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Journal articles on the topic 'Mammalian models'

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Author: Grafiati
Published: 10 December 2022
Last updated: 29 January 2023

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1

Neuweiler, Gerhard. "Bats, models for mammalian ecology?" Trends in Ecology & Evolution 19, no. 1 (January 2004): 10. http://dx.doi.org/10.1016/j.tree.2003.09.019.

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2

Kundi. "SPINAL CORD INJURY: CURRENT MAMMALIAN MODELS." American Journal of Neuroscience 4, no. 1 (January 1, 2013): 1–12. http://dx.doi.org/10.3844/amjnsp.2013.1.12.

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3

Fryxell, John M. "Life-history models reconstruct mammalian evolution." Proceedings of the National Academy of Sciences 117, no. 4 (January 8, 2020): 1839–41. http://dx.doi.org/10.1073/pnas.1921256117.

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4

Ma'ayan, Avi, Robert D. Blitzer, and Ravi Iyengar. "Toward Predictive Models of Mammalian Cells." Annual Review of Biophysics and Biomolecular Structure 34, no. 1 (June 2005): 319–49. http://dx.doi.org/10.1146/annurev.biophys.34.040204.144415.

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5

Herzel, Hanspeter, and Nils Blüthgen. "Mathematical models in mammalian cell biology." Genome Biology 9, no. 7 (2008): 316. http://dx.doi.org/10.1186/gb-2008-9-7-316.

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6

Etienne, A. S. "Mammalian Navigation, Neural Models and Biorobotics." Connection Science 10, no. 3-4 (September 1998): 271–89. http://dx.doi.org/10.1080/095400998116440.

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7

Belser, Jessica A., and Terrence M. Tumpey. "H5N1 pathogenesis studies in mammalian models." Virus Research 178, no. 1 (December 2013): 168–85. http://dx.doi.org/10.1016/j.virusres.2013.02.003.

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8

Selman, Colin, and Dominic J. Withers. "Mammalian models of extended healthy lifespan." Philosophical Transactions of the Royal Society B: Biological Sciences 366, no. 1561 (January 12, 2011): 99–107. http://dx.doi.org/10.1098/rstb.2010.0243.

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Over the last two centuries, there has been a significant increase in average lifespan expectancy in the developed world. One unambiguous clinical implication of getting older is the risk of experiencing age-related diseases including various cancers, dementia, type-2 diabetes, cataracts and osteoporosis. Historically, the ageing process and its consequences were thought to be intractable. However, over the last two decades or so, a wealth of empirical data has been generated which demonstrates that longevity in model organisms can be extended through the manipulation of individual genes. In particular, many pathological conditions associated with the ageing process in model organisms, and importantly conserved from nematodes to humans, are attenuated in long-lived genetic mutants. For example, several long-lived genetic mouse models show attenuation in age-related cognitive decline, adiposity, cancer and glucose intolerance. Therefore, these long-lived mice enjoy a longer period without suffering the various sequelae of ageing. The greatest challenge in the biology of ageing is to now identify the mechanisms underlying increased healthy lifespan in these model organisms. Given that the elderly are making up an increasingly greater proportion of society, this focused approach in model organisms should help identify tractable interventions that can ultimately be translated to humans.
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9

Ishikawa, Makoto, Takeshi Yoshitomi, Charles F. Zorumski, and Yukitoshi Izumi. "Experimentally Induced Mammalian Models of Glaucoma." BioMed Research International 2015 (2015): 1–11. http://dx.doi.org/10.1155/2015/281214.

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A wide variety of animal models have been used to study glaucoma. Although these models provide valuable information about the disease, there is still no ideal model for studying glaucoma due to its complex pathogenesis. Animal models for glaucoma are pivotal for clarifying glaucoma etiology and for developing novel therapeutic strategies to halt disease progression. In this review paper, we summarize some of the major findings obtained in various glaucoma models and examine the strengths and limitations of these models.
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10

Cockerell, Alaina, Liam Wright, Anish Dattani, Ge Guo, Austin Smith, Krasimira Tsaneva-Atanasova, and David M. Richards. "Biophysical models of early mammalian embryogenesis." Stem Cell Reports 18, no. 1 (January 2023): 26–46. http://dx.doi.org/10.1016/j.stemcr.2022.11.021.

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11

Cooke, Gerard. "Toxicology of Tributyltin in Mammalian Animal Models." Immunology‚ Endocrine & Metabolic Agents in Medicinal Chemistry 6, no. 1 (February 1, 2006): 63–71. http://dx.doi.org/10.2174/187152206775528815.

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12

NIU, Yi-dong, and Shu-long LIANG. "Mammalian Models Based on RCAS-TVA Technique." Zoological Research 29, no. 3 (October 28, 2008): 335–45. http://dx.doi.org/10.3724/sp.j.1141.2008.00335.

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13

NIU, Yi-dong. "Mammalian Models Based on RCAS-TVA Technique." Zoological Research 29, no. 3 (October 28, 2008): 335–45. http://dx.doi.org/10.3724/sp.j.1141335.

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14

Szalai, Robert, Alan Champneys, Martin Homer, Dáibhid Ó Maoiléidigh, Helen Kennedy, and Nigel Cooper. "Comparison of nonlinear mammalian cochlear-partition models." Journal of the Acoustical Society of America 133, no. 1 (January 2013): 323–36. http://dx.doi.org/10.1121/1.4768868.

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15

Rusak, Benjamin. "The Mammalian Circadian System: Models and Physiology." Journal of Biological Rhythms 4, no. 2 (June 1989): 9–22. http://dx.doi.org/10.1177/074873048900400203.

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16

Lu, W.-J., and J. M. Abrams. "Lessons from p53 in non-mammalian models." Cell Death & Differentiation 13, no. 6 (March 24, 2006): 909–12. http://dx.doi.org/10.1038/sj.cdd.4401922.

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17

Iyengar, R. "Quantitative Models of Mammalian Cell Signaling Pathways." Science Signaling 1, no. 7 (February 19, 2008): tr1. http://dx.doi.org/10.1126/stke.17tr1.

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18

Comet, Jean-Paul, Gilles Bernot, Aparna Das, Francine Diener, Camille Massot, and Amélie Cessieux. "Simplified Models for the Mammalian Circadian Clock." Procedia Computer Science 11 (2012): 127–38. http://dx.doi.org/10.1016/j.procs.2012.09.014.

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19

Willmann, Raffaella, Stefanie Possekel, Judith Dubach-Powell, Thomas Meier, and Markus A. Ruegg. "Mammalian animal models for Duchenne muscular dystrophy." Neuromuscular Disorders 19, no. 4 (April 2009): 241–49. http://dx.doi.org/10.1016/j.nmd.2008.11.015.

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20

Camacho, Paula, Huimin Fan, Zhongmin Liu, and Jia-Qiang He. "Large Mammalian Animal Models of Heart Disease." Journal of Cardiovascular Development and Disease 3, no. 4 (October 5, 2016): 30. http://dx.doi.org/10.3390/jcdd3040030.

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21

McChesney, J., and S. Kouzi. "Microbial Models of Mammalian Metabolism: Sclareol Metabolism." Planta Medica 56, no. 06 (December 1990): 693. http://dx.doi.org/10.1055/s-2006-961374.

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22

Nas, John Sylvester, Trisha Jaden Galang, Anlie Bacod, Cher Agape Cervantes, Jubilee Ivy Estrilles, Rheaa Esguera, Ryan Miguel Milleza, Paula Angeli Servino, and Laarni Hannah Lacorte. "Mammalian models of pathogen-associated muscle degeneration." Exploratory Animal and Medical Research 12, no. 2 (December 1, 2022): 134–48. http://dx.doi.org/10.52635/eamr/12.2.134-148.

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23

Abourashed, E. A., A. M. Clark, and C. D. Hufford. "Microbial Models of Mammalian Metabolism of Xenobiotics: An Updated Review." Current Medicinal Chemistry 6, no. 5 (May 1999): 359–74. http://dx.doi.org/10.2174/0929867306666220320215539.

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The utilization of microbes as models for mammalian metabolism of xenobiotics has been well established since the concept was first introduced by Smith and Rosazza in the early seventies. The core assumption of this concept rests on the fact that fungi are eukaryotic organisms that possess metabolizing enzyme systems simifar to those pre­sent in mammalian systems. Hence, the outcome of xenobiotic metabolism in both systems is expected to be similar, if not identical, and, thus, fungi can be used to predict the outcome of mammalian metabolism of various xenobiotics, including drugs. Utilizing microbial models offers a number of advantages over the use of animals in metabolism studies, mainly reduction in use of animals, ease of setup and manipulation, higher yield and diversity of metabolite production, and lower cost of production. In a continuation to our contribution to this field, this review will outline the results of studies that were conducted over the last seven years to emphasize the similarities between the microbial and mammalian metabolic pathways of xenobiotics through the endorsement of the concept of 'microbial models of mammalian metabolism'.
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24

Guerrero, N., M. M. Meynard, J. Borgonovo, K. Palma, M. L. Concha, and C. Hetz. "Prion Function and Pathophysiology in Non-Mammalian Models." Current Molecular Medicine 17, no. 1 (April 10, 2017): 13–23. http://dx.doi.org/10.2174/1566524017666170220100715.

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25

Basu, Niladri, and Jessica Head. "Mammalian wildlife as complementary models in environmental neurotoxicology." Neurotoxicology and Teratology 32, no. 1 (January 2010): 114–19. http://dx.doi.org/10.1016/j.ntt.2008.12.005.

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26

Tou, Janet, April Ronca, Richard Grindeland, and Charles Wade. "Models to Study Gravitational Biology of Mammalian Reproduction1." Biology of Reproduction 67, no. 6 (December 1, 2002): 1681–87. http://dx.doi.org/10.1095/biolreprod.102.007252.

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27

Bardet, Pierre-Luc, Vincent Laudet, and Jean-Marc Vanacker. "Studying non-mammalian models? Not a fool's ERRand!" Trends in Endocrinology & Metabolism 17, no. 4 (May 2006): 166–71. http://dx.doi.org/10.1016/j.tem.2006.03.005.

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28

Giannaccini, Martina, Alfred Cuschieri, Luciana Dente, and Vittoria Raffa. "Non-mammalian vertebrate embryos as models in nanomedicine." Nanomedicine: Nanotechnology, Biology and Medicine 10, no. 4 (May 2014): 703–19. http://dx.doi.org/10.1016/j.nano.2013.09.010.

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29

Schaefer, Matthias, Madeleine Meusburger, and Frank Lyko. "Non-mammalian models for epigenetic analyses in cancer." Human Molecular Genetics 16, R1 (April 15, 2007): R1—R6. http://dx.doi.org/10.1093/hmg/ddm004.

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30

Lander, Anthony, Tim King, and Nigel A. Brown. "Left – right development: Mammalian phenotypes and conceptual models." Seminars in Cell & Developmental Biology 9, no. 1 (February 1998): 35–41. http://dx.doi.org/10.1006/scdb.1997.0185.

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31

Moustafa, Ibrahim M., Akira Uchida, Yao Wang, Neela Yennawar, and Craig E. Cameron. "Structural models of mammalian mitochondrial transcription factor B2." Biochimica et Biophysica Acta (BBA) - Gene Regulatory Mechanisms 1849, no. 8 (August 2015): 987–1002. http://dx.doi.org/10.1016/j.bbagrm.2015.05.010.

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32

Baillie-Benson, Peter, Naomi Moris, and Alfonso Martinez Arias. "Pluripotent stem cell models of early mammalian development." Current Opinion in Cell Biology 66 (October 2020): 89–96. http://dx.doi.org/10.1016/j.ceb.2020.05.010.

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33

Cifelli, Richard L. "Early mammalian radiations." Journal of Paleontology 75, no. 6 (November 2001): 1214–26. http://dx.doi.org/10.1017/s002233600001725x.

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The seventy-fifth anniversary of the Journal of Paleontology presents a felicitous opportunity to review major changes in interpretation of mammalian phylogeny. Founding of the journal coincides with the nascence of the career of the most influential paleomammalogist of the past century, George Gaylord Simpson (1902-1984). It occurred at a time when now-archaic models for mammalian systematics and evolution, such as the aristogenesis of H. F. Osborn (1857-1935) and the typological concept of taxa, were prevalent (e.g., Simpson, 1945). These models were soon to give way to “new ways of going at things” (Laporte, 2000, p. 87); most significantly, the incorporation of quantitative methods and the evolutionary synthesis (Simpson, 1944). Subsequent decades witnessed the rise and/or sophistication of other applications and perspectives in fossil-based interpretation of mammalian systematics, including form-function analysis (e.g., Szalay, 1994) and, particularly, cladistic approaches (e.g., McKenna, 1975). Within these broad ideological frameworks, major paradigm shifts have resulted from new discoveries, conceptual changes, or (most commonly) a combination of both. Finally, mammalian systematics currently lie at the verge of a monumental paradigm shift, providing important direction for the future.
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34

Shorter, Kimberly R., Janet P. Crossland, Denessia Webb, Gabor Szalai, Michael R. Felder, and Paul B. Vrana. "Peromyscus as a Mammalian Epigenetic Model." Genetics Research International 2012 (March 7, 2012): 1–11. http://dx.doi.org/10.1155/2012/179159.

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Deer mice (Peromyscus) offer an opportunity for studying the effects of natural genetic/epigenetic variation with several advantages over other mammalian models. These advantages include the ability to study natural genetic variation and behaviors not present in other models. Moreover, their life histories in diverse habitats are well studied. Peromyscus resources include genome sequencing in progress, a nascent genetic map, and >90,000 ESTs. Here we review epigenetic studies and relevant areas of research involving Peromyscus models. These include differences in epigenetic control between species and substance effects on behavior. We also present new data on the epigenetic effects of diet on coat-color using a Peromyscus model of agouti overexpression. We suggest that in terms of tying natural genetic variants with environmental effects in producing specific epigenetic effects, Peromyscus models have a great potential.
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35

Das, Tirtha K., and Ross L. Cagan. "Non-mammalian models of multiple endocrine neoplasia type 2." Endocrine-Related Cancer 25, no. 2 (February 2018): T91—T104. http://dx.doi.org/10.1530/erc-17-0411.

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Twenty-five years ago, RET was identified as the primary driver of multiple endocrine neoplasia type 2 (MEN2) syndrome. MEN2 is characterized by several transformation events including pheochromocytoma, parathyroid adenoma and, especially penetrant, medullary thyroid carcinoma (MTC). Overall, MTC is a rare but aggressive type of thyroid cancer for which no effective treatment currently exists. Surgery, radiation, radioisotope treatment and chemotherapeutics have all shown limited success, and none of these approaches have proven durable in advanced disease. Non-mammalian models that incorporate the oncogenic RET isoforms associated with MEN2 and other RET-associated diseases have been useful in delineating mechanisms underlying disease progression. These models have also identified novel targeted therapies as single agents and as combinations. These studies highlight the importance of modeling disease in the context of the whole animal, accounting for the complex interplay between tumor and normal cells in controlling disease progression as well as response to therapy. With convenient access to whole genome sequencing data from expanded thyroid cancer patient cohorts, non-mammalian models will become more complex, sophisticated and continue to complement future mammalian studies. In this review, we explore the contributions of non-mammalian models to our understanding of thyroid cancer including MTC, with a focus onDanio rerioandDrosophila melanogaster(fish and fly) models.
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36

Kunes, Kay C., Scott C. Clark, Daniel L. Cox, and Rajiv R. P. Singh. "Left handed β helix models for mammalian prion fibrils." Prion 2, no. 2 (April 2008): 81–90. http://dx.doi.org/10.4161/pri.2.2.7059.

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37

Dobson, F. Stephen, Bertram Zinner, and Marina Silva. "Testing models of biological scaling with mammalian population densities." Canadian Journal of Zoology 81, no. 5 (May 1, 2003): 844–51. http://dx.doi.org/10.1139/z03-060.

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Two hypotheses have been suggested to explain the form of interspecific scaling of organismal characteristics to body size, such as the well-known increase in total metabolism with body mass. A hypothesis based on simple Euclidean geometry suggests that the scaling of many biological variables to body size should have a scaling exponent of 2/3, or [Formula: see text]0.667. On the other hand, according to a hypothesis based on fractal dimensions, the relationship between biological variables and body mass should have a scaling exponent of 0.750. We conducted a power analysis of the predicted exponents of scaling under the Euclidean and fractal hypotheses, using average adult body masses and population densities collected from the published literature on mammalian species. The collected data reflect 987 mammal populations from a broad variety of terrestrial habitats. Using statistical methods we determined the sample sizes required to decide between the values of the scaling exponent of the density-to-mass relationship based on the Euclidean (–0.667) and fractal (–0.750) hypotheses. Non-linearities in the dataset and insufficient power plagued our tests of the predictions. We found that mammalian species weighing less than 100 kg had a linear scaling pattern, sufficient power to reveal a difference between the scaling coefficients –0.667 and –0.750, and an actual scaling coefficient of –0.719 (barely significantly different from –0.667 but not from –0.750). Thus, our results support the fractal hypothesis, though the support was not particularly strong, which suggests that the relationship between body mass and population density should have a scaling exponent of –0.750.
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38

Kendall, S. DiSean, Stacey J. Adam, and Christopher M. Counter. "Genetically Engineered Human Cancer Models Utilizing Mammalian Transgene Expression." Cell Cycle 5, no. 10 (April 26, 2006): 1074–79. http://dx.doi.org/10.4161/cc.5.10.2734.

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39

Goodhead, Dudley T. "Saturable Repair Models of Radiation Action in Mammalian Cells." Radiation Research Supplement 8 (November 1985): S58. http://dx.doi.org/10.2307/3583513.

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40

Marder, Eve. "Non-mammalian models for studying neural development and function." Nature 417, no. 6886 (May 2002): 318–21. http://dx.doi.org/10.1038/417318a.

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41

Martínez-Arroyo, Ana M., Jose M. Míguez-Forján, Jose Remohí, Antonio Pellicer, and Jose V. Medrano. "Understanding Mammalian Germ Line Development with In Vitro Models." Stem Cells and Development 24, no. 18 (September 15, 2015): 2101–13. http://dx.doi.org/10.1089/scd.2015.0060.

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42

Vonk, Willianne IM, Cisca Wijmenga, and Bart van de Sluis. "Relevance of animal models for understanding mammalian copper homeostasis." American Journal of Clinical Nutrition 88, no. 3 (September 1, 2008): 840S—845S. http://dx.doi.org/10.1093/ajcn/88.3.840s.

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43

Lossi, L., C. Cantile, I. Tamagno, and A. Merighi. "Apoptosis in the mammalian CNS: Lessons from animal models." Veterinary Journal 170, no. 1 (July 2005): 52–66. http://dx.doi.org/10.1016/j.tvjl.2004.05.010.

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44

Peterson, Randall T., Richard Nass, Windy A. Boyd, Jonathan H. Freedman, Ke Dong, and Toshio Narahashi. "Use of non-mammalian alternative models for neurotoxicological study." NeuroToxicology 29, no. 3 (May 2008): 546–55. http://dx.doi.org/10.1016/j.neuro.2008.04.006.

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45

McEwen, Bruce F., and Yimin Dong. "Contrasting models for kinetochore microtubule attachment in mammalian cells." Cellular and Molecular Life Sciences 67, no. 13 (March 25, 2010): 2163–72. http://dx.doi.org/10.1007/s00018-010-0322-x.

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46

Goodhead, Dudley T. "Saturable Repair Models of Radiation Action in Mammalian Cells." Radiation Research 104, no. 2 (November 1985): S58. http://dx.doi.org/10.2307/3576633.

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47

Tsakovska, Ivanka, Iglika Lessigiarska, Tatiana Netzeva, and Andrew P Worth. "A Mini Review of Mammalian Toxicity (Q)SAR Models." QSAR & Combinatorial Science 27, no. 1 (January 2008): 41–48. http://dx.doi.org/10.1002/qsar.200710107.

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48

Abourashed, E. A., A. M. Clark, and C. D. Hufford. "ChemInform Abstract: Microbial Models of Mammalian Metabolism of Xenobiotics." ChemInform 30, no. 29 (June 14, 2010): no. http://dx.doi.org/10.1002/chin.199929295.

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49

Shorthouse, David, George Hedger, Heidi Koldsø, and Mark S. P. Sansom. "Molecular simulations of glycolipids: Towards mammalian cell membrane models." Biochimie 120 (January 2016): 105–9. http://dx.doi.org/10.1016/j.biochi.2015.09.033.

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50

Cardoso-Moreira, Margarida, Ioannis Sarropoulos, Britta Velten, Matthew Mort, David N. Cooper, Wolfgang Huber, and Henrik Kaessmann. "Developmental Gene Expression Differences between Humans and Mammalian Models." Cell Reports 33, no. 4 (October 2020): 108308. http://dx.doi.org/10.1016/j.celrep.2020.108308.

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