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All sourcesNASA NTRSDOE OSTI
NASA NTRS2020

Circulating miRNA Signature Predicts Health Risks Associated with Cancer and Spaceflight

Biological risks associated with space radiation and microgravity are major concerns for long-term space travel. Through a Systems Biology approach, our previous NASA work has shown both TGF signaling pathways and miRNAs have a critical impact on defining health risks with and without space irradiation. We hypothesize that circulating microRNA (miRNA) signatures are driving microvascular disease and muscle degeneration associated with accelerating aging and will be enhanced by exposure to the space environment (radiation and microgravity). We are investigating this hypothesis with both in vivo and in vitro models to test novel antagonist therapies to these miRNA signatures as countermeasures to reduce space radiation-induced health risks. A comprehensive Systems Biology approach is utilized to examine the influence by high atomic number by high (H) atomic number (Z) and energy (E) (HZE) irradiation. To simulate low-dose exposure due to galactic cosmic rays (GCR), we used ions, energy, and doses determined by a NASA consensus formula of 7 different ions to represent GCR (referred to as GCR sim model). To similate high-dose radiation exposure due to solar particle events (SPE), we used a solar particle event (SPE) sim model which gave a total dose of 1Gy protons with energy ranges from 50MeV to 150MeV. C57BL/6 wild-type female mice were utilized for the irradiations with our established simulated microgravity model (hindlimb suspension model) and an in vitro 3D microvasculature tissue model under simulated microgravity (clinostat) conditions was also irradiated. To expand on the circulating miRNA signature determined from our preliminary data, we determined a group of conserved miRNAs which are commonly being regulated in the majority of the organs and tissues throughout the host using our established techniques. MiRNA-sequencing was done on serum (at time of sacrifice), liver, heart, and muscle (soleus muscle) tissue for all radiation groups. Additional validation of the key miRNAs was performed by droplet digital PCR (ddPCR). This revealed a key circulating miRNA signature (consisting of multiple miRNAs) impacting cardiovascular and muscular disease risk. Further in vitro experiments with CRISPR/Cas9 system to knockout the key miRNA signatures, novel self-delivering antagomirs, overexpression of the miRNAs test the functional impact of the miRNA signatures on both microvascular disease and muscle degeneration due to space irradiation. The current work has started to allow the possible development of a novel minimally invasive miRNA based radioprotector to be used as a countermeasure for space radiation. Collectively, understanding of how whole body space radiation impacts microvascular and tissue degeneration through circulating miRNAs will greatly enhance health risk prognostication and provide possible new mechanisms for protection against space radiation. This work is supported by the Translational Research Institute through NASA Cooperative Agreement NNX16AO69A (T-0404) awarded to AB.

Beheshti, Afshin↗
NASA NTRSDate not supplied

Evaluation of Correction Methods for NASA GeneLab Transcriptomic Datasets

Conducting space biology experiments aboard the International Space Station, particularly those utilizing complex model organisms like mice, is expensive and difficult due to limited crew availability, hardware, and space. As a result, sample numbers from these studies are low, reducing the statistical power of any one experiment. Aggregating spaceflight datasets serves as a method to increase sample numbers, allowing for novel insights through bioinformatic analysis of ‘omics data from merged datasets. However, aggregating datasets can introduce unwanted variation including 1) differences in sample handling, processing, and sequencing platforms between datasets (technical variation) as well as 2) differences in experimental design between datasets such as sex or age of the model organism used. In the present study, NASA GeneLab-hosted RNAseq datasets from rodent liver tissues were used to evaluate several statistical methods to correct for this unwanted variation through two approaches, reference-based and standard. The following correction algorithms were applied with (reference-based) and/or without (standard) considering Universal Mouse RNA Reference samples: ComBat and ComBat_seq from the SVA package, median polish, empirical Bayes, and ANOVA-based algorithms from the MBatch package, and negative binomial regression normalization in the DESeq2 package. For each approach, after the correction algorithm was applied, differential gene expression (DGE) analysis of flight and ground control samples was performed with the combined data. The robustness of each tool was evaluated using BatchQC, to determine statistical differences between datasets before and after correction, Principal Component Analysis, to evaluate global gene expression in samples before and after correction, and by comparing DGE analysis of individual datasets and combined datasets before and after correction. The results showed that the reference-based approach introduced several additional (and likely artificial) DEGs when compared with the standard approach. Thus, the most robust standard correction will be implemented in the GeneLab Visualization 2.0 platform when datasets are combined.

GeneLab, RNA-seq, Batch Correction↗
NASA NTRS2020

Maternal provisioning gives young-of-the-year Hammerheads a head start in early life

For species that do not provide parental care after birth, excess maternal provisioning during development, beyond what is required for embryogenesis, provides offspring with resources to increase their chances of survival. Maternally derived resources are expected to be important for buffering offspring against limited food resources at birth or time needed to learn how to properly feed. Young-of-the-year (YOY) cryptic Scalloped Hammerheads (Sphyrna lewini) and Carolina Hammerheads (Sphyrna gilberti) were sampled from nurseries along the US Atlantic Coast and compared for a number of biological condition metrics across three developmental stages. Large declines in liver lipid content and hepatosomatic indices were found in neonatal sharks, using umbilical scar healing as a proxy for time since birth. Feeding commenced quickly as 96% of sharks had prey remnants in their stomachs. The combination of rapid exhaustion of maternally provided resources and high occurrence of stomachs with prey contents indicate that nursery quality, with respect to prey availability, may be important for YOY hammerhead survivorship. While externally the two species are morphologically similar, longer length-at-birth in S. lewini and higher hepatic condition in neonatal S. gilberti suggest that aspects of reproductive biology, including physiology, may differ between species. While more information is needed to distinguish life history differences between these two species, data collected from YOY may serve as a useful proxy to inform management when adult samples of cryptic species are difficult to collect.

none↗
NASA NTRSDate not supplied

Minipigs as a Model to Study Separate and Combined Effects of Space Radiation and Altered Gravity on Cardiovascular disease, Spaceflight-Associated Neuro-Ocular Syndrome (SANS), and Central Nervous System (CNS) effects

Minipigs are a well-characterized animal model used to study thecardiovascular system, metabolism, bone, gastrointestinal tract,pancreas, liver, kidney, lung, immune system, central nervous system(white matter) etc. Due to their anatomical, physiological, andbiochemical similarities to humans, minipigs are considered atranslational model in biomedical research and development. Also, their large size and cognitive abilities allow collection of supplementary information regarding the central nervous system(BMed) and Spaceflight-Associated Neuro-Ocular Syndrome (SANS) risk.

Janapriya Saha↗
NASA NTRS2021

Topical: Stem Cell-Based Tissue Regenerative Health in Space

The maintenance of healthy adult tissues in mammals requires a complex homeostasis of molecular, cellular, tissue, and metabolic processes which are fundamentally different from the development and aging processes that bookend life. Cellular homeostasis in the adult requires molecular maintenance and repair of non-dividing cells such as cardiomyocytes and neurons, but also stem cell-based tissue regeneration via direct replacement of cell loss, such as in the blood, immune system, bone, skin, liver, intestine, and other tissues. Because stem cell-based tissue regenerative health requires constant proliferation and differentiation of stem cell progenitors in the bone marrow, and other adult stem cell niches, it is uniquely sensitive to the stresses of spaceflight including exposure to space radiation and mechanical unloading in microgravity. A key central hypothesis in this field is that those spaceflight stress factors can have profound negative effects on long-term tissue regenerative health mediated by adult stem cells, and that unmitigated, they may lead to premature tissue aging and functional failure. Specifically, it is thought that mechanical unloading due to lack of weight-bearing in space reduces mitogenic mechanotransduction necessary to promote adult stem cell proliferation and differentiation, and that space radiation can also lead to activation of cell cycle arrest mechanisms, further reducing adult stem cell proliferation. These hypotheses are being tested in low earth orbit(LEO)using a variety of cellular and whole organism tissue model systems, suggesting that spaceflight consistently interferes with stem cell tissue regenerative processes such as in mammalian embryoid bodies, regenerating newt tails, and mouse bone marrow hematopoietic and osteoprogenitor cells. Furthermore, potential molecular mechanisms integrating both space radiation and mechanical disuse via oxidative stress and the cell cycle inhibitor Cdkn1aare now under study using single cell (scRNAseq) expressome analysis of bone marrow osteoprogenitors, both under stretch loading, and spaceflight conditions including in various mouse transgenic null backgrounds relevant to these mechanisms. Future work in the area of tissue regenerative health in space for the coming decade should seek to understand the responses the various tissue regenerative stem cell niches in humans and relevant model organisms, and how they respond to long-term exposure to the space environment. Special emphases of future work should be on how regenerative deficits in whole-organism stem cell niches may lead to tissue degeneration and premature aging, and on the long-term proliferation and differentiation of stem cell derived tissue organoid models in the deep space environment outside of LEO.

Eduardo A C Almeida↗
NASA NTRSDate not supplied

Guanine Oxidation in the Genome, not RNA Editing, Accounts for Single Nucleotide Variation in the Exome of Mice Flown on Board the ISS

We have conducted a further analysis of single nucleotide variation (somatic mutation) in mice flown aboard the ISS. We used data archived in GeneLab from a cohort of 18-week-old mice were flown to the ISS, housed in the Rodent Habitat and therefore subjected to microgravity for 37 days. Mice of similar age, sex and the same strain were used for ground controls housed in identical hardware and simulating, but not matching ISS environmental conditions (temperature, humidity and gas atmosphere). Primary data consists of next generation RNA sequencing for the tissues examined: eye, liver, skeletal muscle and kidney. We used novel software, developed at NASA Ames Research Center and deployed on the NASA Ames Supercomputer, to perform variant calling for single point mutations. Unexpectedly, we discovered a high degree of somatic mutation in ISS-flown mice, compared to controls. We found that the extent of somatic mutation correlated with the degree of gene expression in the four tissue types, with the highest degree of somatic mutation observed in genes with the highest degree of expression. Careful analysis that included measurement of specific nucleotide changes that occurred demonstrated that guanine substitutions were the most frequent, consistent with the hypothesis that reactive oxygen species-mediated guanine oxidation was responsible for the hypermutation events. By contrast, adenine substitutions would be expected if gene editing were responsible for the somatic mutation. These types of substitutions were much less frequent. The implication of these findings for astronaut health in a variety of mission scenarios will be discussed.

International Space Station↗
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↗
NASA NTRSDate not supplied

Batch Effect Correction Methods for NASA GeneLab Transcriptomic Datasets

RNA sequencing (RNA-seq) data from space biology experiments promise to yield invaluable insights into the effects of spaceflight on terrestrial biology. However, sample numbers from each study are low due to limited crew availability, hardware, and space. To increase statistical power, spaceflight RNA-seq datasets from different missions are often aggregated together. However, this can introduce technical variation or "batch effects", often due to differences in sample handling, sample processing, and sequencing platforms. Several computational methods have been developed to correct for technical batch effects, thereby reducing their impact on true biological signals. In this study, we combined 7 mouse liver RNA-seq datasets from NASA GeneLab (part of the NASA Open Science Data Repository) to evaluate several common batch effect correction methods (ComBat and ComBat-seq from the sva R package, and Median Polish, Empirical Bayes, and ANOVA from the MBatch R package). We quantitatively evaluated the ability of these methods to correct for technical batch variables in space biology RNA-seq data using the following criteria: BatchQC, principal component analysis, dispersion separability criterion, log fold change correlation, and differential gene expression analysis. Each batch variable / correction method combination was then assessed using a custom scoring approach to identify the optimal correction method for the combined dataset, by geometrically probing the space of all allowable scoring functions to yield an aggregate volume-based scoring measure. Finally, we describe the way in which the GeneLab multi-study analysis and visualization portal will allow users to examine the presence or absence of batch effects using multiple metrics. If the user chooses to perform batch effect correction, the scoring approach described here can be implemented to identify the optimal correction method to use for their specific combined dataset prior to analysis.

Lauren M. Sanders↗
NASA NTRSDate not supplied

Biological Research and Space Health Enabled by Machine Learning to Support Deep Space Missions

A key science goal of the NASA “Moon to Mars” campaign is to understand how biology responds to the Lunar, Martian, and deep space environments in order to advance fundamental knowledge and support human space missions. Through artificial intelligence (AI) and machine learning (ML), a paradigm shift has begun in space biosciences and engineered astronaut health systems, to enable Earth-independence and mission operations autonomy. We describe current AI/ML methods to support 1) fundamental biology, 2) in situ analytics, 3) high performance computing, 4) automated science, 5) self-driving labs, 6) remote data management, 7) integrated mission biomonitoring, and 8) a Precision Space Health system. AI/ML approaches that can be integrated to support these domains include active learning, explainable AI, adaptive learning, causal inference, knowledge graphs, federated learning, transfer learning, and large language models. Finally, we present results from several current ML projects that are underway in the space biology field to address key challenges of small sample n, high feature count, heterogeneity, and sparse data. These include 1) connecting omics to phenotypic data using an ensemble model to infer causality of rodent liver health disruption, 2) usage of explainable ML to interrogate muscular underpinnings of muscle atrophy, 3) ML models analyzing and determining directed acyclic graphs of human health risk leveraging rodent bone datasets, 4) usage of large pre-trained models connecting biomedical knowledgebases with small spaceflight datasets to understand gene-to-gene interactions, and 5) a suite of benchmarked open science datasets enabling programmers to identify best algorithms to answer space biology questions.

space biology↗
NASA NTRSDate not supplied

Developing Open-Source Training Materials for AI/ML and Space Biological Sciences Using NASA Cloud-Based Data

Artificial Intelligence (AI) and Machine Learning (ML) has gained significant traction in the biological and biomedical research fields in the last two decades, in part thanks to an increasing culture of open data sharing and reuse. Due to its capability for identifying complex relationships and patterns, AI/ML methodology is particularly well suited to recognize and predict biological patterns from high-dimensional next-generation sequencing data (e.g. whole genome sequencing, transcriptomic sequencing), as well as from biological or medical imaging data (e.g. microscopy, computed tomography, ultrasound, magnetic resonance imaging, radiography). These methodologies hold particular promise for space biosciences research and automated space health monitoring systems. However, there are many key considerations for properly training, validating, and testing a machine learning model in biological research or clinical application. Even with the positive culture of Open Science and data sharing, inexperienced researchers working quickly without proper checks can produce models that perform poorly outside of the immediate training dataset. Lessons learned from biological AI/ML research indicate that Open Science principles such as data sharing and open-source code must go hand-in-hand with publicly available, high-quality training curricula in best practices, with modules centered on real-life scientific use cases and data so future AI/ML practitioners gain experience on real problems. Here we present the development of open-source training materials for AI/ML and space biosciences, as part of the NASA Transform to Open Science Training (TOPST) initiative. We develop 4 independent training programs, focused on the following topics: 1) Fundamentals of Machine Learning and Space Biosciences Domain, 2) Open Science, Artificial Intelligence, and Ethical Best Practices for Data Sharing and Analysis, 3) Using AI/ML Classification to Identify Gene Networks Affected By Space Exposure in Mouse Liver, and 4) Using Neural Networks to Find DNA Damage Patterns in Immune Cells after Radiation. All programs leverage cloud-based NASA biological datasets. The curriculum we present will enable worldwide access to training in AI/ML and scientific analysis.

James Andrew Casaletto↗
NASA NTRSDate not supplied

Somatic Mutation in Mice on the International Space Station (ISS): Guanine Substitution Suggests Link to Cancer Risk

We conducted comprehensive analysis of single nucleotide somatic mutations in mice exposed to microgravity and other factors aboard the International Space Station (ISS), using data archived in GeneLab. Animals in the experimental cohort consisted of mice that spent 37 days on the ISS within the Rodent Habitat. Ground control animals consisted of mice of identical age, sex, strain, in a terrestrial Rodent Habitat controlled for temperature, humidity and carbon dioxide levels, to match ISS conditions as closely as possible. RNA extracted from eye, liver, skeletal muscle, and kidney tissue specimens was subjected to next-generation sequencing to acquire primary data. Our analysis employed cutting-edge software developed at NASA Ames Research Center, executed on the NASA Ames Supercomputer and on another high-performance computer, for accurate variant calling of single point mutations. ISS-flown mice exhibited a notably heightened level of somatic mutation compared to control mice. The degree of somatic mutation correlated with the degree of gene expression across the four tissue types, i.e., the greatest rate of mutation accumulation was seen in highly expressed genes. We discovered that guanine substitutions were the most common type of somatic mutation. This observation is consistent with the hypothesis that DNA mutation events stem from reactive oxygen/nitrogen/chlorine species-mediated guanine oxidation induced by the spaceflight environment. Since guanine oxidation is a prominent feature of the DNA mutation landscape that accompanies malignant transformation, our findings suggest a possible link between the spaceflight environment and cancer risk that is independent of radiation carcinogenesis.

ISS↗
NASA NTRSDate not supplied

Creating Benchmark Data for Artificial Intelligence and Machine Learning Space Biology Research

To identify an appropriate AI/ML approach for a specific problem, the best practice is to measure algorithm performance through the benchmarking process. A scientific benchmark consists of an AI-ready dataset and a reference implementation on a specific scientific question. The NASA Science Mission Directorate (SMD) has started the “Benchmark Initiative for AI/ML to create scientific benchmark datasets in three applications: 1) scientific benchmarking, which finds the best algorithm for a specific problem; 2) application benchmarking, which measures algorithm performance against a set of parameters; and 3) system benchmarking, which evaluates performance of hardware and software architecture. Currently, there are no standardized datasets available to benchmark AI/ML algorithms in the domain of space biology. In this work, we constructed two AI/ML-ready biological datasets from experiments in space-flown mice: cellular imaging and RNA-seq. First, radiation-exposed immune cells harbor DNA damage foci that can be fluorescently marked to visualize the amount of damage following exposure to ionizing radiation. However, such large datasets are difficult to analyze visually, due to imaging inconsistencies and human bias, and classical image processing approaches can fail on imaging artifacts. AI/ML are therefore exciting alternative, providing the speed of machines and the accuracy of humans. We have made this dataset available at https://registry.opendata.aws/bps_microscopy/. Second, high-throughput nucleic acid sequencing (DNA-seq, RNA-seq) has become widespread in biomedical research due to the growing availability and affordability of these assays. However, most sequencing datasets suffer from high dimensionality and low sample count. In this work, we used a generative adversarial network to synthesize a standardized, AI-ready, publicly available benchmark dataset for space biology RNA-seq data with sufficient space-flown and ground control mouse liver samples from NASA GeneLab. This dataset is available at https://registry.opendata.aws/bps_rnaseq/. These datasets are now fully open the Space Biology community to test their favorite AI/ML approaches.

James Casaletto↗
NASA NTRSDate not supplied

Developing Open-Source Training Materials for AI/ML and Space Biological Sciences Using NASA Cloud-Based Data

Artificial Intelligence (AI) and Machine Learning (ML) has gained significant traction in the biological and biomedical research fields, in part due to a culture of open data sharing and reuse. AI/ML methodology is well-suited to recognize and predict biological patterns from high-dimensional next-generation sequencing data (e.g. whole genome sequencing, transcriptomic sequencing), as well as from biological or medical imaging data (e.g. microscopy, computed tomography, ultrasound, magnetic resonance imaging, radiography). These methodologies hold particular promise for space biosciences research and automated space health monitoring systems. However, there are key considerations for properly training, validating, and testing a machine learning model in biological research or clinical application. Inexperienced researchers can produce models that perform poorly outside of the training dataset. Open Science principles such as data sharing and open-source code must go hand-in-hand with publicly available, high-quality training curricula in best practices, with modules centered on real-life scientific use cases and data so future AI/ML practitioners gain experience on real problems. Here we present the development of open-source training materials for AI/ML and space biosciences, as part of the NASA Transform to Open Science Training (TOPST) initiative. We develop 4 independent training programs, focused on the following topics: 1) Fundamentals of Machine Learning and Space Biosciences Domain, 2) Open Science, Artificial Intelligence, and Ethical Best Practices for Data Sharing and Analysis, 3) Using AI/ML Classification to Identify Gene Networks Affected By Space Exposure in Mouse Liver, and 4) Using Neural Networks to Find DNA Damage Patterns in Immune Cells after Radiation. All programs leverage cloud-based NASA biological datasets. The curriculum we present will enable worldwide access to training in AI/ML and scientific analysis.

James Casaletto↗
NASA NTRS2024

Metabolic Stress in Space: ROS-Induced Mutations in Mice Hint at A New Path to Cancer

Long-duration spaceflight beyond Earth's magnetosphere poses serious health risks, including muscle atrophy, bone loss, liver and kidney damage, and the Spaceflight-Associated Neuro-ocular Syndrome (SANS). RNA-seq of mice aboard the International Space Station (ISS) for 37 days revealed extraordinary hypermutation in tissue-specific genes, with guanine base conversion predominating, potentially contributing to spaceflight-associated health risks. Our results suggest that the genome-wide accelerated mutation that we measured, seemingly independent of radiation dose, was induced by oxidative damage from higher atmospheric carbon dioxide (CO 2 ) levels and increased reactive oxygen species (ROS) on the ISS. This accelerated mutation, faster via RNA transcription than replication and more numerous than by radiation alone, unveils novel hotspots in the mammalian proteome. Notably, these hotspots correlate with commonly mutated genes across various human cancers, highlighting the ISS as a crucial platform for studying accelerated mutation, genome instability, and the induction of disease-causing mutations in model organisms. Our results suggest that metabolic processes can contribute to somatic mutation, and thus may play a role in the development of cancer. A metabolic link to genetic instability potentially has far-reaching implications for various diseases, with implications for human health on Earth and in space.

mice↗
DOE OSTI2021

Method and composition for targeted delivery of therapeutic agents

Functionalized single walled or multi-walled carbon nanotubes (f-CNTs) can be delivered into mammals to targeted organs, such as the kidney and the liver. These f-CNTs may be non-covalently linked or covalently linked to therapeutic agents. In particular, the application delivers carbon nanotube-therapeutic agent conjugates to a target organ, thereby preventing or reducing damages to the organ caused by other agents or procedure.

McDevitt, Michael R.↗
DOE OSTI2022

First-collision source treatment for ray effect mitigation in discrete-ordinate radiation transport solutions

Deterministic transport codes play a fundamental role in the modelling and simulation of neutron transport. One of the most common deterministic methods is the method of discrete ordinates, also known as the S method. While offering significant advantages over other deterministic methods or stochastic methods like Monte Carlo, the method of discrete ordinates suffers from non-physical artifacts in its local solution due to its discretization of angle. These artifacts, referred to as ray effects because of their ray-like appearance, tend to be worse in problems with small sources in areas with little scattering. Significant effort has gone into developing methods to mitigate ray effects, such as the first-collision source treatment, which separates the angular flux into the uncollided and collided fluxes and solving them using non-traditional techniques such as ray tracing. One such code capable of doing this is Lawrence Livermore National Laboratory's deterministic transport code ARDRA. Current ray tracing methods typically trace to a set of points inside a zone to compute an overall flux. However, this approach has significant drawbacks, such as a low order of convergence and not being conservative. Therefore, a new method has been developed that traces instead to a set of points on each of a zone's surfaces and computing the currents, before using these to obtain the flux. A comparison between these two ray tracing methods showed significant advantages to the new surface method, including inherent conservation, a higher convergence rate, and an increase in calculable information like leakage. This work performed under the auspices of the U.S. Department of Energy by Lawrence Liver- more National Laboratory under Contract DE-AC52-07NA27344. (authors)

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2024

Production of Medically Desirable Radioisotopes in the EIRENE Molten Salt Reactor

Radioisotopes play a vital role in nuclear medicine, enabling the performance of diagnostic procedures such as Positron Emission Tomography (PET) and Single Photon Emission Computed Tomography (SPECT), in addition to innovative targeted therapies for selectively delivering cytotoxic radiation doses to tumor cells while minimizing damage to healthy tissue [1]. Beta emitting radionuclides such as 90Y and 131I find wide use in radio-immunotherapy, with the radiopharmaceuticals 90Y-ibritumomab tiuxetan (Zevalin®) and 131I-tositumomab (Bexxar®) having received FDA approval for treatment of non-Hodgkin’s lymphoma through targeting the CD20 surface receptor, which is commonly expressed in many B cell non-Hodgkin’s lymphoma subtypes [2, 3]. 90Y has also been successfully applied for treatment of brain tumors [4, 5] and liver cancer [6]. The favorable chelation chemistry of 90Y enables its use with a variety of ligands for the development of radiopharmaceuticals suitable for targeting different carcinoma types [1].

42 - ENGINEERING↗
DOE OSTI2025

Improving Surface Quality of Titanium Electrodeposition from a Deep Eutectic Solvent with Organic and Inorganic Additives

Titanium electroreduction is desired for a variety of medical, electronic, and bonding applications but has not been possible until recently. Titanium electrodeposition with leveler and brightener additives in the deep eutectic solvent ethaline has been studied for effects on spectroscopic reflectance. Polymeric leveling agents and several small‐molecule brighteners produce level/bright films (respectively) with high values for both specular and diffuse reflectance. The application of these leveling and brightening agents can produce films with appearances and surface roughnesses that may be suitable for application in medical implants such as stents, corrosion protection of electronics for wearable technology, and as interlayers between dissimilar metals.

deep eutectic solvents↗