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An aspiration for EIMO datascope is to realize artificial intelligence-enhanced solutions for analysis of crew health & performance data and to facilitate clinical decision support for autonomous medical operations. A vision proposed to the meeting participants was that of a “system of systems,” whereby EIMO will utilize AI-supported natural language processing and machine learning techniques to synthesize embedded reference databases and real-time data streams [input vectors] from multiple data sources to continuously and seamlessly assess crew health & performance. Constituent input vectors may include environmental controls, countermeasures data, behavioral data, physiologic wearables, point-of-care laboratory tests, personalized medical records, inventory trade space risk assessments, COTS medical databases, and ground support inputs. An ideal AI capability would possess trained fusion algorithms to cross reference input vectors with medical ‘knowledge’ [cultivated database] to stratify relevant data streams for predictive and actionable capabilities. In addition, EIMO will ideally have a degree of mobility, in that it can be accessed and can push/pull data within and between multiple vehicles/habitats.

This invited presentation will offer an overview of the biomedical research – both from a biomedical sciences and engineering perspective – of human physiology and performance during spaceflight. Focused on the unique expertise within the Biomedical Sciences Branch, NASA Johnson Space Center, the talk will elucidate the critical role of biomedical laboratories in synergistic activities in clinical sciences, space physiology, applied research, technology development, and operational support for human space exploration. Together, the efforts within this branch play a crucial role in supporting astronauts' health, performance, and safety. The branch Scientists, Researchers, and Engineers conduct biomedical research in flight on board the International Space Station and on-earth space environment analogs. This dual approach allows for a nuanced understanding of the effects of micro- and planetary gravitation fields on human physiology and the assessment of potential clinical and biomedical interventions (countermeasures) to mitigate astronaut clinical and performance decrements. The talk aims to underscore the synergistic collaborative efforts of biomedical engineers, physiologists, clinicians, and computational researchers, emphasizing these experts' pivotal role in advancing human spaceflight's frontiers.

BACKGROUND: Exercise is a fundamental component of maintaining astronaut health on long-duration space missions, where the microgravity environment poses unique challenges to physiological homeostasis. Accurate quantification of energy expenditure during such exercises is crucial for optimizing nutritional and physical health strategies for spacefarers. OBJECTIVE: This study aims to develop a comprehensive model to estimate caloric expenditure during exercise in a microgravity environment, employing a combination of spirometry, heart rate data, and other relevant parameters. By assessing energy utilization under these conditions, we seek to facilitate enhanced health management protocols for astronauts in space. METHODS & OUTCOMES: A multivariate predictive model will be constructed, utilizing spirometry and heart rate data, coupled with additional physiological and environmental parameters. A systematic approach will be applied to analyze the relationship between these variables and energy expenditure during various exercises. The proposed model will subsequently undergo rigorous validation to ensure accuracy and reliability. This research is expected to yield a precise and reliable predictive model, contributing to improved strategies for exercise prescription and nutritional intake, addressing the unique challenges presented by microgravity environments. We anticipate that our findings will support the development of more effective health maintenance protocols for astronauts during extended space missions, mitigating the adverse effects of space travel on the human body. SIGNIFICANCE: The development of an accurate and adaptable model to quantify caloric expenditure during exercise in space represents a pivotal advancement in space medicine. The insights gained from this study have the potential to inform the design of enhanced health and wellness strategies, ensuring the well-being and operational effectiveness of astronauts in long-duration space missions.

Addressing the hazards and defining solutions will require a combination of human health and performance and engineering solutions. These solutions will be balanced with acceptable risks imposed on the crew and mission parameters such as duration, vehicle designs, operational considerations, and cost.

Data flows and storage/retrieval capacity are severely constrained during missions in space and challenges will become even greater during exploration class missions. There is a need for an artificial intelligence (AI)-based clinical decision support system (CDSS) to monitor and analyze data to provide real-time consultative support for crew medical officer (CMO) decision-making. EIMO is defined as the gradual transition of medical care and decision making from terrestrial to space-based assets, enabling support of astronaut health and performance and reducing overall mission risk. While a hallmark of this paradigm shift from low-earth orbit is that on-board care will increasingly become the responsibility of the astronauts for primary management and decision making, terrestrial assets will continue to be paramount in pre-mission screening and planning, as well as prevention, health maintenance and long-term care contingencies. New capabilities and systems that enable progressively more robust and resilient systems and crews will be necessary to reduce risk and increase probability of deep space exploration mission success. An aspiration for EIMO is to develop AI-enhanced solutions for analysis of crew health & performance data and to facilitate clinical decision support for autonomous medical operations. A “system of systems” approach is envisioned whereby EIMO will deploy AI-supported natural language processing and machine learning (ML) techniques to utilize embedded reference databases and real-time data streams [input vectors] from multiple data sources. Constituent input vectors may include environmental controls, countermeasures data, behavioral data, physiologic wearables, point-of-care laboratory tests, personalized medical records, inventory trade space risk assessments, COTS medical databases, and ground support inputs. An ideal AI capability would possess trained fusion algorithms to cross reference input vectors with medical ‘knowledge’ [cultivated database] to stratify relevant data streams for predictive and actionable capabilities. In addition, EIMO will feature mobility, in that it can be accessed and can push/pull data within and between multiple vehicles/habitats. Large amounts and variable sources of data can be leveraged to diagnose, inform treatment strategies, and potentially predict medical events and performance decrements. Inclusion of advanced training tools using extended reality will enable increasingly autonomous medical care to aid a CMO when ground support is unavailable or time-delayed beyond required action window, e.g., emergent medical situations. EIMO CDSS would require very large datasets to train pre-flight and significant amounts of data are needed to support ML via in-flight CDSS operations. An additional challenge will be to find sufficient data to train a model relevant to astronaut demographics. The rapid, accelerating evolution of this field creates a propitious solution space to leverage multi-modal AI through public-private partnership(s). The status of multi-modal AI systems today would preclude their use for long duration missions as they remain unreliable and are subject to “digital hallucinations” and other errors that could pose operational risk. A federated labs structure is being considered to test and optimize data flow from the multiple input vectors leading to field testing in suitable ground/flight analogs. Critical to the success of an EIMO CDSS will be integration and interoperability and success will be defined by a system that can serve as an in-flight medical consult for the CMO providing critical support during medical contingencies. Benefits to terrestrial medicine may be significant as an outflow of the EIMO medical system, particularly for remote areas and communities lacking significant infrastructure, personnel and resources.

Lymphocytes are naturally exposed to genotoxic stresses. DNA damage occurs during the entire lymphocyte’s life span and is induced mainly by reactive oxygen species (ROS), replication fork collapse, or telomere shortening during the immune response or intense cell proliferation phases. Strong evidence for the influence of immune function on DNA repair comes from studies of SCID disease. SCID mice not only have a deficient V(D)J recombination but are also unable to repair double strand breaks, leading to increased radiation sensitivity. The leukocytes’ transcriptome of 8 ISS crew members revels a dysregulated immune function and activation of cellular survival pathways in response to space environment. We have performed PCR analysis in peripheral mononuclear cells from the same crew members. A list of 62 genes were carefully selected addressing immunological and cell survival pathways. Differentially expressed genes indicated changes in chemokine receptor activity, chemokine binding, toll-like receptors, adhesion molecules and cellular response to DNA damage.

Space radiation poses an increased risk for cardiovascular diseases (CVD) during and after spaceflight, that should be quantified to plan future space missions such as the flight to Mars. The radiation-induced CVD risk is evaluated using a linear no-threshold dose response excess relative risk (ERR) model, so that the risk is function of the radiation dose and of the background (i.e., non-exposed) incidence rate. The background CVD risk can be evaluated using clinical prediction models (CPMs). The first CPMs developed in the 1960’s have identified CVD risk factors such as blood pressure, diabetes, cholesterol, and smoking. In this work, the steps used to integrate the CVD risk provided by a CPM into the NASA Radiation Risk Model are described. Simulation results calculated using available data for relevant mission scenarios are also shown, and the challenges in CVD risk predictions are discussed.

Spaceflight drives adaptive changes in healthy individuals appropriate for sensorimotor function in a microgravity environment. These changes are maladaptive for return to earth's gravity. The inter-individual variability of sensorimotor decrements is striking, although poorly understood. The goal of this study is to identify a set of behavioral, neuroimaging and genetic measures that can be used to predict early post-flight performance on a set of sensorimotor tasks. Astronauts are recruited who previously participated in sensorimotor field tests and/or posturography soon after long-duration spaceflight. Behavioral tests include assessments of sensory dependency and adaptability. Visual dependency involves treadmill walking while viewing a moving virtual visual scene. Vestibular perceptual thresholds are measured while seated during lateral translations. Proprioception dependency is measured during one-legged stance on a horizontal air-bearing surface. Ground assessment of adaptability is performed(1) during treadmill walking with a virtual linear hallway and a moving walking surface, and (2) during multiple trials of navigating an obstacle course while wearing reversing prisms(adaptive Functional Mobility Test, aFMT). The neuroimaging tests will characterize individual differences in regional brain volumes (using Structural MRI) and white matter microstructure(using Diffusion Tensor Imaging) to serve as potential predictors of adaptive capacity. The genetic tests will utilize saliva samples to examine variations in four genes chosen because of their ability to differentiate sensorimotor adaptation ability in a normative population, including Catechol-O-methyltransferase (COMT), Dopamine Receptor D2 (DRD2), Brain-derived neurotrophic factor (BDNF) and the α2-adrenergic receptor. Twenty-seven ISS crewmembers have been tested to date, including 6 from this past year. This cohort includes 10first-time fliers, 6F, and mission durations lasting 178.6 ± 30.5 days, mean ± std. We are utilizing a combination of three post-flight functional task outcomes: tandem walk, recovery from fall and dynamic posturography. There is considerable variability among the post-flight performance outcomes for the 27participants to date. Based on a partial sample using an ordinal scale survey, 70% indicated their ability to perform functional tasks were more impacted postflight relative to inflight with 50% indicating they needed to restrict movements for a longer period postflight relative to inflight. While there is a strong association within tests obtained at different R+0 timepoints, by R+24 hr performance on one post-flight test does not necessarily correlate with performance on other post-flight tests. There are apparent relationships between individual measures and specific post-flight outcome measures, e.g., the cumulative time to complete the aFMT is significantly correlated to pre-to-post-flight changes in tandem walk (rho= 0.64, p = 0.001). Preliminary statistical analysis indicates combining biomarkers will increase predictive power and this will be explored with future analyses. Our preliminary findings underscore the importance of a comprehensive post-flight test battery including different types of tasks with varying sensory feedback. We expect that understanding the relationships between these sensorimotor biomarkers and post-flight functional task performance will improve both our understanding of the individual variability and our strategy to optimize sensorimotor countermeasures

While both microgravity and radiation are major biological stressors associated with the spaceflight environment, their cumulative impact on host-pathogen interactions and infectious disease risks are rarely considered. This is critical to address, since the cumulative effects of these stressors during spaceflight may result in unexpected negative impacts on crew health and performance that neither condition alone would predict, thus limiting the ability to develop effective countermeasures. Previously, we showed that both spaceflight and spaceflight analogue culture increased the virulence and pathogenesis-related characteristics of the foodborne pathogen, Salmonella Typhimurium (S. Typhimurium), which is responsible for disqualification of food destined for the International Space Station and Salmonella spp. have been found aboard NASA spacecraft. Recently, we demonstrated that spaceflight-analogue culture of S. Typhimurium increased its ability to infect 3-D biomimetic human intestinal tissue models. In a separate study, we showed low dose radiation damaged our 3-D intestinal models. The primary objective of this proposal is to evaluate the possibility that low dose radiation will exacerbate the already increased bacterial pathogenicity of S. Typhimurium observed following spaceflight analogue culture. In addition, we will determine the impact of a radiation countermeasure to provide protection against both radiation and pathogen-induced tissue damage and inflammation. Hypothesis: The already enhanced infection potential of spaceflight analogue cultured S. Typhimurium will be further exacerbated when used to infect host cells exposed to low dose radiation and this enhanced pathogenicity can be mitigated by a radioprotective compound. Aims: 1. Characterize the impact of spaceflight-analogue culture on the ability of S. Typhimurium to infect 3-D biomimetic intestinal tissue models before and after exposure to low dose radiation. 2. Evaluate the ability of the radioprotective compound, EC-18, to protect 3-D intestinal models from low dose radiation, S. Typhimurium infection, and the cumulative impact of these stressors. Significance: Current infectious disease risk assessments for spaceflight do not consider the potential for increased susceptibility to infection and disease resulting from exposure to low dose radiation, which is a critical consideration. This study will provide key evidence to determine if exposure to low dose radiation may be a factor in astronaut susceptibility to infection during long duration exploration missions and the impact of selected countermeasures to mitigate that risk to crew health. This study has been initiated and data collection is beginning.

While the NASA Human Research Program (HRP) utilizes a Crew Health and Performance (CHP) System to represent all the Agency’s efforts to ensure the health and performance of NASA astronauts, there is no shared mental model of a CHP system at NASA. Some groups may consider a CHP system to be only a medical kit, while others may not be using the concept at all. To facilitate the integration of functions and capabilities to ensure astronaut health and performance during vehicle development, HRP has proposed a CHP Shared Mental Model derived from the NASA Human Health, Medical, and Performance Spaceflight Standards (NASA-STD-3001 Vol.1/Vol.2). [1] Even though many vehicle, ground, and communication systems as well as mission operations are modeled for the Artemis Campaigns, no mission level CHP system model was created to achieve the intent of the HRP CHP Shared Mental Model. The lack of this model renders it difficult to visualize and understand how the many programs work together to provide the necessary cross program functions and capabilities to ensure the health and performance of the crew throughout an Artemis mission. For this purpose, the Exploration Medical Capability (ExMC) element of HRP developed a CHP system model for the Artemis III and IV missions to provide a view of how each program contributes to and interacts with the overall CHP system. To develop the 2023 Artemis CHP system model, ExMC leveraged existing data and models from the Moon to Mars Program Office, the Office of the Chief Health and Medical Officer (OCHMO) and the Orion, Gateway, Extravehicular Activity and Human Surface Mobility (EHP) and Human Landing System (HLS) programs. By using a Model-Based Systems Engineering (MBSE) approach, existing requirements, functions, and concepts of operations were combined to create a single system model focused on representing CHP from the launch to the return to Earth segments of the Artemis III and IV missions. Additionally, by incorporating the HRP Systems Platform for Aggregating and Relating Capabilities, or SPARC tool, the data from the programs was also related back to the 2nd volume of the NASA Human Health, Medical, and Performance Spaceflight Standard (NASA-STD-3001, Vol.2) and the human system risks identified by the Human System Risk Board (HSRB). The first version of the 2023 Artemis CHP system model was baselined in Fall of 2023 after the model was demonstrated to be a potentially useful tool for systems engineers integrating CHP capabilities in vehicle development as well as members of the Health and Medical Technical Authority providing oversight of those programs. The model may also be useful to any stakeholder of astronaut health and performance by providing insights on how an Artemis mission satisfies the NASA Human Health, Medical, and Performance Spaceflight Standards as well as how they mitigate the HSRB Human System Risks. This presentation highlights how the model was developed and the possible benefits of the model. [1] NASA HRP (2022), Crew Health and Performance System Whitepaper

Compared to the current paradigm for crew health in low-Earth orbit and Lunar missions that rely on constant communication with Mission Control, there is an anticipated shift in medical operations for deep-space exploration missions. This shift stems from mission constraints imposed by the considerable distance from Earth, which include resource limitations due to a lack of resupply, mass, power, volume, and data limitations, challenges imposed by communication latency and the inability to evacuate in case of emergencies. To transition towards a more self-reliant medical approach, a comprehensive strategy is essential to progressively enable crew autonomy and mitigate mission success risks in the challenging environment of space. This transformative shift is collectively referred to as "Earth-Independent Medical Operations" (EIMO), signifying the gradual transfer of medical care and decision-making from terrestrial resources to space-based assets. This transition is aimed at bolstering astronaut health and performance while simultaneously reducing the overall risks associated with space missions. The constraints related to EIMO necessitate an integrated development of medical systems, featuring interoperability with mission planning, vehicle design, spacesuit design, and data architecture. This integration is vital in establishing a robust medical infrastructure that not only supports the well-being of astronauts but also ensures the success of the mission as a whole. The Human Research Program (HRP) Exploration Medical Capability (ExMC) Element has developed a model-based Concept of Operations (ConOps) outlining an initial vision for EIMO. Within this ConOps, a comprehensive view is presented, encompassing stakeholder needs, system objectives, and system goals associated with EIMO. Additionally, it provides illustrative examples of the various activities (scenarios) for which the system will be employed during missions. The selection of these activities has been meticulous, aiming to encompass a wide spectrum of medical conditions, including those falling under different risk categories, such as low-likelihood-low-consequence, low-likelihood-high-consequence, and high-likelihood-low-consequence. The selection of these activities (scenarios) effectively encompasses the wide range of medical events situated within an assumed probability-consequence bell curve. In each scenario, at least one of the five main EIMO components identified is captured. Those EIMO components are: Pre-mission Planning, Acute and Emergent Management Decision Making, Prolonged Medical Management Decision Making, Supplies and Resource Management, and Task Load Management. The ConOps was developed by a multidisciplinary team consisting of systems engineers, scientists, and clinicians across NASA and aims to serve as an initial recommendation to gradually and safely enabling crew autonomy for Mars missions and beyond.

Human Research Program (HRP) Research Operations and Integration (ROI) ISS facilities overview.

Currently, discrete projects within NASA’s Human Research Program (HRP) take aim at providing future exploration missions with the capabilities necessary to operate within a progressively Earth-independent operative environment. Realizing an entire ecosystem that can accomplish these goals requires a clear visualization on how each project contributes to the overall objectives of Human Health and Performance (HH&P) in that context. The Exploration Medical Capability (ExMC) Element has developed an architectural framework for exploration class medical operations by utilizing a systems engineering approach using model-based systems engineering (MBSE). This model, a System of Systems Architecture (SoSA), captures a means of integrating various independent but related efforts into a comprehensive view associated with HRP’s goal of providing progressively Earth-Independent Medical Operations (EIMO). This level of visibility is realized by identifying interfaces between development efforts and existing programs that contribute to the overall mission of EIMO even if those extend past the traditional boundary of HRP. Potential interfaces that are considered important when constructing an architecture include program/project Needs, Goals, and Objectives (NGOs), capabilities, envisioned use cases/scenarios, and system/subsystem structure. This presentation will focus on the processes that were used to generate the SoSA along with a means to realize its utility amongst other MBSE products from ExMC. Evaluation of the best architecture moving forward can then be achieved that promotes cohesion between various functional areas of HH&P. Additionally, the SoSA is developed with a long-term vision for the dynamic integration of novel technological developments that may be implemented in the future. The SoSA is intended to enable Stakeholders the ability to view the broader interconnections that exist and begin to create avenues of communication between various efforts. The benefits of a SoSA provide an essential step towards addressing the unique challenges of attaining the goals of broader exploration class medical missions of the future.

INTRODUCTION: On the International Space Station (ISS), exercise feedback from astronauts is very important to diagnose and mitigate any form-related injuries and ensure efficacious exercise prescriptions and systems. Going forward, exploration exercise efforts seek to gain further quantitative data of human and system performance. Currently, methods of collecting in-flight exercise data on the ISS are limited to marker-based motion capture (MoCap) where astronauts must wear reflective markers over their clothes and specialized cameras are used. The main objective of this work was to investigate the following alternative tracking options: markerless video-based MoCap and inertial measurement units (IMUs). These were compared against traditional marker-based MoCap to evaluate kinematic accuracy and inform feasible methods for future exercise data collections on the ISS, especially in support of future Vibration Isolation and Stabilization (VIS) system development. METHODS: Three test subjects performed a variety of flight-like resistance and aerobic exercises using the Miniature Exercise Device (MED-2), Concept-2 rowing ergometer, barbell mockup, bench (e.g., for bench press, hip thruster, and cycling), and a custom structure for dips. These were intended also to represent exercises which could be performed on the multi-modality European Enhanced Exploration Exercise Device (E4D) [1]. The marker-based MoCap data, collected through a 16-camera OptiTrack MoCap system, was regarded as the gold standard to compare the data against. Passive markers were affixed to each subject according to a modified full body Plug-in Gait marker set [2] with 46 total markers. The markerless MoCap data was collected using two GoPro Hero7 cameras and one GoPro Hero11 camera. For the IMU data, a full body set of 17 Xsens DOTs were placed on the subject: 10 upper body and 7 lower body IMUs. Biomechanical modeling and evaluation was conducted through OpenSim [3] (MoCap), OpenSense [4] (IMU), OpenCap [5] (markerless), ENABLE [6] (markerless), and other modeling software. Secondary objectives included comparing the volume of equipment, reducing mass and crew set-up time. RESULTS AND DISCUSSION: While there were issues with initial processing for the IMUs and markerless MoCap, the results aided in the understanding of each sensor, developing end-to-end processes, and identifying future needs. Some observed concerns with the markerless MoCap approaches included being cognizant of a cluttered background, number of people in field of view, camera number and placement. Some challenges with the IMUs included possible sliding, early deactivation possibly due to exercise pose, and large quantity sensor synchronization. Overall, the markerless MoCap option may be the preferred method of data collection and processing as it provides a solution for certain IMU shortcomings and may be least in equipment volume, upmass, and crew setup time. CONCLUSIONS: While this work was mainly focused on ISS data collection, these sensor data along with continued evaluation and development efforts will help to establish best methods for exercise data collection on Gateway, for other Artemis missions, and beyond. Details on the latest end-to-end processing of the data and results will be presented, along with lessons learned and recommended sensor selection and methods.

Future Human Exploration missions will face new constraints as crews move further from terrestrial communication, resupply, and the real-time support enjoyed by Low Earth Orbit missions today. Exploration crews will need to be more self-reliant and able to respond to emergencies without immediate support from ground-based personnel. A new generation of technologies, employing advanced analytical and predictive modeling techniques, is needed to assist the crew’s work, help maintain their health, and inform the decisions they make on these future Exploration missions. The Crew Health and Performance Integrated Data Architecture (CHP-IDA) project is laying a foundation for these future technologies by integrating sources of data generated by and around the crew and then providing them though common data models and Application Programming Interfaces to external systems. The combined data model makes comprehensive Crew Health and Performance data accessible and more meaningful to the decision-making process. This presentation will describe the currently ongoing effort to develop and evaluate a path-to-flight concept of the CHP-IDA software, current integrations, updates to the architecture, and examples of exploration scenarios in which CHP-IDA would be used.

We envision an artificial intelligence (AI) based system that will provide support and recommendations to the crew medical officer (CMO) and ground flight surgeon during long-duration space missions. Such a system would be pretrained on the knowledgebase of clinical knowledge on Earth, minimizing the amount of Earth data that needs to be transferred into space. Then during deployment, the system would be constantly refined through active learning from diverse streams of data from sensors in the spacecraft, data collected daily from individual astronauts, and human-in-the-loop feedback from the crew. The model could be interrogated for predictions and recommendations on personalized crew health based on the overall status of the spacecraft, medicinal stores, and status of other crew members. Adaptation techniques would be used to incorporate spaceflight data that have very different distributions from the training data due to the extreme environment. Edge computing and the most advanced neuromorphic processing would enable computation in scenarios with low power and bandwidth, while dimensionality reduction would be employed to ensure that the input data streams from spaceflight are as small as possible. In order to realize this long-term vision, several hardware and software aspects need to be developed and assembled. First, models pretrained on Earth biomedical data would need to be evaluated for predictive accuracy, and the best one selected. That model would need to be adapted to learn from diverse, sparse, and inconsistently measured data streams, as well as human-in-the-loop feedback. A data integration, standardization, and dimensionality reduction methodology would need to be developed to handle all data types and feed them into the model. Once the software and data infrastructure is developed, it would need to be integrated with small footprint compute processors and tested in high-radiation, high-vibration, unregulated temperature situations. As a short-term goal, we recommend to focus on the development of the data and model software structure. Several large language models (LLM) already exist that have been trained on Earth biomedical and clinical knowledgebases, including BioMedLLM, Med-PaLM, SPOKE LLM, and Foresight. These models need to be evaluated for accuracy and the best one chosen for a proof-of-concept structure, while maintaining awareness of the accelerating AI field and incorporating any newly improved model architectures as needed. Then, we recommend to develop a database of synthetic data types to mimic the diverse data streams that are expected in a long-duration space mission. This should include environmental and microbial data from the spacecraft, non-invasive data from wearables and point-of-care devices employed by astronauts, and more invasive molecular and physiological monitoring of clinical and biomarker data from astronauts. The data standardization methodology should be developed, and these data streams used to refine the clinical LLM. Several scenarios should be developed that could plausibly come up in a long-duration space mission, and changes or aberrations introduced to the data at specific times to mimic these scenarios. Then, question and answer tasks should be designed to interrogate the model for predictions and recommendations, with acceptable answers already identified.

Hydrogen sulfide (H2S) has not historically been detected in spacecraft, but the possible evolution of volatile components from polar ice on the lunar surface is a potential concern for NASA’s planned Artemis missions. Numerous case reports and occupational epidemiological studies document that exposure to H2S at high concentrations has effects on the respiratory system, potentially leading to unconsciousness followed by debilitating neurological effects. Studies in rodents demonstrate sensitivity of the respiratory system to lower concentrations of H2S. Adverse respiratory effects have also been indicated in workers chronically exposed to H2S. The objective of the present publication is to develop Spacecraft Maximum Allowable Concentrations (SMACs) for H2S for all current standard durations of exposure for spaceflight (1-hour, 24-hours, 7-days, 30-days, 180-days, and 1000-days). Summary sources and literature review were used to identify relevant studies to inform SMAC development. Space flight mission specific activities and most probable exposure scenarios were used to determine the relevant toxicity endpoints and supporting studies. SMACs were established for hydrogen sulfide of 5 ppm for 1-hour, 1.3 ppm for 24-hours, 1.3 ppm for 7-days, 1.3 ppm for 30-days, and 0.3 ppm for 180-days. Data are not sufficient currently to establish a 1000-day SMAC value. SMACs for H2S will support development of handling protocols and proper containment for lunar sample collection.

Motion sickness represents one of the greatest clinical challenges impacting crew activities during and following g-transitions. Our overall goal is to characterize the effectiveness of motion sickness countermeasures during controlled laboratory experiments using capsule wave motion simulation and during field testing in operational environments. This laboratory study focused on prevention of motion sickness using intranasal scopolamine using a double-blinded repeated measures design in 30 subjects (19M, 11F). Intranasal scopolamine was provided by Defender Pharmaceuticals, Inc. (DPI-386 Nasal Gel, referred to as Inscop) self-administered by a nasal pump (Aptar Pharma) that delivers 0.4 mg dose (0.2 mg / nostril). During each session, subjects were exposed to complex wave motion on a six degree-of-freedom platform that included pitch, roll and heave at provocative stimulus frequencies (0.1-0.25 Hz) while seated in an illuminated cabin deprived of external visual cues. Motion sickness symptoms were compared across treatment and placebo control sessions counterbalanced across subjects and separated by at least one week. The time-to-motion sickness endpoint was based on severe malaise, defined as symptom score of ≥8 points using the Pensacola Diagnostic Index [1]. The bioavailability of scopolamine for each session was estimated from plasma concentrations obtained every 15 min[2]. Side effects during the treatment session were minimal, and performance was not impaired on a test battery including motion perception tracking, tablet-based eye-hand coordination and psychomotor vigilance testing. The plasma concentration remained near-peak levels throughout the 45 min motion sickness testing for most subjects. The percentile ranking on the Motion Sickness Susceptibility Questionnaire [MSSQ, 3]was moderately correlated with the motion sickness time-to-endpoint during the placebo control session (rho = –0.3, p=0.056). Seventeen subjects did not reach an endpoint during their placebo session and were eliminated from subsequent analysis. Another subject was excluded due to insufficient plasma concentration during the treatment session. For the remaining 12 subjects, the change in time-to-motion sickness endpoint between placebo and treatment sessions was moderately correlated with plasma concentration (rho =0.48, p = 0.056), improving on average 4.4 ± 18.4 min, mean ± std with Inscop versus placebo. Our results are consistent with previous findings that intranasal delivery of scopolamine can be effective at reducing motion sickness symptoms with minimal cognitive or sedative side effects. Future work is needed to optimize the delivery of Inscop for rescue (treatment) of symptoms following g-transitions as one of the key advantages of this formulation is self-administration in a suited environment. We will also explore how the combination of Inscop with non-pharmaceutical sensory aids, e.g., vibrotactile feedback of Earth vertical, may further mitigate motion sickness and improve task performance.

The scope of this report is limited to evaluating drug stability with respect to spaceflight. The effects of spaceflight on drug pharmacokinetics (PK) or drug pharmacodynamics (PD) are not addressed in this report: these topics will be reviewed in future reports. This is the first report of the available evidence pertaining to the chemical and physical stability of drugs in the context of drug impurity, drug repackaging, and exposure to ionizing space radiation. Previous studies are discussed within this report and, where possible, the data are interpreted and contextualized with regards to drug stability.

Welcome to the October 2023 issue of the Lifetime Surveillance of Astronaut Health (LSAH) Newsletter! This issue will showcase our Space Radiation Analysis Group (SRAG), highlighting their contributions towards keeping our astronauts safe during spaceflight.

This panel presents the findings from a series of Technical Interchange Meetings (TIMs) hosted by the Exploration Medical Capability Element (ExMC) in NASA’s Human Research Program. The topics for the TIMs were derived from a 2-day conference of senior leaders and subject matters experts that collectively outlined a multi-faceted strategy designed to optimize crew health and performance through an increasingly autonomous medical approach. The first abstract in this panel outlines the scope of issues related to data collection, usage, transmission and computing capacity to facilitate EIMO. The second presentation provides an overview of the challenges in developing curricula and advanced training tools to baseline knowledge, skills and abilities (KSA), verify clinical competency and assure retention during prolonged durations inherent in exploration-class missions. An overview of the complicated medical supply and resource chain necessary to facilitate EIMO is provided in the third presentation of this panel. The final presentation in this EIMO panel surveys the breadth and depth of demands on cognitive load expected to be experienced by crew on an exploration mission and proposes strategies to mitigate the prospect of cognitive overload through methods to shift task load from the crew to multi-modal artificial intelligence based medical support systems. Taken together, these presentations summarize the challenges to be expected and potential solution spaces to be explored and developed to progressively enable increasing autonomous medical operations to support crewed missions beyond low earth orbit. Through EIMO focused pre-mission planning, integrated data architecture design, innovative training development and AI-assisted task load management, the gradual transition of medical care and decision making from terrestrial to space-based assets enabling support of astronaut health and performance and reducing overall mission risk is achievable.

On 26th October 2023, ExMC convened a panel of Subject Matter Experts (SMEs) from NASA, broadly representing Headquarters, the Human Research Program, Medical Operations, the Human Health and Performance Directorate at JSC, the Health and Medical Technical Authority, the Flight Operations Directorate, and representation from other Centers including: Ames Research Center (ARC), and Glenn Research Center (GRC). Representatives from the Canadian Space Agency also participated in this TIM. In addition, SMEs from industry included representation from the following entities: Axiom Space, Space Exploration Technologies Corporation, Level Ex, Shiny Box Interactive. Additional SMEs from academia included: University of Houston, Touro University, Weill Cornell Medical College and the Translational Research Institute for Space Health at Baylor College of Medicine. This group of stakeholders discussed the training issues related to facilitating EIMO. Sub-topics for this discussion included the following: - Pre-launch medical curriculum development - Ground-based training on medical hardware - Dental health - Behavioral health - Telemedicine - Just-in-time training - Simulation-based training (extended reality)

This project will leverage existing capabilities of an augmented reality (AR) sensorimotor assessment tool. Currently, the tool mimics the capabilities of validated 2-dimensional (2D) assessments. While this modality is well-aligned with seated interactions with flight-deck displays, extravehicular activity (EVA) tasks will require full body mobility wherein astronauts reach and bend in a 3-dimensional (3D) space. These types of motions, naturally including head movements, are known to elicit experiences of disorientation. Thus, sensorimotor assessments that include these stimulations and larger movements are important for understanding astronaut readiness prior to initial EVAs. The proposed effort will extend the current 2D eye-hand coordination task in AR to a 3D task designed to evaluate dynamic balance and eye-hand-body coordination. The new module will extend the range of motion and interaction space such that it better aligns with operational task needs for astronaut readiness evaluation.