Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “spinal cord injuries”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

6 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Bioactive scaffolds with enhanced supramolecular motion promote recovery from spinal cord injury

The signaling of cells by scaffolds of synthetic molecules that mimic proteins is known to be effective in the regeneration of tissues. Here, we describe peptide amphiphile supramolecular polymers containing two distinct signals and test them in a mouse model of severe spinal cord injury. One signal activates the transmembrane receptor β1-integrin and a second one activates the basic fibroblast growth factor 2 receptor. By mutating the peptide sequence of the amphiphilic monomers in nonbioactive domains, we intensified the motions of molecules within scaffold fibrils. This resulted in notable differences in vascular growth, axonal regeneration, myelination, survival of motor neurons, reduced gliosis, and functional recovery. Here, we hypothesize that the signaling of cells by ensembles of molecules could be optimized by tuning their internal motions.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Topological network analysis of patient similarity for precision management of acute blood pressure in spinal cord injury

Background: Predicting neurological recovery after spinal cord injury (SCI) is challenging. Using topological data analysis, we have previously shown that mean arterial pressure (MAP) during SCI surgery predicts long-term functional recovery in rodent models, motivating the present multicenter study in patients. Methods: Intra-operative monitoring records and neurological outcome data were extracted (n = 118 patients). We built a similarity network of patients from a low-dimensional space embedded using a non-linear algorithm, Isomap, and ensured topological extraction using persistent homology metrics. Confirmatory analysis was conducted through regression methods. Results: Network analysis suggested that time outside of an optimum MAP range (hypotension or hypertension) during surgery was associated with lower likelihood of neurological recovery at hospital discharge. Logistic and LASSO (least absolute shrinkage and selection operator) regression confirmed these findings, revealing an optimal MAP range of 76–[104-117] mmHg associated with neurological recovery. Conclusions: We show that deviation from this optimal MAP range during SCI surgery predicts lower probability of neurological recovery and suggest new targets for therapeutic intervention. Funding: NIH/NINDS: R01NS088475 (ARF); R01NS122888 (ARF); UH3NS106899 (ARF); Department of Veterans Affairs: 1I01RX002245 (ARF), I01RX002787 (ARF); Wings for Life Foundation (ATE, ARF); Craig H. Neilsen Foundation (ARF); and DOD: SC150198 (MSB); SC190233 (MSB); DOE: DE-AC02-05CH11231 (DM).

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

SIRT1: A promising therapeutic target for chronic pain

Abstract Chronic pain remains an unresolved problem. Current treatments have limited efficacy. Thus, novel therapeutic targets are urgently required for the development of more effective analgesics. An increasing number of studies have proved that sirtuin 1 (SIRT1) agonists can relieve chronic pain. In this review, we summarize recent progress in understanding the roles and mechanisms of SIRT1 in mediating chronic pain associated with peripheral nerve injury, chemotherapy‐induced peripheral neuropathy, spinal cord injury, bone cancer, and complete Freund's adjuvant injection. Emerging studies have indicated that SIRT1 activation may exert positive effects on chronic pain relief by regulating inflammation, oxidative stress, and mitochondrial dysfunction. Therefore, SIRT1 agonists may serve as potential therapeutic drugs for chronic pain.

Song, Fan‐He↗
DOE OSTI2022

Feature Selection Techniques for a Machine Learning Model to Detect Autonomic Dysreflexia

Feature selection plays a crucial role in the development of machine learning algorithms. Understanding the impact of the features on a model, and their physiological relevance can improve the performance. This is particularly helpful in the healthcare domain wherein disease states need to be identified with relatively small quantities of data. Autonomic Dysreflexia (AD) is one such example, wherein mismanagement of this neurological condition could lead to severe consequences for individuals with spinal cord injuries. We explore different methods of feature selection needed to improve the performance of a machine learning model in the detection of the onset of AD. We present different techniques used as well as the ideal metrics using a dataset of thirty-six features extracted from electrocardiograms, skin nerve activity, blood pressure and temperature. The best performing algorithm was a 5-layer neural network with five relevant features, which resulted in 93.4% accuracy in the detection of AD. The techniques in this paper can be applied to a myriad of healthcare datasets allowing forays into deeper exploration and improved machine learning model development. Through critical feature selection, it is possible to design better machine learning algorithms for detection of niche disease states using smaller datasets.

electrocardiography↗
DOE OSTI2025

Motion of Molecules in Supramolecular Scaffolds Enhances Bone Regeneration

The regeneration of human tissues is a great scientific challenge and a critical factor to achieve a long healthspan and prevent disabilities due to injury or disease. Materials chemistry can contribute to this goal with the development of bioactive supramolecular systems that can signal cells for regeneration. Recent work in our laboratory using in vivo models of spinal cord injury and cartilage regeneration has demonstrated that the motion of bioactive molecules in supramolecular scaffolds enhances receptor signaling. We report here on a novel molecular strategy to control supramolecular motion in filamentous assemblies using bone regeneration as a functional target. The supramolecular assemblies are composed of monomers that arrange, by design, with either parallel or antiparallel β-sheets, and some of them contain a terminal peptide sequence that binds BMP-2. We found that parallel β-sheet supramolecular assemblies promote greater osteogenic differentiation of progenitor cells in vitro relative to antiparallel assemblies, as well as superior quality of newly regenerated bone in a rat model of spinal fusion. Furthermore, these assemblies drastically reduce the dangerous supraphysiological dose of BMP-2 used clinically for spinal fusion. Here, we attribute the enhanced bioactivity to the weaker nature of hydrogen bonds in parallel relative to antiparallel β-sheet assemblies, which in turn allows greater supramolecular motion and cell signaling of the growth factor-binding molecules.

Anatomy↗
DOE OSTI2023

Supramolecular polymers: Dynamic assemblies of “dancing” monomers

The objective of this perspective on supramolecular polymers is to highlight how their dynamic nature based on noncovalent bonding among monomers can have a profound impact on their functions. We focus here on peptide amphiphile supramolecular polymers developed in our laboratory and use recent results on their dynamic behavior to reflect on the exciting functions that might emerge in these systems. We are greatly motivated by recent results that demonstrate unprecedented bioactivity in supramolecular polymers to address the enormous scientific challenge of finding strategies to reverse paralysis caused by traumatic injuries or disease. We suggest that future opportunities exist for novel functions in supramolecular polymers by tuning dynamic behavior through the chemical design of monomers, and also by gaining an understanding of the precise spatiotemporal mechanisms linked to supramolecular motion in these emerging polymers.

36 MATERIALS SCIENCE↗