Academic literature on the topic 'Tumor cell computation'
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Journal articles on the topic "Tumor cell computation"
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Ryser, Marc D., Byung-Hoon Min, Kimberly D. Siegmund, and Darryl Shibata. "Spatial mutation patterns as markers of early colorectal tumor cell mobility." Proceedings of the National Academy of Sciences 115, no. 22 (2018): 5774–79. http://dx.doi.org/10.1073/pnas.1716552115.
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A growing body of evidence suggests that a subset of human cancers grows as single clonal expansions. In such a nearly neutral evolution scenario, it is possible to infer the early ancestral tree of a full-grown tumor. We hypothesized that early tree reconstruction can provide insights into the mobility phenotypes of tumor cells during their first few cell divisions. We explored this hypothesis by means of a computational multiscale model of tumor expansion incorporating the glandular structure of colorectal tumors. After calibrating the model to multiregional and single gland data from 19 human colorectal tumors using approximate Bayesian computation, we examined the role of early tumor cell mobility in shaping the private mutation patterns of the final tumor. The simulations showed that early cell mixing in the first tumor gland can result in side-variegated patterns where the same private mutations could be detected on opposite tumor sides. In contrast, absence of early mixing led to nonvariegated, sectional mutation patterns. These results suggest that the patterns of detectable private mutations in colorectal tumors may be a marker of early cell movement and hence the invasive and metastatic potential of the tumor at the start of the growth. In alignment with our hypothesis, we found evidence of early abnormal cell movement in 9 of 15 invasive colorectal carcinomas (“born to be bad”), but in none of 4 benign adenomas. If validated with a larger dataset, the private mutation patterns may be used for outcome prediction among screen-detected lesions with unknown invasive potential.
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Bernardis, Alessia, Marco Bullo, Luca Giovanni Campana, et al. "Electric field computation and measurements in the electroporation of inhomogeneous samples." Open Physics 15, no. 1 (2017): 790–96. http://dx.doi.org/10.1515/phys-2017-0092.
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AbstractIn clinical treatments of a class of tumors,e.g. skin tumors, the drug uptake of tumor tissue is helped by means of a pulsed electric field, which permeabilizes the cell membranes. This technique, which is called electroporation, exploits the conductivity of the tissues: however, the tumor tissue could be characterized by inhomogeneous areas, eventually causing a non-uniform distribution of current. In this paper, the authors propose a field model to predict the effect of tissue inhomogeneity, which can affect the current density distribution. In particular, finite-element simulations, considering non-linear conductivity against field relationship, are developed. Measurements on a set of samples subject to controlled inhomogeneity make it possible to assess the numerical model in view of identifying the equivalent resistance between pairs of electrodes.
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Eddy, James A., Vésteinn Thorsson, Andrew E. Lamb, et al. "CRI iAtlas: an interactive portal for immuno-oncology research." F1000Research 9 (August 24, 2020): 1028. http://dx.doi.org/10.12688/f1000research.25141.1.
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The Cancer Research Institute (CRI) iAtlas is an interactive web platform for data exploration and discovery in the context of tumors and their interactions with the immune microenvironment. iAtlas allows researchers to study immune response characterizations and patterns for individual tumor types, tumor subtypes, and immune subtypes. iAtlas supports computation and visualization of correlations and statistics among features related to the tumor microenvironment, cell composition, immune expression signatures, tumor mutation burden, cancer driver mutations, adaptive cell clonality, patient survival, expression of key immunomodulators, and tumor infiltrating lymphocyte (TIL) spatial maps. iAtlas was launched to accompany the release of the TCGA PanCancer Atlas and has since been expanded to include new capabilities such as (1) user-defined loading of sample cohorts, (2) a tool for classifying expression data into immune subtypes, and (3) integration of TIL mapping from digital pathology images. We expect that the CRI iAtlas will accelerate discovery and improve patient outcomes by providing researchers access to standardized immunogenomics data to better understand the tumor immune microenvironment and its impact on patient responses to immunotherapy.
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Neeru Saxena. "Ensemble XMOB Approach for Brain Tumor Detection Based on Feature Extraction." Tuijin Jishu/Journal of Propulsion Technology 45, no. 03 (2024): 593–607. http://dx.doi.org/10.52783/tjjpt.v45.i03.7253.
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Brain tumors are a serious health threat in adults. These fast-growing abnormal cell masses disrupt normal brain function. Doctors use various imaging techniques to identify the specific type, size, and location of brain tumors in patients. Accurately identifying and classifying brain tumors is crucial for understanding how they develop and progress. Magnetic Resonance Imaging (MRI), a well-established medical imaging technique, plays a vital role in this process by assisting radiologists in investigating the location of the tumor. Previous models frequently encounter a compromise between accuracy and computational efficiency, lacking an approach that successfully integrates both aspects.This study introduces an innovative ensemble model termed as “XMob Approach” that combines the deep features extraction abilities of Xception with computational efficiency of MobleNet for binary classification of brain Tumor. The Xmob Approach leverages the strengths of both architectures : Xception depthwise seperable convolutions allow for detailed feature extraction whereas MobileNet’s lightweight structure ensures efficient computation making it suitable for real life application. This combination aims to enhance in medical diagnostics, promising enhanced accuracy and efficiency. This study explores the potential of integrating these pre-trained architectures to provide real-time, automated diagnostic assistance, improving the speed and precision of brain tumor detection. In our methodology pre-processed MRI scans undergo feature extraction through Xception model, capturing complicated patterns indicative of tumor presence. Simultaneously MobileNet processed these images emphasizing computational efficiency without compromising on performance.The output of both the modesl are then integrated using ensemble technique to improve overall classification accuracy. By integrating the complementary strengths of Xception and MobileNet , the XMob Approach represent a significant step towards the field of medical diagnostic promising improved outcomes for patients through advanced technology. DOI: https://doi.org/10.52783/tjjpt.v45.i03.7253
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Zhang, Bing, and Michal Bassani-Sternberg. "Current perspectives on mass spectrometry-based immunopeptidomics: the computational angle to tumor antigen discovery." Journal for ImmunoTherapy of Cancer 11, no. 10 (2023): e007073. http://dx.doi.org/10.1136/jitc-2023-007073.
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Identification of tumor antigens presented by the human leucocyte antigen (HLA) molecules is essential for the design of effective and safe cancer immunotherapies that rely on T cell recognition and killing of tumor cells. Mass spectrometry (MS)-based immunopeptidomics enables high-throughput, direct identification of HLA-bound peptides from a variety of cell lines, tumor tissues, and healthy tissues. It involves immunoaffinity purification of HLA complexes followed by MS profiling of the extracted peptides using data-dependent acquisition, data-independent acquisition, or targeted approaches. By incorporating DNA, RNA, and ribosome sequencing data into immunopeptidomics data analysis, the proteogenomic approach provides a powerful means for identifying tumor antigens encoded within the canonical open reading frames of annotated coding genes and non-canonical tumor antigens derived from presumably non-coding regions of our genome. We discuss emerging computational challenges in immunopeptidomics data analysis and tumor antigen identification, highlighting key considerations in the proteogenomics-based approach, including accurate DNA, RNA and ribosomal sequencing data analysis, careful incorporation of predicted novel protein sequences into reference protein database, special quality control in MS data analysis due to the expanded and heterogeneous search space, cancer-specificity determination, and immunogenicity prediction. The advancements in technology and computation is continually enabling us to identify tumor antigens with higher sensitivity and accuracy, paving the way toward the development of more effective cancer immunotherapies.
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Bloom, Alexander B., and Muhammad H. Zaman. "Influence of the microenvironment on cell fate determination and migration." Physiological Genomics 46, no. 9 (2014): 309–14. http://dx.doi.org/10.1152/physiolgenomics.00170.2013.
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Several critical cell functions are influenced not only by internal cellular machinery but also by external mechanical and biochemical cues from the surrounding microenvironment. Slight changes to the microenvironment can result in dramatic changes to the cell's phenotype; for example, a change in the nutrients or pH of a tumor microenvironment can result in increased tumor metastasis. While cellular fate and the regulators of cell fate have been studied in detail for several decades now, our understanding of the extracellular regulators remains qualitative and far from comprehensive. In this review, we discuss the microenvironment influence on cell fate in terms of adhesion, migration, and differentiation and focus on both developments in experimental and computation tools to analyze cellular fate.
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Trusov, P. V., N. V. Zaitseva, and V. М. Chigvintsev. "Predicting a risk of tumor evolution considering regulatory mechanisms of the body and angiogenesis." Health Risk Analysis, no. 4 (December 2023): 134–45. http://dx.doi.org/10.21668/health.risk/2023.4.13.eng.
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Adverse environmental and lifestyle factors produce considerable effects on occurrence of cancerous tumors, both directly and indirectly through impaired functionality of the body protection mechanisms. Investigation of these effects has practical significance for risk assessment and development of effective cancer preventive strategies. Mathematical modeling is an eligible method for considering complex multicomponent interactions between elements of various systems involved in tumor growth. This article presents an approach to assessing risks of cancerous tumors by using a created predictive model that describes dynamics of abnormal cells considering regulatory mechanisms and angiogenesis. An evolution approach is applied to estimate accumulated functional disorders of the immune system due to natural ageing and chemical environmental exposures. The Monte Carlo simulation is employed to estimate a likely outcome of cancerous tumor evolution given different possible properties of abnormal cells. The article provides the results of accomplished computation experiments aimed at describing dynamics of changes in cell population properties in an analyzed organ tissue. Development of a vessel system is described considering different effects of the most significant factors. Computation results are analyzed within various scenarios that describe cancerous tumor growth in dynamics considering how angiogenesis develops under different parameters of the immune system dysfunction and different properties of abnormal cells. Risks of tumor development are assessed considering parameters that determine the overall state of the body (the immune system) and properties of abnormal cells. This approach makes it possible to develop a system of preventive and sanitary-hygienic activities in areas where envi-ronmental conditions are unfavorable in order to reduce cancer incidence.
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Trusov, P. V., N. V. Zaitseva, and V. М. Chigvintsev. "Predicting a risk of tumor evolution considering regulatory mechanisms of the body and angiogenesis." Health Risk Analysis, no. 4 (December 2023): 134–45. http://dx.doi.org/10.21668/health.risk/2023.4.13.
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Adverse environmental and lifestyle factors produce considerable effects on occurrence of cancerous tumors, both directly and indirectly through impaired functionality of the body protection mechanisms. Investigation of these effects has practical significance for risk assessment and development of effective cancer preventive strategies. Mathematical modeling is an eligible method for considering complex multicomponent interactions between elements of various systems involved in tumor growth. This article presents an approach to assessing risks of cancerous tumors by using a created predictive model that describes dynamics of abnormal cells considering regulatory mechanisms and angiogenesis. An evolution approach is applied to estimate accumulated functional disorders of the immune system due to natural ageing and chemical environmental exposures. The Monte Carlo simulation is employed to estimate a likely outcome of cancerous tumor evolution given different possible properties of abnormal cells. The article provides the results of accomplished computation experiments aimed at describing dynamics of changes in cell population properties in an analyzed organ tissue. Development of a vessel system is described considering different effects of the most significant factors. Computation results are analyzed within various scenarios that describe cancerous tumor growth in dynamics considering how angiogenesis develops under different parameters of the immune system dysfunction and different properties of abnormal cells. Risks of tumor development are assessed considering parameters that determine the overall state of the body (the immune system) and properties of abnormal cells. This approach makes it possible to develop a system of preventive and sanitary-hygienic activities in areas where envi-ronmental conditions are unfavorable in order to reduce cancer incidence.
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Satomi, Kaishi, Ryo Nishikawa, Masao Matsutani, Koichi Ichimura, and Hirokazu Takami. "PATH-42. PROGNOSTIC FACTORS OF CNS GERM CELL TUMORS; MOLECULAR AND HISTOPATHOLOGICAL ANALYSES ON 154 CASES FROM THE IGCT CONSORTIUM." Neuro-Oncology 23, Supplement_6 (2021): vi124—vi125. http://dx.doi.org/10.1093/neuonc/noab196.494.
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Abstract BACKGROUND Germ cell tumors (GCTs) preferentially occurs in pediatric and young adult age groups. Chemo- and radiation therapies cause long-term sequelae in their later lives. We searched for clinical and histopathological features to predict the prognosis and affect treatment response, with a future goal of treatment stratification. METHODS A total of 154 GCT cases were included in the analysis. Total of 114 germinoma cases underwent measurement of tumor cell content on H-E specimen, and 82 GCT cases underwent 450K methylation analysis. 12p gain was determined on methylation-based copy number computation and FISH. Association with progression-free and overall survival (PFS/OS) was investigated. RESULTS The tumor cell content was widely distributed from < 5% to 90% in the specimens, with a median value of 50%. Patients with a higher tumor cell content (≥50%) showed shorter PFS than those with a lower tumor cell content (< 50 %) (p=0.03). In multivariate analysis with tumor location, tumor cell content was the sole statistically significant prognostic factor (p=0.04). 12p gain was found in 25-out-of-82 cases (30%) and was more frequent in NGGCTs, particularly in cases with malignant components. The presence of 12p gain correlated with shorter PFS and OS, even with histology and tumor markers incorporated in the multivariate analysis. Among NGGCTs, 12p gain still had prognostic significance for PFS and OS. The 12p copy number status was shared among histological components in mixed GCTs. Whole-genome amplification was suggested by FISH. CONCLUSIONS We found that tumor cell content significantly affected the prognosis of germinomas. 12p gain predicts the presence of malignant components of NGGCTs, and poor prognosis of the patients. Furthermore, 12p is likely to be an early event in the tumorigenesis of GCT. These potentially open the possibility of leveraging these pathological and molecular factors in future clinical trials when stratifying the treatment intensity.
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Takami, Hirokazu, Kaishi Satomi, Kohei Fukuoka, et al. "BOT-3 Prognostic Factors of CNS Germ Cell Tumors; Molecular and Histopathological Analyses on 154 Cases from the iGCT Consortium." Neuro-Oncology Advances 3, Supplement_6 (2021): vi8—vi9. http://dx.doi.org/10.1093/noajnl/vdab159.031.
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Abstract Background: Germ cell tumors (GCTs) preferentially occurs in pediatric and young adult age groups. Chemo- and radiation therapies cause long-term sequelae in their later lives. We searched for clinical and histopathological features to predict the prognosis and affect treatment response, with a future goal of treatment stratification.Methods: A total of 154 GCT cases were included in the analysis. Total of 114 germinoma cases underwent measurement of tumor cell content on H-E specimen, and 82 GCT cases underwent 450K methylation analysis. 12p gain was determined on methylation-based copy number computation and FISH. Association with progression-free and overall survival (PFS/OS) was investigated. Results: The tumor cell content was widely distributed from <5% to 90% in the specimens, with a median value of 50%. Patients with a higher tumor cell content (>=50%) showed shorter PFS than those with a lower tumor cell content (<50 %) (p=0.03). In the multivariate analysis with tumor location, tumor cell content was the sole statistically significant prognostic factor (p=0.04). 12p gain was found in 25-out-of-82 cases (30%) and was more frequent in NGGCTs, particularly in cases with malignant components. The presence of 12p gain correlated with shorter PFS and OS, even with histology and tumor markers incorporated in the multivariate analysis. Among NGGCTs, 12p gain still had prognostic significance for PFS and OS. The 12p copy number status was shared among histological components in mixed GCTs. Whole-genome amplification was suggested by FISH.Conclusions: We found that tumor cell content significantly affected the prognosis of germinomas. 12p gain predicts the presence of malignant components of NGGCTs, and poor prognosis of the patients. Furthermore, 12p is likely to be an early event in the tumorigenesis of CNS GCT. These potentially open the possibility of leveraging these pathological and molecular factors in the future clinical trials when stratifying the treatment intensity.
Dissertations / Theses on the topic "Tumor cell computation"
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Kumar, Manu Prajapati. "Computational analysis of cell-cell communication in the tumor microenvironment." Thesis, Massachusetts Institute of Technology, 2019. https://hdl.handle.net/1721.1/123063.
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Thesis: Ph. D., Massachusetts Institute of Technology, Department of Biological Engineering, 2019<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (pages 147-168).<br>Cell-cell communication between malignant, immune, and stromal cells influences many aspects of in vivo tumor biology, including tumorigenesis, tumor progression, and therapeutic resistance. As a result, targeting receptor-ligand interactions, for instance with immune check-point inhibitors, can provide significant benefit for patients. However, our knowledge of this complex network of cell-cell interactions in a tumor microenvironment is still incomplete, and there is a need for systematic approaches to study cell-cell communication. This thesis presents computational approaches for characterizing cell-cell communication networks in three different experimental studies. In the first study, we modeled metastatic triple negative breast cancer in the liver using a microphysiological system and identified inflammatory cytokines secreted by the microenvironment that result in the proliferation of dormant metastases. In the second study, we used single-cell RNA sequencing (scRNA-seq) to quantify receptor-ligand interactions in six syngeneic mouse tumor models. To identify specific receptor-ligand interactions that predict tumor growth rate and immune infiltration, we used receptor-ligand interactions as features in regression models. For the third study, we extended our scRNA-seq approach to include inferences of single-cell signaling pathway and transcription factor activity. We then identified protein-protein interaction networks that connect extra-cellular receptor-ligand interactions to intra-cellular signal transduction pathways. Using this approach, we compared inflammatory versus genetic models of colorectal cancer and identified cancer-associated-fibroblasts as drivers of a partial epithelial-to-mesenchymal transition in tumor cells via MAPK1 and MAPK14 signaling. Overall, the methods developed in this thesis provide a foundational computational framework for constructing "multi-scale" models of communication networks in multi-cellular tissues.<br>by Manu Prajapati Kumar.<br>Ph. D.<br>Ph.D. Massachusetts Institute of Technology, Department of Biological Engineering
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Marjanovic, Nemanja. "Application of the single cell genomics in deciphering tumor heterogeneity and its role in tumor progression and drug resistance." Thesis, Massachusetts Institute of Technology, 2021. https://hdl.handle.net/1721.1/130830.
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Thesis: Ph. D., Massachusetts Institute of Technology, Computational and Systems Biology Program, February, 2021<br>Cataloged from the official PDF of thesis. "February 2021."<br>Includes bibliographical references.<br>Tumor progression, from the single mutated cell to the advanced stages of cancer, represents an evolutionary process. During tumor progression, cancer cells acquire new genetic mutations, becoming more heterogeneous, leading to tumor progression and resistance to therapy. However, clear genetic drivers of progression, metastasis, and therapeutic resistance are identified in only a subset of tumors, pointing to non-genetic contributors to cancer progression. Also, somatic evolution in cancer is occurring at the level of the single cell. Therefore, the application of the single cell genomic method is crucial for deciphering phenotypic heterogeneity. Here, we profiled single cell transcriptomes from genetically engineered mouse lung tumors at seven stages spanning tumor progression from atypical adenomatous hyperplasia to lung adenocarcinoma. The diversity of transcriptional states spanned by tumor cells increased over time and was reproducible across tumors and mice, but was not explained by genomic copy number variation. Cancer cells progressively adopted alternate lineage identities, computationally predicted to be mediated through a common transitional, high-plasticity cell state (HPCS). HPCS cells prospectively isolated from mouse tumors had robust potential for phenotypic switching and tumor formation and were more chemoresistant in mice. Our study reveals transitions that connect cell states across tumor evolution and motivates therapeutic targeting of the HPCS.<br>by Nemanja Marjanovic.<br>Ph. D.<br>Ph.D. Massachusetts Institute of Technology, Computational and Systems Biology Program
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Windes, Peter. "Computational Modeling of Intracapillary Bacteria Transport in Tumor Microvasculature." Thesis, Virginia Tech, 2016. http://hdl.handle.net/10919/77502.
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The delivery of drugs into solid tumors is not trivial due to obstructions in the tumor microenvironment. Innovative drug delivery vehicles are currently being designed to overcome this challenge. In this research, computational fluid dynamics (CFD) simulations were used to evaluate the behavior of several drug delivery vectors in tumor capillaries—specifically motile bacteria, non-motile bacteria, and nanoparticles. Red blood cells, bacteria, and nanoparticles were imposed in the flow using the immersed boundary method. A human capillary model was developed using a novel method of handling deformable red blood cells (RBC). The capillary model was validated with experimental data from the literature. A stochastic model of bacteria motility was defined based on experimentally observed run and tumble behavior. The capillary and bacteria models were combined to simulate the intracapillary transport of bacteria. Non-motile bacteria and nanoparticles of 200 nm, 300 nm, and 405 nm were also simulated in capillary flow for comparison to motile bacteria. Motile bacteria tended to swim into the plasma layer near the capillary wall, while non-motile bacteria tended to get caught in the bolus flow between the RBCs. The nanoparticles were more impacted by Brownian motion and small scale fluid fluctuations, so they did not trend toward a single region of the flow. Motile bacteria were found to have the longest residence time in a 1 mm long capillary as well as the highest average radial velocity. This suggests motile bacteria may enter the interstitium at a higher rate than non-motile bacteria or nanoparticles of diameters between 200–405 nm.<br>Master of Science
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Lumb, Craig Nicholas. "Computational studies of signalling at the cell membrane." Thesis, University of Oxford, 2012. http://ora.ox.ac.uk/objects/uuid:d5b2db00-1050-4191-8eff-3521a4885a0c.
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In order to associate with the cytoplasmic leaflet of the plasma membrane, many cytosolic signalling proteins possess a distinct lipid binding domain as part of their overall fold. Here, a multiscale simulation approach has been used to investigate three membrane-binding proteins involved in cellular processes such as growth and proliferation. The pleckstrin homology (PH) domain from the general receptor for phosphoinositides 1 (GRP1-PH) binds phosphatidylinositol (3,4,5)-trisphosphate (PI(3,4,5)P₃) with high affinity and specificity. To investigate how this peripheral protein is able to locate its target lipid in the complex membrane environment, Brownian dynamics (BD) simulations were employed to explore association pathways for GRP1-PH binding to PI(3,4,5)P₃ embedded in membranes with different surface charge densities and distributions. The results indicated that non-PI(3,4,5)P₃ lipids can act as decoys to disrupt PI(3,4,5)P₃ binding, but that at approximately physiological anionic lipid concentrations steering towards PI(3,4,5)P₃ is actually enhanced. Atomistic molecular dynamics (MD) simulations revealed substantial membrane penetration of membrane-bound GRP1-PH, evident when non-equilibrium, steered MD simulations were used to forcibly dissociate the protein from the membrane surface. Atomistic and coarse grained (CG) MD simulations of the phosphatase and tensin homologue deleted on chromosome ten (PTEN) tumour suppressor, which also binds PI(3,4,5)P₃, detected numerous non-specific protein-lipid contacts and anionic lipid clustering around PTEN that can be modulated by selective in silico mutagenesis. These results suggested a dual recognition model of membrane binding, with non-specific membrane interactions complementing the protein-ligand interaction. Molecular docking and MD simulations were used to characterise the lipid binding properties of kindlin-1 PH. Simulations demonstrated that a dynamic salt bridge was responsible for controlling the accessibility of the binding site. Electrostatics calculations applied to a variety of PH domains suggested that their molecular dipole moments are typically aligned with their ligand binding sites, which has implications for steering and ligand electrostatic funnelling.
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Su, Hai. "Nuclei/Cell Detection in Microscopic Skeletal Muscle Fiber Images and Histopathological Brain Tumor Images Using Sparse Optimizations." UKnowledge, 2014. http://uknowledge.uky.edu/cs_etds/24.
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Nuclei/Cell detection is usually a prerequisite procedure in many computer-aided biomedical image analysis tasks. In this thesis we propose two automatic nuclei/cell detection frameworks. One is for nuclei detection in skeletal muscle fiber images and the other is for brain tumor histopathological images.
For skeletal muscle fiber images, the major challenges include: i) shape and size variations of the nuclei, ii) overlapping nuclear clumps, and iii) a series of z-stack images with out-of-focus regions. We propose a novel automatic detection algorithm consisting of the following components: 1) The original z-stack images are first converted into one all-in-focus image. 2) A sufficient number of hypothetical ellipses are then generated for each nuclei contour. 3) Next, a set of representative training samples and discriminative features are selected by a two-stage sparse model. 4) A classifier is trained using the refined training data. 5) Final nuclei detection is obtained by mean-shift clustering based on inner distance. The proposed method was tested on a set of images containing over 1500 nuclei. The results outperform the current state-of-the-art approaches.
For brain tumor histopathological images, the major challenges are to handle significant variations in cell appearance and to split touching cells. The proposed novel automatic cell detection consists of: 1) Sparse reconstruction for splitting touching cells. 2) Adaptive dictionary learning for handling cell appearance variations. The proposed method was extensively tested on a data set with over 2000 cells. The result outperforms other state-of-the-art algorithms with F1 score = 0.96.
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Paye, Julie Melissa Davis. "Effect of the Insulin-like Growth Factor (IGF) Axis on the Transport Properties of Endothelial and Epithelial Cells In Vitro." Diss., Virginia Tech, 2003. http://hdl.handle.net/10919/29071.
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The overall objective of this research consists of two main parts: (1) provide evidence that autocrine production of IGF-I modulates tight junction permeability and (2) demonstrate the ability of IGFBPs to regulate IGF-I delivery across cell layers. To meet the first objective, parental and IGF-I secreting bovine mammary epithelial cells were tested for cell layer permeability, tight and adherens junction proteins, IGF-IR, and a downstream signaling components of IGF-IR. In comparison with parental cells, IGF-I secreting cells had high levels of IGF-IRs, but low levels of the junction components E-cadherin, b-catenin, and occludin. The differences in parental and IGF-I secreting cells was not due to extracellular stimuli since inclusion of IGF-I, IGFBP-3, or co-culture with SV40-IGF-I cells did not alter the barrier properties of parental cells, suggesting that intracrine signaling may alter cell connectivity. The second objective focused on exogenous rather than endogenous IGF-I and the role of IGFBPs and IGF-IRs in ligand transcytosis. Bovine aortic endothelial cells (BAECs) cultured on surfaces optimized to minimize paracellular transport were utilized to investigate the kinetics involved in the transport of insulin-like growth factor-I from the apical side of confluent monolayers to the basolateral side. Binding competitors were used to determine the role of the cell surface insulin-like growth factor-I receptor (IGF-IR) and cell surface insulin-like growth factor binding proteins (IGFBPs) in this transport process. Although IGFBPs initially retard delivery of IGF-I, using a computation model, this report shows that pulse durations of less than 6 hrs resulted in enhanced delivery of IGF-I in the presence of IGFBPs, above that for delivery in the absence of IGFBPs. In addition, the model was utilized to identify key parameters to target when developing engineered growth factors for the treatment of diseases. It is shown that the sorting factions and internalization rates are reasonable targets for the design of engineered growth factors. Since the sorting fractions are dictated by binding affinities in the acidic environment of the endosomes, it may be beneficial to design and analog of IGF-I that is more resistant to changes in pH, similar to those develop from epidermal growth factor.<br>Ph. D.
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Favara, David M. "The biology of ELTD1/ADGRL4 : a novel regulator of tumour angiogenesis." Thesis, University of Oxford, 2017. http://ora.ox.ac.uk/objects/uuid:0d00af0a-bb43-44bc-ba0b-1f8acbe34bc5.
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<strong>Background:</strong> Our laboratory identified ELTD1, an orphan GPCR belonging to the adhesion GPCR family (aGPCR), as a novel regulator of angiogenesis and a potential anti-cancer therapeutic target. ELTD1 is normally expressed in both endothelial cells and vascular smooth muscle cells and expression is significantly increased in the tumour vasculature. The aim of this project was to analyse ELTD1's function in endothelial cells and its role in breast cancer. <strong>Method:</strong> 62 sequenced vertebrate genomes were interrogated for ELTD1 conservation and domain alterations. A phylogenetic timetree was assembled to establish time estimates for ELTD1's evolution. After ELTD1 silencing, mRNA array profiling was performed on primary human umbilical vein endothelial cells (HUVECs) and validated with qPCR and confocal microscopy. ELTD1's signalling was investigated by applying the aGPCR âStinger/tethered-agonist Hypothesis'. For this, truncated forms of ELTD1 and peptides analogous to the proposed tethered agonist region were designed. FRET-based 2<sup>nd</sup> messenger (Cisbio IP-1;cAMP) and luciferase-reporter assays (NFAT; NFÎoB; SRE; SRF-RE; CREB) were performed to establish canonical GPCR activation. To further investigate ELTD1's role in endothelial cells, ELTD1 was stably overexpressed in HUVECS. Functional angiogenesis assays and mRNA array profiling were then performed. To investigate ELTD1 in breast cancer, a panel of cell lines representative of all molecular subtypes were screened using qPCR. Furthermore, an exploratory pilot study was performed on matched primary and regional nodal secondary breast cancers (n=43) which were stained for ELTD1 expression. Staining intensity was then scored and compared with relapse free survival and overall survival. <strong>Results:</strong> ELTD1 arose 435 million years ago (mya) in bony fish and is present in all subsequent vertebrates. ELTD1 has 3 evolutionary variants of which 2 are most common: one variant with 3 EGFs and a variant with 2 EGFs. Additionally, ELTD1 may be ancestral to members of aGPCR family 2. HUVEC mRNA expression profiling after ELTD1 silencing showed upregulation of the mitochondrial citrate transporter SLC25A1, and ACLY which converts cytoplasmic citrate to Acetyl CoA, feeding fatty acid and cholesterol synthesis, and acetylation. A review of lipid droplet (fatty acid and cholesterol) accumulation by confocal microscopy and flow cytometry (FACS) revealed no changes with ELTD1 silencing. Silencing was also shown to affect the Notch pathway (downregulating the Notch ligand JAG1 and target gene HES2; upregulating the Notch ligand DLL4) and inducing KIT, a mediator of haematopoietic (HSC) and endothelial stem cell (ESC) maintenance. Signalling experiments revealed that unlike other aGPCRs, ELTD1 does not couple to any canonical GPCR pathways (Gαi, Gαs, Gαq, Gα12/13). ELTD1 overexpression in HUVECS revealed that ELTD1 induces an endothelial tip cell phenotype by promoting sprouting and capillary formation, inhibiting lumen anastomoses in mature vessels and lowering proliferation rate. There was no effect on wound healing or adhesion to angiogenesis associated matrix components. Gene expression changes following ELTD1 overexpression included upregulation of angiogenesis associated ANTRX1 as well as JAG1 and downregulation of migration associated CCL15 as well as KIT and DLL4. In breast cancer, none of the representative breast cancer cell lines screened expressed ELTD1. ELTD1 breast cancer immunohistochemistry revealed higher levels of vascular ELTD1 staining intensity within the tumour stroma contrasted to normal stroma and expression within tumour epithelial cells. Additionally, ELTD1 expression in tumour vessels was differentially expressed between the primary breast cancer microenvironment and that of the matched regional node. Due to the small size of the pilot study population, survival comparisons between the various subgroups did not yield significant results. <strong>Conclusion:</strong> ELTD1 is a novel regulator of endothelial metabolism through its suppression of ACLY and the related citrate transporter SLC25A1. ELTD1 also represses KIT, which is known to mediate haematopoietic and endothelial progenitors stem cell maintenance, a possible mechanism through which endothelial cells maintain terminal endothelial differentiation. ELTD1 does not signal like other adhesion GPCRS with CTF and FL forms of ELTD1 not signalling canonically. Additionally, ELTD1 regulates various functions of endothelial cell behaviour and function, inducing an endothelial tip cell phenotype and is highly evolutionarily conserved. Lastly, ELTD1 is differentially expressed in tumour vessels between primary breast cancer and regional nodal metastases and is also expressed in a small subset of breast cancer cells in vivo despite no cancer cell lines expressing ELTD1. The pilot study investigating ELTD1 in the primary breast cancer and regional involved nodes will be followed up with a larger study including the investigation of ELTD1 in distant metastases.
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Sarr, Abdoulaye. "Viab-Cell, développement d'un logiciel viabiliste sur processeur multicoeurs pour la simulation de la morphogénèse." Thesis, Brest, 2016. http://www.theses.fr/2016BRES0105/document.
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Ce travail présente un modèle théorique de morphogenèse animale, sous la forme d’un système complexe émergeant de nombreux comportements, processus internes, expressions et interactions cellulaires. Son implémentation repose sur un automate cellulaire orienté système multi-agents avec un couplage énergico-génétique entre les dynamiques cellulaires et les ressources.Notre objectif est de proposer des outils permettant l’étude numérique du développement de tissus cellulaires à travers une approche hybride (discrète/continue et qualitative/quantitative) pour modéliser les aspects génétiques, énergétiques et comportementaux des cellules. La modélisation de ces aspects s’inspire des principes de la théorie de la viabilité et des données expérimentales sur les premiers stades de division de l’embryon du poisson-zèbre.La théorie de la viabilité appliquée à la morphogenèse pose cependant de nouveaux défis en informatique pour pouvoir implémenter des algorithmes dédiés aux dynamiques morphologiques. Le choix de données biologiques pertinentes à considérer dans le modèle à proposer, la conception d’un modèle basé sur une théorie nouvelle, l’implémentation d’algorithmes adaptés reposant sur des processeurs puissants et le choix d’expérimentations pour éprouver nos propositions sont les enjeux fondamentaux de ces travaux. Les hypothèses que nous proposons sont discutées au moyen d’expérimentations in silico qui ont porté principalement sur l’atteignabilité et la capturabilité de formes de tissus ; sur la viabilité de l’évolution d’un tissu pour un horizon de temps ; sur la mise en évidence de nouvelles propriétés de tissus et la simulation de mécanismes tissulaires essentiels pour leur contrôlabilité face à des perturbations ; sur de nouvelles méthodes de caractérisation de tissus pathologiques, etc. De telles propositions doivent venir en appoint aux expérimentations in vitro et in vivo et permettre à terme de mieux comprendre les mécanismes régissant le développement de tissus. Plus particulièrement, nous avons mis en évidence lors du calcul de noyaux de viabilité les relations de causalité ascendante reliant la maintenance des cellules en fonction des ressources énergétiques disponibles et la viabilité du tissu en croissance. La dynamique de chaque cellule est associée à sa constitution énergétique et génétique. Le modèle est paramétré à travers une interface permettant de prendre en compte le nombre de coeurs à solliciter pour la simulation afin d’exploiter la puissance de calcul offerte par les matériels multi-coeurs<br>This work presents a theoretical model of animal morphogenesis, as a complex system from which emerge cellular behaviors, internal processes, interactions and expressions. Its implementation is based on a cellular automaton oriented multi-agent system with an energico-genetic coupling between the cellular dynamics and resources. Our main purpose is to provide tools for the numerical study of tissue development through a hybrid approach (discrete/continuous and qualitative/quantitative) that models genetic, behavioral and energetic aspects of cells. The modeling of these aspects is based on the principles of viability theory and on experimental data on the early stages of the zebrafish embryo division. The viability theory applied to the morphogenesis, however, raises new challenges in computer science to implement algorithms dedicated to morphological dynamics. The choice of relevant biological data to be considered in the model to propose, the design of a model based on a new theory, the implementation of suitable algorithms based on powerful processors and the choice of experiments to test our proposals are fundamental issues of this work. The assumptions we offer are discussed using in silico experiments that focused on the reachability and catchability of tissue forms ; on the viability of the evolution of a tissue for a time horizon ; on the discovery of new tissue properties and simulation of tissue mechanisms that are fondamental for their controllability face to disruptions ; on new pathological tissue characterization methods, etc. Such proposals must come extra to support experiments in vitro and in vivo and eventually allow a better understanding of the mechanisms governing the development of tissues.In particular, we have highlighted through the computing of viability kernels the bottom causal relationship between the maintenance of cells according to available energy resources and the viability of the tissue in growth. The model is set through an interface that takes into account the number of cores to solicit for simulation in order to exploit the computing power offered by multicore hardware
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Sun, Roger. "Utilisation de méthodes radiomiques pour la prédiction des réponses à l’immunothérapie et combinaisons de radioimmunothérapie chez des patients atteints de cancers Radiomics to Assess Tumor Infiltrating CD8 T-Cells and Response to Anti-PD-1/PD-L1 Immunotherapy in Cancer Patients: An Imaging Biomarker Multi-Cohort Study Imagerie médicale computationnelle (radiomique) et potentiel en immuno-oncologie Radiomics to Predict Outcomes and Abscopal Response of Cancer Patients Treated with Immunotherapy Combined with Radiotherapy Using a Validated Signature of CD8 Cells." Thesis, université Paris-Saclay, 2020. http://www.theses.fr/2020UPASL023.
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Depuis l’arrivée des inhibiteurs de points de contrôle immunitaire, l’immunothérapie a profondément modifié la prise en charge de nombreux cancers, permettant parfois des réponses tumorales prolongées chez des patients atteints de cancers aux stades très avancés. Cependant, malgré des progrès thérapeutiques constants et des associations de traitements combinant par exemple radiothérapie et immunothérapie, la majorité des patients traités ne présentent pas de bénéfices à ces traitements. Ceci explique l’importance de la recherche de biomarqueurs innovants de réponse à l’immunothérapie.L’application de l’intelligence artificielle en imagerie est une discipline récente et en pleine expansion. L’analyse informatique de l’image, appelée également radiomique, permet d’extraire des images médicales de l’information non exploitable à l’œil nu, potentiellement représentative de l’architecture des tissus sous-jacents et de leur composition biologique et cellulaire, et ainsi de développer des biomarqueurs grâce à l’apprentissage automatique (« machine learning »). Cette approche permettrait d’évaluer de façon non invasive la maladie tumorale dans sa globalité, avec la possibilité d’être répétée facilement dans le temps pour appréhender les modifications tumorales survenant au cours de l’histoire de la maladie et de la séquence thérapeutique.Dans le cadre de cette thèse, nous avons évalué si une approche radiomique permettait d’évaluer l’infiltration tumorale lymphocytaire, et pouvait être associée à la réponse de patients traités par immunothérapie. Dans un deuxième temps, nous avons évalué si cette signature permettait d’évaluer la réponse clinique de patients traités par radiothérapie et immunothérapie, et dans quelle mesure elle pouvait être utilisée pour évaluer l’hétérogénéité spatiale tumorale. Les défis spécifiques posés par les données d’imagerie de haute dimension dans le développement d’outils prédictifs applicables en clinique sont discutés dans cette thèse<br>With the advent of immune checkpoint inhibitors, immunotherapy has profoundly changed the therapeutic strategy of many cancers. However, despite constant therapeutic progress and combinations of treatments such as radiotherapy and immunotherapy, the majority of patients treated do not benefit from these treatments. This explains the importance of research into innovative biomarkers of response to immunotherapyComputational medical imaging, known as radiomics, analyzes and translates medical images into quantitative data with the assumption that imaging reflects not only tissue architecture, but also cellular and molecular composition. This allows an in-depth characterization of tumors, with the advantage of being non-invasive allowing evaluation of tumor and its microenvironment, spatial heterogeneity characterization and longitudinal assessment of disease evolution.Here, we evaluated whether a radiomic approach could be used to assess tumor infiltrating lymphocytes and whether it could be associated with the response of patients treated with immunotherapy. In a second step, we evaluated the association of this radiomic signature with clinical response of patients treated with radiotherapy and immunotherapy, and we assessed whether it could be used to assess tumor spatial heterogeneity.The specific challenges raised by high-dimensional imaging data in the development of clinically applicable predictive tools are discussed in this thesis
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Shahbandi, Nazgol. "Interaction of Brain Cancer Stem Cells and the Tumour Microenvironment: A Computational Study." Thesis, 2012. http://hdl.handle.net/10012/6440.
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Glioblastoma multiforme (GBM) is one of the most common and aggressive primary brain tumours, with a median patient survival time of 6-12 months in adults. It has been recently suggested that a typically small sub-population of brain tumour cells, in possession of certain defining properties of stem cells, is responsible for initiating and maintaining the tumour. More recent experiments have studied the interactions between this subpopulation of brain cancer cells and tumour microenvironmental factors such as hypoxia and high acidity. In this thesis a computational approach (based on Gillespie’s algorithm and cellular automata) is proposed to investigate the tumour heterogeneities that develop when exposed to various microenvironmental conditions of the cancerous tissue. The results suggest that microenvironmental conditions highly affect the characterization of cancer cells, including the self-renewal, differentiation and dedifferentiation properties of cancer cells.
Book chapters on the topic "Tumor cell computation"
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Jurtz, Vanessa Isabell, and Lars Rønn Olsen. "Computational Methods for Identification of T Cell Neoepitopes in Tumors." In Methods in Molecular Biology. Springer New York, 2018. http://dx.doi.org/10.1007/978-1-4939-8868-6_9.
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Owolabi, Kolade M., Albert Shikongo, and Edson Pindza. "Numerical Solution for a Tumor Cells Dynamics Within Their Micro-environment." In Computational Methods for Biological Models. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-5001-0_3.
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Ono, Naoaki, Chika Iwamoto, and Kenoki Ohuchida. "Construction of Classifier of Tumor Cell Types of Pancreas Cancer Based on Pathological Images Using Deep Learning." In Multidisciplinary Computational Anatomy. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-4325-5_17.
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Celik, Sefa, Funda Ozkok, Sevim Akyuz, and Aysen E. Ozel. "The Importance of Anthraquinone and Its Analogues and Molecular Docking Calculation." In Computational Models for Biomedical Reasoning and Problem Solving. IGI Global, 2019. http://dx.doi.org/10.4018/978-1-5225-7467-5.ch007.
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In drug-delivery systems containing nano-drug structures, targeting the tumorous tissue by anthraquinone molecules with high biological activity, and reaching and destroying tumors by their tumor-killing effect reveals remarkable results for the treatment of tumors. The various biological activities of anthraquinones and their derivatives depend on molecular conformation; hence, their intra-cell interaction mechanisms including deoxyribonucleic acid (DNA), ribonucleic acid (RNA), enzymes, and hormones. Computer-based drug design plays an important role in the design of drugs and the determination of goals for them. Molecular docking has been widely used in structure-based drug design. The effects of anthraquinone analogues in tumor cells as a result of their interaction with DNA strand has increased the number of studies done on them, and they have been shown to have a wide range of applications in chemistry, medicine, pharmacy, materials, and especially in the field of biomolecules.
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Niida, Atsushi, and Watal M. Iwasaki. "Agent-Based Modeling and Analysis of Cancer Evolution." In Simulation Modeling. IntechOpen, 2022. http://dx.doi.org/10.5772/intechopen.100140.
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Before the development of the next-generation sequencing (NGS) technology, carcinogenesis was regarded as a linear evolutionary process, driven by repeated acquisition of multiple driver mutations and Darwinian selection. However, recent cancer genome analyses employing NGS revealed the heterogeneity of mutations in the tumor, which is known as intratumor heterogeneity (ITH) and generated by branching evolution of cancer cells. In this chapter, we introduce a simulation modeling approach useful for understanding cancer evolution and ITH. We first describe agent-based modeling for simulating branching evolution of cancer cells. We next demonstrate how to fit an agent-based model to observational data from cancer genome analyses, employing approximate Bayesian computation (ABC). Finally, we explain how to characterize the dynamics of the simulation model through sensitivity analysis. We not only explain the methodologies, but also introduce exemplifying applications. For example, simulation modeling of cancer evolution demonstrated that ITH in colorectal cancer is generated by neutral evolution, which is caused by a high mutation rate and stem cell hierarchy. For cancer genome analyses, new experimental technologies are actively being developed; these will unveil various aspects of cancer evolution when combined with the simulation modeling approach.
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Talbot Hugo, Lekkal Myriam, Bessard-Duparc Remi, and Cotin Stephane. "Interactive Planning of Cryotherapy Using Physics-Based Simulation." In Studies in Health Technology and Informatics. IOS Press, 2014. https://doi.org/10.3233/978-1-61499-375-9-423.
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Cryotherapy is a rapidly growing minimally invasive technique for the treatment of certain tumors. It consists in destroying cancer cells by extreme cold delivered at the tip of a needle-like probe. As the resulting iceball is often smaller than the targeted tumor, a key to the success of cryotherapy is the planning of the position and orientation of the multiple probes required to treat a tumor, while avoiding any damage to the surrounding tissues. In order to provide such a planning tool, a number of challenges need to be addressed such as fast and accurate computation of the freezing process or interactive positioning of the virtual cryoprobes in the pre-operative image volume. To address these challenges, we present an approach which relies on an advanced computational framework, and a gesture-based planning system using contact-less technology to remain compatible with a use in a sterile environment.
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Salunke, Ujwala Udhavrao, and Bhushan Rajendra Mote. "Brain Tumor Detection." In Advances in Computational Intelligence and Robotics. IGI Global, 2025. https://doi.org/10.4018/979-8-3693-9521-9.ch016.
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Brain tumor formation is due to fast and unchecked growth of cells. Early identification of cancer is a very necessary. Brain tumors can be classified into various types of groups with respect to the type they are, anatomical structure from where they originate, rate of growth, and the stage at which the tumor is developed; Thus, developing automated segmentation and classification techniques is critical to accelerate and enhance the diagnosis process of brain tumors. Neuroimaging techniques like CT and MRI can quickly and safely detect tumors. The capability of ML and AI is demonstrated in creating algorithms that would automatically classify and segment using various imaging methods. In this review, several forms of brain tumors are covered along with datasets used in various studies, methods applied for enhancement, procedures carried out for segmentation, methods adopted for feature extraction, classification paradigms, methodologies on machine learning, approaches used for deep learning, and strategies for transfer learning pertaining to the study on brain tumors
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Sakthivel, Shalini, Manjita Srivastava, Muneesh Kumar Barman, et al. "Cancer Stem Cells and Advanced Novel Technologies in Oncotherapy." In Research Anthology on Bioinformatics, Genomics, and Computational Biology. IGI Global, 2023. http://dx.doi.org/10.4018/979-8-3693-3026-5.ch020.
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Self-renewal is the most important property of stem cells. Parallel to this, cancer stem cells (CSCs) have an indefinite proliferative ability that drives tumorigenesis. The conventional treatment of cancer includes chemotherapy, radiotherapy, and surgery, which decreases the tumour size. Contrary, targeted therapy against CSCs initially does not shrink the tumour but ultimately causes tumour degeneration. Nanobiotechnology, RNA interference, microRNA are emerging fields with a vital role in targeted therapy against CSCs. The non-protein encoding microRNAs has a major role in cancer treatment since they regulate gene expression during post-transcription. RNAi technology can silence the gene of interest with potency and specificity inhibiting tumour growth. In nanoparticles-based RNA interference, nanocarriers protect RNAi molecules from immune recognition and enzymatic degradation. The cancer cell gene expression profiling using next-generation sequencing helps in understanding the underlying cancer cell mechanisms. The current chapter deals with novel concepts in the treatment of cancer.
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Ghosh, Joyeta, and Asmita Roy. "Reframing Cancer as an Immunological Disorder Through Personalized Nutrition and AI Integration." In Advances in Computational Intelligence and Robotics. IGI Global, 2025. https://doi.org/10.4018/979-8-3693-9725-1.ch012.
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Recent advances in cancer research have revolutionized our understanding of cancer as not merely a disease of uncontrolled cell growth but fundamentally as an immunological disorder where malignant cells evade and suppress immune surveillance. This chapter presents a novel framework for conceptualizing and treating cancer through an integrative approach that combines immunological insights with personalized nutrition and artificial intelligence-driven interventions. The chapter synthesizes current evidence on immunonutrition in oncology, examining specific nutrients and bioactive compounds that enhance anti-tumor immunity. Furthermore, we discuss how artificial intelligence algorithms can be leveraged to develop personalized nutritional protocols based on individual patient parameters, including genetic profiles and immune biomarkers.
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Frometa-Castillo, Terman, Anil Pyakuryal, Amadeo Wals-Zurita, and Asghar Mesbahi. "Biologically Effective Dose (BED) or Radiation Biological Effect (RBEf)?" In Recent Techniques and Applications in Ionizing Radiation Research. IntechOpen, 2020. http://dx.doi.org/10.5772/intechopen.92029.
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The current radiosensitive studies are described with linear-quadratic (LQ) cell survival (S) model for one fraction with a dose d. As result of assuming all sublethally damaged cells (SLDCs) are completely repaired during the interfractions, that is, no presence of SLDCs, the survived cells are calculated for a n-fractionated regimen with the LQ S(n,D) model. A mathematically processed subpart of LQS(n,D) is the biologically effective dose (BED) that is used for evaluating a so-called “biological dose.” The interactions of ionizing radiation with a living tissue can produce partial death or sublethal damage from healthy or sublethally damaged cells. The proportions of the killed and sub-lethally damaged cells define the radiation biological effects (RBEfs). Computational simulations using RBEFs for fractionated regimens let calculating tumor control probability. While the derivation of the LQ S(n,D) considers a 100% cell repair, that is, 0% of sublethally damaged cells (SLDCs), the radiobiological simulators take into account the presence of SLDCs, as well as a cell repair <100% during the interfractions and interruption. Given “biological dose” does not exist, but RBEf, there was need for creating the BED. It is shown how some uses of BED, like the derivation of EQ2D expression, can be done directly with the LQ S(n,D).
Conference papers on the topic "Tumor cell computation"
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Rizwan, Muhammad, Po Tsang B. Huang, and Mehboob Ali. "A Bi-Fold Multi-Classification Scheme for Brain Tumor Using Deep Convolutional Neural Network." In 14th International Seminar on Industrial Engineering and Management. Trans Tech Publications Ltd, 2025. https://doi.org/10.4028/p-mk5lge.
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A brain tumor is an uncontrolled and unstructured growth of brain cells inside the skull. Most of the time, tumors are misclassified due to the complexity of the lesion and as a result, the survival rate of the patient effects adversely. Magnetic resonance imaging (MRI) is usually used to identify various types of brain tumors. Due to the advancement in computer-aided systems and bio-informatics ML and DL algorithms has been applied to assist neurologist in decision-making. However, the current techniques are error-prone and time-consuming. Therefore, a Bi-fold CNN model for tumor detection and classification has been proposed with validation accuracies of 99.13% and 98.10% respectively. The proposed system consists of two ends-to-end connected CNN models, where the first has been used to detect tumors while the second is used to classify them into glioma, meningioma, and pituitary tumors. The respective accuracies on blind testing are 99.36% and 97.35%. The model has been compared with ResNet50, Xception, InsepectionV3, and EfficeintNetV2S. The comparison results showed the novelty and superiority of our proposed system. The publicly available dataset has been used in this research. In addition, due to its structural specifications, it has less computational complexity as compared to the existing methods.
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Zaman, Muhammad H. "Multiscale Modeling of Tumor Cell Migration." In FROM PHYSICS TO BIOLOGY: The Interface between Experiment and Computation - BIFI 2006 II International Congress. AIP, 2006. http://dx.doi.org/10.1063/1.2345628.
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Li, Xiuyan, Zhenyu Yang, Qi Wang, Yukuan Sun, and Aidong Liu. "Vision Transformer for Cell Tumor Image Classification." In 2023 3rd International Conference on Frontiers of Electronics, Information and Computation Technologies (ICFEICT). IEEE, 2023. http://dx.doi.org/10.1109/icfeict59519.2023.00039.
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He, Zhi Zhu, and Jing Liu. "Investigation of Tumor Growth Based on Phase Field Model." In ASME 2011 International Mechanical Engineering Congress and Exposition. ASMEDC, 2011. http://dx.doi.org/10.1115/imece2011-65744.
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This paper presents and investigates the tumor growth based on a phase model. The tumor core is necrotic and inhibitor chemical species are considered. The interface of tumor and health tissue is tracked using a phase field equation. The reformulation of a classical model, accounting for cell-proliferation, apoptosis, cell-to-cell and cell-to-matrix adhesion, is derived. The advantages of the finite difference methodology employed are generality and relative simplicity implication. We present simulations of the nonlinear evolution of growing tumors morphology and discuss the effects of tumor microenvironment. Mechanisms reflecting the tumor growth and development behavior was preliminarily interpreted. Recently numerous mathematical have been developed to investigate the growth dynamics of tumor [1–8]. One of most significant model developed by Wise [8] is based on Cahn-Hilliard equation, which is conservation phase field method. Allen-Chan nonconservation phase field has been developed to track the moving interface for multiphase simulation by Sun [9]. Allen-Chan equation is second order, while Cahn-Hilliard equation is fourth order in space. Thus, we introduce the Allen-Chan phase method [9–10] to simulate the tumor growth, which is very simple for numerical simulation The computation domain is illustrated in Fig. 1, where ΩH denotes host tissue, the tumor domains is comprised of viable tumor cell ΩV and dead tumor cell ΩD. The numerical results are presented at Fig. (2–4). One can find that the growth of tumor strongly depend on the nutrients and nonlinear unstable growth may lead to finger shaped pattern, which is in agreement with recent experimental observations [7] of in vivo tumor. In summary, a phase method has been developed to study diffusion and consumption of the nutrients and tumor cell proliferation, necrosis and migration, which discloses the evolution of complex shape of tumor.
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Ahmed, Meraj, Tam Thien Nguyen, Lahcen Akerkouch, Margherita Tavasso, Ankur Deep, and Trung B. Le. "Numerical Modelling of Tumor Transport in Fluid Flows." In 2025 Design of Medical Devices Conference. American Society of Mechanical Engineers, 2025. https://doi.org/10.1115/dmd2025-1060.
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Abstract Cancer metastasis leads to the transport and widespread of malignant cells from the primary tumor to other parts of the body by exploiting body fluids (lymphatic fluid, bloodstream, and interstitial fluid). While the transport of a single cancer cell in fluid flow has been studied in the past, it is unclear how a group of cancer cells (tumor) migrate under the impact of hydrodynamic force in vasculature. In this work, we address this knowledge gap by investigating the migration process of a cancer spheroid tumor in a micro-channel with a constriction using both experimental and computational methods. The Dissipative Particle Dynamics method was employed to simulate the mechanical components of the spheroid tumor and immersed boundary method is used for interaction of spheroid with the surrounding fluid. Our results suggest that the mechanical response of the spheroid tumor differs from a single cell. Our computational framework provides new capabilities for designing bioengineering devices for cell manipulation.
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Pishko, Gregory L., Garrett W. Astary, Thomas H. Mareci, and Malisa Sarntinoranont. "High Resolution DCE-MRI Vascular Characterization of Murine Sarcoma and Human Renal Cell Carcinoma for Computational Modeling." In ASME 2008 Summer Bioengineering Conference. American Society of Mechanical Engineers, 2008. http://dx.doi.org/10.1115/sbc2008-193179.
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Non-uniform extravasation from blood vessels, elevated interstitial fluid pressure (IFP), and transport by bulk fluid motion in the extracellular space have all been determined to contribute to the non-uniform tissue distribution of systemically delivered agents in solid tumors. The aforementioned factors can lead to inadequate and uneven uptake in tumor tissue which has been shown to be a major obstacle to macromolecules in clinical cancer therapy [1]. Recently developed computational tumor models have described blood flow either in a single vessel or capillary network with variations in space and time [2]. These studies do not account for heterogeneous tissue transport properties in regions of leakier vessels [3].
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Sarkar, Saugata, and Marissa Nichole Rylander. "Treatment Planning Model for Nanotube-Mediated Laser Cancer Therapy." In ASME 2008 Summer Bioengineering Conference. American Society of Mechanical Engineers, 2008. http://dx.doi.org/10.1115/sbc2008-192997.
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The goal of the project is to develop an effective treatment planning computational tool for nanotube-mediated laser therapy that maximizes tumor destruction and minimizes tumor recurrence. Laser therapies can provide a minimally invasive treatment alternative to surgical resection of tumors. However, the effectiveness of these therapies is limited due to nonspecific heating of target tissue and diffusion limited thermal deposition which often leads to healthy tissue injury and extended treatment durations. These therapies can be further compromised due to induction of molecular chaperones called heat shock protein (HSP) in tumor regions where non-lethal temperature elevation occurs causing enhanced tumor cell viability and imparting resistance to chemotherapy and radiation treatments which are generally employed in conjunction with hyperthermia.
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Park, Taehyun, Daniel Sangwon Park, and Michael C. Murphy. "High Flow Rate Device for Circulating Tumor Cell Capture." In ASME 2011 International Mechanical Engineering Congress and Exposition. ASMEDC, 2011. http://dx.doi.org/10.1115/imece2011-63750.
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Circulating tumor cells (CTCs) were captured at high flow rates with a high recovery rate using a small footprint disposable polymer micro device. A new concept of target cell capture was introduced to break through the barriers limiting current approaches. Several potential designs were parametrically simulated using computational fluid dynamics (CFD) to achieve the best performance. The high flow rate device (HFRD) was fabricated in polymethyl methacrylate (PMMA) based on simulation results. Antibodies (anti-EpCAM) were immobilized on the PMMA device with surface treatments including UV modification and amine functionalization. A novel rare cell sample preparation method was established to provide an exact number of initial target cells to accurately test the rare cell performance. The precisely prepared samples of rare target tumor cells were spiked in a solution containing human erythrocytes, with a 40% hematocrit. The mean recovery rate with the HFRD was 85% at a 750 μL/min flow rate.
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Aghaamoo, Mohammad, Jie Xu, and Xiaolin Chen. "A Computational Study on Non-Uniform Cross-Sectional Deformability-Based CTC Separation Devices." In ASME 2015 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2015. http://dx.doi.org/10.1115/imece2015-50481.
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Cancer is one of the most dangerous diseases widespread around the world. Developing the most efficient cures for cancer strongly relies on a comprehensive understanding of cancer cells. Circulating Tumor Cells (CTCs) are cancer cells detached from the primary tumor site and released into the blood. CTCs are the main source of cancer metastasis. Devising devices to identify and separate these cells from the blood is of great importance since these cells represent cancer in many aspects. Because of the rarity of CTCs in the blood, designing efficient CTC separation devices has become a challenging issue. Among different CTC separation devices, deformability-based CTC separation devices have become very popular recently because of their simplicity and their relatively low cost. In this research, we investigate numerically the deformability-based CTC separation microfilters. Specifically, we study non-uniform cross-sectional microfilters because of their ability in unclogging. Different microfilter geometries are selected for this study including conical-shaped and rectangular cross-section microfilters with different channel profiles. In this study, we mainly focus on the effect of different design parameters on system performance criteria. The main performance criteria are: critical pressure of the system, system throughput and cell clogging in filtration. Critical pressure, which is defined as the maximum pressure for a cancer cell to squeeze through the microfilter, is an important design aspect. Applying a pressure lower than the critical pressure causes the cell to get stuck in the microfilter, while applying much higher pressure on the system may result in cellular damage which has negative effect on the viability of the cell for post processing. System throughput is also of great importance. A high-throughput CTC filtration system is always more desirable in clinic applications. System clogging, which decreases the CTC separation efficiency, is one of the challenging issues in these devices. In this research, we first simulate how a cell behaves in a passing event process through the microfilter. Specifically, we focus on how different cells squeeze through the microfilter. This gives us more insight through the separation process. Second, we investigate the effect of different microfilter geometries on the critical pressure required for separation of cancer cells. Third, the effect of applied inlet pressure on the system performance is studied. Our results indicate that the critical pressure varies significantly with microfilter geometry. Results also show that the device throughput is strongly related to the applied pressure. Moreover, the filtration simulation demonstrates that system clogging occurs if unsuitable pressure is applied on the system.
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Whitney, Jon, Saugata Sarkar, Xuanfeng Ding, et al. "Computational Models and Digital Image Analysis of Carbon Nanotube Mediated Laser Cancer Therapy." In ASME 2011 Summer Bioengineering Conference. American Society of Mechanical Engineers, 2011. http://dx.doi.org/10.1115/sbc2011-53984.
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Laser-based photothermal therapy can provide a minimally invasive treatment alternative to surgical resection of tumors. The selectivity and effectiveness of laser therapy can be greatly enhanced when photo-absorbing nanoparticles such as multi-walled carbon nanotubes (MWNTs) are introduced into the tissue [1,2]. The effectiveness of nanoparticle enhanced laser treatment can be determined through a combined approach using experimental measurement and computational models. This approach allows ideal laser parameters (e.g. irradiance, pulse duration) and nanoparticle properties (e.g. concentration and delivery method) to be selected to maximize treatment efficacy. We developed a computational model to predict the temperature response of tissue representative phantoms and in vivo murine renal cancer (RENCA) kidney tumors to MWNTs used in combination with external laser irradiation. The accuracy of the computational model prediction of temperature was verified by comparing with experimental measurements of temperature using magnetic resonance thermometry (MRTI). In addition, an image analysis technique is introduced for measuring the spatial viability of cancer cells suspended in tissue phantoms following nanoparticle enhanced laser therapy and correlating cell viability with thermal exposure. Spatial viability and thermal measurements are combined to predict cell death as a function of temperature in tissue phantoms.