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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 685 records · Page 38
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Data-driven key performance indicators and datasets for building energy flexibility: A review and perspectives

Energy flexibility, through short-term demand-side management (DSM) and energy storage technologies, is now seen as a major key to balancing the fluctuating supply in different energy grids with the energy demand of buildings. This is especially important when considering the intermittent nature of ever-growing renewable energy production, as well as the increasing dynamics of electricity demand in buildings. This paper provides a holistic review of (1) data-driven energy flexibility key performance indicators (KPIs) for buildings in the operational phase and (2) open datasets that can be used for testing energy flexibility KPIs. The review identifies a total of 48 data-driven energy flexibility KPIs from 87 recent and relevant publications. These KPIs were categorized and analyzed according to their type, complexity, scope, key stakeholders, data requirement, baseline requirement, resolution, and popularity. Moreover, 330 building datasets were collected and evaluated. Of those, 16 were deemed adequate to feature building performing demand response or building-to-grid (B2G) services. The DSM strategy, building scope, grid type, control strategy, needed data features, and usability of these selected 16 datasets were analyzed. This review reveals future opportunities to address limitations in the existing literature: (1) developing new data-driven methodologies to specifically evaluate different energy flexibility strategies and B2G services of existing buildings; (2) developing baseline-free KPIs that could be calculated from easily accessible building sensors and meter data; (3) devoting non-engineering efforts to promote building energy flexibility, standardizing data-driven energy flexibility quantification and verification processes; and (4) curating and analyzing datasets with proper description for energy flexibility assessm.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2015

Sinomenine inhibits breast cancer cell invasion and migration by suppressing NF-κB activation mediated by IL-4/miR-324-5p/CUEDC2 axis

Nuclear factor kappa-light-chain-enhancer of activated B cells (NF-κB) is a vital transcription factor that regulates multiple important biological processes, including the epithelial–mesenchymal transition (EMT) and metastasis of breast cancer. Sinomenine is an isoquinoline well known for its remarkable curative effect on rheumatic and arthritic diseases and can induce apoptosis of several cancer cell types. Recently, sinomenine was reported as a tumor suppressor via inhibiting cell proliferation and inducing apoptosis. However, the role and mechanism of sinomenine in invasion and metastasis of breast cancer are largely unknown. Here, we report that sinomenine suppressed the invasion and migration of MDA-MB-231 and 4T1 breast cancer cells in a dose-dependent manner. We detected binding of NF-κB to the inhibitor of NF-κB (IκB) after the MDA-MB-231 cells were treated with 0.25, 0.5, and 1 mM sinomenine. Co-IP analysis revealed that sinomenine enhanced the binding of NF-κB and IκB in a dose-dependent manner, suggesting that sinomenine had an effect on inactivation of NF-κB. Western blotting and ELISA approaches indicated that the suppression effect was closely associated with the phosphorylation of IκB kinase (IKK) and its negative regulator CUEDC2. Sinomenine treatment decreased miR-324-5p expression, thus increased the level of its target gene CUEDC2, and then blocked the phosphorylation of IKK through altering the upstream axis. Finally, transfection of a miR-324-5p mimic inhibited the suppression of invasion and metastasis of MDA-MB-231 and 4T1 cell by sinomenine, providing evidence that sinomenine treatment suppressed breast cancer cell invasion and metastasis via regulation of the IL4/miR-324-5p/CUEDC2 axis. Our findings reveal a novel mechanism by which sinomenine suppresses cancer cell invasion and metastasis, i.e., blocking NF-κB activation. - Highlights: • Sinomenine reduced invasion and migration of MDA-MB-231 and 4T1 breast cancer cells. • Sinomenine enhanced combination of NF-κB with IκB and blocked NF-κB activation. • Sinomenine promoted CUEDC2 expression and suppressed IκB kinase phosphorylation. • Sinomenine reduced IL-4 and miR-324-5p levels. • MiR-324-5p inhibited the suppression of invasion and metastasis by sinomenine.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

CD4 down regulation and raft dissociation by the non-depleting YTS177 antibody hinder murine T helper cell activities

Non-depleting YTS177 anti-CD4 monoclonal antibody (MoAb) has been reported to lead to antigen-specific immunotolerance in allograft transplantation and autoimmune diabetes, as well as possibly to inhibition of allergic inflammation in mice. However, the molecular mechanisms underlying hyporesponsive T cell responses induced by YTS177 MoAb remain elusive. Herein, we demonstrate that the YTS177 MoAb increases the levels of anergy factors p27{sup kip1} and Cbl-b, inhibits IL-2 production, and impairs calcium mobilization in activated T cells in vitro. YTS177 MoAb suppresses OVA-driven proliferation of DO11.10 CD4{sup +} T cells in vivo as well. Mechanistically, YTS177 MoAb induces tolerance by causing CD4 down-regulation through clathrin-dependent and raft dissociation. The results obtained in this study lead us to propose novel protective or curative approaches to CD4 T cell-mediated diseases.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2017

shRNA interference of NLRP3 inflammasome alleviate ischemia reperfusion-induced myocardial damage through autophagy activation

Myocardial ischemia-reperfusion (I/R) injury always occur during the recovery of myocardial blood supply with high morbidity and mortality. Although, various therapeutic schedules were applied in clinic, there are real problems that have to be resolved on curative effect. Nod-like receptor protein 3 (NLRP3) inflammasome has moderation effects on cellular damage and inflammatory reaction after I/R injury. Our research aims to investigate a more effective approach to restrain the activation of NLRP3 inflammasome in treating myocardial I/R injury. Results indicated that cell viability, Bax/Bcl-2 expression were affected hardly by sh-NLRP3 transfection in normal cells. However, the decreased cell viability and increased Bax/Bcl-2 expression level caused by I/R were remarkably suppressed through sh-NLRP3 transfection. Besides that, the reduced levels of pro-autophagy proteins (Beclin1, Agt7, LC3II/LC3I) while enhanced level of anti-autophagy protein (p62) and apoptosis-related proteins (Bax/Bcl-2) were significantly repressed via sh-NLRP3 transfection. Nevertheless, the autophagy inhibitor 3 MA could reverse the results. Moreover, in vivo experiment suggested that NLRP3 was up-regulated in wild type (WT) rats with I/R injury. The expansion of infarct size induced by ischemia was tremendously constricted in NLRP3 knockout (KO) rats. NLRP3 silence had nearly no impact on myocardial enzymes (AST, LDH and CK) expressions, inflammatory factors (TNF-α and IL-1β) expressions and cell apoptosis in rats without I/R injury. Nonetheless, the elevated levels of myocardial enzymes, inflammatory factors and cell apoptosis caused by I/R injury were vastly inhibited in NLRP3 KO rats. Furthermore, NLRP3 KO itself would lead to higher level of pro-autophagy proteins (Beclin1, Agt7, LC3II/LC3I) while lower level of anti-autophagy protein (p62) in vivo. The decreased expressions of pro-autophagy proteins while increased expressions of anti-autophagy protein induced by I/R injury were remarkably suppressed by NLRP3 KO. Taken together, our study indicated that shRNA interference of NLRP3 inflammasome attenuated myocardial I/R injury via autophagy activation. These findings demonstrated that NLRP3 KO may a promising therapy in myocardial I/R injury.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2018

GSDME mediates caspase-3-dependent pyroptosis in gastric cancer

Highlights: • Pyroptosis can be fired in gastric cancer. • GSDME switches chemotherapy induced apoptosis to pyroptosis in GSDME high expressed gastric cancer cells. • GSDME mediates cancer cell pyroptosis dependent on caspase-3 cleavage. Gastric cancer is a malignancy that starts from the cells in the stomach with relatively low overall survival rate. Chemotherapy following resection surgery has been recommended as a curative strategy for gastric cancer. However, the mechanism of the chemotherapy drugs on gastric cancer is not completely understood. Pyroptosis is a form of programmed cell death and plays critical role in immunity. The role of pyroptosis on cancer cells is less known. In this study, we treated SGC-7901 and MKN-45 with 5-FU and found that the cell viability was significantly decreased. The release of LDH and the percentage of PI and APC Annexin-V double positive cells after 5-FU treatment were elevated compared to control group. Moreover, there were large bubbles blowing from the membrane of 5-FU-treated cells and the cleavage of GSDME but not GSDMD, which were blocked by the silence or specific inhibitor of caspase-3. Additionally, GSDME knockout by CRISPR-Cas9 switched 5-FU induced pyroptosis into apoptosis in SGC-7901. In conclusion, our findings firstly revealed that GSDME switches chemotherapy drug-induced caspase-3 dependent apoptosis into pyroptosis in gastric cancer cells.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2018

Uncovering transcription factor and microRNA risk regulatory pathways associated with osteoarthritis by network analysis

Osteoarthritis (OA) is the most common form of joint disease. The development of inflammation have been considered to play a key role during the progression of OA. Regulatory pathways are known to play crucial roles in many pathogenic processes. Thus, deciphering these risk regulatory pathways is critical for elucidating the mechanisms underlying OA. We constructed an OA-specific regulatory network by integrating comprehensive curated transcription and post-transcriptional resource involving transcription factor (TF) and microRNA (miRNA). To deepen our understanding of underlying molecular mechanisms of OA, we developed an integrated systems approach to identify OA-specific risk regulatory pathways. In this study, we identified 89 significantly differentially expressed genes between normal and inflamed areas of OA patients. We found the OA-specific regulatory network was a standard scale-free network with small-world properties. It significant enriched many immune response-related functions including leukocyte differentiation, myeloid differentiation and T cell activation. Finally, 141 risk regulatory pathways were identified based on OA-specific regulatory network, which contains some known regulator of OA. The risk regulatory pathways may provide clues for the etiology of OA and be a potential resource for the discovery of novel OA-associated disease genes.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2018

Lnc-SNHG1 may promote the progression of non-small cell lung cancer by acting as a sponge of miR-497

Highlights: • lnc-SNHG1 promotes the progression of non-small cell lung cancer(NSCLC). • lnc-SNHG1 might modulate cell proliferation, migration and invasion by sponging miR-497. • The effect of lnc-SNHG1 on NSCLC could be rescued by miR-497. • lnc-SNHG1 promotes cancer cell motility in a miR-497-dependent manner via IGF1-R. Lnc-SNHG1 (small nucleolar RNA host gene 1) is considered an important regulating factor in several types of cancers. However, the biological functions and underlying molecular mechanisms in which lnc-SNHG1 is involved in non-small cell lung cancer (NSCLC) still need to be explored. In this study, we investigated the detailed effects and possible molecular mechanisms. The transcript level of lnc-SNHG1 was higher in lung adenocarcinoma specimens and NSCLC cell lines than in noncancer tissue and cells. The level of expression was positively correlated with invasiveness and was negatively correlated with the level of miR-497 in vivo and in vitro. In exploring the regulatory mechanism, we found that lnc-SNHG1 might modulate tumor growth by sponging miR-497. The inhibitory effect of si-lnc-SNHG1 on NSCLC cell proliferation, migration and invasion could be rescued by miR-497 inhibition, while the overexpression of miR-497 could reverse the effect of lnc-SNHG1 overexpression. Furthermore, our study demonstrated that the lnc-SNHG1 regulated the expression of the insulin-like growth factor 1 receptor (IGF1-R) by acting as a sponge of miR-497 in NSCLC. lnc-SNHG1 could be a novel biomarker as well as a curative target.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2019

Building fault detection and diagnostics: Achieved savings, and methods to evaluate algorithm performance

Fault detection and diagnosis (FDD) represents one of the most active areas of research and commercial product development in the buildings industry. This paper addresses two questions concerning FDD implementation and advancement 1) What are today's users of FDD saving and spending on the technology? 2) What methods and datasets can be used to evaluate and benchmark FDD algorithm performance? Relevant to the first question, 26 organizations that use FDD across a total 550 buildings and 97 M sf achieved median savings of 8%. Twenty-seven FDD users reported that the median base cost for FDD software, annual recurring software cost, and annual labor cost were $8, $2.7 and $8 per monitoring point, with a median implementation size of approximately 1300 points. To address the second question, this paper describes a systematic methodology for evaluating the performance of FDD algorithms, curates an initial test dataset of air handling unit (AHU) system faults, and completes a trial to demonstrate the evaluation process on three sample FDD algorithms. The work provided a first step toward a standard evaluation of different FDD technologies. Finally, it showed the test methodology is indeed scalable and repeatable, provided an understanding of the types of insights that can be gained from algorithm performance testing, and highlighted the priorities for further expanding the test dataset.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

Ten questions on future and extreme weather data for building simulation and analysis in a changing climate

Weather plays a significant role in building operations as it directly influences HVAC loads and in turn the building energy and thermal performance. In a changing climate, future trends and extreme weather events become critical concerns in the global building decarbonization and clean energy transition. This paper aims to address ten key questions concerning extreme and future weather data for building applications, and more importantly to identify research gaps and guide the curation and selection of future and extreme weather data for use in building performance simulation and assessment. The paper intends to inform architects and engineers, operators, owners, policy makers, and other stakeholders on considering the impacts of future and extreme weather data and adopting strategies for selecting and applying this data in various use cases related to building design, operation, and retrofit for energy efficiency, electrification, and climate resilience.

Yan, Da↗
DOE OSTI2021

Audacity of huge: overcoming challenges of data scarcity and data quality for machine learning in computational materials discovery

Machine learning (ML)-accelerated discovery requires large amounts of high-fidelity data to reveal predictive structure–property relationships. For many properties of interest in materials discovery, the challenging nature and high cost of data generation has resulted in a data landscape that is both scarcely populated and of dubious quality. Data-driven techniques starting to overcome these limitations include the use of consensus across functionals in density functional theory, the development of new functionals or accelerated electronic structure theories, and the detection of where computationally demanding methods are most necessary. When properties cannot be reliably simulated, large experimental data sets can be used to train ML models. In the absence of manual curation, increasingly sophisticated natural language processing and automated image analysis are making it possible to learn structure–property relationships from the literature. Finally, models trained on these data sets will improve as they incorporate community feedback.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Accelerating materials science with high-throughput computations and machine learning

With unprecedented amounts of materials data generated from experiments as well as high-throughput density functional theory calculations, machine learning techniques has the potential to greatly accelerate materials discovery and design. In this report we review our efforts in the Materials Virtual Lab to integrate software automation, data generation and curation and machine learning to (i) design and optimize technological materials for energy storage, energy efficiency and high-temperature alloys; (ii) develop scalable quantum-accurate models, and (iii) enhance the speed and accuracy in interpreting characterization spectra.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Large language model-driven database for thermoelectric materials

Thermoelectric materials have the ability to convert waste heat into electricity, offering a valuable solution for energy harvesting. However, their widespread use is hindered by low conversion efficiency, the reliance on expensive rare earth elements, and the environmental and regulatory concerns associated with lead-based materials. A fast and cost-effective way to identify highly efficient thermoelectric materials is through data-driven methods. These approaches rely on robust and comprehensive datasets to train models. Although there are several databases on thermoelectric materials, there is still a need to collect and integrate experimental data from peer-reviewed research articles to capture diverse compositions and properties of materials. Here, in this work, we developed a comprehensive database of 7,123 thermoelectric compounds, containing key information such as chemical composition, structural detail, seebeck coefficient, electrical and thermal conductivity, power factor, and figure of merit (ZT). We used the GPTArticleExtractor workflow, powered by large language models (LLM), to extract and curate data automatically from the scientific literature published in Elsevier journals. This process enabled the creation of a structured database that addresses the challenges of manual data collection. The open access database could stimulate data-driven research and advance thermoelectric material analysis and discovery.

Database↗
DOE OSTI2025

Challenges in predicting protein-protein interactions of understudied viruses: Arenavirus-human interactions

Understanding protein-protein interactions (PPIs) between viruses and host organisms is crucial for uncovering infection mechanisms and identifying potential therapeutic targets. The ability to generalize PPI predictive models across understudied viruses presents a significant challenge. In this work, we use arenavirus-human PPIs to illustrate the difficulties associated with model generalization, which are compounded by a lack of both positive and negative data. We employ a Transfer Learning approach to investigate arenavirus-human PPIs by utilizing models trained on better-studied virus-human and human-human PPIs. Additionally, we curate and assess four types of negative sampling datasets to evaluate their impact on model performance. Despite the overall high accuracies (93–99 %) and AUPRC scores (0.8–0.9) appearing promising, further analysis indicates that these performance metrics can be misleading due to data leakage, data bias, and overfitting, especially concerning under-represented viral proteins. We reveal these gaps and assess the impact of data imbalance using standard k-fold cross-validation and Independent Blind Testing with a Balanced Dataset, resulting in a drop in accuracy below 50 %. We propose a viral protein-specific evaluation framework that categorizes viral proteins into majority and minority classes based on their representation in the dataset, enabling comparison of model performance across these groups using balanced accuracies. This framework offers a more robust evaluation of model generalizability, addressing biases inherent in standard evaluation techniques and paving the way for more reliable PPI prediction models for understudied viruses.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2018

Spatial distribution of marker gene activity in the mouse lung during alveolarization

Here, this data is a curated collection of visual images of gene expression patterns from the pre- and post-natal mouse lung, accompanied by associated mRNA probe sequences and RNA-Seq expression profiles. Mammalian lungs undergo significant growth and cellular differentiation before and after the transition to breathing air. Documenting normal lung development is an important step in understanding abnormal lung development, as well as the challenges faced during a preterm birth. Images in this dataset indicate the spatial distribution of mRNA transcripts for over 500 different genes that are active during lung development, as initially determined via RNA-Seq. Images were systematically acquired using high-throughput in situ hybridization with non-radioactive digoxigenin-labeled mRNA probes across mouse lungs from developmental time points E16.5, E18.5, P7, and P28.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Characterizing Long COVID: Deep Phenotype of a Complex Condition

Background: Numerous publications describe the clinical manifestations of post-acute sequelae of SARS-CoV-2 (PASC or "long COVID"), but they are difficult to integrate because of heterogeneous methods and the lack of a standard for denoting the many phenotypic manifestations. Patient-led studies are of particular importance for understanding the natural history of COVID-19, but integration is hampered because they often use different terms to describe the same symptom or condition. This significant disparity in patient versus clinical characterization motivated the proposed ontological approach to specifying manifestations, which will improve capture and integration of future long COVID studies. Methods: The Human Phenotype Ontology (HPO) is a widely used standard for exchange and analysis of phenotypic abnormalities in human disease but has not yet been applied to the analysis of COVID-19. Funding: We identified 303 articles published before April 29, 2021, curated 59 relevant manuscripts that described clinical manifestations in 81 cohorts three weeks or more following acute COVID-19, and mapped 287 unique clinical findings to HPO terms. We present layperson synonyms and definitions that can be used to link patient self-report questionnaires to standard medical terminology. Long COVID clinical manifestations are not assessed consistently across studies, and most manifestations have been reported with a wide range of synonyms by different authors. Across at least 10 cohorts, authors reported 31 unique clinical features corresponding to HPO terms; the most commonly reported feature was Fatigue (median 45.1%) and the least commonly reported was Nausea (median 3.9%), but the reported percentages varied widely between studies. Interpretation: Translating long COVID manifestations into computable HPO terms will improve analysis, data capture, and classification of long COVID patients. If researchers, clinicians, and patients share a common language, then studies can be compared/pooled more effectively. Furthermore, mapping lay terminology to HPO will help patients assist clinicians and researchers in creating phenotypic characterizations that are computationally accessible, thereby improving the stratification, diagnosis, and treatment of long COVID.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2026

Agentic framework for programmatic crystal structure generation using a fine-tuned worker–supervisor large language model

Platinum group metals (PGMs) underpin many catalytic technologies but face severe supply constraints, motivating the search for alternative materials and computational methods to accelerate discovery. While atomistic simulation tools such as Pymatgen and ASE have streamlined structure manipulation, they require detailed inputs, limiting accessibility for experimentalists and slowing early-stage exploration. Here, in this study, we present an AI-driven agentic framework that orchestrates worker–supervisor large language models (LLMs). The worker translates natural-language prompts of varying abstraction into valid crystallographic structures using a compact LLM fine-tuned with low-rank adaptation on a curated text–code–CIF dataset, emphasizing energy-efficient training. Benchmarking against the baseline CodeGen-350M-mono model shows that fine-tuning reduces hallucination rates from 100% to as low as 5% and improves structural match accuracy to up to 82% for fully specified inputs. Accuracy declines with decreasing prompt detail but remains nontrivial even when only stoichiometry and space group are provided, underscoring the LLM’s capacity for crystallographic inference. The supervisor Claude LLM evaluates the outputs and triggers iterative refinement through the worker’s built-in structure manipulation capabilities (e.g., supercell scaling, strain, vacancy, and substitution operations). We further demonstrate use cases for technologically relevant catalysts, including IrO 2 , pyrochlore Pb 2 Ir 2 O 7 , Ni 2 FeO 4 , and Ni 3 Mo, where the framework generates physically consistent structures that can be refined via geometry optimization. This work introduces a low-energy, language-driven pathway for integrating human and machine intelligence in materials design, paving the way for AI-assisted synthesis planning and high-throughput screening of complex oxides.

AI agent↗
DOE OSTI2010

Predicting Grade 3 Acute Diarrhea During Radiation Therapy for Rectal Cancer Using a Cutoff-Dose Logistic Regression Normal Tissue Complication Probability Model

Purpose: Understanding the dose-volume relationship of small bowel irradiation and severe acute diarrhea may help reduce the incidence of this side effect during adjuvant treatment for rectal cancer. Methods and Materials: Consecutive patients treated curatively for rectal cancer were reviewed, and the maximum grade of acute diarrhea was determined. The small bowel was outlined on the treatment planning CT scan, and a dose-volume histogram was calculated for the initial pelvic treatment (45 Gy). Logistic regression models were fitted for varying cutoff-dose levels from 5 to 45 Gy in 5-Gy increments. The model with the highest LogLikelihood was used to develop a cutoff-dose normal tissue complication probability (NTCP) model. Results: There were a total of 152 patients (48% preoperative, 47% postoperative, 5% other), predominantly treated prone (95%) with a three-field technique (94%) and a protracted venous infusion of 5-fluorouracil (78%). Acute Grade 3 diarrhea occurred in 21%. The largest LogLikelihood was found for the cutoff-dose logistic regression model with 15 Gy as the cutoff-dose, although the models for 20 Gy and 25 Gy had similar significance. According to this model, highly significant correlations (p <0.001) between small bowel volumes receiving at least 15 Gy and toxicity exist in the considered patient population. Similar findings applied to both the preoperatively (p = 0.001) and postoperatively irradiated groups (p = 0.001). Conclusion: The incidence of Grade 3 diarrhea was significantly correlated with the volume of small bowel receiving at least 15 Gy using a cutoff-dose NTCP model.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2010

Influence of Irradiated Lung Volumes on Perioperative Morbidity and Mortality in Patients After Neoadjuvant Radiochemotherapy for Esophageal Cancer

Purpose: In some randomized trials, the treatment outcome of locally advanced esophageal cancer has been significantly improved by neoadjuvant radiochemotherapy (RCT). However, increased perioperative pulmonary toxicity in terms of acute respiratory distress syndrome (ARDS) has been linked to radiation exposure of the lungs. In our study we evaluated perioperative morbidity and mortality in patients with cancer Stages IIA-IVA treated with curative intent either with surgery alone (S) or with neoadjuvant RCT followed by surgery (RCTS). Patients and Methods: Between 1996 and 2003, 55 patients received S, and 98 received RCTS. In the RCTS group, most patients received two cycles of 5-fluorouracil plus cisplatinum simultaneously with normofractionated radiotherapy (40Gy). Four weeks later they underwent surgery. Endpoints were the incidence of acute lung injury (ALI), ARDS, other postoperative complications, and mortality within 31 days. Results: Between both groups there were no significant differences between the incidence and severity of ALI and ARDS (RCTS: 42.9%, 42.9%; S: 45.5%, 38.2%). Furthermore, there were no significant differences in the incidences of pneumonia, pleural effusion, and pneumothorax (RCTS 29.6% vs. S 16.4%, p = 0.07). Perioperative complication rates and mortality did not vary significantly (mortality after RCTS 5.1% vs. S 3.6%). A detailed analysis of 54 RCTS patients according to lung dose-volume histograms did not show any correlation between ARDS and pulmonary exposure. In univariate analysis, only respiratory comorbidity correlated with ARDS. Conclusion: Neoadjuvant cisplatinum and 5-fluorouracil-based RCT apparently has no detrimental impact on the postoperative course.

62 RADIOLOGY AND NUCLEAR MEDICINE↗