxiety.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Tomato deploys defence and growth simultaneously to resist bacterial wilt disease

Abstract Plant disease limits crop production, and host genetic resistance is a major means of control. Plant pathogenic Ralstonia causes bacterial wilt disease and is best controlled with resistant varieties. Tomato wilt resistance is multigenic, yet the mechanisms of resistance remain largely unknown. We combined metaRNAseq analysis and functional experiments to identify core Ralstonia ‐responsive genes and the corresponding biological mechanisms in wilt‐resistant and wilt‐susceptible tomatoes. While trade‐offs between growth and defence are common in plants, wilt‐resistant plants activated both defence responses and growth processes. Measurements of innate immunity and growth, including reactive oxygen species production and root system growth, respectively, validated that resistant plants executed defence‐related processes at the same time they increased root growth. In contrast, in wilt‐susceptible plants roots senesced and root surface area declined following Ralstonia inoculation. Wilt‐resistant plants repressed genes predicted to negatively regulate water stress tolerance, while susceptible plants repressed genes predicted to promote water stress tolerance. Our results suggest that wilt‐resistant plants can simultaneously promote growth and defence by investing in resources that act in both processes. Infected susceptible plants activate defences, but fail to grow and so succumb to Ralstonia , likely because they cannot tolerate the water stress induced by vascular wilt.

Meline, Valerian↗
NASA NTRS2025

Potential Health Impacts, Treatments, and Countermeasures of Martian Dust on Future Human Space Exploration

The challenges of human space exploration produce some of humanity’s greatest technological and scientific advances, not excluding innovations in medicine. The microgravity environment causes a whole host of physiological changes, and exposure to dust on the Moon caused considerable pulmonary distress to astronauts during the Apollo missions. As the National Aeronautics and Space Administration (NASA)and other space organizations prepare for long duration exploration missions to Mars, the hazards and consequences of the Martian surface need to be accounted for. This review investigates how substances analogous to the hazardous components of Martian dust have caused disease in people on Earth. Because of the small grain size of Martian dust, dust on Mars is more likely to cause lung irritation, absorb into the bloodstream, and lead to diseases in astronauts. Toxic components of Martian dust to astronaut health include perchlorates, silica, nanophase iron oxides, and gypsum in addition to trace amounts of toxic metals whose abundances are debated: chromium, beryllium, arsenic, and cadmium. Predicted effects of dust exposure ranges from asymptomatic to life threatening, with many substances being carcinogenic and most damage impacting the pulmonary system. The longer transit time for astronauts to return home makes the operations of performing emergency medical treatment more difficult and increases both the likelihood and consequences of developing chronic disease. Exposure mitigation needs to be prioritized; however, supplements may be taken to prevent disease from breakthrough exposures and treatment regimens could lessen morbidity and mortality. Treatments and equipment need to be carefully thought out and transported with the astronauts to be prepared for all possible scenarios.

Justin L Wang↗