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1دورية أكاديمية
المؤلفون: Ryan T. Woodall, Cora C. Esparza, Margarita Gutova, Maosen Wang, Jessica J. Cunningham, Alexander B. Brummer, Caleb A. Stine, Christine C. Brown, Jennifer M. Munson, Russell C. Rockne
المصدر: APL Bioengineering, Vol 8, Iss 2, Pp 026106-026106-12 (2024)
مصطلحات موضوعية: Biotechnology, TP248.13-248.65, Medical technology, R855-855.5
الوصف: Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is a routine method to noninvasively quantify perfusion dynamics in tissues. The standard practice for analyzing DCE-MRI data is to fit an ordinary differential equation to each voxel. Recent advances in data science provide an opportunity to move beyond existing methods to obtain more accurate measurements of fluid properties. Here, we developed a localized convolutional function regression that enables simultaneous measurement of interstitial fluid velocity, diffusion, and perfusion in 3D. We validated the method computationally and experimentally, demonstrating accurate measurement of fluid dynamics in situ and in vivo. Applying the method to human MRIs, we observed tissue-specific differences in fluid dynamics, with an increased fluid velocity in breast cancer as compared to brain cancer. Overall, our method represents an improved strategy for studying interstitial flows and interstitial transport in tumors and patients. We expect that our method will contribute to the better understanding of cancer progression and therapeutic response.
وصف الملف: electronic resource
العلاقة: https://doaj.org/toc/2473-2877Test
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2دورية أكاديمية
المؤلفون: Lucas Vu, Krystine Garcia‐Mansfield, Antonio Pompeiano, Jiyan An, Victoria David‐Dirgo, Ritin Sharma, Vinisha Venugopal, Harkeerat Halait, Guido Marcucci, Ya‐Huei Kuo, Lisa Uechi, Russell C. Rockne, Patrick Pirrotte, Robert Bowser
المصدر: Annals of Clinical and Translational Neurology, Vol 10, Iss 11, Pp 2025-2042 (2023)
مصطلحات موضوعية: Neurosciences. Biological psychiatry. Neuropsychiatry, RC321-571, Neurology. Diseases of the nervous system, RC346-429
الوصف: Abstract Objective Amyotrophic lateral sclerosis (ALS) is a heterogeneous disease with a complex etiology that lacks biomarkers predicting disease progression. The objective of this study was to use longitudinal cerebrospinal fluid (CSF) samples to identify biomarkers that distinguish fast progression (FP) from slow progression (SP) and assess their temporal response. Methods We utilized mass spectrometry (MS)‐based proteomics to identify candidate biomarkers using longitudinal CSF from a discovery cohort of SP and FP ALS patients. Immunoassays were used to quantify and validate levels of the top biomarkers. A state‐transition mathematical model was created using the longitudinal MS data that also predicted FP versus SP. Results We identified a total of 1148 proteins in the CSF of all ALS patients. Pathway analysis determined enrichment of pathways related to complement and coagulation cascades in FPs and synaptogenesis and glucose metabolism in SPs. Longitudinal analysis revealed a panel of 59 candidate markers that could segregate FP and SP ALS. Based on multivariate analysis, we identified three biomarkers (F12, RBP4, and SERPINA4) as top candidates that segregate ALS based on rate of disease progression. These proteins were validated in the discovery and a separate validation cohort. Our state‐transition model determined that the overall variance of the proteome over time was predictive of the disease progression rate. Interpretation We identified pathways and protein biomarkers that distinguish rate of ALS disease progression. A mathematical model of the CSF proteome determined that the change in entropy of the proteome over time was predictive of FP versus SP.
وصف الملف: electronic resource
العلاقة: https://doaj.org/toc/2328-9503Test
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3دورية أكاديمية
المؤلفون: Vikram Adhikarla, Dennis Awuah, Enrico Caserta, Megan Minnix, Maxim Kuznetsov, Amrita Krishnan, Jefferey Y. C. Wong, John E. Shively, Xiuli Wang, Flavia Pichiorri, Russell C. Rockne
المصدر: Frontiers in Immunology, Vol 15 (2024)
مصطلحات موضوعية: radionuclide, combination therapy, myeloma, CAR T cells, daratumumab, mathematical model, Immunologic diseases. Allergy, RC581-607
الوصف: IntroductionCancer combination treatments involving immunotherapies with targeted radiation therapy are at the forefront of treating cancers. However, dosing and scheduling of these therapies pose a challenge. Mathematical models provide a unique way of optimizing these therapies. MethodsUsing a preclinical model of multiple myeloma as an example, we demonstrate the capability of a mathematical model to combine these therapies to achieve maximum response, defined as delay in tumor growth. Data from mice studies with targeted radionuclide therapy (TRT) and chimeric antigen receptor (CAR)-T cell monotherapies and combinations with different intervals between them was used to calibrate mathematical model parameters. The dependence of progression-free survival (PFS), overall survival (OS), and the time to minimum tumor burden on dosing and scheduling was evaluated. Different dosing and scheduling schemes were evaluated to maximize the PFS and optimize timings of TRT and CAR-T cell therapies. ResultsTherapy intervals that were too close or too far apart are shown to be detrimental to the therapeutic efficacy, as TRT too close to CAR-T cell therapy results in radiation related CAR-T cell killing while the therapies being too far apart result in tumor regrowth, negatively impacting tumor control and survival. We show that splitting a dose of TRT or CAR-T cells when administered in combination is advantageous only if the first therapy delivered can produce a significant benefit as a monotherapy. DiscussionMathematical models are crucial tools for optimizing the delivery of cancer combination therapy regimens with application along the lines of achieving cure, maximizing survival or minimizing toxicity.
وصف الملف: electronic resource
العلاقة: https://www.frontiersin.org/articles/10.3389/fimmu.2024.1358478/fullTest; https://doaj.org/toc/1664-3224Test
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4دورية أكاديمية
المؤلفون: Mari Amirbekyan, Vikram Adhikarla, Jeffrey P. Cheng, Eleni H. Moschonas, Corina O. Bondi, Russell C. Rockne, Anthony E. Kline, Margarita Gutova
المصدر: Scientific Reports, Vol 13, Iss 1, Pp 1-11 (2023)
الوصف: Abstract Efficacious stem cell-based therapies for traumatic brain injury (TBI) depend on successful delivery, migration, and engraftment of stem cells to induce neuroprotection. L-myc expressing human neural stem cells (LMNSC008) demonstrate an inherent tropism to injury sites after intranasal (IN) administration. We hypothesize that IN delivered LMNSC008 cells migrate to primary and secondary injury sites and modulate biomarkers associated with neuroprotection and tissue regeneration. To test this hypothesis, immunocompetent adult female rats received either controlled cortical impact injury or sham surgery. LMNSC008 cells or a vehicle were administered IN on postoperative days 7, 9, 11, 13, 15, and 17. The distribution and migration of eGFP-expressing LMNSC008 cells were quantified over 1 mm-thick optically cleared (CLARITY) coronal brain sections from TBI and SHAM controls. NSC migration was observed along white matter tracts projecting toward the hippocampus and regions of TBI. ELISA and Nanostring assays revealed a shift in tissue gene expression in LMNSC008 treated rats relative to controls. LMNSC008 treatment reduced expression of genes and pathways involved in inflammatory response, microglial function, and various cytokines and receptors. Our proof-of-concept studies, although preliminary, support the rationale of using intranasal delivery of LMNSC008 cells for functional studies in preclinical models of TBI and provide support for potential translatability in TBI patients.
وصف الملف: electronic resource
العلاقة: https://doaj.org/toc/2045-2322Test
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5دورية أكاديمية
المؤلفون: Alexander B. Brummer, Agata Xella, Ryan Woodall, Vikram Adhikarla, Heyrim Cho, Margarita Gutova, Christine E. Brown, Russell C. Rockne
المصدر: Frontiers in Immunology, Vol 14 (2023)
مصطلحات موضوعية: dynamical systems, latent variables, CAR T-cells, antigen binding, allee effect, SINDy, Immunologic diseases. Allergy, RC581-607
الوصف: In the development of cell-based cancer therapies, quantitative mathematical models of cellular interactions are instrumental in understanding treatment efficacy. Efforts to validate and interpret mathematical models of cancer cell growth and death hinge first on proposing a precise mathematical model, then analyzing experimental data in the context of the chosen model. In this work, we present the first application of the sparse identification of non-linear dynamics (SINDy) algorithm to a real biological system in order discover cell-cell interaction dynamics in in vitro experimental data, using chimeric antigen receptor (CAR) T-cells and patient-derived glioblastoma cells. By combining the techniques of latent variable analysis and SINDy, we infer key aspects of the interaction dynamics of CAR T-cell populations and cancer. Importantly, we show how the model terms can be interpreted biologically in relation to different CAR T-cell functional responses, single or double CAR T-cell-cancer cell binding models, and density-dependent growth dynamics in either of the CAR T-cell or cancer cell populations. We show how this data-driven model-discovery based approach provides unique insight into CAR T-cell dynamics when compared to an established model-first approach. These results demonstrate the potential for SINDy to improve the implementation and efficacy of CAR T-cell therapy in the clinic through an improved understanding of CAR T-cell dynamics.
وصف الملف: electronic resource
العلاقة: https://www.frontiersin.org/articles/10.3389/fimmu.2023.1115536/fullTest; https://doaj.org/toc/1664-3224Test
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6دورية أكاديمية
المؤلفون: Heyrim Cho, Ya-Huei Kuo, Russell C. Rockne
المصدر: Mathematical Biosciences and Engineering, Vol 19, Iss 8, Pp 8505-8536 (2022)
مصطلحات موضوعية: next generation sequencing data, cell state evolution, phenotype structured models, partial differential equation, hematopoeisis, Biotechnology, TP248.13-248.65, Mathematics, QA1-939
الوصف: Single-cell sequencing technologies have revolutionized molecular and cellular biology and stimulated the development of computational tools to analyze the data generated from these technology platforms. However, despite the recent explosion of computational analysis tools, relatively few mathematical models have been developed to utilize these data. Here we compare and contrast two cell state geometries for building mathematical models of cell state-transitions with single-cell RNA-sequencing data with hematopoeisis as a model system; (i) by using partial differential equations on a graph representing intermediate cell states between known cell types, and (ii) by using the equations on a multi-dimensional continuous cell state-space. As an application of our approach, we demonstrate how the calibrated models may be used to mathematically perturb normal hematopoeisis to simulate, predict, and study the emergence of novel cell states during the pathogenesis of acute myeloid leukemia. We particularly focus on comparing the strength and weakness of the graph model and multi-dimensional model.
وصف الملف: electronic resource
العلاقة: https://doaj.org/toc/1551-0018Test
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7دورية أكاديمية
المؤلفون: Seth Hilliard, Karen Mosoyan, Sergio Branciamore, Grigoriy Gogoshin, Alvin Zhang, Diana L. Simons, Russell C. Rockne, Peter P. Lee, Andrei S. Rodin
المصدر: iScience, Vol 26, Iss 2, Pp 106041- (2023)
مصطلحات موضوعية: Molecular network, Neural networks, Artificial intelligence, Science
الوصف: Summary: Modern artificial neural networks (ANNs) have long been designed on foundations of mathematics as opposed to their original foundations of biomimicry. However, the structure and function of these modern ANNs are often analogous to real-life biological networks. We propose that the ubiquitous information-theoretic principles underlying the development of ANNs are similar to the principles guiding the macro-evolution of biological networks and that insights gained from one field can be applied to the other. We generate hypotheses on the bow-tie network structure of the Janus kinase - signal transducers and activators of transcription (JAK-STAT) pathway, additionally informed by the evolutionary considerations, and carry out ANN simulation experiments to demonstrate that an increase in the network’s input and output complexity does not necessarily require a more complex intermediate layer. This observation should guide novel biomarker discovery—namely, to prioritize sections of the biological networks in which information is most compressed as opposed to biomarkers representing the periphery of the network.
وصف الملف: electronic resource
العلاقة: http://www.sciencedirect.com/science/article/pii/S2589004223001189Test; https://doaj.org/toc/2589-0042Test
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8دورية أكاديمية
المؤلفون: Reuben Franklin, Yiming Guo, Shiyang He, Meijuan Chen, Fei Ji, Xinyue Zhou, David Frankhouser, Brian T. Do, Carmen Chiem, Mihyun Jang, M. Andres Blanco, Matthew G. Vander Heiden, Russell C. Rockne, Maria Ninova, David B. Sykes, Konrad Hochedlinger, Rui Lu, Ruslan I. Sadreyev, Jernej Murn, Andrew Volk, Sihem Cheloufi
المصدر: Nature Communications, Vol 13, Iss 1, Pp 1-17 (2022)
مصطلحات موضوعية: Science
الوصف: Cell fate commitment involves transcription factor activity and changes in chromatin architecture. Here the authors show that CAF-1 maintains lineage fidelity by controlling chromatin accessibility at specific sites; suppressing CAF-1 triggers differentiation of myeloid stem and progenitor cells into a mixed lineage state.
وصف الملف: electronic resource
العلاقة: https://doaj.org/toc/2041-1723Test
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9دورية أكاديمية
المؤلفون: Justin Gomez, Nathanael Holmes, Austin Hansen, Vikram Adhikarla, Margarita Gutova, Russell C. Rockne, Heyrim Cho
المصدر: Mathematical Biosciences and Engineering, Vol 19, Iss 3, Pp 2592-2615 (2022)
مصطلحات موضوعية: neural stem cell therapy, intranasal drug administration, mathematical oncology, agent based modeling, glioma, lm-nsc008, Biotechnology, TP248.13-248.65, Mathematics, QA1-939
الوصف: Neural stem cells (NSCs) offer a potential solution to treating brain tumors. This is because NSCs can circumvent the blood-brain barrier and migrate to areas of damage in the central nervous system, including tumors, stroke, and wound injuries. However, for successful clinical application of NSC treatment, a sufficient number of viable cells must reach the diseased or damaged area(s) in the brain, and evidence suggests that it may be affected by the paths the NSCs take through the brain, as well as the locations of tumors. To study the NSC migration in brain, we develop a mathematical model of therapeutic NSC migration towards brain tumor, that provides a low cost platform to investigate NSC treatment efficacy. Our model is an extension of the model developed in Rockne et al. (PLoS ONE 13, e0199967, 2018) that considers NSC migration in non-tumor bearing naive mouse brain. Here we modify the model in Rockne et al. in three ways: (i) we consider three-dimensional mouse brain geometry, (ii) we add chemotaxis to model the tumor-tropic nature of NSCs into tumor sites, and (iii) we model stochasticity of migration speed and chemosensitivity. The proposed model is used to study migration patterns of NSCs to sites of tumors for different injection strategies, in particular, intranasal and intracerebral delivery. We observe that intracerebral injection results in more NSCs arriving at the tumor site(s), but the relative fraction of NSCs depends on the location of injection relative to the target site(s). On the other hand, intranasal injection results in fewer NSCs at the tumor site, but yields a more even distribution of NSCs within and around the target tumor site(s).
وصف الملف: electronic resource
العلاقة: https://doaj.org/toc/1551-0018Test
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10دورية أكاديمية
المؤلفون: Bihong T. Chen, Taihao Jin, Ningrong Ye, Sean W. Chen, Russell C. Rockne, Stephanie Yoon, Isa Mambetsariev, Ebenezer Daniel, Ravi Salgia
المصدر: Brain Sciences, Vol 13, Iss 7, p 1057 (2023)
مصطلحات موضوعية: brain metastases, lung cancer, mutation status, spatial distribution, Neurosciences. Biological psychiatry. Neuropsychiatry, RC321-571
الوصف: Non-small cell lung cancer (NSCLC) has a high rate of brain metastasis. The purpose of this study was to assess the differential distribution of brain metastases from primary NSCLC based on mutation status. Brain MRI scans of patients with brain metastases from primary NSCLC were retrospectively analyzed. Brain metastatic tumors were grouped according to mutation status of their primary NSCLC and the neuroimaging features of these brain metastases were analyzed. A total of 110 patients with 1386 brain metastases from primary NSCLC were included in this study. Gray matter density at the tumor center peaked at ~0.6 for all mutations. The median depths of tumors were 7.9 mm, 8.7 mm and 9.1 mm for EGFR, ALK and KRAS mutation groups, respectively (p = 0.044). Brain metastases for the EGFR mutation-positive group were more frequently located in the left cerebellum, left cuneus, left precuneus and right precentral gyrus. In the ALK mutation-positive group, brain metastases were more frequently located in the right middle occipital gyrus, right posterior cingulate, right precuneus, right precentral gyrus and right parietal lobe. In the KRAS mutation-positive patient group, brain metastases were more frequently located in the posterior left cerebellum. Our study showed differential spatial distribution of brain metastases in patients with NSCLC according to their mutation status. Information regarding distribution of brain metastases is clinically relevant as it could be helpful to guide treatment planning for targeted therapy, and for predicting prognosis.
وصف الملف: electronic resource
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