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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2014

WE-G-BRE-07: Proton Therapy Enhanced by Tumor-Targeting Gold Nanoparticles: A Pilot in Vivo Experiment at The Proton Therapy Center at MD Anderson Cancer Center

Purpose: Assess tumor-growth delay and survival in a mouse model of prostate cancer treated with tumor-targeting gold nanoparticles (AuNPs) and proton therapy. Methods: We first examined the accumulation of targeting nanoparticles within prostate tumors by imaging AuNPs with ultrasound-guided photoacoustics at 24h after the intravenous administration of goserelin-conjugated AuNPs (gAuNP) in three mice. Nanoparticles were also imaged at the cellular level with TEM in PC3 cells incubated with gAuNP for 24h. Pegylated AuNPs (pAuNP) were also imaged in vivo and in vitro for comparison. PC3 cells were then implanted subcutaneously in nude mice; 51mice with 8–10mm tumors were included. AuNPs were injected intravenously at 0.2%w/w final gold concentration 24h before irradiation. A special jig was designed to facilitate tumor irradiation perpendicular to the proton beam. Proton energy was set to 180MeV, the radiation field was 18×18cm{sup 2}, and 9cm or 13.5cm thick solid-water compensators were used to position the tumors at either the beam entrance (BE) or the SOBP. Physical doses of 5Gy were delivered to all tumors on a patient beam line at MD Anderson's Proton Therapy Center. Results: The photoacoustic experiment reveled that our nanoparticles leak from the tumor-feeding vasculature and accumulate within the tumor volume over time. Additionally, TEM images showed gAuNP are internalized in cancer cells, accumulating within the cytoplasm, whereas pAuNP are not. Tumor-growth was delayed by 11 or 32days in mice receiving gAuNP irradiated at the BE or the SOBP, relative to proton radiation alone. Survival curves (ongoing experiment) reveal that gAuNPs improved survival by 36% or 74% for tumors irradiated at the BE or SOBP. Conclusion: These important, albeit preliminary, in vivo findings reveal nanoparticles to be potent sensitizers to proton therapy. Further, conjugation of AuNPs to tumor-specific antigens that promote enhanced cellular internalization improved both tumor-growth delay and survival of mice after proton therapy.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

Concepts of association between cancer and ionising radiation: accounting for specific biological mechanisms

The probability that an observed cancer was caused by radiation exposure is usually estimated using cancer rates and risk models from radioepidemiological cohorts and is called assigned share (AS). This definition implicitly assumes that an ongoing carcinogenic process is unaffected by the studied radiation exposure. However, there is strong evidence that radiation can also accelerate an existing clonal development towards cancer. In this work, we define different association measures that an observed cancer was newly induced, accelerated, or retarded. The measures were quantified exemplarily by Monte Carlo simulations that track the development of individual cells. Three biologically based two-stage clonal expansion (TSCE) models were applied. In the first model, radiation initiates cancer development, while in the other two, radiation has a promoting effect, i.e. radiation accelerates the clonal expansion of pre-cancerous cells. The parameters of the TSCE models were derived from breast cancer data from the atomic bomb survivors of Hiroshima and Nagasaki. For exposure at age 30, all three models resulted in similar estimates of AS at age 60. For the initiation model, estimates of association were nearly identical to AS. However, for the promotion models, the cancerous clonal development was frequently accelerated towards younger ages, resulting in associations substantially higher than AS. This work shows that the association between a given cancer and exposure in an affected person depends on the underlying biological mechanism and can be substantially larger than the AS derived from classic radioepidemiology.

63 RADIATION, THERMAL, AND OTHER ENVIRON. POLLUTAN↗
DOE OSTI2015

Expression of WNT genes in cervical cancer-derived cells: Implication of WNT7A in cell proliferation and migration

According to the multifactorial model of cervical cancer (CC) causation, it is now recognized that other modifications, in addition to Human papillomavirus (HPV) infection, are necessary for the development of this neoplasia. Among these, it has been proposed that a dysregulation of the WNT pathway might favor malignant progression of HPV-immortalized keratinocytes. The aim of this study was to identify components of the WNT pathway differentially expressed in CC vs. non-tumorigenic, but immortalized human keratinocytes. Interestingly, WNT7A expression was found strongly downregulated in cell lines and biopsies derived from CC. Restoration of WNT7A in CC-derived cell lines using a lentiviral gene delivery system or after adding a recombinant human protein decreases cell proliferation. Likewise, WNT7A silencing in non-tumorigenic cells markedly accelerates proliferation. Decreased WNT7A expression was due to hypermethylation at particular CpG sites. To our knowledge, this is the first study reporting reduced WNT7A levels in CC-derived cells and that ectopic WNT7A restoration negatively affects cell proliferation and migration. - Highlights: • WNT7A is expressed in normal keratinocytes or cervical cells without lesion. • WNT7A is significantly reduced in cervical cancer-derived cells. • Restoration of WNT7A expression in HeLa decreases proliferation and cell migration. • Silencing of WNT7A in HaCaT induces an increased proliferation and migration rate. • Decreased WNT7A expression in this model is due to hypermethylation.

60 APPLIED LIFE SCIENCES↗
NASA NTRS2012

Probability of Causation for Space Radiation Carcinogenesis Following International Space Station, Near Earth Asteroid, and Mars Missions

Cancer risk is an important concern for International Space Station (ISS) missions and future exploration missions. An important question concerns the likelihood of a causal association between a crew members radiation exposure and the occurrence of cancer. The probability of causation (PC), also denoted as attributable risk, is used to make such an estimate. This report summarizes the NASA model of space radiation cancer risks and uncertainties, including improvements to represent uncertainties in tissue-specific cancer incidence models for never-smokers and the U.S. average population. We report on tissue-specific cancer incidence estimates and PC for different post-mission times for ISS and exploration missions. An important conclusion from our analysis is that the NASA policy to limit the risk of exposure-induced death to 3% at the 95% confidence level largely ensures that estimates of the PC for most cancer types would not reach a level of significance. Reducing uncertainties through radiobiological research remains the most efficient method to extend mission length and establish effective mitigators for cancer risks. Efforts to establish biomarkers of space radiation-induced tumors and to estimate PC for rarer tumor types are briefly discussed.

Cucinotta, Francis A.↗
DOE OSTI2013

Trisomy of the Dscr1 gene suppresses early progression of pancreatic intraepithelial neoplasia driven by oncogenic Kras

Highlights: •A single extra copy of Dscr1 restrains progression of PanIN-1A to PanIN-1B lesions. •Dscr1 trisomy attenuates calcineurin–NFAT pathway in neoplastic ductal epithelium. •Dscr1 trisomy leads to upregulation of p15{sup INK4b} in neoplastic ductal epithelium. •A single extra copy of Dscr1 reduces epithelial proliferation in early PanIN lesions. •Dscr1 trisomy may protect Down syndrome individuals from pancreatic cancer. -- Abstract: Individuals with Down syndrome exhibit remarkably reduced incidence of most solid tumors including pancreatic cancer. Multiple mechanisms arising from the genetic complexity underlying Down syndrome has been suggested to contribute to such a broad cancer protection. In this study, utilizing a genetically engineered mouse model of pancreatic cancer, we demonstrate that trisomy of the Down syndrome critical region-1 (Dscr1), an endogenous calcineurin inhibitor localized on chromosome 21, suppresses the progression of pancreatic intraepithelial neoplasia-1A (PanIN-1A) to PanIN-1B lesions without affecting the initiation of PanIN lesions mediated by oncogenic Kras{sup G12D}. In addition, we show that Dscr1 trisomy attenuates nuclear localization of nuclear factor of activated T-cells (NFAT) accompanied by upregulation of the p15{sup Ink4b} tumor suppressor and reduction of cell proliferation in early PanIN lesions. Our data suggest that attenuation of calcineurin–NFAT signaling in neoplastic pancreatic ductal epithelium by a single extra copy of Dscr1 is sufficient to inhibit the progression of early PanIN lesions driven by oncogenic Kras, and thus may be a potential mechanism underlying reduced incidence of pancreatic cancer in Down syndrome individuals.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2012

3,3′-Diindolylmethane, but not indole-3-carbinol, inhibits histone deacetylase activity in prostate cancer cells

Increased consumption of cruciferous vegetables is associated with a reduced risk of developing prostate cancer. Indole-3-carbinol (I3C) and 3,3′-diindolylmethane (DIM) are phytochemicals derived from cruciferous vegetables that have shown promise in inhibiting prostate cancer in experimental models. Histone deacetylase (HDAC) inhibition is an emerging target for cancer prevention and therapy. We sought to examine the effects of I3C and DIM on HDACs in human prostate cancer cell lines: androgen insensitive PC-3 cells and androgen sensitive LNCaP cells. I3C modestly inhibited HDAC activity in LNCaP cells by 25% but no inhibition of HDAC activity was detected in PC-3 cells. In contrast, DIM significantly inhibited HDAC activity in both cell lines by as much as 66%. Decreases in HDAC activity correlated with increased expression of p21, a known target of HDAC inhibitors. DIM treatment caused a significant decrease in the expression of HDAC2 protein in both cancer cell lines but no significant change in the protein levels of HDAC1, HDAC3, HDAC4, HDAC6 or HDAC8 was detected. Taken together, these results show that inhibition of HDAC activity by DIM may contribute to the phytochemicals' anti-proliferative effects in the prostate. The ability of DIM to target aberrant epigenetic patterns, in addition to its effects on detoxification of carcinogens, may make it an effective chemopreventive agent by targeting multiple stages of prostate carcinogenesis. -- Highlights: ► DIM inhibits HDAC activity and decreases HDAC2 expression in prostate cancer cells. ► DIM is significantly more effective than I3C at inhibiting HDAC activity. ► I3C has no effect on HDAC protein expression. ► Inhibition of HDAC activity by DIM is associated with increased p21 expression. ► HDAC inhibition may be a novel epigenetic mechanism for cancer prevention with DIM.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

Disordered methionine metabolism in MTAP/CDKN2A-deleted cancers leads to dependence on PRMT5

5-Methylthioadenosine phosphorylase (MTAP) is a key enzyme in the methionine salvage pathway. The MTAP gene is frequently deleted in human cancers because of its chromosomal proximity to the tumor suppressor gene CDKN2A. By interrogating data from a large-scale short hairpin RNA–mediated screen across 390 cancer cell line models, we found that the viability of MTAP-deficient cancer cells is impaired by depletion of the protein arginine methyltransferase PRMT5. MTAP-deleted cells accumulate the metabolite methylthioadenosine (MTA), which we found to inhibit PRMT5 methyltransferase activity. Deletion of MTAP in MTAP-proficient cells rendered them sensitive to PRMT5 depletion. Conversely, reconstitution of MTAP in an MTAP-deficient cell line rescued PRMT5 dependence. Thus, MTA accumulation in MTAP–deleted cancers creates a hypomorphic PRMT5 state that is selectively sensitized toward further PRMT5 inhibition. Inhibitors of PRMT5 that leverage this dysregulated metabolic state merit further investigation as a potential therapy for MTAP/CDKN2A-deleted tumors.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Sequentially calibrating a Bayesian microsimulation model to incorporate new information and assumptions

Background: Microsimulation models are mathematical models that simulate event histories for individual members of a population. They are useful for policy decisions because they simulate a large number of individuals from an idealized population, with features that change over time, and the resulting event histories can be summarized to describe key population-level outcomes. Model calibration is the process of incorporating evidence into the model. Calibrated models can be used to make predictions about population trends in disease outcomes and effectiveness of interventions, but calibration can be challenging and computationally expensive. Methods: This paper develops a technique for sequentially updating models to take full advantage of earlier calibration results, to ultimately speed up the calibration process. A Bayesian approach to calibration is used because it combines different sources of evidence and enables uncertainty quantification which is appealing for decision-making. We develop this method in order to re-calibrate a microsimulation model for the natural history of colorectal cancer to include new targets that better inform the time from initiation of preclinical cancer to presentation with clinical cancer (sojourn time), because model exploration and validation revealed that more information was needed on sojourn time, and that the predicted percentage of patients with cancers detected via colonoscopy screening was too low. Results: The sequential approach to calibration was more efficient than recalibrating the model from scratch. Incorporating new information on the percentage of patients with cancers detected upon screening changed the estimated sojourn time parameters significantly, increasing the estimated mean sojourn time for cancers in the colon and rectum, providing results with more validity. Conclusions: A sequential approach to recalibration can be used to efficiently recalibrate a microsimulation model when new information becomes available that requires the original targets to be supplemented with additional targets.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

Models for predicting objective function weights in prostate cancer IMRT

Purpose: To develop and evaluate the clinical applicability of advanced machine learning models that simultaneously predict multiple optimization objective function weights from patient geometry for intensity-modulated radiation therapy of prostate cancer. Methods: A previously developed inverse optimization method was applied retrospectively to determine optimal objective function weights for 315 treated patients. The authors used an overlap volume ratio (OV) of bladder and rectum for different PTV expansions and overlap volume histogram slopes (OVSR and OVSB for the rectum and bladder, respectively) as explanatory variables that quantify patient geometry. Using the optimal weights as ground truth, the authors trained and applied three prediction models: logistic regression (LR), multinomial logistic regression (MLR), and weighted K-nearest neighbor (KNN). The population average of the optimal objective function weights was also calculated. Results: The OV at 0.4 cm and OVSR at 0.1 cm features were found to be the most predictive of the weights. The authors observed comparable performance (i.e., no statistically significant difference) between LR, MLR, and KNN methodologies, with LR appearing to perform the best. All three machine learning models outperformed the population average by a statistically significant amount over a range of clinical metrics including bladder/rectum V53Gy, bladder/rectum V70Gy, and dose to the bladder, rectum, CTV, and PTV. When comparing the weights directly, the LR model predicted bladder and rectum weights that had, on average, a 73% and 74% relative improvement over the population average weights, respectively. The treatment plans resulting from the LR weights had, on average, a rectum V70Gy that was 35% closer to the clinical plan and a bladder V70Gy that was 29% closer, compared to the population average weights. Similar results were observed for all other clinical metrics. Conclusions: The authors demonstrated that the KNN and MLR weight prediction methodologies perform comparably to the LR model and can produce clinical quality treatment plans by simultaneously predicting multiple weights that capture trade-offs associated with sparing multiple OARs.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2012

Lung Cancer Survival Prediction using Ensemble Data Mining on Seer Data

We analyze the lung cancer data available from the SEER program with the aim of developing accurate survival prediction models for lung cancer. Carefully designed preprocessing steps resulted in removal/modification/splitting of several attributes, and 2 of the 11 derived attributes were found to have significant predictive power. Several supervised classification methods were used on the preprocessed data along with various data mining optimizations and validations. In our experiments, ensemble voting of five decision tree based classifiers and meta-classifiers was found to result in the best prediction performance in terms of accuracy and area under the ROC curve. We have developed an on-line lung cancer outcome calculator for estimating the risk of mortality after 6 months, 9 months, 1 year, 2 year and 5 years of diagnosis, for which a smaller non-redundant subset of 13 attributes was carefully selected using attribute selection techniques, while trying to retain the predictive power of the original set of attributes. Further, ensemble voting models were also created for predicting conditional survival outcome for lung cancer (estimating risk of mortality after 5 years of diagnosis, given that the patient has already survived for a period of time), and included in the calculator. The on-line lung cancer outcome calculator developed as a result of this study is available at http://info.eecs.northwestern.edu:8080/LungCancerOutcomeCalculator/.

Agrawal, Ankit↗
DOE OSTI2019

Selective Information Extraction Strategies for Cancer Pathology Reports with Convolutional Neural Networks

To trust model predictions, it is important to ensure new data scored by the model comes from the same population used for model training. If the model is used to score new data different than the model’s training data, then predictions and model performance metrics cannot be trusted. Identifying and excluding these anomalous data points is an important task when using models in the real world. Traditional machine learning algorithms and classifiers don’t have the capability to abstain in this case. Here we propose a data-novelty detection algorithm for the Convolutional Neural Network classifier, yielding a rejection score for each new data point scored. It is a post-modeling procedure which examines the distribution of convolution filters to determine if the prediction should be trusted. We apply this algorithm to an information extraction model for a natural language text corpus. We evaluated the algorithm performance using a primary cancer site classification model applied to cancer pathology reports. Results demonstrate that the algorithm is an effective way to exclude cancer pathology reports from model scoring when they do not contain the expected information necessary to accurately classify the primary cancer type.

Yoon, Hong-Jun↗
DOE OSTI2020

Lung cancer mortality among construction workers: implications for early detection

Objectives This study examined predictors of lung cancer mortality, beyond age and smoking, among construction workers employed at US Department of Energy (DOE) sites to better define eligibility for low-dose CT (LDCT) lung cancer screening. Methods Predictive models were based on 17 069 workers and 352 lung cancer deaths. Risk factors included age, gender, race/ethnicity, cigarette smoking, years of trade or DOE work, body mass index (BMI), chest X-ray results, spirometry results, respiratory symptoms, beryllium sensitisation and personal history of cancer. Competing risk Cox models were used to obtain HRs and to predict 5-year risks. Results Factors beyond age and smoking included in the final predictive model were chest X-ray changes, abnormal lung function, chronic obstructive pulmonary disease (COPD), respiratory symptoms, BMI, personal history of cancer and having worked 5 or more years at a DOE site or in construction. Risk-based LDCT eligibility demonstrated improved sensitivity, specificity and positive predictive value compared with current US Preventive Services Task Force guidelines. The risk of lung cancer death from 5 years of work in the construction industry or at a DOE site was comparable with the risk from a personal cancer history, a family history of cancer or a diagnosis of COPD. LDCT eligibility criteria used for DOE construction workers, which includes factors beyond age and smoking, identified 86% of participants who eventually would die from lung cancer compared with 51% based on age and smoking alone. Conclusions Results support inclusion of risk from occupational exposures and non-malignant respiratory clinical findings in LDCT clinical guidelines.

Public, Environmental & Occupational Health↗
DOE OSTI2016

TH-CD-207A-08: Simulated Real-Time Image Guidance for Lung SBRT Patients Using Scatter Imaging

Purpose: To develop a comprehensive Monte Carlo-based model for the acquisition of scatter images of patient anatomy in real-time, during lung SBRT treatment. Methods: During SBRT treatment, images of patient anatomy can be acquired from scattered radiation. To rigorously examine the utility of scatter images for image guidance, a model is developed using MCNP code to simulate scatter images of phantoms and lung cancer patients. The model is validated by comparing experimental and simulated images of phantoms of different complexity. The differentiation between tissue types is investigated by imaging objects of known compositions (water, lung, and bone equivalent). A lung tumor phantom, simulating materials and geometry encountered during lung SBRT treatments, is used to investigate image noise properties for various quantities of delivered radiation (monitor units(MU)). Patient scatter images are simulated using the validated simulation model. 4DCT patient data is converted to an MCNP input geometry accounting for different tissue composition and densities. Lung tumor phantom images acquired with decreasing imaging time (decreasing MU) are used to model the expected noise amplitude in patient scatter images, producing realistic simulated patient scatter images with varying temporal resolution. Results: Image intensity in simulated and experimental scatter images of tissue equivalent objects (water, lung, bone) match within the uncertainty (∼3%). Lung tumor phantom images agree as well. Specifically, tumor-to-lung contrast matches within the uncertainty. The addition of random noise approximating quantum noise in experimental images to simulated patient images shows that scatter images of lung tumors can provide images in as fast as 0.5 seconds with CNR∼2.7. Conclusions: A scatter imaging simulation model is developed and validated using experimental phantom scatter images. Following validation, lung cancer patient scatter images are simulated. These simulated patient images demonstrate the clinical utility of scatter imaging for real-time tumor tracking during lung SBRT.

60 APPLIED LIFE SCIENCES↗
NASA NTRS2011

Space Radiation Cancer Risk Projections and Uncertainties - 2010

Uncertainties in estimating health risks from galactic cosmic rays greatly limit space mission lengths and potential risk mitigation evaluations. NASA limits astronaut exposures to a 3% risk of exposure-induced death and protects against uncertainties using an assessment of 95% confidence intervals in the projection model. Revisions to this model for lifetime cancer risks from space radiation and new estimates of model uncertainties are described here. We review models of space environments and transport code predictions of organ exposures, and characterize uncertainties in these descriptions. We summarize recent analysis of low linear energy transfer radio-epidemiology data, including revision to Japanese A-bomb survivor dosimetry, longer follow-up of exposed cohorts, and reassessments of dose and dose-rate reduction effectiveness factors. We compare these projections and uncertainties with earlier estimates. Current understanding of radiation quality effects and recent data on factors of relative biological effectiveness and particle track structure are reviewed. Recent radiobiology experiment results provide new information on solid cancer and leukemia risks from heavy ions. We also consider deviations from the paradigm of linearity at low doses of heavy ions motivated by non-targeted effects models. New findings and knowledge are used to revise the NASA risk projection model for space radiation cancer risks.

Cucinotta, Francis A.↗
DOE OSTI2024

Path-BigBird: An AI-Driven Transformer Approach to Classification of Cancer Pathology Reports

PURPOSE Surgical pathology reports are critical for cancer diagnosis and management. To accurately extract information about tumor characteristics from pathology reports in near real time, we explore the impact of using domain-specific transformer models that understand cancer pathology reports. METHODS We built a pathology transformer model, Path-BigBird, by using 2.7 million pathology reports from six SEER cancer registries. We then compare different variations of Path-BigBird with two less computationally intensive methods: Hierarchical Self-Attention Network (HiSAN) classification model and an offthe-shelf clinical transformer model (Clinical BigBird). We use five pathology information extraction tasks for evaluation: site, subsite, laterality, histology, and behavior. Model performance is evaluated by using macro and micro F 1 scores. RESULTS We found that Path-BigBird and Clinical BigBird outperformed the HiSAN in all tasks. Clinical BigBird performed better on the site and laterality tasks. Versions of the Path-BigBird model performed best on the two most difficult tasks: subsite (micro F 1 score of 72.53, macro F 1 score of 35.76) and histology (micro F 1 score of 80.96, macro F 1 score of 37.94). The largest performance gains over the HiSAN model were for histology, for which a Path-BigBird model increased the micro F 1 score by 1.44 points and the macro F 1 score by 3.55 points. Overall, the results suggest that a Path-BigBird model with a vocabulary derived from wellcurated and deidentified data is the best-performing model. CONCLUSION The Path-BigBird pathology transformer model improves automated information extraction from pathology reports. Although Path-BigBird outperforms Clinical BigBird and HiSAN, these less computationally expensive models still have utility when resources are constrained.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

Quantification of fibronectin as a method to assess ex vivo extracellular matrix remodeling

Altered architecture, composition and quality of the extracellular matrix (ECM) are pathological hallmarks of several inflammatory and fibro-proliferative pathological processes such as osteoarthritis (OA), rheumatoid arthritis (RA), fibrosis and cancer. One of the most important components of the ECM is fibronectin. Fibronectin serves as an adhesion molecule anchoring cells to the underlying basement membrane through direct interaction with integrin receptors. Fibronectin hereby modulates the properties of the ECM and affects cellular processes. Quantification of fibronectin remodeling could therefore be used to assess the changes in the ECM that occur during progression of fibro-proliferative pathologies. Ex vivo models are becoming state-of-the-art tools to study ECM remodeling as the cellular composition and the organization of the ECM are preserved. Ex vivo models may therefore be a valuable tool to study the ECM remodeling that occurs during progression of fibro-proliferative pathologies. The aim of this study was to quantify fibronectin remodeling in ex vivo models of cartilage and cancer. A competitive The enzyme-linked immunosorbent assay (ELISA) against the C-terminus of fibronectin was developed (FBN-C). The assay was evaluated in relation to specificity, technical performance and as a marker for quantification of fibronectin in cartilage and cancer ex vivo models. The ELISA was specific and technically stable. Cleavage of tumor tissue with MMP-2 released significantly higher levels of FBN-C compared to tissue with buffer only and western blot analysis revealed that FBN-C recognizes both full length and degraded fibronectin. When ex vivo cartilage cultures were stimulated with the anabolic factor TGFβ and catabolic factors TNF-α and OSM, significantly higher levels of FBN-C were found in the conditioned media. Lastly, FBN-C was released from a cancer ex vivo model. In conclusion, we were able to quantify fibronectin remodeling in ex vivo models of cartilage and cancer. Quantification of fibronectin remodeling could be a valuable tool to understand ECM remodeling in ex vivo models of fibro-proliferative pathologies.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2012

Possible action mechanism of the electromagnetic fields in the liver cancer development: A mathematical proposal

Currently it is known that electromagnetic field exposure can induce biological changes, although the precise effects and action mechanism of the interaction between the electromagnetic field and biological systems are not well understood. In this work we propose a possible action mechanism, concerning the effect that the extremely low frequency electromagnetic field exposure has on the early stage of liver cancer development. The model is developed studying the phenomena called oxidative stress that it appears after it is applied a carcinogenic agent used to induce hepatic cancer chemically in an experimental animal model. This physical-chemical process involves the movement of magnetic field dependent free charged particles, called free radicals. We will consider the use of the radical pairs theory as a framework, in which we will describe the spin density operator evolution by implementing the stochastic Liouville equation with hyperfine interaction. This describes how the selectivity of the interaction between spin states of the free radicals with the applied electromagnetic field, influences the development of pre-neoplastic lesions in the liver. AIP Publishing is retracting this article due to the substantial use of content in the Results and Conclusions section without proper citation of a previously published paper in Chemical Physics Letters 361 (2012) 219-225. This article is retracted from the scientific record with effect from 15 October 2015.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2014

Systems biology perspectives on the carcinogenic potential of radiation

This review focuses on recent experimental and modeling studies that attempt to define the physiological context in which high linear energy transfer (LET) radiation increases epithelial cancer risk and the efficiency with which it does so. Radiation carcinogenesis is a two-compartment problem: ionizing radiation can alter genomic sequence as a result of damage due to targeted effects (TE) from the interaction of energy and DNA; it can also alter phenotype and multicellular interactions that contribute to cancer by poorly understood nontargeted effects (NTE). Rather than being secondary to DNA damage and mutations that can initiate cancer, radiation NTE create the critical context in which to promote cancer. Systems biology modeling using comprehensive experimental data that integrates different levels of biological organization and time-scales is a means of identifying the key processes underlying the carcinogenic potential of high-LET radiation. We hypothesize that inflammation is a key process, and thus cancer susceptibility will depend on specific genetic predisposition to the type and duration of this response. Systems genetics using novel mouse models can be used to identify such determinants of susceptibility to cancer in radiation sensitive tissues following high-LET radiation. Improved understanding of radiation carcinogenesis achieved by defining the relative contribution of NTE carcinogenic effects and identifying the genetic determinants of the high-LET cancer susceptibility will help reduce uncertainties in radiation risk assessment.

59 BASIC BIOLOGICAL SCIENCES↗