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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 253 records · Page 14
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Targeting Drugs Against Fibroblast Growth Factor(s)-Induced Cell Signaling

Background:: The fibroblast growth factor (FGF) family is comprised of 23 highly regulatedmonomeric proteins that regulate a plethora of developmental and pathophysiological processes,including tissue repair, wound healing, angiogenesis, and embryonic development. Binding ofFGF to fibroblast growth factor receptor (FGFR), a tyrosine kinase receptor, is facilitated by a glycosaminoglycan,heparin. Activated FGFRs phosphorylate the tyrosine kinase residues that mediateinduction of downstream signaling pathways, such as RAS-MAPK, PI3K-AKT, PLCγ, andSTAT. Dysregulation of the FGF/FGFR signaling occurs frequently in cancer due to gene amplification,FGF activating mutations, chromosomal rearrangements, integration, and oncogenic fusions.Aberrant FGFR signaling also affects organogenesis, embryonic development, tissue homeostasis,and has been associated with cell proliferation, angiogenesis, cancer, and other pathophysiologicalchanges. Objective:: This comprehensive review will discuss the biology, chemistry, and functions of FGFs,and its current applications toward wound healing, diabetes, repair and regeneration of tissues, andfatty liver diseases. In addition, specific aberrations in FGFR signaling and drugs that target FGFRand aid in mitigating various disorders, such as cancer, are also discussed in detail. Conclusion:: Inhibitors of FGFR signaling are promising drugs in the treatment of several types ofcancers. The clinical benefits of FGF/FGFR targeting therapies are impeded due to the activationof other RTK signaling mechanisms or due to the mutations that abolish the drug inhibitory activityon FGFR. Thus, the development of drugs with a different mechanism of action for FGF/FGFRtargeting therapies is the recent focus of several preclinical and clinical studies.

Pharmacology & Pharmacy↗
DOE OSTI2023

Phenopacket-tools: Building and validating GA4GH Phenopackets

The Global Alliance for Genomics and Health (GA4GH) is a standards-setting organization that is developing a suite of coordinated standards for genomics. The GA4GH Phenopacket Schema is a standard for sharing disease and phenotype information that characterizes an individual person or biosample. The Phenopacket Schema is flexible and can represent clinical data for any kind of human disease including rare disease, complex disease, and cancer. It also allows consortia or databases to apply additional constraints to ensure uniform data collection for specific goals. We present phenopacket-tools, an open-source Java library and command-line application for construction, conversion, and validation of phenopackets. Phenopacket-tools simplifies construction of phenopackets by providing concise builders, programmatic shortcuts, and predefined building blocks (ontology classes) for concepts such as anatomical organs, age of onset, biospecimen type, and clinical modifiers. Phenopacket-tools can be used to validate the syntax and semantics of phenopackets as well as to assess adherence to additional user-defined requirements. The documentation includes examples showing how to use the Java library and the command-line tool to create and validate phenopackets. We demonstrate how to create, convert, and validate phenopackets using the library or the command-line application. Source code, API documentation, comprehensive user guide and a tutorial can be found at https://github.com/phenopackets/phenopacket-tools. The library can be installed from the public Maven Central artifact repository and the application is available as a standalone archive. The phenopacket-tools library helps developers implement and standardize the collection and exchange of phenotypic and other clinical data for use in phenotype-driven genomic diagnostics, translational research, and precision medicine applications.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Inhibition of FOXP3 by stapled alpha-helical peptides dampens regulatory T cell function

Despite continuing advances in the development of novel cellular-, antibody-, and chemotherapeutic-based strategies to enhance immune reactivity, the presence of regulatory T cells (Treg cells) remains a complicating factor for their clinical efficacy. To overcome dosing limitations and off-target effects from antibody-based Treg cell deletional strategies or small molecule drugging, we investigated the ability of hydrocarbon stapled alpha-helical (SAH) peptides to target FOXP3, the master transcription factor regulator of Treg cell development, maintenance, and suppressive function. Using the crystal structure of the FOXP3 homodimer as a guide, we developed SAHs in the likeness of a portion of the native FOXP3 antiparallel coiled-coil homodimerization domain (SAH-FOXP3) to block this key FOXP3 protein-protein interaction (PPI) through molecular mimicry. We describe the design, synthesis, and biochemical evaluation of single- and double-stapled SAHs covering the entire coiled-coil expanse. We show that lead SAH-FOXP3s bind FOXP3, are cell permeable and nontoxic to T cells, induce dose-dependent transcript and protein level alterations of FOXP3 target genes, impede Treg cell function, and lead to Treg cell gene expression changes in vivo consistent with FOXP3 dysfunction. These results demonstrate a proof of concept for rationally designed FOXP3-directed peptide therapeutics that could be used as approaches to amplify endogenous immune responsiveness.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Interactions Between Diabetes Mellitus and Osteoarthritis: From Animal Studies to Clinical Data

Diabetes mellitus (DM) and osteoarthritis (OA) are commonly known metabolic diseases that affect a large segment of the world population. These two conditions share several risk factors such as obesity and aging; however, there is still no consensus regarding the direct role of DM on OA development and progression. Interestingly, both animal and human studies have yielded conflicting results, with some showing a significant role for DM in promoting OA, while others found no significant interactions between these conditions. In this review, we will discuss preclinical and clinical data that assessed the interaction between DM and OA. We will also discuss possible mechanisms associated with the effect of high glucose on the articular cartilage and chondrocytes. An emerging theme dominates the breath of published work in this area: most of the studies discussed in this review do not take into consideration the role of other factors such as the type of diabetes, age, biological sex, type of animal model, body mass index, and the use of pain medications when analyzing and interpreting data. Therefore, future studies should be more rigorous when designing experiments looking at DM and its effects on OA and should carefully account for these confounding factors, so that better approaches can be developed for monitoring and treating patients at risk of OA and DM.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

A hidden Markov model for population-level cervical cancer screening data

The Cancer Registry of Norway has been administrating a national cervical cancer screening program since 1992 by coordinating triennial cytology exam screenings for the female population between 25 and 69 years of age. Up to 80% of cancers are prevented through mass screening, but this comes at the expense of considerable screening activity and leads to overtreatment of clinically asymptomatic precancers. Here, we present a continuous-time, time-inhomogeneous hidden Markov model which was developed to understand the screening process and cervical cancer carcinogenesis in detail. By leveraging 1.7 million individual's multivariate time-series of medical exams performed over a 25-year period, we simultaneously estimate all model parameters. We show that an age-dependent model reflects the Norwegian screening program by comparing empirical survival curves from observed registry data and data simulated from the proposed model. The model can be generalized to include more detailed individual-level covariates as well as new types of screening exams. By utilizing individual screening histories and covariate data, the proposed model shows potential for improving strategies for cancer screening programs by personalizing recommended screening intervals.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

A Review of Cardiac Radioablation (CR) for Arrhythmias: Procedures, Technology, and Future Opportunities

Cardiac radioablation (CR), a new treatment for cardiac arrhythmias such as ventricular tachycardia and atrial fibrillation, has had promising clinical outcomes to date. There is consequent desire for rapid clinical adoption. However, CR presents unique challenges to radiation therapy, and it is paramount that clinical adoption be performed safely and effectively. Recent reviews comprehensively detail patient selection, clinical history, treatment outcomes, and treatment toxicities but only briefly mention the technical aspects of CR. To address this knowledge gap, this review collates currently available knowledge regarding CR technology choice and procedural details to help inform and guide clinics considering implementing their own CR program, to aid technique standardization, and to highlight areas that require further development or verification.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2021

Wide-Scale Clinical Implementation of Knowledge-Based Planning: An Investigation of Workforce Efficiency, Need for Post-automation Refinement, and Data-Driven Model Maintenance

Our purpose was to investigate the effect of automated knowledge-based planning (KBP) on real-world clinical workflow efficiency, assess whether manual refinement of KBP plans improves plan quality across multiple disease sites, and develop a data-driven method to periodically improve KBP automated planning routines.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2024

Subject-specific multi-scale modeling of the fate of inhaled aerosols

Determining the fate of inhaled aerosols in the respiratory system is essential in assessing the potential toxicity of inhaled airborne materials, responses to airborne pathogens, or in improving inhaled drug delivery. The availability of high-resolution clinical lung imaging and advances in the reconstruction of lung airways from CT images have led to the development of subject-specific in-silico 3D models of aerosol dosimetry, often referred to as computational fluid-particle-dynamics (CFPD) models. As CFPD models require extensive computing resources, they are typically confined to the upper and large airways. These models can be combined with lower-dimensional models to form multiscale models that predict the transport and deposition of inhaled aerosols in the entire respiratory tract. Understanding where aerosols deposit is only the first of potentially several key events necessary to predict an outcome, being a detrimental health effect or a therapeutic response. To that end, multiscale approaches that combine CFPD with physiologically-based pharmacokinetics (PBPK) models have been developed to evaluate the absorption, distribution, metabolism, and excretion (ADME) of toxic or medicinal chemicals in one or more compartments of the human body. CFPD models can also be combined with host cell dynamics (HCD) models to assess regional immune system responses. Here, this paper reviews the state of the art of these different multiscale approaches and discusses the potential role of personalized or subject-specific modeling in respiratory health.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

A Double-Pronged Sword: XJB-5-131 Is a Suppressor of Somatic Instability and Toxicity in Huntington’s Disease

Due to large increases in the elderly populations across the world, age-related diseases are expected to expand dramatically in the coming years. Among these, neurodegenerative diseases will be among the most devastating in terms of their emotional and economic impact on patients, their families, and associated subsidized health costs. There is no currently available cure or rescue for dying brain cells. Viable therapeutics for any of these disorders would be a breakthrough and provide relief for the large number of affected patients and their families. Neurodegeneration is accompanied by elevated oxidative damage and inflammation. While natural antioxidants have largely failed in clinical trials, preclinical phenotyping of the unnatural, mitochondrial targeted nitroxide, XJB-5-131, bodes well for further translational development in advanced animal models or in humans. Here we consider the usefulness of synthetic antioxidants for the treatment of Huntington’s disease. The mitochondrial targeting properties of XJB-5-131 have great promise. It is both an electron scavenger and an antioxidant, reducing both somatic expansion and toxicity simultaneously through the same redox mechanism. By quenching reactive oxygen species, XJB-5-131 breaks the cycle between the rise in oxidative damage during disease progression and the somatic growth of the CAG repeat which depends on oxidation.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Sequence-Defined Nanotubes Assembled from IR780-Conjugated Peptoids for Chemophototherapy of Malignant Glioma

Near-infrared (NIR) laser-induced phototherapy through NIR agents has demonstrated the great potential for cancer therapy. However, insufficient tumor killing due to the nonuniform heat or cytotoxic singlet oxygen ( 1 O 2 ) distribution over tumors from phototherapy results in tumor recurrence and inferior outcomes. To achieve high tumor killing efficacy, one of the solutions is to employ the combinational treatment of phototherapy with other modalities, especially with chemotherapeutic agents. In this paper, a simple and effective multimodal therapeutic system was designed via combining chemotherapy, photothermal therapy (PTT), and photodynamic therapy (PDT) to achieve the polytherapy of malignant glioma which is one of the most aggressive tumors in the brain. IR-780 (IR780) dye-labeled tube-forming peptoids (PepIR) were synthesized and self-assembled into crystalline nanotubes (PepIR nanotubes). These PepIR nanotubes showed an excellent efficacy for PDT/PTT because the IR780 photosensitizers were effectively packed and separated from each other within crystalline nanotubes by tuning IR780 density; thus, a self-quenching of these IR780 molecules was significantly reduced. Moreover, the efficient DOX loading achieved due to the nanotube large surface area contributed to an efficient and synergistic chemotherapy against glioma cells. Given the unique properties of peptoids and peptoid nanotubes, we believe that the developed multimodal DOX-loaded PepIR nanotubes in this work offer great promises for future glioma therapy in clinic.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

Development of LpxH Inhibitors Chelating the Active Site Dimanganese Metal Cluster of LpxH

Abstract Despite the widespread emergence of multidrug‐resistant nosocomial Gram‐negative bacterial infections and the major public health threat it brings, no new class of antibiotics for Gram‐negative pathogens has been approved over the past five decades. Therefore, there is an urgent medical need for developing effective novel antibiotics against multidrug‐resistant Gram‐negative pathogens by targeting previously unexploited pathways in these bacteria. To fulfill this crucial need, we have been investigating a series of sulfonyl piperazine compounds targeting LpxH, a dimanganese‐containing UDP‐2,3‐diacylglucosamine hydrolase in the lipid A biosynthetic pathway, as novel antibiotics against clinically important Gram‐negative pathogens. Inspired by a detailed structural analysis of our previous LpxH inhibitors in complex with K. pneumoniae LpxH ( Kp LpxH), here we report the development and structural validation of the first‐in‐class sulfonyl piperazine LpxH inhibitors, JH‐LPH‐45 ( 8 ) and JH‐LPH‐50 ( 13 ), that achieve chelation of the active site dimanganese cluster of Kp LpxH. The chelation of the dimanganese cluster significantly improves the potency of JH‐LPH‐45 ( 8 ) and JH‐LPH‐50 ( 13 ). We expect that further optimization of these proof‐of‐concept dimanganese‐chelating LpxH inhibitors will ultimately lead to the development of more potent LpxH inhibitors for targeting multidrug‐resistant Gram‐negative pathogens.

Pharmacology & Pharmacy↗
DOE OSTI2024

Reducing Sample Size While Improving Equity in Vaccine Clinical Trials: A Machine Learning-Based Recruitment Methodology with Application to Improving Trials of Hepatitis C Virus Vaccines in People Who Inject Drugs

Despite the availability of direct-acting antivirals that cure individuals infected with the hepatitis C virus (HCV), developing a vaccine is critically needed in achieving HCV elimination. HCV vaccine trials have been performed in populations with high incidence of new HCV infection such as people who inject drugs (PWID). Developing strategies of optimal recruitment of PWID for HCV vaccine trials could reduce sample size, follow-up costs and disparities in enrollment. We investigate trial recruitment informed by machine learning and evaluate a strategy for HCV vaccine trials termed PREDICTEE—Predictive Recruitment and Enrichment method balancing Demographics and Incidence for Clinical Trial Equity and Efficiency. PREDICTEE utilizes a survival analysis model applied to trial candidates, considering their demographic and injection characteristics to predict the candidate’s probability of HCV infection during the trial. The decision to recruit considers both the candidate’s predicted incidence and demographic characteristics such as age, sex, and race. We evaluated PREDICTEE using in silico methods, in which we first generated a synthetic candidate pool and their respective HCV infection events using HepCEP, a validated agent-based simulation model of HCV transmission among PWID in metropolitan Chicago. We then compared PREDICTEE to conventional recruitment of high-risk PWID who share drugs or injection equipment in terms of sample size and recruitment equity, with the latter measured by participation-to-prevalence ratio (PPR) across age, sex, and race. Comparing conventional recruitment to PREDICTEE found a reduction in sample size from 802 (95%: 642–1010) to 278 (95%: 264–294) with PREDICTEE, while also reducing screening requirements by 30%. Simultaneously, PPR increased from 0.475 (95%: 0.356–0.568) to 0.754 (95%: 0.685–0.834). Even when targeting a dissimilar maximally balanced population in which achieving recruitment equity would be more difficult, PREDICTEE is able to reduce sample size from 802 (95%: 642–1010) to 304 (95%: 288–322) while improving PPR to 0.807 (95%: 0.792–0.821). PREDICTEE presents a promising strategy for HCV clinical trial recruitment, achieving sample size reduction while improving recruitment equity.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

Statistically-driven Experimental Design to Improve Reference-free Quantification of Small Molecules by Liquid Chromatography-Mass Spectrometry

Non-targeted analysis of small molecules and metabolites in unknown, complex samples using liquid chromatography-tandem mass spectrometry remains challenging. One of the main bottlenecks is the extensive unannotated regions of metabolomics mass spectrometry data, resulting in knowledge gaps. Small molecule annotation in mass spectrometry data has conventionally relied on reference standards and libraries for compound identification and confirmation, which can constrain compound identification to those molecules already known, thus limiting the ability to discover new knowledge and new markers. Retention time prediction can facilitate and expedite unknown compound identification in non-targeted analysis of complex metabolomics samples. Additionally, accurate retention time predictions can also inform sample mixture design for LC-MS/MS analyses. However, current machine learning-based methods for retention time prediction are typically developed for specific chromatographic platforms and are not generalizable across scales. And while technologies and methods to improve reference-free metabolite identification for more comprehensive annotation of unknowns has received much attention, development of the same for quantitation without reference standards has been much more limited, despite its importance in toxicological, environmental, food safety, forensics, and clinical applications. We believe that a reference-free quantitation strategy that exploits mass spectrometry data already collected for reference-free identification can provide much more insight on unknowns, and move the metabolomics field for more complete unknowns characterization. As such, we pursue two efforts to improve upon current state-of-the-art methods in non-targeted analysis: (1) machine learning-based retention time prediction and (2) statistical design of experiments framework for reference-free quantitation. In this work, we develop and demonstrate (1) a generalizable retention time prediction capability across chromatographic conditions and scales, and (2) a statistical design-based framework for response factor contribution elucidation and reference-free quantitation. Evaluation of our retention time prediction model, PrediToR, showed approximately 24% improvement over current models, and we observed approximately 10X improvement in concentration estimation accuracy from our statistical design-based response factor model over a primarily ionization efficiency-based model. We expect that future efforts to improve upon these new capabilities will further advance non-targeted analysis of small molecules towards truly reference-free metabolomics.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Development of 225 Ac Production from Low Isotopic Dilution 229 Th

The promise of 225 Ac targeted alpha therapies has been on the horizon for the last two decades. TerraPower Isotopes are uniquely suited to produce clinically relevant quantities of 225 Ac through the decay of 229 Th. Herein, a rapid processing scheme to isolate radionuclidic and radioisotopically pure 225 Ac in good yield (98%) produced from 229 Th that contains significant quantities of 228 Th activity is described. The characterization of each step of the process is presented along with the detailed characterization of the resulting 225 Ac isotopic starting material that will support the cancer research and development efforts.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Molecular basis for antiviral activity of two pediatric neutralizing antibodies targeting SARS-CoV-2 Spike RBD

Neutralizing antibodies (NAbs) hold great promise for clinical interventions against SARS-CoV-2 variants of concern (VOCs). Understanding NAb epitopedependent antiviral mechanisms is crucial for developing vaccines and therapeutics against VOCs. Here we characterized two potent NAbs, EH3 and EH8, isolated from an unvaccinated pediatric patient with exceptional plasma neutralization activity. EH3 and EH8 cross-neutralize the early VOCsandmediatestrong Fc-dependent effector activity in vitro. Structural analyses of EH3 and EH8 in complex with the receptor-binding domain (RBD) revealed the molecular determinantsoftheepitope-drivenprotectionandVOCevasion.WhileEH3represents the prevalent IGHV3-53 NAb whose epitope substantially overlaps with the ACE2 binding site, EH8 recognizes a narrow epitope exposed in both RBD-up and RBD-down conformations. When tested in vivo, a single-dose prophylactic administration of EH3 fully protected stringent K18-hACE2 micefrom lethal challenge with Delta VOC. Our study demonstrates that protective NAbs responses converge in pediatric and adult SARS-CoV-2 patients.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Tumor-derived LIF promotes chemoresistance via activating tumor-associated macrophages in gastric cancers

Chemotherapy is the preferred clinical treatment for advanced stage gastric cancer (GC) patients, of which efficacy could be markedly impaired due to the development of chemoresistance. Alternatively activated or M2-type tumor associated macrophages (TAMs) are recruited under chemotherapy and are highly implicated in the chemoresistance development, but underlying molecular mechanism for TAM activation is largely unknown. Here, we present that tumor-derived Leukemia inhibitory factor (LIF) induced by chemo drugs represses the chemo sensitivity of gastric tumor cells in a TAM-dependent manner. Mechanistically, cisplatin-induced HIF1α signaling activation directly drive the transcription of LIF, which promotes the resistance of gastric tumors to chemo drug. Further study revealed that tumor cell-derived LIF stimulates macrophages into tumor-supporting M2-type phenotype via activating STAT3 signaling pathway. Therapeutically, blocking LIF efficiently elevates chemo sensitivity of tumor cells and further represses the growth rates of tumors under chemotherapy. Therefore, our study reveals a novel insight in understanding the cross talking between tumor cells and immune cells and provides new therapeutic targets for gastric cancer.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

C10-Benzoate Esters of Anhydrotetracycline Inhibit Tetracycline Destructases and Recover Tetracycline Antibacterial Activity

Tetracyclines (TCs) are an important class of antibiotics threatened by enzymatic inactivation. These tetracycline-inactivating enzymes, also known as tetracycline destructases (TDases), are a subfamily of class A flavin monooxygenases (FMOs) that catalyze hydroxyl group transfer and oxygen insertion (Baeyer–Villiger type) reactions on TC substrate scaffolds. Semisynthetic modification of TCs (e.g., tigecycline, omadacycline, eravacycline, and sarecycline) has proven effective in evading certain resistance mechanisms, such as ribosomal protection and efflux, but does not protect against TDase-mediated resistance. Here, we report the design, synthesis, and evaluation of a new series of 22 semisynthetic TDase inhibitors that explore D-ring substitution of anhydrotetracycline (aTC) including 14 C10-benzoate ester and eight C9-benzamides. Overall, the C10-benzoate esters displayed enhanced bioactivity and water solubility compared to the corresponding C9-benzamides featuring the same heterocyclic aryl side chains. The C10-benzoate ester derivatives of aTC were prepared in a high-yield one-step synthesis without the need for protecting groups. The C10-esters are water-soluble, stable toward hydrolysis, and display dose-dependent rescue of tetracycline antibiotic activity in E. coli expressing two types of tetracycline destructases, represented by TetX7 (Type 1) and Tet50 (Type 2). The best inhibitors recovered tetracycline antibiotic activity at concentrations as low as 2 μM, producing synergistic scores <0.5 in the fractional inhibitory concentration index (FICI) against TDase-expressing strains of E. coli and clinical P. aeruginosa. The C10-benzoate ester derivatives of aTC reported here are promising new leads for the development of tetracycline drug combination therapies to overcome TDase-mediated antibiotic resistance.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Preparing for the next pandemic via transfer learning from existing diseases with hierarchical multi-modal BERT: a study on COVID-19 outcome prediction

Abstract Developing prediction models for emerging infectious diseases from relatively small numbers of cases is a critical need for improving pandemic preparedness. Using COVID-19 as an exemplar, we propose a transfer learning methodology for developing predictive models from multi-modal electronic healthcare records by leveraging information from more prevalent diseases with shared clinical characteristics. Our novel hierarchical, multi-modal model ( $${\textsc {TransMED}}$$ T R A N S MED ) integrates baseline risk factors from the natural language processing of clinical notes at admission, time-series measurements of biomarkers obtained from laboratory tests, and discrete diagnostic, procedure and drug codes. We demonstrate the alignment of $${\textsc {TransMED}}$$ T R A N S MED ’s predictions with well-established clinical knowledge about COVID-19 through univariate and multivariate risk factor driven sub-cohort analysis. $${\textsc {TransMED}}$$ T R A N S MED ’s superior performance over state-of-the-art methods shows that leveraging patient data across modalities and transferring prior knowledge from similar disorders is critical for accurate prediction of patient outcomes, and this approach may serve as an important tool in the early response to future pandemics.

59 BASIC BIOLOGICAL SCIENCES↗