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18 results for “Growth Kinetics”
Tumor growth kinetics of human LM2-4LUC+ triple negative breast carcinoma cells
<p><strong>Cell culture and data set</strong></p> <p>Tumor growth data used in this study were obtained from experiments involving the use of a LM2-4<sup>LUC+</sup> cells (or LM2-4), a metastatic variant of the human triple-negative breast carcinoma MDA-MB-231 cells. Animal studies were performed as described previously under Roswell Park Comprehensive Cancer Center (RPCCC) Institutional Animal Care and Use Committee (IACUC) protocol number 1227M [1-7]. Tumor growth data were pooled from eight separate experiments conducted with a total of 581 observations, and represent control (vehicle-treated) animals from published studies [1-7]. Vehicle formulation was carboxymethylcellulose sodium (USP, 0.5% w/v), NaCl (USP, 1.8% w/v), Tween-80 (NF, 0.4% w/v), benzyl alcohol (NF, 0.9% w/v), and reverse osmosis deionized water (added to final volume) and adjusted to pH 6 (see [3]) and was given at 10ml/kg/day for 7-14 days prior after tumor implantation and before tumor resection [1-7].</p> <ul> </ul> <p><strong>Tumor injections</strong></p> <p>LM2-4<sup>LUC+</sup> cells were orthotopically implanted (10<sup>6</sup> cells per injection) into the right inguinal mammary fat pads of 6- to 8-week-old female severe combined immunodeficient (SCID) mice.</p> <p><strong>Tumor measurements</strong></p> <p>Tumor size was measured regularly with calipers to a maximum volume of 2 cm<sup>3</sup>, calculated by the formula </p> <p><span class="math-tex">\(V = \frac{\pi}{6} w^2 L\)</span></p> <p>(ellipsoid) where <em>L</em> is the largest and <em>w</em> is the smallest tumor diameter.</p> <p><strong>Please cite: </strong>Vaghi C, Rodallec A, Fanciullino R, Ciccolini J, Mochel JP, et al. (2020) Population modeling of tumor growth curves and the reduced Gompertz model improve prediction of the age of experimental tumors, PLoS Comput Biol, 16, p. e1007178. <a href="https://doi.org/10.1371/journal.pcbi.1007178">https://doi.org/10.1371/journal.pcbi.1007178</a></p> <p> </p> <p>In the file, the columns correspond to:</p> <ul> <li>ID: identifier of the animal</li> <li>Time: day of the tumor measurement after implantation</li> <li>Observation: tumor measurement (in mm<sup>3</sup>)</li> </ul> <p> </p> <p><strong>References</strong></p> <p>[1] Benzekry, S., Lamont, C., Beheshti, A., Tracz, A., Ebos, J. M. L., Hlatky, L., & Hahnfeldt, P. (2014). Classical mathematical models for description and prediction of experimental tumor growth. PLoS Comput Biol, <em>10</em>(8), e1003800. http://doi.org/10.1371/journal.pcbi.1003800</p> <p>[2] Benzekry S, Tracz A, Mastri M, Corbelli R, Barbolosi D, Ebos JML. (2016) Modeling Spontaneous Metastasis Following Surgery: An In Vivo-In Silico Approach. Cancer Res.;76(3):535–547. doi:10.1158/0008-5472.CAN-15-1389.</p> <p>[3] Ebos JML, Lee CR, Bogdanovic E, Alami J, Van Slyke P, Francia G, et al. (2008) Vascular Endothelial Growth Factor-Mediated Decrease in Plasma Soluble Vascular Endothelial Growth Factor Receptor-2 Levels as a Surrogate Biomarker for Tumor Growth. Cancer Res.;68(2):521–529. doi:10.1158/0008-5472.CAN-07-3217.</p> <p>[4] Ebos JML, Mastri M, Lee CR, Tracz A, Hudson JM, Attwood K, et al. (2014) Neoadjuvant antiangiogenic therapy reveals contrasts in primary and metastatic tumor efficacy. EMBO Mol Med;6:1561–76. https://doi.org/10.15252/emmm.201403989</p> <p>[5] Ebos JML, Lee CR, Cruz-Munoz W, Bjarnason GA, Christensen JG, Kerbel RS. (2009) Accelerated metastasis after short-term treatment with a potent inhibitor of tumor angiogenesis. Cancer Cell;15:232–9. https://doi.org/10.1016/j.ccr.2009.01.021</p> <p>[6] Mastri M, Tracz A, Lee CR, Dolan M, Attwood K, Christensen JG, et al. (2018) A Transient Pseudosenescent Secretome Promotes Tumor Growth after Antiangiogenic Therapy Withdrawal. Cell Rep.; 25 (13):3706–20 e8. Epub 2018/12/28. https://doi.org/10.1016/j.celrep.2018.12.017</p> <p>[7] Vaghi C, Rodallec A, Fanciullino R, Ciccolini J, Mochel JP, et al. (2020) Population modeling of tumor growth curves and the reduced Gompertz model improve prediction of the age of experimental tumors, PLoS Comput Biol, 16, p. e1007178. <a href="https://doi.org/10.1371/journal.pcbi.1007178">https://doi.org/10.1371/journal.pcbi.1007178</a></p>
Figure 1 in Kinetics of Dermatophagoides pteronyssinus and Dermatophagoides farinae growth and an analysis of the allergen expression in semi-synthetic culture medium
Figure 1 Mite growth curve of Dermatophagoides pteronyssinus (a) andD. farinae (b) in the semisynthetic culture medium. Data showing the kinetics of the mites' growth in semi-synthetic culture medium are expressed as arithmetic mean ± standard error of triplicates, at each time of growth.
Figure 3 in Kinetics of Dermatophagoides pteronyssinus and Dermatophagoides farinae growth and an analysis of the allergen expression in semi-synthetic culture medium
Figure 3 Kinetics of Der 1 and Der 2 major allergen levels during the growth ofDermatophagoides pteronyssinus (a) andD. farinae (b) in the semi-synthetic culture medium. Data showing the kinetics
Figure 2 in Kinetics of Dermatophagoides pteronyssinus and Dermatophagoides farinae growth and an analysis of the allergen expression in semi-synthetic culture medium
Figure 2 SDS-PAGE IgE-immunoblotting of extracts fromDermatophagoides pteronyssinus (a) and D. farinae (b) mites along their growth. Mw: Molecular weight marker. Numbers on lines indicate
Kinetics of Guided Growth of Horizontal GaN Nanowires on Flat and Faceted Sapphire Surfaces_experimental dataset
<p>This dataset contains the raw experimental data for the particle Rothman et al., Kinetics of Guided Growth of Horizontal GaN Nanowires on Flat and Faceted Sapphire Surfaces, <em>Nanomaterials</em> <strong>2021</strong>, <em>11</em>(3), 624. </p>
Growth kinetics of the HSJD-DIPG-07 cell line in non-adherent culture
<p>The growth kinetics of the HSJD-DIPG-07 cell line when grown as neurospheres in non-adherent culture conditions.</p>
Sporadic Angiomyolipomas (AMLs) Growth Kinetics While on Everolimus
ClinicalTrials.gov study NCT02539459. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Tumor growth kinetics of subcutaneously implanted Lewis Lung carcinoma cells
<p><strong>Please cite</strong><br> Benzekry, S., Lamont, C., Beheshti, A., Tracz, A., Ebos, J. M. L., Hlatky, L., & Hahnfeldt, P. (2014). Classical mathematical models for description and prediction of experimental tumor growth. <em>PLoS Computational Biology</em>, <em>10</em>(8), e1003800. http://doi.org/10.1371/journal.pcbi.1003800</p> <p><strong>Cell culture</strong><br> Murine Lewis lung carcinoma (LLC) cells, originally derived from a spontaneous tumor in a C57BL/6 mouse [1], were obtained from American Type Culture Collection (Manassas, VA). </p> <p><strong>Tumor injections</strong><br> For the subcutaneous mouse syngeneic lung tumor model, C57BL/6 male mice with an average lifespan of 878 days were used [2]. At time of injection mice were 6 to 8 weeks old (Jackson Laboratory, Bar Harbor, Maine). Subcutaneous injections of 10<sup>6</sup> LLC cells in 0.2 ml phosphate-buffered saline (PBS) were performed on the caudal half of the back in anesthetized mice.</p> <p><strong>Tumor measurements</strong><br> Tumor size was measured regularly with calipers to a maximum of 1.5 cm<sup>3</sup> for the lung data set. Largest (L) and smallest (w) diameters were measured subcutaneously using calipers and the formula V = <span class="math-tex">\(\frac{\pi}{6}w^2 L\)</span> was then used to compute the volume (ellipsoid). Volumes ranged 14–1492 mm<sup>3</sup> over time spans from 4 to 22 days for the lung tumor model (two experiments of 10 animals each).</p> <p>[1] Bertram JS, Janik P (1980) Establishment of a cloned line of Lewis Lung Carcinoma cells adapted to cell culture. Cancer Lett 11: 63–73. Available: http://www.ncbi.nlm.nih.gov/pubmed/7226139. Accessed 9 July 2013.</p> <p>[2] Kunstyr I, Leuenberger HG (1975) Gerontological data of C57BL/6J mice. I. Sex differences in survival curves. J Gerontol 30: 157–162. Available: http:// www.ncbi.nlm.nih.gov/pubmed/1123533. Accessed 9 July 2013.</p>
Tumor growth kinetics of human MDA-MB-231 cells transfected with dTomato lentivirus
<p>Tumor growth data involve human MDA-MB-231 cells stably transfected with dTomato lentivirus.</p> <p>Animals were orthotopically implanted (80,000 cells at injection) into the mammary fat pads of 6-week-old female nude mice.</p> <p>Tumor size was monitored regularly with fluorescence imaging. The data comprised a total of 64 observations.</p> <p> </p> <p>In the file, the columns correspond to:</p> <ul> <li>ID: identifier of the animal</li> <li>Time: day of the tumor measurement after implantation</li> <li>Observation: tumor measurement (in phot./s)</li> </ul> <p> </p> <p><strong>Please cite: </strong>Vaghi C, Rodallec A, Fanciullino R, Ciccolini J, Mochel JP, et al. (2020) Population modeling of tumor growth curves and the reduced Gompertz model improve prediction of the age of experimental tumors, PLoS Comput Biol, 16, p. e1007178. <a href="https://doi.org/10.1371/journal.pcbi.1007178">https://doi.org/10.1371/journal.pcbi.1007178</a></p>
Data access - Operando characterization and theoretical modelling of metal|electrolyte interphase growth kinetics in solid-state-batteries - part I: experiments
<p>The zip file contains XPS and EIS data used in parts 1 and 2 of the publication entitled: "New insights into the kinetics of metal|electrolyte interphase growth in solid-state-batteries via an <em>operando</em> XPS analysis"</p> <p>Folders description: </p> <p>- "XPS" contains three subfolders with the XPS data and fitting models (in .vms format, CasaXPS) corresponding to the reference Na metal sample, the Na|NZSPas interface and Na|NZSPpolished interface</p> <p>- "EIS" contains two subfolders with the EIS data from the Na|NZSPas and Na|NZSPpolished symmetrical cells. The raw data is stored as .mpr files (EC-lab), and the fitted data is stored as .eis3 files (RelaxIS)</p>
Data from: Growth profiling, kinetics and substrate utilization of low-cost dairy waste for production of β-cryptoxanthin by Kocuria marina DAGII
Dairy industry produces enormous amount of cheese whey compromising of major milk nutrients but remains unutilized all over the globe. The present study investigates the production of β-Cryptoxanthin (β-CRX) by Kocuria marina DAGII using cheese whey as substrate. Response surface methodology (RSM) and artificial neural network (ANN) was implemented to obtain the maximum β-CRX yield. Significant factors viz. yeast extract, peptone, cheese whey and initial pH were the input variables in both the optimizing studies and β-CRX yield and biomass were taken as output variables. The ANN topology of 4-9-2 was found to be optimum when trained with feed-forward back propagation algorithm. Experimental values of β-CRX yield (17.14 mg/L) and biomass (5.35 g/L) were compared and ANN predicted (16.99 mg/L and 5.33 g/L respectively) values were found to be more accurate compared to RSM predicted values (16.95 mg/L and 5.23 g/L respectively). Detailed kinetic analysis of cellular growth, substrate consumption and product formation revealed that growth inhibition took place at substrate concentrations higher than 12%(v/v) of cheese whey. Han and Levenspiel model was the best fitted substrate inhibition model that described the cell growth in cheese whey with a R2 and MSE of 0.9982 and 0.00477%, respectively. The potential importance of this study lies in the development, optimization, modelling and characterization of a suitable cheese whey supplemented medium for increased β-CRX production.
Insights on SEI Growth and Properties in Na-Ion Batteries via Physically Driven Kinetic Monte Carlo Model
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Data from: Growth profiling, kinetics and substrate utilization of low-cost dairy waste for production of β-cryptoxanthin by Kocuria marina DAGII
Open the record for dataset details and reuse information.
High-throughput investigation of ferrite growth kinetics in graded ternary Fe-C-X alloys
<p>The composition dependence of ferrite growth kinetics in Fe-C-X ternary alloyed steels, where X = Ni, Mn, Mo, Cr, Si, was investigated using a high-throughput approach. Compositionally graded samples were subjected to in situ time- and space-resolved X-ray diffraction during intercritical annealing. To this end, diffusion couples were created between a binary Fe-C and different Fe-C-X alloys, using hot uniaxial compression and high temperature diffusion treatments. In-situ high-energy X-ray diffraction experiments were performed to collect ferrite growth kinetics along the composition gradient of the diffusion couples. A large dataset describing the austenite-to-ferrite phase transformation kinetics was generated using a very limited number of experiments. This dataset of unprecedented size was compared to the kinetics predicted by the classical local-equilibrium (LE) and para-equilibrium (PE) models as well as a modified version of the three-jump solute drag (SD) model, which accounts for the different interactions between the elements present at the austenite/ferrite interface. The comparison showed that both LE and PE models fail to capture the effect of both composition and temperature on the kinetics of ferrite growth for the different Fe-C-X systems. The SD model calculations matched experimental transformation kinetics at all investigated temperatures and over almost all the investigated composition ranges of Si, Cr, Mn, Ni, and Mo.</p>
A Kinetic Analysis of Auxin-mediated Changes in Transcript Abundance in Arabidopsis Reveals New Mediators of Root Growth and Development
GEO Series GSE42007. Arabidopsis thaliana. 48 samples. Type: Expression profiling by array.
Cord Blood-Derived Mesenchymal Stem Cells with Distinct Growth Kinetics, Differentiation Potentials, Expression Profiles
GEO Series GSE6029. Homo sapiens. 18 samples. Type: Expression profiling by array.
Uncoupling growth and product formation kinetics to design improved strains for recombinant protein production in escherichia coli
GEO Series GSE29486. Escherichia coli. 11 samples. Type: Expression profiling by array.
Leveraging single cell transcriptomics and custom high throughput functional screening assays to resolve cell type, growth kinetic, and stemness heterogeneity within Comma-1D
GEO Series GSE182589. Mus musculus. 1 samples. Type: Expression profiling by high throughput sequencing.
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