Hasil untuk "q-bio.TO"

Benjamin K. Schneider, Sebastien Benzekry, Jonathan P. Mochel

Non-small cell lung cancer (NSCLC) is often intrinsically resistant to several first- and second-line therapeutics and can rapidly acquire further resistance after a patient begins receiving treatment. Treatment outcomes are therefore significantly impacted by the optimization of therapeutic scheduling. Previous preclinical research has suggested scheduling bevacizumab in sequence with combination antiproliferatives could significantly improve clinical outcomes. Mathematical modeling is a well-suited tool for investigating this proposed scheduling modification. To address this critical need, individual patient tumor data from 11 clinical trials in NSCLC has been collated and used to develop a semi-mechanistic model of NSCLC growth and response to the various therapeutics represented in those trials. Precise estimates of clinical parameters fundamental to cancer modeling have been produced - such as the rate of acquired resistance to various pharmaceuticals, the relationship between drug concentration and cancer cell death, as well as the fine temporal dynamics of vascular remodeling in response to bevacizumab. In a reserved portion of the dataset, this model was used to predict the efficacy of individual treatment time courses with a mean error rate of 59.7% after a single tumor measurement and 11.7% after three successive tumor measurements. A delay of 9.6 hours between pemetrexed-cisplatin and bevacizumab administration is predicted to optimize the benefit of sequential administration. At this gap, approximately 93.5% of simulated patients benefited from a gap in sequential administration compared with concomitant administration. Of those simulated patients, the mean improvement in tumor reduction was 20.7%. This result suggests that scheduling a modest gap between the administration of bevacizumab and its partner antiproliferatives could meaningfully improve patient outcomes in NSCLC.

Keith L Chambers, Mary R Myerscough, Helen M Byrne

Atherosclerotic plaques form in artery walls due to a chronic inflammatory response driven by lipid accumulation. A key component of the inflammatory response is the interaction between monocyte-derived macrophages and extracellular lipid. Although concentrations of low-density lipoprotein (LDL) and high-density lipoprotein (HDL) particles in the blood are known to affect plaque progression, their impact on the lipid load of plaque macrophages remains unexplored. In this paper, we develop a lipid-structured mathematical model to investigate the impact of blood LDL/HDL levels on plaque composition, and lipid distribution in plaque macrophages. A reduced subsystem, derived by summing the equations of the full model, describes the dynamics of biophysical quantities relating to plaque composition (e.g. total number of macrophages, total amount of intracellular lipid). We also derive a continuum approximation of the model to facilitate analysis of the macrophage lipid distribution. The results, which include time-dependent numerical solutions and asymptotic analysis of the unique steady state solution, indicate that plaque lipid content is sensitive to the influx of LDL relative to HDL capacity. The macrophage lipid distribution evolves in a wave-like manner towards an equilibrium profile which may be monotone decreasing, quasi-uniform or unimodal, attaining its maximum value at a non-zero lipid level. Our model also reveals that macrophage uptake may be severely impaired by lipid accumulation. We conclude that lipid accumulation in plaque macrophages may serve as a partial explanation for the defective uptake of apoptotic cells (efferocytosis) often reported in atherosclerotic plaques.

Ivana Pajic-Lijakovic, Milan Milivojevic

Morphogenesis, tissue regeneration and cancer invasion involve transitions in tissue morphology. These transitions, caused by collective cell migration (CCM), have been interpreted as active wetting/de-wetting transitions. This phenomenon is considered on model system such as wetting of cell aggregate on rigid substrate which includes cell aggregate movement and isotropic/anisotropic spreading of cell monolayer around the aggregate depending on the substrate rigidity and aggregate size. This model system accounts for the transition between 3D epithelial aggregate and 2D cell monolayer as a product of: (1) tissue surface tension, (2) surface tension of substrate matrix, (3) cell-matrix interfacial tension, (4) interfacial tension gradient, (5) viscoelasticity caused by CCM, and (6) viscoelasticity of substrate matrix. These physical parameters depend on the cell contractility and state of cell-cell and cell matrix adhesion contacts, as well as, the stretching/compression of cellular systems caused by CCM. Despite extensive research devoted to study cell wetting, we still do not understand interplay among these physical parameters which induces oscillatory trend of cell rearrangement. This review focuses on these physical parameters in governing the cell rearrangement in the context of epithelial aggregate wetting.de-wetting, and on the modelling approaches aimed at reproducing and understanding these biological systems. In this context, we do not only review previously-published bio-physics models for cell rearrangement caused by CCM, but also propose new extensions of those models in order to point out the interplay between cell-matrix interfacial tension and epithelial viscoelasticity and the role of the interfacial tension gradient in cell spreading.

Chay Paterson

In this thesis we develop minimal models of the relationship between motility, growth, and evolution of cancer cells. We utilise simple simulations of a population of individual cells in space to examine how changes in mechanical properties of invasive cells and their surroundings can affect the speed of cell migration. We also find that the growth rate of large lesions depends weakly on the migration speed of escaping cells, and has stronger and more complex dependencies on the rates of other stochastic processes in the model, namely the rate at which cells transition to being motile and the reverse rate at which cells cease to be motile. To examine how the rates of growth and evolution of an ensemble of cancerous lesions depends on their geometry and underlying fitness landscape, we develop an analytical framework in which the spatial structure is coarse grained and the cancer treated as a continuously growing system with stochastic migration events. Both approaches conclude that the whole ensemble can undergo migration-driven exponential growth regardless of the dependence of size on time of individual lesions, and that the relationship between growth rate and rate of migration is determined by the geometrical constraints of individual lesions. We also find that linear fitness landscapes result in faster-than-exponential growth of the ensemble, and we can determine the expected number of driver mutations present in several important cases of the model. Finally, we study data from a clinical study of the effectiveness of a new low-dose combined chemotherapy. This enables us to test some important hypotheses about the growth rate of pancreatic cancers and the speed with which evolution occurs in reality. Despite this, we find that the frequency of resistant mutants is far too high to be explained without resorting to novel mechanisms of cross-resistance to multiple drugs.

Allison Koenecke, Michael Powell, Ruoxuan Xiong et al.

In severe viral pneumonia, including Coronavirus disease 2019 (COVID-19), the viral replication phase is often followed by hyperinflammation, which can lead to acute respiratory distress syndrome, multi-organ failure, and death. We previously demonstrated that alpha-1 adrenergic receptor ($α_1$-AR) antagonists can prevent hyperinflammation and death in mice. Here, we conducted retrospective analyses in two cohorts of patients with acute respiratory distress (ARD, n=18,547) and three cohorts with pneumonia (n=400,907). Federated across two ARD cohorts, we find that patients exposed to $α_1$-AR antagonists, as compared to unexposed patients, had a 34% relative risk reduction for mechanical ventilation and death (OR=0.70, p=0.021). We replicated these methods on three pneumonia cohorts, all with similar effects on both outcomes. All results were robust to sensitivity analyses. These results highlight the urgent need for prospective trials testing whether prophylactic use of $α_1$-AR antagonists ameliorates lower respiratory tract infection-associated hyperinflammation and death, as observed in COVID-19.

R. Tonn

J. Degnan

Darina Roitshtain, Lauren Wolbromsky, Evgeny Bal et al.

We present cytometric classification of live healthy and cancer cells by using the spatial morphological and textural information found in the label-free quantitative phase images of the cells. We compare both healthy cells to primary tumor cell and primary tumor cells to metastatic cancer cells, where tumor biopsies and normal tissues were isolated from the same individuals. To mimic analysis of liquid biopsies by flow cytometry, the cells were imaged while unattached to the substrate. We used low-coherence off-axis interferometric phase microscopy setup, which allows a single-exposure acquisition mode, and thus is suitable for quantitative imaging of dynamic cells during flow. After acquisition, the optical path delay maps of the cells were extracted, and used to calculate 15 parameters derived from cellular 3-D morphology and texture. Upon analyzing tens of cells in each group, we found high statistical significance in the difference between the groups in most of the parameters calculated, with the same trends for all statistically significant parameters. Furthermore, a specially designed machine learning algorithm, implemented on the phase map extracted features, classified the correct cell type (healthy/cancer/metastatic) with 81%-93% sensitivity and 81%-99% specificity. The quantitative phase imaging approach for liquid biopsies presented in this paper could be the basis for advanced techniques of staging freshly isolated live cancer cells in imaging flow cytometers.

Finn Kuusisto, Vitor Santos Costa, Zhonggang Hou et al.

There is a growing need for fast and accurate methods for testing developmental neurotoxicity across several chemical exposure sources. Current approaches, such as in vivo animal studies, and assays of animal and human primary cell cultures, suffer from challenges related to time, cost, and applicability to human physiology. We previously demonstrated success employing machine learning to predict developmental neurotoxicity using gene expression data collected from human 3D tissue models exposed to various compounds. The 3D model is biologically similar to developing neural structures, but its complexity necessitates extensive expertise and effort to employ. By instead focusing solely on constructing an assay of developmental neurotoxicity, we propose that a simpler 2D tissue model may prove sufficient. We thus compare the accuracy of predictive models trained on data from a 2D tissue model with those trained on data from a 3D tissue model, and find the 2D model to be substantially more accurate. Furthermore, we find the 2D model to be more robust under stringent gene set selection, whereas the 3D model suffers substantial accuracy degradation. While both approaches have advantages and disadvantages, we propose that our described 2D approach could be a valuable tool for decision makers when prioritizing neurotoxicity screening.

Felix Jost, Enrico Schalk, Daniela Weber et al.

Neutropenia is an adverse event commonly arising during intensive chemotherapy of acute myeloid leukemia (AML). It is often associated with infectious complications. Mathematical modeling, simulation, and optimization of the treatment process would be a valuable tool to support clinical decision making, potentially resulting in less severe side effects and deeper remissions. However, until now, there has been no validated mathematical model available to simulate the effect of chemotherapy treatment on white blood cell (WBC) counts and leukemic cells simultaneously. We developed a population pharmacokinetic/pharmacodynamic (PK/PD) model combining a myelosuppression model considering endogenous granulocyte-colony stimulating factor (G-CSF), a PK model for cytarabine (Ara-C), a subcutaneous absorption model for exogenous G-CSF, and a two-compartment model for leukemic blasts. This model was fitted to data of 44 AML patients during consolidation therapy with a novel Ara-C plus G-CSF schedule from a phase II controlled clinical trial. Additionally, we were able to optimize treatment schedules with respect to disease progression, WBC nadirs, and the amount of Ara-C and G-CSF. The developed PK/PD model provided good prediction accuracies and an interpretation of the interaction between WBCs, G-CSF, and blasts. For 14 patients (those with available bone marrow blast counts), we achieved a median 4.2-fold higher WBC count at nadir, which is the most critical time during consolidation therapy. The simulation results showed that relative bone marrow blast counts remained below the clinically important threshold of 5%, with a median of 60% reduction in Ara-C.

Derek John Curtis, Pauline Holbrook, Sarah Bew et al.

Introduction There is increasing focus on the association between trunk control and functional abilities in children with cerebral palsy (CP). The purpose of this study was to determine the extent of functional change in children with CP who participated in specific trunk and head postural control training combined with physical therapy treatment as usual (TAU). Methods This study included 140 consecutive referrals to a centre specialising in head and trunk postural control (Targeted Training (TT)) between 2009 and 2016. Twenty-five children discontinued therapy due to surgery, health, family issues or poor attendance. The remaining 115 children (46 girls, 69 boys) had a mean age of 6 y 6 mo (SD 2 y 8 mo) with participants from all GMFCS levels. The intervention was a program of TT and ongoing TAU with a mean duration of 11 months. Gross Motor Function Measure (GMFM), Pediatric Evaluation of Disability Inventory functional skills, Chailey Levels of Ability and Segmental Assessment of Trunk Control were administered before and after the intervention. Results There were significant improvements in all outcomes. GMFM improvements exceeded those predicted from the published reference curves, especially for the children with more severe cerebral palsy. Conclusions Functional improvement exceeded the expected norm, especially in those children with more severe gross motor function disability. The other outcomes also showed significant improvements. These findings support the case for further studies and, if needed, tool development to facilitate determination of the critical elements in a combined therapy approach of TT with TAU.

Marta Diaz-delCastillo, Soren H. Christiansen, Camilla K. Appel et al.

Pain remains a major concern in patients suffering from metastatic cancer to the bone and more knowledge of the condition, as well as novel treatment avenues, are called for. Neuropeptide Y (NPY) is a highly conserved peptide that appears to play a central role in nociceptive signaling in inflammatory and neuropathic pain. However, little is known about the peptide in cancer-induced bone pain. Here, we evaluate the role of spinal NPY in the MRMT-1 rat model of cancer-induced bone pain. Our studies revealed an up-regulation of NPY-immunoreactivity in the dorsal horn of cancer-bearing rats 17 days after inoculation, which could be a compensatory antinociceptive response. Consistent with this interpretation, intrathecal administration of NPY to rats with cancer-induced bone pain caused a reduction in nociceptive behaviors that lasted up to 150 min. This effect was diminished by both Y1 (BIBO3304) and Y2 (BIIE0246) receptor antagonists, indicating that both receptors participate in mediating the antinociceptive effect of NPY. Y1 and Y2 receptor binding in the spinal cord was unchanged in the cancer state as compared to sham-operated rats, consistent with the notion that increased NPY results in a net antinociceptive effect in the MRMT-1 model. In conclusion, the data indicate that NPY is involved in the spinal nociceptive signaling of cancer-induced bone pain and could be a new therapeutic target for patients with this condition.

Jicun Wang-Michelitsch, Thomas M Michelitsch

Acute myeloid leukemia (AML) and chronic myeloid leukemia (CML) are two major formsof leukemia developed from myeloid cells (MCs). To understand why AML and CML occurin children, we analyzed the causes and the mechanism of cell transformation of a MC. I. Forthe MCs in marrow cavity, repeated bone-remodeling during bone-growth may be a source ofcell injuries. II. As a type of blood cell, a MC may have higher survivability from DNAchanges and require obtaining fewer cancerous properties for cell transformation than a tissuecell. III. Point DNA mutations (PDMs) and chromosome changes (CCs) are the two majortypes of DNA changes. CCs have three subtypes by effects on a cell: great effect CCs(GECCs), mild-effect CCs (MECCs), and intermediate-effect CCs (IECCs). A GECC affectsone or more genes and can alone trigger cell transformation. PDMs/MECCs are mostly mildand can accumulate in cells. Some of the PDMs/MECCs contribute to cell transformation. AnIECC affects one or more genes and participates in cell transformation. IV. Based on II andIII, we hypothesize that a MC may have two pathways on transformation: a slow and anaccelerated. Slow pathway is driven by accumulation of PDMs/MECCs. Accelerated pathwayis driven by accumulation of PDMs/MECCs/IECC(s). A transformation via slow pathwayoccurs at old age; whereas that via accelerated pathway occurs at any age. Thus, CML andpediatric AML may develop via accelerated pathway, and adult AML may develop via bothpathways. In conclusion, pediatric AML and CML may develop as a result of transformationof a MC via accelerated pathway; and repeated bone-remodeling for bone-growth may be atrigger for the transformation of a MC in a child.

A. Jouvenceau, A. Jouvenceau, LH Eunson et al.

A. Stea, W. Tomlinson, T. Soong et al.

Jingchen Liu, Gongjun Xu, Z. Ying

Jake P. Taylor-King, David Basanta, S. Jonathan Chapman et al.

We consider evolving networks in which each node can have various associated properties (a state) in addition to those that arise from network structure. For example, each node can have a spatial location and a velocity, or some more abstract internal property that describes something like social trait. Edges between nodes are created and destroyed, and new nodes enter the system. We introduce a "local state degree distribution" (LSDD) as the degree distribution at a particular point in state space. We then make a mean-field assumption and thereby derive an integro-partial differential equation that is satisfied by the LSDD. We perform numerical experiments and find good agreement between solutions of the integro-differential equation and the LSDD from stochastic simulations of the full model. To illustrate our theory, we apply it to a simple continuum model for osteocyte network formation within bones, with a view to understanding changes that may take place during cancer. Our results suggest that increased rates of differentiation lead to higher densities of osteocytes but with a lower number of dendrites. To help provide biological context, we also include an introduction to osteocytes, the formation of osteocyte networks, and the role of osteocytes in bona metastasis.

M. N. Hounkonnou

We build a framework for R(p;q)-deformed calculus, which pro- vides a method of computation for deformed R(p;q)-derivative and integration, generalizing known deformed derivatives and integrations of analytic functions defined on a complex disc as particular cases corresponding to conveniently cho- sen meromorphic functions. Under prescribed conditions, we define the R(p;q)- derivative and integration. Relevant examples are also given.

Y. Goldberg, M. Kosorok

We develop methodology for a multistage-decision problem with flexible number of stages in which the rewards are survival times that are subject to censoring. We present a novel Q-learning algorithm that is adjusted for censored data and allows a flexible number of stages. We provide finite sample bounds on the generalization error of the policy learned by the algorithm, and show that when the optimal Q-function belongs to the approximation space, the expected survival time for policies obtained by the algorithm converges to that of the optimal policy. We simulate a multistage clinical trial with flexible number of stages and apply the proposed censored-Q-learning algorithm to find individualized treatment regimens. The methodology presented in this paper has implications in the design of personalized medicine trials in cancer and in other life-threatening diseases.

Y. Mizuno, M. Kumagai, S. Mattessich et al.