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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 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Metal-olsalazine coordination polymers for medical applications

Olsalazine (H 4 olz), a prodrug of the anti-inflammatory 5-aminosalicylic acid, is used as a ligand to synthesize a suite of M(H 2 olz) and M 2 (olz) materials, where M is a dication (e.g. Mg, Ca, Sr, Fe, Co, Ni, Cu, Zn). A family of metal olsalazine coordination polymers, coordination solids, and metal organic frameworks are described, which include 1-, 2-, and 3-dimensional structures. The materials resist degradation at acidic pH and release olsalazine preferentially at neutral pH. The mesoporous M 2 (olz) frameworks exhibit high surface areas with hexagonal pore apertures that are approximately 27 Å in diameter and contain coordinatively unsaturated metal sites. Biologically active molecules containing a Lewis-basic functional group can be grafted directly to the open metal sites of the frameworks. Dissolution of the frameworks under physiological conditions releases olsalazine (H 4 olz) and the grafted molecules so that multiple therapeutic components can be delivered together at different rates.

Levine, Dana A.↗
DOE OSTI2022

Use of lipid particles in medical diagnostics

Disclosed herein are methods for identifying one or more diseased cells in a subject. Some embodiments include providing a biological sample derived from a subject, analyzing the biological sample by mass spectrometry, and determining the abundance of one or more lipids in the biological sample, wherein an altered abundance of the one or more lipids in the biological sample, as compared to a reference level, indicates a presence of one or more diseased cells in the subject from which the biological sample is derived.

Louie, Katherine B.↗
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 OSTI2020

CPAP Ventilators Needed for Rapid Response to COVID-19 by Modification of CPAP Equipment

Early on in the COVID-19 pandemic, potential ventilator shortages were a critical issue identified by national health care providers. Capacity modeling at the time suggested patient demand may exceed ventilator supply. Thus, the challenge became finding an urgent interim solution to meet health care needs. Our initial hypothesis was that CPAP technology could be modified to provide similar functionality to a ventilator, relieving demand and allowing physicians to decide which patients need high end machines, ultimately saving lives. In conjunction with medical experts and pulmonologists, we were able to identify three key thrusts associated with this research problem: (1) modification of CPAP technology to allow for 02 input that would be capable of providing ventilation; (2) development of an alarming function that would provide real-time audible alarms to alert medical personnel to critical conditions, which would be used inline with CPAP technology; and (3) a method of sterilizing expiratory air from such a system in order to protect medical personnel from biohazard, since CPAPs vent to the atmosphere. We were unable to realize results for thrust 1 (CPAP modification for 02); we identified potential safety issues associated with utilizing medical grade oxygen with a common CPAP device. In order to characterize and mitigate these issues, we would need to partner closely with a device manufacturer; such a partnership could not be achieved in the timeframe needed for this rapid response work. However, we determined that some medical grade BiPAP devices do not need this modification and that the significant progress on thrusts 2 and 3 would be sufficient to buy down risk of a massive ventilator shortage. Our team built a prototype alarm system that can be utilized with any assistive respiratory device to alert on all key conditions identified by medical personnel (high pressure, low pressure, apnea, loss of power, low battery). Finally, our team made significant progress in the rapid prototyping and demonstration of an inline UV air purifier device. The device is cost efficient and can be manufactured at scale with both commercially available and additively manufactured parts. Initial tests with SARS-CoV-2 analog bacteriophage MS2 show 99% efficacy at reducing bioburden. Following a successful demonstration of the prototype device with medical personnel, we were able to obtain follow-on (non-LDRD) funding to provide additional device characterization, validation, and production in order to respond to an immediate regional need.

42 ENGINEERING↗
DOE OSTI2022

Supervisory Algorithm for Autonomous Hemodynamic Management Systems

Future military conflicts will require new solutions to manage combat casualties. The use of automated medical systems can potentially address this need by streamlining and augmenting the delivery of medical care in both emergency and combat trauma environments. However, in many situations, these systems may need to operate in conjunction with other autonomous and semi-autonomous devices. Management of complex patients may require multiple automated systems operating simultaneously and potentially competing with each other. Supervisory controllers capable of harmonizing multiple closed-loop systems are thus essential before multiple automated medical systems can be deployed in managing complex medical situations. The objective for this study was to develop a Supervisory Algorithm for Casualty Management (SACM) that manages decisions and interplay between two automated systems designed for management of hemorrhage control and resuscitation: an automatic extremity tourniquet system and an adaptive resuscitation controller. SACM monitors the required physiological inputs for both systems and synchronizes each respective system as needed. We present a series of trauma experiments carried out in a physiologically relevant benchtop circulatory system in which SACM must recognize extremity or internal hemorrhage, activate the corresponding algorithm to apply a tourniquet, and then resuscitate back to the target pressure setpoint. SACM continues monitoring after the initial stabilization so that additional medical changes can be quickly identified and addressed, essential to extending automation algorithms past initial trauma resuscitation into extended monitoring. Overall, SACM is an important step in transitioning automated medical systems into emergency and combat trauma situations. Future work will address further interplay between these systems and integrate additional medical systems.

42 ENGINEERING↗
DOE OSTI2025

Low responsiveness of machine learning models to critical or deteriorating health conditions

Machine learning (ML) based mortality prediction models can be immensely useful in intensive care units. Such a model should generate warnings to alert physicians when a patient’s condition rapidly deteriorates, or their vitals are in highly abnormal ranges. Before clinical deployment, it is important to comprehensively assess a model’s ability to recognize critical patient conditions. We develop multiple medical ML testing approaches, including a gradient ascent method and neural activation map. We systematically assess these machine learning models’ ability to respond to serious medical conditions using additional test cases, some of which are time series. Guided by medical doctors, our evaluation involves multiple machine learning models, resampling techniques, and four datasets for two clinical prediction tasks. We identify serious deficiencies in the models’ responsiveness, with the models being unable to recognize severely impaired medical conditions or rapidly deteriorating health. For in-hospital mortality prediction, the models tested using our synthesized cases fail to recognize 66% of the injuries. In some instances, the models fail to generate adequate mortality risk scores for all test cases. Our study identifies similar kinds of deficiencies in the responsiveness of 5-year breast and lung cancer prediction models. Using generated test cases, we find that statistical machine-learning models trained solely from patient data are grossly insufficient and have many dangerous blind spots. Most of the ML models tested fail to respond adequately to critically ill patients. How to incorporate medical knowledge into clinical machine learning models is an important future research direction.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2026

Identification of drug repurposing candidates for amyotrophic lateral sclerosis using electronic health records: a retrospective cohort study

Amyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative disease with a life expectancy of only 3–5 years and few approved treatments. To identify drug repurposing candidates for the treatment of ALS, we analysed the electronic health records (EHRs) of a large cohort of military veterans with ALS. We analysed the EHRs of individuals in the US Veterans Health Administration (VHA) database who were diagnosed with ALS between Jan 1, 2009 and Dec 31, 2019 to assess medication effects. Individuals without recorded prescriptions after the date of diagnosis were excluded. Two sets of criteria were applied to ascertain exposure. Exposure criteria A were met if the dispense date or the end date of the medication was within 12 months of ALS diagnosis and the end date was at least 6 months after the dispense date. Exposure criteria B were met if there were at least two dispenses within 6 months before diagnosis and 12 months after diagnosis. Propensity score-matched control groups were generated on the basis of confounders included in the EHR, with methodology of potential outcomes used to infer treatment effects. The primary outcome was death. A standard Cox proportional hazards analysis was done to assess association with survival. Survival was defined as the time from diagnosis date recorded in the EHR to death reported in the Department for Veterans Affairs Vital Status File. Follow-up survival time was censored on Dec 31, 2020, for those alive on this date. Downstream protein targets of drugs with clinically significant effects were analysed using the protein–protein interaction networks-based algorithm PathFX. The EHRs of 11 003 individuals with ALS in the VHA database were appropriate for analysis. 162 medications with treatment groups of 30 or more individuals were identified. Among these 162 medications, 27 were associated with statistically significant changes (≥0·1) in the hazard ratio (HR) for death. 18 of the medications were associated with a reduced HR for death (prolonged survival), and nine were associated with an increased HR for death (reduced survival). Drugs associated with reduced HR included HMG-CoA reductase inhibitors (simvastatin, pravastatin, lovastatin, and atorvastatin), PDE5 inhibitors (vardenafil and sildenafil), and α-adrenergic antagonists (tamsulosin and terazosin). The medications associated with an increased HR were drugs used either in the management of clinical features of ALS associated with poor outcomes or in end-of-life care. PathFx analysis identified a complex of proteins interacting with several of the identified drugs. To our knowledge, this analysis is the largest EHR-based study for identifying drug repurposing candidates for ALS. We identified several drugs that warrant further assessment as therapeutic options in ALS, as well as a protein network complex that might serve as a therapeutic target for ALS.

Reimer, Richard J. [Stanford Univ., CA (United Sta↗
DOE OSTI2020

Optimizing Individualized Treatment Planning for Parkinson’s Disease Using Deep Reinforcement Learning

More than one million people currently live with Parkinson's Disease (PD) in the U.S. alone. Medications, such as levodopa, can help manage PD symptoms. However, medication treatment planning is generally based on patient history and limited interaction between physicians and patients during office visits. This limits the extent of benefit that may be derived from the treatment as disease/patient characteristics are generally non-stationary. Wearable sensors that provide continuous monitoring of various symptoms, such as bradykinesia and dyskinesia, can enhance symptom management. However, using such data to overhaul the current static medication treatment planning approach and prescribe personalized medication timing and dosage that accounts for patient/care-giver/physician feedback/preferences remains an open question. We develop a model to prescribe timing and dosage of medications, given the motor fluctuation data collected using wearable sensors in real-time. We solve the resulting model using deep reinforcement learning (DRL). The prescribed policy determines the optimal treatment plan that minimizes patient's symptoms. Our results show that the model-prescribed policy outperforms the static a priori treatment plan in improving patients' symptoms, providing a proof-of-concept that DRL can augment medical decision making for treatment planning of chronic disease patients.

Watts, Jeremy↗
DOE OSTI2023

Wildfire smoke exposure and early childhood respiratory health: a study of prescription claims data

Abstract Wildfire smoke is associated with short-term respiratory outcomes including asthma exacerbation in children. As investigations into developmental wildfire smoke exposure on children’s longer-term respiratory health are sparse, we investigated associations between developmental wildfire smoke exposure and first use of respiratory medications. Prescription claims from IBM MarketScan Commercial Claims and Encounters database were linked with wildfire smoke plume data from NASA satellites based on Metropolitan Statistical Area (MSA). A retrospective cohort of live infants (2010–2016) born into MSAs in six western states (U.S.A.), having prescription insurance, and whose birthdate was estimable from claims data was constructed ( N = 184,703); of these, gestational age was estimated for 113,154 infants. The residential MSA, gestational age, and birthdate were used to estimate average weekly smoke exposure days ( smoke-day ) for each developmental period: three trimesters, and two sequential 12-week periods post-birth. Medications treating respiratory tract inflammation were classified using active ingredient and mode of administration into three categories:: 'upper respiratory', 'lower respiratory', 'systemic anti-inflammatory'. To evaluate associations between wildfire smoke exposure and medication usage, Cox models associating smoke-days with first observed prescription of each medication category were adjusted for infant sex, birth-season, and birthyear with a random intercept for MSA. Smoke exposure during postnatal periods was associated with earlier first use of upper respiratory medications (1–12 weeks: hazard ratio (HR) = 1.094 per 1-day increase in average weekly smoke-day, 95%CI: (1.005,1.191); 13–24 weeks: HR = 1.108, 95%CI: (1.016,1.209)). Protective associations were observed during gestational windows for both lower respiratory and systemic anti-inflammatory medications; it is possible that these associations may be a consequence of live-birth bias. These findings suggest wildfire smoke exposure during early postnatal developmental periods impact subsequent early life respiratory health.

Dhingra, Radhika↗
DOE OSTI2025

Baseline Hypothetical Facility for the Production of 131 I and 99 Mo using Activation Targets

This report describes a hypothetical facility for production of medical radioisotopes via activation under the Proliferation Resistance and Optimization (PRO-X) program. The facility uses neutron activation of non-special nuclear material (SNM) to produce the medical isotopes 131 I and 99 Mo at a throughput of 60 Ci/week of 131 I and 5 Ci/week of 99 Mo. The hypothetical design was carried out using a 10 MWt research reactor. The precursors used for the activation process were TeO2 for 131 I and MoO 3 for 99 Mo. The processes are performed in 3 hot cells used for target receipt, extraction, purification low specific activity (LSA) generator introduction, and packaging. A fourth hotcell is used for waste processing. The hot cell processing area takes up a footprint of 15.4 m 2 with the total footprint of the facility, including space for administrative offices, non-rad labs, quality assurance, and radiation buffer areas set at 763 m 2 . Waste is produced at a weekly rate of 257.8 g low activity solid waste and 8032.7 mL of low activity liquid waste, 8032 mL of which is water. This baseline hypothetical facility for production of medical isotopes via activation was then compared and contrasted to the hypothetical facility for production of medical isotopes via fission products to show the differences in approach for the two production modes. The two production modes had several highlighted differences including the overall facility and hot cell layout, the type and amount of waste produced by the respective facilities, and economic factors impacting production mode. Finally, a decision tree for which production mode might be more beneficial for an entrant into medical isotope production was developed based on the differences examined and the desired output of medical isotopes desired by the entrant.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗