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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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7 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Wearable Sensors Measure Ankle Joint Changes of Patients with Parkinson’s Disease before and after Acute Levodopa Challenge

Background . Previous studies found levodopa could improve the activity of the ankle joints of patients with Parkinson’s disease (PD). But ankle joint movement is composed of four motion ranges. The specific changes of four motion ranges in PD remain unknown. Objective . The purpose of this study was to decompose the complex ankle joint movement, measure ankle joint changes before and after the acute levodopa challenge test (ALCT), and investigate the effects of these parameters on gait performance. Methods . 29 PD patients and 30 healthy control subjects (HC) completed the Instrumented Stand and Walk (ISAW) test and gait parameters were collected by the JiBuEn gait analysis system. The percentage of improvement of gait data and the UPDRS III in the on-drug condition (ON) were determined with respect to the off-drug condition (OFF). Results . We observed a reduction in the heel strike angle (HS), 3-plantarflexion (3-PF) angle, and 4-dorsiflexion (4-DF) angle of ankle joints. We did not find significant difference in the toe-off angle (TO), 1-plantarflexion (1-PF) angle, and 2-dorsiflexion (2-DF) angle among three groups. Stride length improvement rate was significantly correlated with HS ( r s = 0.616, P < 0.001 ) and 3-PF ( r s = 0.639, P < 0.001 ) improvement rates. The improvement in the sum of rigidity items (UPDRS motor subsection item 22) was also correlated with HS ( r s = 0.389, P = 0.037 ) and 3-PF ( r s = 0.373, P = 0.046 ) improvement rates. Conclusions . Exogenous levodopa supplementation can significantly reduce the rigidity of patients with PD, improve their 3-PF and 4-DF of ankle joint kinematic parameters, and ultimately enhance their gait.

Wu, Zhuang↗
DOE OSTI2021

Improving Medication Regimen Recommendation for Parkinson’s Disease Using Sensor Technology

Parkinson’s disease medication treatment planning is generally based on subjective data obtained through clinical, physician-patient interactions. The Personal KinetiGraph™ (PKG) and similar wearable sensors have shown promise in enabling objective, continuous remote health monitoring for Parkinson’s patients. In this proof-of-concept study, we propose to use objective sensor data from the PKG and apply machine learning to cluster patients based on levodopa regimens and response. The resulting clusters are then used to enhance treatment planning by providing improved initial treatment estimates to supplement a physician’s initial assessment. We apply k-means clustering to a dataset of within-subject Parkinson’s medication changes—clinically assessed by the MDS-Unified Parkinson’s Disease Rating Scale-III (MDS-UPDRS-III) and the PKG sensor for movement staging. A random forest classification model was then used to predict patients’ cluster allocation based on their respective demographic information, MDS-UPDRS-III scores, and PKG time-series data. Clinically relevant clusters were partitioned by levodopa dose, medication administration frequency, and total levodopa equivalent daily dose—with the PKG providing similar symptomatic assessments to physician MDS-UPDRS-III scores. A random forest classifier trained on demographic information, MDS-UPDRS-III scores, and PKG time-series data was able to accurately classify subjects of the two most demographically similar clusters with an accuracy of 86.9%, an F1 score of 90.7%, and an AUC of 0.871. A model that relied solely on demographic information and PKG time-series data provided the next best performance with an accuracy of 83.8%, an F1 score of 88.5%, and an AUC of 0.831, hence further enabling fully remote assessments. These computational methods demonstrate the feasibility of using sensor-based data to cluster patients based on their medication responses with further potential to assist with medication recommendations.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

A wearable patch for continuous analysis of thermoregulatory sweat at rest

The body naturally and continuously secretes sweat for thermoregulation during sedentary and routine activities at rates that can reflect underlying health conditions, including nerve damage, autonomic and metabolic disorders, and chronic stress. However, low secretion rates and evaporation pose challenges for collecting resting thermoregulatory sweat for non-invasive analysis of body physiology. Here we present wearable patches for continuous sweat monitoring at rest, using microfluidics to combat evaporation and enable selective monitoring of secretion rate. We integrate hydrophilic fillers for rapid sweat uptake into the sensing channel, reducing required sweat accumulation time towards real-time measurement. Along with sweat rate sensors, we integrate electrochemical sensors for pH, Cl - , and levodopa monitoring. We demonstrate patch functionality for dynamic sweat analysis related to routine activities, stress events, hypoglycemia-induced sweating, and Parkinson’s disease. By enabling sweat analysis compatible with sedentary, routine, and daily activities, these patches enable continuous, autonomous monitoring of body physiology at rest.

47 OTHER INSTRUMENTATION↗
DOE OSTI2026

Bioproduction, bioprotection, and biocontainment in multi-kingdom microbial systems with 3D spatial control

Engineered living materials (ELMs) are a class of hybrid materials that include engineered microbes encapsulated by a polymer matrix. The biotic and abiotic components define the ELMs design space and can be altered to improve performance and function. While current synthetic materials in the field display robust biocompatibility with both native and engineered living systems, we have a limited understanding of how to leverage three-dimensional (3D) form factors to spatially organize and control microbial dynamics within the material. Motivated by this knowledge gap, we employed extrusion-based 3D printing to fabricate multi-kingdom hydrogel constructs for the encapsulation of both single and multi-kingdom microbial systems. Core–shell cubic constructs enabled the spatial organization of a constitutive multi-kingdom system of levodopa (L-DOPA)-producing E. coli and betaxanthins (BXN)-producing S. cerevisiae. This spatial organization in 3D materials can introduce precise control over bioproduction, bioprotection, and biocontainment features that are critical to the efficacy of current ELMs. The relative spatial organization of the organisms, as well as the surface area-to-volume ratio were investigated to determine how these design elements impact microbial behavior (metabolite production, growth, expression, and cell distribution) over time. We demonstrated that F127-bis-urethane methacrylate (F127-BUM) core–shell geometries enable the hierarchical 3D printing of multi-kingdom constructs, offering customizable control over bioproduction, bioprotection, and biocontainment. With the optimization of these core–shell structures for continuous bioproduction, these ELMs could be deployed as compact and sustainable bioreactors in remote environments.

additive manufacturing↗
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↗
NASA NTRS1991

Effects of systemic carbidopa on dopamine synthesis in rat hypothalamus and striatum

Significant concentrations of carbidopa (CD) were found in rat hypothalamus, striatum, and in striatal microdialysis efflux after intraperitoneal administration of the drug. Efflux levels peaked one hour after administration of 100 mg/kg at 0.37 microg/kg or about 2 percent of serum levels. Concurrent CD levels in hypothalamus and striatum were about 2.5 percent and 1.5 percent, respectively, of corresponding serum levels. Levels of dopamine and its principal metabolites in striatal efflux were unaffected. The removal of the brain blood by saline perfusion decreased the striatal and hypothalamic CD concentrations only by 33 percent and 16 percent, respectively. In other rats receiving both CD and levodopa (LD), brain L-dopa, dopamine, and 3,4-dihydroxyphenvlacetic acid (DOPAC) levels after one hour tended to be proportionate to LD dose. When the LD dose remained constant, increasing the CD dose dose-dependently enhanced L-dopa levels in the hypothalamus and striatum. However, dopamine levels did not increase but, in contrast, decreased dose-dependently (although significantly only in the hypothalamus). CD also caused dose-dependent decrease in striatal 3-O-methyldopa (3-OMD) and in striatal and hypothalamic homovanillic acid (HVA), when the LD dose was 50 mg/kg. We conclude that, at doses exceedimg 50 mg/kg, sufficient quantities of CD enter the brain to inhibit dopamine formation, especially in the hypothalamus. Moreover, high doses of LD/CD, both of which are themselves catechols, can inhibit the O-methylation of brain catecholamines formed from the LD.

Kaakkola, S.↗
NASA NTRS1992

Effects of systemic carbidopa on dopamine synthesis in rat hypothalamus and striatum

Significant concentrations of carbidopa (CD) were found in rat hypothalamus, striatum, and in striatal microdialysis efflux after intraperitoneal administration of the drug. Efflux levels peaked one hour after administration of 100 mg/kg at 0.37 micrograms/ml, or about 2% of serum levels. Concurrent CD levels in hypothalamus and striatum were about 2.5% and 1.5%, respectively, of corresponding serum levels. Levels of dopamine and its principal metabolites in striatal efflux were unaffected. The removal of the brain blood by saline perfusion decreased the striatal and hypothalamic CD concentrations only by 33% and 16%, respectively. In other rats receiving both CD and levodopa (LD), brain L-dopa, dopamine and 3,4-dihydroxyphenylacetic acid (DOPAC) levels after one hour tended to be proportionate to LD dose. When the LD dose remained constant, increasing the CD dose dose-dependently enhanced L-dopa levels in the hypothalamus and striatum. However dopamine levels did not increase but, in contrast, decreased dose-dependently (although significantly only in the hypothalamus). CD also caused dose-dependent decrease in striatal 3-O-methyldopa (3-OMD) and in striatal and hypothalamic homovanillic acid (HVA), when the LD dose was 50 mg/kg. We conclude that, at doses exceeding 50 mg/kg, sufficient quantities of CD enter the brain to inhibit dopamine formation, especially in the hypothalamus. Moreover, high doses of LD/CD, both of which are themselves catechols, can inhibit the O-methylation of brain catecholamines formed from the LD.

NASA Discipline Regulatory Physiology↗