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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 613 records · Page 34
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

The loss or absence of minimal residual disease of <0·1% at any time after two cycles of consolidation chemotherapy in CBFB–MYH11 ‐positive acute myeloid leukaemia indicates poor prognosis

Summary No consensus has been reached on the relationship between CBFB‐MYH11 copies and prognosis. Of 1525 acute myeloid leukemia (AML) patients, 58 with CBFB‐MYH11‐positive AML (16/58 patients with c‐kit mutation) were retrospectively analyzed with a median follow‐up duration of 29.8 (range: 4.8–74.4) months. Of these, 25/58 (43.1%) patients underwent allogeneic hematopoietic stem cell transplantation (allo‐HSCT), 10 of whom had the c‐kit mutation. Of the 33 patients who did not undergo allo‐HSCT, recurrence in patients with CBFB‐MYH11/ABL level >0.1% at any time after two consolidation cycles was significantly higher than in patients with CBFB‐MYH11/ABL level <0.1% (61.9% vs. 0%, P = 0.001); further, the 3‐year relapse‐free survival (RFS; 31.4% vs. 100%, P = 0.004) and event‐free survival (EFS; 33.1% vs. 100%, P = 0.004) were significantly decreased in patients with CBFB‐MYH11/ABL level >0.1% at any time after two consolidation cycles. The 3‐year RFS and EFS rates were lower in patients who did not receive allo‐HSCT than in those who did (31.4% vs 84.6%, P = 0.000; 31.4% vs. 80.8%, P = 0.001). CBFB‐MYH11‐positive AML patients with CBFB‐MYH11/ABL level >0.1% at any time after two cycles of consolidation had poor prognoses, and allo‐HSCT could improve their survival.

Duan, Wenbing↗
DOE OSTI2025

Switchgrass Steroidal Saponins Reduce Fungal Disease but Decrease Yeast Fermentation Yield

Increasing the production of bioproducts from lignocellulosic feedstocks requires improvement in both field production and biorefinery efficiency. When plant traits arise that improve field production but decrease biofuel yield, these trade-offs can represent challenges in the entire production process. To examine trade-offs between field and production traits, we examined factors underlying switchgrass resistance to fungal rust pathogens in field conditions and factors that impede yeast fermentation in the lab using repeated measurements on a switchgrass genetic diversity panel. We found that the same switchgrass genotypes that showed high fungal pathogen resistance also showed recalcitrance to yeast fermentation. These switchgrass genotypes were mostly from the Atlantic genetic group, which had high levels of specialized metabolites of the saponin class. Among 1589 metabolites identified through metabolomics, we found that saponins were among the most likely to explain variation in both rust infection and fermentation yield using random forest feature selection, and that only four of these were sufficient to explain 57.9% of the variation in rust susceptibility. Through follow-up testing in recalcitrant biomass, we found that the bacterium Zymomonas mobilis does not suffer the same inhibition as the yeast Saccharomyces cerevisiae, and that the addition of ergosterol (thought to be the fungal cellular target of saponin inhibition) rescues yeast fermentation. Several lines of evidence point to a central role for saponins as key metabolites protecting switchgrass from fungal pathogens and interfering with yeast fermentation, underscoring an ongoing need for collaboration between plant breeders and biofuel production scientists.

VanWallendael, Acer [North Carolina State Universi↗
DOE OSTI2023

Immune checkpoint analysis of T‐cell responses to pp65 and IE‐1 antigens in end‐stage lung diseases

Abstract Lung transplant (LTX) patients are at high risk of cytomegalovirus (CMV) infection, which is often associated with high mortality and morbidity. Reactivation of CMV causes cell injury due to the cytopathic effect of viral replication and triggering of T cell immunity. The aim of this study was to compare expression of immune checkpoints (ICs) (PD‐1, CTLA‐4, LAG‐3 and TIGIT) in CD4, CD8 and CD56 and activation markers CD137, CD154 and CD69 of end‐stage patients awaiting lung transplant. Eighteen pre‐LTX positive for anti‐CMV IgG titres and 18 healthy subjects were enrolled. IC and activation markers have been evaluated through flow cytometric analysis in HC and pre‐LTX patients. Reactive (QF+) and unreactive (QF−) patients were stratified according to QuantiFERON‐CMV assays. ICs' and activation markers' expression were determined before and after in vitro stimulation with pp‐65 and IE‐1 antigens. Lower expression of PD‐1 was observed in CD4 and CD8 cells of pre‐LTX patients than controls, whereas CTLA4 appeared upregulated in CD56 and CD8 cells. TIGIT is increased on the surface of CD4, CD8 and NK cells after peptide stimulation in QF‐negative patients and PD‐1 is only downregulated after stimulation in the QF‐positive patients. This study provides new evidence of immune dysregulation in patients with end‐stage lung disorders, particularly in relation to immune checkpoint cell biology. The change in QF+ mostly happens on cytotoxic cells NK and CD8, while the changes in QF− were observed in adaptive immune cells, including CD4 and CD8.

Bergantini, Laura↗
DOE OSTI2021

Early initiation of prophylactic anticoagulation for prevention of coronavirus disease 2019 mortality in patients admitted to hospital in the United States: cohort study

Early initiation of prophylactic anticoagulation compared with no anticoagulation among patients admitted to hospital with covid-19 was associated with a decreased risk of 30 day mortality and no increased risk of serious bleeding events. These findings provide strong real world evidence to support guidelines recommending the use of prophylactic anticoagulation as initial treatment for patients with covid-19 on hospital admission.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Pharmacologic or genetic interference with atrogene signaling protects against glucocorticoid-induced musculoskeletal and cardiac disease

Despite their beneficial actions as immunosuppressants, glucocorticoids (GC) have devastating effects on the musculoskeletal and cardiac systems, as long-term treated patients exhibit high incidence of falls, bone fractures, and cardiovascular events. Herein, we show that GC upregulate simultaneously in bone, skeletal muscle, and the heart the expression of E3 ubiquitin ligases (atrogenes), known to stimulate the proteasomal degradation of proteins. Activation of vitamin D receptor (VDR) signaling with the VDR ligands calcitriol or eldecalcitol prevented GC-induced atrogene upregulation in vivo and ex vivo in bone/muscle organ cultures and preserved tissue structure/mass and function of the 3 tissues in vivo. Direct pharmacologic inhibition of the proteasome with carfilzomib also conferred musculoskeletal protection. Genetic loss of the atrogene MuRF1-mediated protein ubiquitination in ΔRING mice afforded temporary or sustained protection from GC excess in bone or skeletal and heart muscle. We concluded that the atrogene pathway downstream of MuRF1 underlies GC action in bone, muscle, and the heart, and it can be pharmacologically or genetically targeted to confer protection against the damaging actions of GC simultaneously in the 3 tissues.

Research & Experimental Medicine↗
DOE OSTI2024

Human limits in machine learning: prediction of potato yield and disease using soil microbiome data

Abstract Background The preservation of soil health is a critical challenge in the 21st century due to its significant impact on agriculture, human health, and biodiversity. We provide one of the first comprehensive investigations into the predictive potential of machine learning models for understanding the connections between soil and biological phenotypes. We investigate an integrative framework performing accurate machine learning-based prediction of plant performance from biological, chemical, and physical properties of the soil via two models: random forest and Bayesian neural network. Results Prediction improves when we add environmental features, such as soil properties and microbial density, along with microbiome data. Different preprocessing strategies show that human decisions significantly impact predictive performance. We show that the naive total sum scaling normalization that is commonly used in microbiome research is one of the optimal strategies to maximize predictive power. Also, we find that accurately defined labels are more important than normalization, taxonomic level, or model characteristics. ML performance is limited when humans can’t classify samples accurately. Lastly, we provide domain scientists via a full model selection decision tree to identify the human choices that optimize model prediction power. Conclusions Our study highlights the importance of incorporating diverse environmental features and careful data preprocessing in enhancing the predictive power of machine learning models for soil and biological phenotype connections. This approach can significantly contribute to advancing agricultural practices and soil health management.

Aghdam, Rosa↗
DOE OSTI2023

Multi-dimensional resilience: A quantitative exploration of disease outcomes and economic, political, and social resilience to the COVID-19 pandemic in six countries

The COVID-19 pandemic has highlighted a need for better understanding of countries’ vulnerability and resilience to not only pandemics but also disasters, climate change, and other systemic shocks. A comprehensive characterization of vulnerability can inform efforts to improve infrastructure and guide disaster response in the future. In this paper, we propose a data-driven framework for studying countries’ vulnerability and resilience to incident disasters across multiple dimensions of society. To illustrate this methodology, we leverage the rich data landscape surrounding the COVID-19 pandemic to characterize observed resilience for several countries (USA, Brazil, India, Sweden, New Zealand, and Israel) as measured by pandemic impacts across a variety of social, economic, and political domains. We also assess how observed responses and outcomes (i.e., resilience) of the COVID-19 pandemic are associated with pre-pandemic characteristics or vulnerabilities, including (1) prior risk for adverse pandemic outcomes due to population density and age and (2) the systems in place prior to the pandemic that may impact the ability to respond to the crisis, including health infrastructure and economic capacity. Our work demonstrates the importance of viewing vulnerability and resilience in a multi-dimensional way, where a country’s resources and outcomes related to vulnerability and resilience can differ dramatically across economic, political, and social domains. This work also highlights key gaps in our current understanding about vulnerability and resilience and a need for data-driven, context-specific assessments of disaster vulnerability in the future.

59 BASIC BIOLOGICAL SCIENCES↗