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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 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Effects of processing on the efficacy and metabolites of Cistanche tubulosa using ultra‐performance liquid chromatography coupled with quadrupole time‐of‐flight mass spectrometry

Abstract Cistanche tubulosa (CT), a well‐known traditional Chinese medicine, has always been processed with rice wine for the treatment of kidney‐yang deficiency syndrome (KYDS) since time immemorial. To explore the effect of processing on the efficacy and metabolites of CT in vivo , a comprehensive method using ultra‐performance liquid chromatography coupled with quadrupole time‐of‐flight mass spectrometry was established for the analysis of the altered endogenous metabolites in response to the intervention of the raw and processed CT in KYDS model and the metabolites of the absorbed compounds in rats after gastric perfusion. It was shown that CT could improve KYDS, and the effect of the processed product was more significant. A total of 47 differential metabolites were identified in urine. Pathway analysis proved that purine metabolism; alanine, aspartate, and glutamate metabolism; and citrate cycle were the main pathways. Furthermore, 53 prototypes and 48 metabolites have been detected in rats. This was the first systematic research focus on the metabolites of raw and processed CT in vivo , which could provide a scientific basis for explaining the increasing efficiency of the processed CT. Moreover, it provides a valuable strategy for analyzing the chemical components and metabolites of other TCM prescriptions.

Lei, Huibo↗
DOE OSTI2022

Enzymatic Laser-Induced Graphene Biosensor for Electrochemical Sensing of the Herbicide Glyphosate

Glyphosate is a globally applied herbicide yet it has been relatively undetectable in-field samples outside of gold-standard techniques. Its presumed nontoxicity toward humans has been contested by the International Agency for Research on Cancer, while it has been detected in farmers’ urine, surface waters and crop residues. Rapid, on-site detection of glyphosate is hindered by lack of field-deployable and easy-to-use sensors that circumvent sample transportation to limited laboratories that possess the equipment needed for detection. Herein, the flavoenzyme, glycine oxidase, immobilized on platinum-decorated laser-induced graphene (LIG) is used for selective detection of glyphosate as it is a substrate for GlyOx. The LIG platform provides a scaffold for enzyme attachment while maintaining the electronic and surface properties of graphene. The sensor exhibits a linear range of 10–260 µm, detection limit of 3.03 µm, and sensitivity of 0.991 nA µm -1 . The sensor shows minimal interference from the commonly used herbicides and insecticides: atrazine, 2,4-dichlorophenoxyacetic acid, dicamba, parathion-methyl, paraoxon-methyl, malathion, chlorpyrifos, thiamethoxam, clothianidin, and imidacloprid. Sensor function is further tested in complex river water and crop residue fluids, which validate this platform as a scalable, direct-write, and selective method of glyphosate detection for herbicide mapping and food analysis.

47 OTHER INSTRUMENTATION↗
DOE OSTI2022

Mass Spectrometry-Based Approaches for Clinical Biomarker Discovery in Traumatic Brain Injury

Purpose of Review: Precision treatments to address the multifaceted pathophysiology of traumatic brain injury (TBI) are desperately needed, which has led to the intense study of fluid-based protein biomarkers in TBI. Mass Spectrometry (MS) is increasingly being applied to biomarker discovery and quantification in neurological disease to explore the proteome, allowing for more flexibility in biomarker discovery than commonly encountered antibody-based assays. In this narrative review, we will provide specific examples of how MS technology has advanced translational research in traumatic brain injury (TBI) focusing on clinical studies, and looking ahead to promising emerging applications of MS to the field of Neurocritical Care. Recent Findings: Proteomic biomarker discovery using MS technology in human subjects has included the full range of injury severity in TBI, though critically ill patients can offer more options to biofluids given the need for invasive monitoring. Blood, urine, cerebrospinal fluid, brain specimens, and cerebral extracellular fluid have all been sources for analysis. Emerging evidence suggests there are distinct proteomic profiles in radiographic TBI subtypes, and that biomarkers may be used to distinguish patients sustaining TBI from healthy controls. Metabolomics may offer a window into the perturbations of ongoing cerebral insults in critically ill patients after severe TBI. Summary: Emerging MS technologies may offer biomarker discovery and validation opportunities not afforded by conventional means due to its ability to handle the complexities associated with the proteome. In conclusion, while MS techniques are relatively early in development in the neurosciences space, the potential applications to TBI and neurocritical care are likely to accelerate in the coming decade.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Multiplexed Quantitative Proteomics in Prostate Cancer Biomarker Development

Prostate cancer (PCa) is the most common non-skin cancer among men in the United States. However, the widely used protein biomarker in PCa, prostate-specific antigen (PSA), while useful for initial detection, its use alone cannot detect aggressive PCa and can lead to overtreatment. This chapter provides an overview of PCa protein biomarker development. It reviews the state-of-the-art liquid chromatography-mass spectrometry-based proteomics technologies for PCa biomarker development, such as enhancing the detection sensitivity of low-abundance proteins through antibody-based or antibody-independent protein/peptide enrichment, enriching post-translational modifications such as glycosylation as well as information-rich extracellular vesicles, and increasing accuracy and throughput using advanced data acquisition methodologies. This chapter also summarizes recent PCa biomarker validation studies that applied those techniques in diverse specimen types, including cell lines, tissues, proximal fluids, urine, and blood, developing novel protein biomarkers for various clinical applications, including early detection and diagnosis, prognosis, and therapeutic intervention of PCa.

Prostate cancer, SRM, PRM, DIA, protein biomarker↗
DOE OSTI2025

Development of carbon nanosensor for vinpocetine at zinc oxide modified carbon matrix with anionic surfactant for clinical application

A nano-level sensing method was formulated for the trace-level detection and determination of vinpocetine (VIN) at a zinc oxide nanomaterial-based carbon matrix with immobilised anionic surfactant sodium dodecyl sulfate (ZnO-SDS/CPE) using cyclic voltammetry (CV) and square wave voltammetry (SWV) approaches. The catalytic properties of ZnO impact the electron rate of VIN activity, resulting in a threefold increase in the VIN peak response with the ZnO-SDS-modified sensor. Here, the effects of several factors, including scan rate, pH, accumulation length, modifier amount, and concentration, were investigated on the VIN peak current. The electro-oxidation of VIN by pH study involves one proton and one electron. The CV method also investigated the effect of scan rate. The charge transfer coefficient (α) is obtained to be 0.58, and the heterogeneous rate constant (k°) is estimated to be 4.46 s⁻¹. The concentration effect of VIN was studied using the SWV method. Specifically, the SWV methodology achieved the lowest detection limit compared to previously published approaches, with estimated values of the Limit of Detection (LOD) and Limit of Quantification (LOQ) being 2.2 × 10⁻⁸ M and 7.7 × 10⁻⁸ M, respectively. The trace level of VIN in tablet and urine samples was determined using the modified sensor. Moreover, the sensor demonstrates particular reproducibility and long-term stability, making it suitable for real-time applications.

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

exRNA-eCLIP intersection analysis reveals a map of extracellular RNA binding proteins and associated RNAs across major human biofluids and carriers

Although the role of RNA binding proteins (RBPs) in extracellular RNA (exRNA) biology is well established, their exRNA cargo and distribution across biofluids are largely unknown. To address this gap, we extend the exRNA Atlas resource by mapping exRNAs carried by extracellular RBPs (exRBPs). This map was developed through an integrative analysis of ENCODE enhanced crosslinking and immunoprecipitation (eCLIP) data (150 RBPs) and human exRNA profiles (6,930 samples). Computational analysis and experimental validation identified exRBPs in plasma, serum, saliva, urine, cerebrospinal fluid, and cell-culture-conditioned medium. exRBPs carry exRNA transcripts from small non-coding RNA biotypes, including microRNA (miRNA), piRNA, tRNA, small nuclear RNA (snRNA), small nucleolar RNA (snoRNA), Y RNA, and lncRNA, as well as protein-coding mRNA fragments. Computational deconvolution of exRBP RNA cargo reveals associations of exRBPs with extracellular vesicles, lipoproteins, and ribonucleoproteins across human biofluids. Overall, we mapped the distribution of exRBPs across human biofluids, presenting a resource for the community.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Development of a Universal Second-Tier Newborn Screening LC–MS/MS Method for Amino Acids, Lysophosphatidylcholines, and Organic Acids

First-tier MS-based newborn screening by flow injection analysis can have high presumptive positive rates, often due to isomeric/isobaric compounds or poor biomarker specificity. These presumptive positive samples can be analyzed by second-tier screening assays employing separations such as liquid chromatography–mass spectrometry (LC–MS/MS), which increases test specificity and drastically reduces false positive referrals. The ability to screen for multiple disorders in a single multiplexed test simplifies workflows and maximizes public health laboratories’ resources. In this study, we developed and validated a highly multiplexed second-tier method for dried blood spots using a hydrophilic interaction liquid chromatography (HILIC) column coupled to an MS/MS system. The LC–MS/MS method was capable of simultaneously detecting second-tier biomarkers for maple syrup urine disease, homocystinuria, methylmalonic acidemia, propionic acidemia, glutaric acidemia type 1, glutaric acidemia type 2, guanidinoacetate methyltransferase deficiency, short-chain acyl-CoA dehydrogenase deficiency, adrenoleukodystrophy, and Pompe disease.

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

Evaluating Retention Index Score Assumptions to Refine GC–MS Metabolite Identification

As metabolomics grows into a high-throughput and high demand research field, current metrics for the identification of small molecules in gas chromatography–mass spectrometry (GC–MS) still require manual verification. Though steps have been taken to improve scoring metrics by combining spectral similarity (SS) and retention index (RI), the problem persists. A large body of literature has analyzed and refined SS scores, but few studies have explicitly studied improvements to RI scores. Here, in this study, we examined whether uninvestigated assumptions of the RI score are valid and propose ways to improve them. Query RIs were matched to library RI with a generous window of ±35 to avoid unintentional removal of valid compound identifications. Each match was manually verified as a true positive (TP), true negative, or unknown. Metabolites with at least 30 TP identifications were included in downstream analyses, resulting in a total of 87 metabolites from samples of varying complexity and type (e.g., amino acid mixtures, human urine, fungal species, and so on.). Our results showed that the RI score assumptions of normality, consistent variance across metabolites, and a mean error centered at 0 are often violated. We demonstrated through a cross-validation analysis that modifying these underlying assumptions according to empirical metabolite-specific distributions improved the TP and negative rankings. Further, we statistically determined the minimum number of samples required to estimate distributional parameters for scoring metrics. Overall, this work proposes a robust statistical pipeline to reduce the time bottleneck of metabolite identification by improving RI scores and thus minimize the effort to complete manual verification.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Metabolites of Tobacco- and E-Cigarette-Related Nitrosamines Can Drive Cu 2+ -Mediated DNA Oxidation

Nitrosamine metabolites resulting from cigarette smoking and E-cigarette (E-cig) vaping cause DNA damage that can lead to genotoxicity. While DNA adducts of metabolites of nitrosamines 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanone (NNK) and N-nitrosonornicotine (NNN) are well-known tobacco-related cancer biomarkers, only a few studies implicate NNN and NNK in DNA oxidation in humans. NNK and NNN were found in the urine of E-cigarette users who never smoked cigarettes. This paper proposes the first chemical pathways of DNA oxidation driven by NNK and NNN metabolites in redox reactions with Cu 2+ and NADPH leading to reactive oxygen species (ROS). A microfluidic array with thin films of DNA and metabolic enzymes that make metabolites of NNN and NNK in the presence of Cu 2+ and NADPH was used to estimate relative rates of DNA oxidation. Detection by electrochemiluminescence (ECL) employed a new ECL dye [Os(tpy-benz-COOH) 2 ] 2+ that is selective for and sensitive to the primary DNA oxidation product 8-oxo-7,8-dihydro-2-deoxyguanosine (8-oxodG) in DNA. Enzyme–DNA films on magnetic beads were used to produce nitrosamine metabolites that enter ROS-forming redox cycles with Cu 2+ and NADPH, and liquid chromatography–mass spectrometry (LC–MS) was used to quantify 8-oxodG and identify metabolites. ROS were detected by optical sensors. Metabolites of NNK and NNN + Cu 2+ + NADPH generated relatively high rates of DNA oxidation. Lung is the exposure route in smoking and vaping, human lung tissue contains Cu 2+ and NADPH, and lung microsomal enzymes gave the highest rates of DNA oxidation in this study. Furthermore, E-cigarette vapor contains 6-fold more copper than that in cigarette smoke, which could exacerbate DNA oxidation.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Sequential Ammonia and Carbon Dioxide Adsorption on Pyrolyzed Biomass to Recover Waste Stream Nutrients

Untreated fecal waste in unsewered communities and reactive nitrogen (N) loading from wastewater and urine disposal are important environmental challenges. Through pyrolysis, untreated fecal waste can be converted into amine-rich carbon dioxide (CO 2 ) sorbents, the chemisorption of which tunes the surface chemistry toward ammonia (NH 3 ) uptake. We compared the N-loading potential within amine-rich pyrolyzed human solid waste through exposure to CO 2 followed by NH 3 with the N-loading potential on carboxyl-rich, pyrolyzed oxidized wood using sequential adsorption of isotopically labeled CO 2 and NH 3 . The total N content of pyrolyzed oxidized wood exposed once to NH3 was similar to native, pyrolyzed human solid waste (3.7% compared to 4.6%). However, sorption kinetics and spectroscopic evidence revealed distinct N forms between the two materials affecting subsequent CO 2 and NH 3 uptake. Tertiary amines comprised 70% of N in pyrolyzed waste, compared to only 53% in pyrolyzed oxidized wood. The high heat of adsorption of CO 2 on pyrolyzed human solid waste (50 kJ mol-1) at low concentrations (0.05 mmol CO 2 g -1 ) was indicative of CO 2 reactivity with tertiary amines, consistent with the formation of carbamic acid (Figure S1). Conversely, after exposure to NH 3 , pyrolyzed oxidized wood chemisorbed CO 2 at much higher concentrations (0.7 mmol CO 2 g-1), pointing to primary amines as the active functional moieties. Adsorption kinetics of pyrolyzed oxidized wood revealed continued, albeit diminishing NH 3 uptake with each consecutive CO 2 treatment, averaging 5.9 mmol NH 3 g -1 for the first NH 3 exposure event, and still 3.5 and 2.9 mmol NH 3 g -1 for the second and third. Penetration of 15 NH 3 and 13 CO 2 measured by NanoSIMS reached over 7 µm deep into both materials, explaining the large NH 3 capture. The significantly lower NH 3 adsorption compared to pyrolyzed oxidized wood may be a result of potassium (K) carbonate precipitation precluding a Lewis base interaction between NH 3 and chemisorbed CO 2 , suggested by a 2-fold increase in surface K concentrations after exposure of pyrolyzed human solid waste to CO 2 +NH 3 detected by XPS.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

A resource of lipidomics and metabolomics data from individuals with undiagnosed diseases

Every year individuals experience symptoms that remain undiagnosed by healthcare providers. In the United States, these rare diseases are defined as a condition that affects fewer than 200,000 individuals. However, there are an estimated 7000 rare diseases, and there are an estimated 25–30 million Americans in total (7.6–9.2% of the population as of 2018) affected by such disorders. The NIH Common Fund Undiagnosed Diseases Network (UDN) seeks to provide diagnoses for individuals with undiagnosed disease. Mass spectrometry-based metabolomics and lipidomics analyses could advance the collective understanding of individual symptoms and advance diagnoses for individuals with heretofore undiagnosed disease. Here, we report the mass spectrometry-based metabolomics and lipidomics analyses of blood plasma, urine, and cerebrospinal fluid from 148 patients within the UDN and their families, as well as from a reference population of over 100 individuals with no known metabolic diseases. The raw and processed data are available to the research community so that they might be useful in the diagnoses of current or future patients suffering from undiagnosed disorders.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Machine learning approaches to predict gestational age in normal and complicated pregnancies via urinary metabolomics analysis

The elucidation of dynamic metabolomic changes during gestation is particularly important for the development of methods to evaluate pregnancy status or achieve earlier detection of pregnancy-related complications. Some studies have constructed models to evaluate pregnancy status and predict gestational age using omics data from blood biospecimens; however, less invasive methods are desired. Here we propose a model to predict gestational age, using urinary metabolite information. In our prospective cohort study, we collected 2741 urine samples from 187 healthy pregnant women, 23 patients with hypertensive disorders of pregnancy, and 14 patients with spontaneous preterm birth. Using gas chromatography-tandem mass spectrometry, we identified 184 urinary metabolites that showed dynamic systematic changes in healthy pregnant women according to gestational age. A model to predict gestational age during normal pregnancy progression was constructed; the correlation coefficient between actual and predicted weeks of gestation was 0.86. The predicted gestational ages of cases with hypertensive disorders of pregnancy exhibited significant progression, compared with actual gestational ages. This is the first study to predict gestational age in normal and complicated pregnancies by using urinary metabolite information. Minimally invasive urinary metabolomics might facilitate changes in the prediction of gestational age in various clinical settings.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

Machine learning prediction of incidence of Alzheimer’s disease using large-scale administrative health data

Nationwide population-based cohort provides a new opportunity to build an automated risk prediction model based on individuals’ history of health and healthcare beyond existing risk prediction models. We tested the possibility of machine learning models to predict future incidence of Alzheimer’s disease (AD) using large-scale administrative health data. From the Korean National Health Insurance Service database between 2002 and 2010, we obtained de-identified health data in elders above 65 years (N = 40,736) containing 4,894 unique clinical features including ICD-10 codes, medication codes, laboratory values, history of personal and family illness and socio-demographics. To define incident AD we considered two operational definitions: “definite AD” with diagnostic codes and dementia medication (n = 614) and “probable AD” with only diagnosis (n = 2026). We trained and validated random forest, support vector machine and logistic regression to predict incident AD in 1, 2, 3, and 4 subsequent years. For predicting future incidence of AD in balanced samples (bootstrapping), the machine learning models showed reasonable performance in 1-year prediction with AUC of 0.775 and 0.759, based on “definite AD” and “probable AD” outcomes, respectively; in 2-year, 0.730 and 0.693; in 3-year, 0.677 and 0.644; in 4-year, 0.725 and 0.683. The results were similar when the entire (unbalanced) samples were used. Important clinical features selected in logistic regression included hemoglobin level, age and urine protein level. This study may shed a light on the utility of the data-driven machine learning model based on large-scale administrative health data in AD risk prediction, which may enable better selection of individuals at risk for AD in clinical trials or early detection in clinical settings.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Morphine and high-fat diet differentially alter the gut microbiota composition and metabolic function in lean versus obese mice

There are known associations between opioids, obesity, and the gut microbiome, but the molecular connection/mediation of these relationships is not understood. To better clarify the interplay of physiological, genetic, and microbial factors, this study investigated the microbiome and host inflammatory responses to chronic opioid administration in genetically obese, diet-induced obese, and lean mice. Samples of feces, urine, colon tissue, and plasma were analyzed using targeted LC-MS/MS quantification of metabolites, immunoassays of inflammatory cytokine levels, genome-resolved metagenomics, and metaproteomics. Genetic obesity, diet-induced obesity, and morphine treatment in lean mice each showed increases in distinct inflammatory cytokines. Metagenomic assembly and binning uncovered over 400 novel gut bacterial genomes and species. Morphine administration impacted the microbiome’s composition and function, with the strongest effect observed in lean mice. This microbiome effect was less pronounced than either diet or genetically driven obesity. Based on inferred microbial physiology from the metaproteome datasets, a high-fat diet transitioned constituent microbes away from harvesting diet-derived nutrients and towards nutrients present in the host mucosal layer. Considered together, these results identified novel host-dependent phenotypes, differentiated the effects of genetic obesity versus diet induced obesity on gut microbiome composition and function, and showed that chronic morphine administration altered the gut microbiome.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Electrospun porous La–Sr–Co–Ni–O nanofibers for highly sensitive non-enzymatic glucose detection

Glucose biosensors are widely used for clinical, industrial, and environmental applications. Nonenzymatic electrochemical glucose biosensors based on metal oxides with a perovskite structure have exhibited high sensitivity, excellent stability, and cost efficiency. In this work, porous La–Sr–Co–Ni–O (LSCNO) nanofibers, with an ABO 3 -type perovskite structure, were prepared through optimizing the A-site and B-site elements by electrospinning, followed with calcination at 700 °C for 5 h. Characterized by scanning and transmission electron microscopy, X-ray diffraction, and X-ray photoelectron spectroscopy, fabricated nanofibers were confirmed to be porous and nanosized polycrystalline grains with high crystallinity. A novel La 0.75 Sr 0.25 Co 0.5 Ni 0.5 O 3 -based nonenzymatic electrochemical biosensor was developed, which is sensitive to glucose because of an electrochemically catalytic mechanism, a mediated electron transfer involving Ni(II)/Ni(III) or Co(II)/Co(III), accompanying with gluconic acid complexation. The glucose biosensor presented a linear response in the range of 0.1–1.0 mM with a calibration sensitivity of 924 ± 28 μA mM –1 , a proportion of the variance of 0.9926, and a lower limit of detection of 0.083 mM, respectively, demonstrating an outstanding analytical performance. The biosensor showed no response to the most widely used anionic surfactant, sodium dodecyl sulfate, and low sensitivity to other biomolecules, such as fructose, lactose, galactose, mannose, dopamine, and ascorbic acid. A urine sample was tested by this novel nonenzymatic electrochemical biosensor by standard addition method, suggesting a potential application for clinical test.

36 MATERIALS SCIENCE↗
DOE OSTI2023

A novel inclusion complex of oxybenzone with C -methylresorcin[4]arene deters skin permeation

Oxybenzone (OXB), a very widely used sunscreen ingredient has the potential to block both UVA and UVB but can penetrate through skin. Studies have revealed its presence in the blood and urine of most humans, which may lead to long-term health effects. As the confined cavities of macrocycles can alter the physical and chemical properties of encapsulated guests, in this study, we investigated the formation of host–guest complexes between C-methylresorcin[4]arene and OXB. Combined experimental (NMR spectroscopy, UV/vis absorption, and fluorescence spectroscopy) and theoretical investigation confirmed the formation of a weak host–guest complex that had a 1 : 1 stoichiometry. Furthermore, skin permeation testing revealed that complexation by C-methylresorcin[4]arene significantly reduced the skin permeation of OXB which can potentially limit the harmful effects of this organic sunscreen.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Discovery of an ultraspecific triuret hydrolase (TrtA) establishes the triuret biodegradation pathway

Triuret (carbonyldiurea) is an impurity found in industrial urea fertilizer (<0.1% w/w) that is applied, worldwide, around 300 million pounds each year on agricultural lands. In addition to anthropogenic sources, endogenous triuret has been identified in amoeba and human urine, the latter being diagnostic for hypokalemia. The present study is the first to describe the metabolic breakdown of triuret, which funnels into biuret metabolism. We identified the gene responsible for triuret decomposition ( trtA ) in bacterial genomes, clustered with biuH , which encodes biuret hydrolase and has close protein sequence homology. TrtA is a member of the isochorismatase-like hydrolase (IHL) protein family, similarly to BiuH, and has a catalytic efficiency (k cat/ K M ) of 6 x 10 5 M –1 s –1 , a K M for triuret of 20 μM, and exquisite substrate specificity. Indeed, TrtA has four orders of magnitude less activity with biuret. Crystal structures of TrtA in apo and holo form were solved and compared with the BiuH structure. The high substrate selectivity was found to be conveyed by second shell residues around each active site. Mutagenesis of residues conserved in TrtA to the alternate consensus found in BiuHs revealed residues critical to triuret hydrolase activity but no single mutant evolved more biuret activity, and likely a combination of mutations is required to interconvert between TrtA, BiuH functions. TrtA-mediated triuret metabolism is relatively rare in recorded genomes (1–2%), but is largely found in plant-associated, nodulating, and endophytic bacteria. This study suggests functions for triuret hydrolase in certain eukaryotic intermediary processes and prokaryotic intermediary or biodegradative metabolism.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Discordant Clinical and Microbiological Outcomes Are Associated With Late Clinical Relapse in Clinical Trials for Complicated Urinary Tract Infections

Abstract Background Current US Food and Drug Administration guidance recommends that the primary endpoint for complicated urinary tract infection clinical trials be a composite of the clinical and microbiological responses, assessed at a fixed point after therapy. Although some participants meet the criteria for clinical success, they do not meet the criteria for microbiological eradication and are classified as treatment failures. These discordant outcomes have raised questions about the utility of the microbiological endpoint. Methods We analyzed participant data from 13 phase 3 clinical trials submitted to the US Food and Drug Administration (N = 4842). Outcomes were determined at the test of cure (TOC) visit, recommended to occur at least 5 days after therapy and at the late follow-up visit, recommended to occur 21 to 28 days after randomization. Clinical and microbiological success were defined as the resolution of complicated urinary tract infection symptoms present at study entry, with no new symptoms (clinical cure), and a reduction in density of the original pathogen to <103 CFU/mL on urine culture (microbiological eradication). Results Among included participants, 70.7% were concordant successes at the TOC visit, 18.0% were discordant failures (clinical cure/microbiological persistence), and 6.7% were concordant failures (clinical failure/microbiological persistence). Discordant participants were at an increased risk for clinical failure at the late follow-up visit, and the risk of late clinical failure increased with time. Conclusions Discordant clinical and microbiological outcomes at the TOC visit were associated with an increased risk of late clinical failure. Microbiological outcomes appear to be an important component of the endpoint.

Immunology↗