We examined whether mechanical stretch affected expression of muscle-specific microRNAs (miRNAs) that regulate proliferation (miR-133a) or differentiation (miR-1, -206). Real-time quantitative RT-PCR was used to assess miRNA regulation in the murine myoblast cell line C2C12 exposed to stretch regimens that promote either proliferation (high stretch: 17%, 1 Hz) or differentiation (moderate stretch: 10%, 0.5 Hz) after adding media that promotes differentiation. Controls consisted of myoblasts cultured under static conditions. While miRNA expression was not affected by high stretch, a significant effect of stretch (P < 0.05) was seen after 4 days with the moderate stretch regimen. All three microRNAs were upregulated by stretch, with the most significant increase for miR-1. Myoblast maturation was enhanced with a moderate stretch regimen, as assessed by a higher percentage of nuclei in straited fibers and an increase in Mef2c gene expression. Correspondingly, HDAC4 protein expression, a direct target of miR-1 and repressor of Mef2, was decreased with the moderate stretch regimen. Over-expression of miR-1 abrogated the effect of stretch on miR-1, miR-133a and miR-206 levels compared to its negative control but did not alter miR-133a or miR-206 levels. Treatment with an antisense mRNA to miR-1 similarly diminished the stretch-mediated response. Results indicate that the differential response of skeletal myoblasts to moderate and high stretch cyclic stretch regimens is due, in part, to muscle specific miRNA expression.
Citation: Caroline Rhim, William E. Kraus, George A. Truskey. Biomechanical effects on microRNA expression in skeletal muscle differentiation[J]. AIMS Bioengineering, 2020, 7(3): 147-164. doi: 10.3934/bioeng.2020014
[1] | Zi Sang, Zhipeng Qiu, Xiefei Yan, Yun Zou . Assessing the effect of non-pharmaceutical interventions on containing an emerging disease. Mathematical Biosciences and Engineering, 2012, 9(1): 147-164. doi: 10.3934/mbe.2012.9.147 |
[2] | Hao Wang, Di Zhu, Shiqi Li, Robert A. Cheke, Sanyi Tang, Weike Zhou . Home quarantine or centralized quarantine? A mathematical modelling study on the COVID-19 epidemic in Guangzhou in 2021. Mathematical Biosciences and Engineering, 2022, 19(9): 9060-9078. doi: 10.3934/mbe.2022421 |
[3] | Maryam Al-Yahyai, Fatma Al-Musalhi, Ibrahim Elmojtaba, Nasser Al-Salti . Mathematical analysis of a COVID-19 model with different types of quarantine and isolation. Mathematical Biosciences and Engineering, 2023, 20(1): 1344-1375. doi: 10.3934/mbe.2023061 |
[4] | Julijana Gjorgjieva, Kelly Smith, Gerardo Chowell, Fabio Sánchez, Jessica Snyder, Carlos Castillo-Chavez . The Role of Vaccination in the Control of SARS. Mathematical Biosciences and Engineering, 2005, 2(4): 753-769. doi: 10.3934/mbe.2005.2.753 |
[5] | Fang Wang, Lianying Cao, Xiaoji Song . Mathematical modeling of mutated COVID-19 transmission with quarantine, isolation and vaccination. Mathematical Biosciences and Engineering, 2022, 19(8): 8035-8056. doi: 10.3934/mbe.2022376 |
[6] | Robert G. McLeod, John F. Brewster, Abba B. Gumel, Dean A. Slonowsky . Sensitivity and uncertainty analyses for a SARS model with time-varying inputs and outputs. Mathematical Biosciences and Engineering, 2006, 3(3): 527-544. doi: 10.3934/mbe.2006.3.527 |
[7] | Fernando Saldaña, Hugo Flores-Arguedas, José Ariel Camacho-Gutiérrez, Ignacio Barradas . Modeling the transmission dynamics and the impact of the control interventions for the COVID-19 epidemic outbreak. Mathematical Biosciences and Engineering, 2020, 17(4): 4165-4183. doi: 10.3934/mbe.2020231 |
[8] | Abba B. Gumel, C. Connell McCluskey, James Watmough . An sveir model for assessing potential impact of an imperfect anti-SARS vaccine. Mathematical Biosciences and Engineering, 2006, 3(3): 485-512. doi: 10.3934/mbe.2006.3.485 |
[9] | Kai Wang, Zhenzhen Lu, Xiaomeng Wang, Hui Li, Huling Li, Dandan Lin, Yongli Cai, Xing Feng, Yateng Song, Zhiwei Feng, Weidong Ji, Xiaoyan Wang, Yi Yin, Lei Wang, Zhihang Peng . Current trends and future prediction of novel coronavirus disease (COVID-19) epidemic in China: a dynamical modeling analysis. Mathematical Biosciences and Engineering, 2020, 17(4): 3052-3061. doi: 10.3934/mbe.2020173 |
[10] | Xinmiao Rong, Liu Yang, Huidi Chu, Meng Fan . Effect of delay in diagnosis on transmission of COVID-19. Mathematical Biosciences and Engineering, 2020, 17(3): 2725-2740. doi: 10.3934/mbe.2020149 |
We examined whether mechanical stretch affected expression of muscle-specific microRNAs (miRNAs) that regulate proliferation (miR-133a) or differentiation (miR-1, -206). Real-time quantitative RT-PCR was used to assess miRNA regulation in the murine myoblast cell line C2C12 exposed to stretch regimens that promote either proliferation (high stretch: 17%, 1 Hz) or differentiation (moderate stretch: 10%, 0.5 Hz) after adding media that promotes differentiation. Controls consisted of myoblasts cultured under static conditions. While miRNA expression was not affected by high stretch, a significant effect of stretch (P < 0.05) was seen after 4 days with the moderate stretch regimen. All three microRNAs were upregulated by stretch, with the most significant increase for miR-1. Myoblast maturation was enhanced with a moderate stretch regimen, as assessed by a higher percentage of nuclei in straited fibers and an increase in Mef2c gene expression. Correspondingly, HDAC4 protein expression, a direct target of miR-1 and repressor of Mef2, was decreased with the moderate stretch regimen. Over-expression of miR-1 abrogated the effect of stretch on miR-1, miR-133a and miR-206 levels compared to its negative control but did not alter miR-133a or miR-206 levels. Treatment with an antisense mRNA to miR-1 similarly diminished the stretch-mediated response. Results indicate that the differential response of skeletal myoblasts to moderate and high stretch cyclic stretch regimens is due, in part, to muscle specific miRNA expression.
[1] | Burkholder TJ, Lieber RL (2001) Sarcomere length operating range of vertebrate muscles during movement. J Exp Biol 204: 1529-1536. |
[2] |
Zhang SJ, Truskey GA, Kraus WE (2007) Effect of cyclic stretch on β1D-integrin expression and activation of FAK and RhoA. Am J Physiol Cell Physiol 292: C2057-2069. doi: 10.1152/ajpcell.00493.2006
![]() |
[3] | Vandenburgh HH (1992) Mechanical forces and their second messengers in stimulating cell growth in vitro. Am J Physiol 262: R350-R355. |
[4] |
Vandenburgh HH, Karlisch P (1989) Longitudinal growth of skeletal myotubes in vitro in a new horizontal mechanical cell stimulator. In Vitro Cell Dev Biol 25: 607-616. doi: 10.1007/BF02623630
![]() |
[5] |
Kumar A, Murphy R, Robinson P, et al. (2004) Cyclic mechanical strain inhibits skeletal myogenesis through activation of focal adhesion kinase, Rac-1 GTPase, and NF-κB transcription factor. FASEB J 18: 1524-1535. doi: 10.1096/fj.04-2414com
![]() |
[6] |
Rauch C, Loughna PT (2005) Static stretch promotes MEF2A nuclear translocation and expression of neonatal myosin heavy chain in C2C12 myocytes in a calcineurin- and p38-dependent manner. Am J Physiol Cell Physiol 288: C593-C605. doi: 10.1152/ajpcell.00346.2004
![]() |
[7] | Kook SH, Lee HJ, Chung WT, et al. (2008) Cyclic mechanical stretch stimulates the proliferation of C2C12 myoblasts and inhibits their differentiation via prolonged activation of p38 MAPK. Mol Cells 25: 479-486. |
[8] |
Zhao X, Ito A, Kane CD, et al. (2001) The modular nature of histone deacetylase HDAC4 confers phosphorylation-dependent intracellular trafficking. J Biol Chem 276: 35042-35048. doi: 10.1074/jbc.M105086200
![]() |
[9] |
Zhou X, Richon VM, Wang AH, et al. (2000) Histone deacetylase 4 associates with extracellular signal-regulated kinases 1 and 2, and its cellular localization is regulated by oncogenic Ras. Proc Natl Acad Sci USA 97: 14329-14333. doi: 10.1073/pnas.250494697
![]() |
[10] |
McKinsey TA, Zhang CL, Lu J, et al. (2000) Signal-dependent nuclear export of a histone deacetylase regulates muscle differentiation. Nature 408: 106-111. doi: 10.1038/35040593
![]() |
[11] |
Miska EA, Langley E, Wolf D, et al. (2001) Differential localization of HDAC4 orchestrates muscle differentiation. Nucleic Acids Res 29: 3439-3447. doi: 10.1093/nar/29.16.3439
![]() |
[12] |
Chen JF, Mandel EM, Thomson JM, et al. (2006) The role of microRNA-1 and microRNA-133 in skeletal muscle proliferation and differentiation. Nat Genet 38: 228-233. doi: 10.1038/ng1725
![]() |
[13] |
Rao PK, Kumar RM, Farkhondeh M, et al. (2006) Myogenic factors that regulate expression of muscle-specific microRNAs. Proc Natl Acad Sci USA 103: 8721-8726. doi: 10.1073/pnas.0602831103
![]() |
[14] |
Kim HK, Lee YS, Sivaprasad U, et al. (2006) Muscle-specific microRNA miR-206 promotes muscle differentiation. J Cell Biol 174: 677-687. doi: 10.1083/jcb.200603008
![]() |
[15] |
Baskerville S, Bartel DP (2005) Microarray profiling of microRNAs reveals frequent coexpression with neighboring miRNAs and host genes. RNA 11: 241-247. doi: 10.1261/rna.7240905
![]() |
[16] |
Wienholds E, Plasterk RHA (2005) MicroRNA function in animal development. FEBS Lett 579: 5911-5922. doi: 10.1016/j.febslet.2005.07.070
![]() |
[17] |
Lagos-Quintana M, Rauhut R, Yalcin A, et al. (2002) Identification of tissue-specific microRNAs from mouse. Curr Biol 12: 735-739. doi: 10.1016/S0960-9822(02)00809-6
![]() |
[18] |
Sempere LF, Freemantle S, Pitha-Rowe I, et al. (2004) Expression profiling of mammalian microRNAs uncovers a subset of brain-expressed microRNAs with possible roles in murine and human neuronal differentiation. Genome Biol 5: R13. doi: 10.1186/gb-2004-5-3-r13
![]() |
[19] |
Ma K, Chan JKL, Zhu G, et al. (2005) Myocyte enhancer factor 2 acetylation by p300 enhances its DNA binding activity, transcriptional activity, and myogenic differentiation. Mol Cell Biol 25: 3575-3582. doi: 10.1128/MCB.25.9.3575-3582.2005
![]() |
[20] |
Wang DZ (2006) Micro or mega: how important are MicroRNAs in muscle? Cell Cycle 5: 1015-1016. doi: 10.4161/cc.5.10.2742
![]() |
[21] |
McCarthy JJ (2008) MicroRNA-206: the skeletal muscle-specific myomiR. Biochim Biophys Acta 1779: 682-691. doi: 10.1016/j.bbagrm.2008.03.001
![]() |
[22] |
Yuasa K, Hagiwara Y, Ando M, et al. (2008) MicroRNA-206 is highly expressed in newly formed muscle fibers: implications regarding potential for muscle regeneration and maturation in muscular dystrophy. Cell Struct Funct 33: 163-169. doi: 10.1247/csf.08022
![]() |
[23] |
Cheng CS, El-Abd Y, Bui K, et al. (2014) Conditions that promote primary human skeletal myoblast culture and muscle differentiation in vitro. Am J Physiol Cell Physiol 306: C385-C395. doi: 10.1152/ajpcell.00179.2013
![]() |
[24] |
Kuang W, Tan J, Duan Y, et al. (2009) Cyclic stretch induced miR-146a upregulation delays C2C12 myogenic differentiation through inhibition of Numb. Biochem Bioph Res Commun 378: 259-263. doi: 10.1016/j.bbrc.2008.11.041
![]() |
[25] |
McCarthy JJ, Esser KA (2007) MicroRNA-1 and microRNA-133a expression are decreased during skeletal muscle hypertrophy. J Appl Physiol 102: 306-313. doi: 10.1152/japplphysiol.00932.2006
![]() |
[26] |
Care A, Catalucci D, Felicetti F, et al. (2007) MicroRNA-133 controls cardiac hypertrophy. Nat Med 13: 613-618. doi: 10.1038/nm1582
![]() |
[27] |
Carson JA, Wei L (2000) Integrin signaling's potential for mediating gene expression in hypertrophying skeletal muscle. J Appl Physiol 88: 337-343. doi: 10.1152/jappl.2000.88.1.337
![]() |
[28] |
Blau HM, Pavlath GK, Hardeman EC, et al. (1985) Plasticity of the differentiated state. Science 230: 758-766. doi: 10.1126/science.2414846
![]() |
[29] |
Livak KJ, Schmittgen TD (2001) Analysis of relative gene expression data using real-time quantitative PCR and the 2−ΔΔCT method. Methods 25: 402-408. doi: 10.1006/meth.2001.1262
![]() |
[30] | Zar JH (1999) Biostatistical Analysis. Upper Saddle River NJ: Prentice Hall. |
[31] |
Feng Y, Cao JH, Li XY, et al. (2011) Inhibition of miR-214 expression represses proliferation and differentiation of C2C12 myoblasts. Cell Biochem Funct 29: 378-383. doi: 10.1002/cbf.1760
![]() |
[32] |
Wei X, Li H, Zhang B, et al. (2016) miR-378a-3p promotes differentiation and inhibits proliferation of myoblasts by targeting HDAC4 in skeletal muscle development. RNA Biol 13: 1300-1309. doi: 10.1080/15476286.2016.1239008
![]() |
[33] |
Liu L, Li TM, Liu XR, et al. (2019) MicroRNA-140 inhibits skeletal muscle glycolysis and atrophy in endotoxin-induced sepsis in mice via the WNT signaling pathway. Am J Physiol Cell Physiol 317: C189-C199. doi: 10.1152/ajpcell.00419.2018
![]() |
[34] |
Su Y, Yu Y, Liu C, et al. (2020) Fate decision of satellite cell differentiation and self-renewal by miR-31-IL34 axis. Cell Death Differ 27: 949-965. doi: 10.1038/s41418-019-0390-x
![]() |
[35] |
Ge Y, Sun Y, Chen J (2011) IGF-II is regulated by microRNA-125b in skeletal myogenesis. J Cell Biol 192: 69-81. doi: 10.1083/jcb.201007165
![]() |
[36] |
McCarthy JJ, Esser KA, Andrade FH (2007) MicroRNA-206 is overexpressed in the diaphragm but not the hindlimb muscle of mdx mouse. Am J Physiol Cell Physiol 293: C451-C457. doi: 10.1152/ajpcell.00077.2007
![]() |
[37] |
Farh KKH, Grimson A, Jan C, et al. (2005) The widespread impact of mammalian MicroRNAs on mRNA repression and evolution. Science 310: 1817-1821. doi: 10.1126/science.1121158
![]() |
[38] |
Sayed D, Hong C, Chen IY, et al. (2007) MicroRNAs play an essential role in the development of cardiac hypertrophy. Circ Res 100: 416-424. doi: 10.1161/01.RES.0000257913.42552.23
![]() |
[39] |
Van Rooij E, Olson EN (2007) MicroRNAs: powerful new regualtors of heart disease and provocative therapeutic targets. J Clin Invest 117: 2369-2376. doi: 10.1172/JCI33099
![]() |
[40] |
Van Rooij E, Sutherland LB, Liu N, et al. (2006) A signature pattern of stress-responsive microRNAs that can evoke cardiac hypertrophy and heart failure. Proc Natl Acad Sci USA 103: 18255-18260. doi: 10.1073/pnas.0608791103
![]() |
[41] |
Yang B, Lin H, Xiao J, et al. (2007) The muscle-specific microRNA miR-1 regulates cardiac arrhythmogenic potential by targeting GJA1 and KCNJ2. Nat Med 13: 486-491. doi: 10.1038/nm1569
![]() |
[42] |
Potthoff MJ, Olson EN (2007) MEF2: a central regulator of diverse developmental programs. Development 134: 4131-4140. doi: 10.1242/dev.008367
![]() |
[43] |
Chan JKL, Sun L, Yang XJ, et al. (2003) Functional characterization of an amino-terminal region of HDAC4 that possesses MEF2 binding and transcriptional repressive activity. J Biol Chem 278: 23515-234521. doi: 10.1074/jbc.M301922200
![]() |
[44] |
Liu N, Williams AH, Kim Y, et al. (2007) An intragenic MEF2-dependent enhancer directs muscle-specific expression of microRNAs 1 and 133. Proc Natl Acad Sci USA 104: 20844-20849. doi: 10.1073/pnas.0710558105
![]() |
[45] |
Zhao Y, Samal E, Srivastava D (2005) Serum response factor regulates a muscle-specific microRNA that targets Hand2 during cardiogenesis. Nature 436: 214-220. doi: 10.1038/nature03817
![]() |
[46] |
Van Rooij E, Liu N, Olson EN (2008) MicroRNAs flex their muscles. Trends Genet 24: 159-166. doi: 10.1016/j.tig.2008.01.007
![]() |
![]() |
![]() |
1. | Luis Almonte-Vega, Monica Colón-Vargas, Ligia Luna-Jarrín, Joel Martinez, Jordy Rodriguez-Rinc, Anarina L. Murillo, Mugdha Thakur, Baltazar Espinoza, Rohan Patil, Leon Arriola, Viswanathan Arunachalam, Anuj Mubayi, Cost analysis of treatment strategies for the control of HSV–2 infection in the U.S.: A mathematical modeling-based case study, 2020, 324, 00255564, 108347, 10.1016/j.mbs.2020.108347 | |
2. | Erivelton G. Nepomuceno, Ricardo H.C. Takahashi, Luis A. Aguirre, Reducing vaccination level to eradicate a disease by means of a mixed control with isolation, 2018, 40, 17468094, 83, 10.1016/j.bspc.2017.09.004 | |
3. | Yuqin Zhao, Dobromir T. Dimitrov, Hao Liu, Yang Kuang, Mathematical Insights in Evaluating State Dependent Effectiveness of HIV Prevention Interventions, 2013, 75, 0092-8240, 649, 10.1007/s11538-013-9824-7 | |
4. | B. Ainseba, M. Iannelli, Optimal Screening in Structured SIR Epidemics, 2012, 7, 0973-5348, 12, 10.1051/mmnp/20127302 | |
5. | Cameron Browne, Hayriye Gulbudak, Glenn Webb, Modeling contact tracing in outbreaks with application to Ebola, 2015, 384, 00225193, 33, 10.1016/j.jtbi.2015.08.004 | |
6. | Swati DebRoy, Olivia Prosper, Austin Mishoe, Anuj Mubayi, Challenges in modeling complexity of neglected tropical diseases: a review of dynamics of visceral leishmaniasis in resource limited settings, 2017, 14, 1742-7622, 10.1186/s12982-017-0065-3 | |
7. | Ginny Sprang, Miriam Silman, Posttraumatic Stress Disorder in Parents and Youth After Health-Related Disasters, 2013, 7, 1935-7893, 105, 10.1017/dmp.2013.22 | |
8. | Attila Dénes, Abba B. Gumel, Modeling the impact of quarantine during an outbreak of Ebola virus disease, 2019, 4, 24680427, 12, 10.1016/j.idm.2019.01.003 | |
9. | Barbara Nussbaumer-Streit, Verena Mayr, Andreea Iulia Dobrescu, Andrea Chapman, Emma Persad, Irma Klerings, Gernot Wagner, Uwe Siebert, Claudia Christof, Casey Zachariah, Gerald Gartlehner, Quarantine alone or in combination with other public health measures to control COVID-19: a rapid review, 2020, 14651858, 10.1002/14651858.CD013574 | |
10. | Carmen Coll, Elena Sánchez, Quarantine in an epidemic model with seasonality, 2020, 114, 1578-7303, 10.1007/s13398-019-00753-x | |
11. | Maia Martcheva, 2015, Chapter 9, 978-1-4899-7611-6, 215, 10.1007/978-1-4899-7612-3_9 | |
12. | Gerardo Chowell, Hiroshi Nishiura, Transmission dynamics and control of Ebola virus disease (EVD): a review, 2014, 12, 1741-7015, 10.1186/s12916-014-0196-0 | |
13. | Jing-An Cui, Fangyuan Chen, Effects of isolation and slaughter strategies in different species on emerging zoonoses, 2017, 14, 1551-0018, 1119, 10.3934/mbe.2017058 | |
14. | Govind Prasad Sahu, Joydip Dhar, Dynamics of an SEQIHRS epidemic model with media coverage, quarantine and isolation in a community with pre-existing immunity, 2015, 421, 0022247X, 1651, 10.1016/j.jmaa.2014.08.019 | |
15. | Bhavani Shankara Bagepally, Madhumitha Haridoss, Meenakumari Natarajan, Kathiresan Jeyashree, Manickam Ponnaiah, Cost-effectiveness of surgical mask, N-95 respirator, hand-hygiene and surgical mask with hand hygiene in the prevention of COVID-19: Cost effectiveness analysis from Indian context, 2021, 10, 22133984, 100702, 10.1016/j.cegh.2021.100702 | |
16. | Mohammad A. Safi, Mudassar Imran, Abba B. Gumel, Threshold dynamics of a non-autonomous SEIRS model with quarantine and isolation, 2012, 131, 1431-7613, 19, 10.1007/s12064-011-0148-6 | |
17. | Jidi Zhao, Huajie Jin, Xun Li, Jianguo Jia, Chao Zhang, Huijuan Zhao, Wuren Ma, Zhuozhu Wang, Yi He, Jimmy Lee, Donglan Zhang, Bo Yin, Weiwei Zheng, Haiyin Wang, Mark Pennington, Disease Burden Attributable to the First Wave of COVID-19 in China and the Effect of Timing on the Cost-Effectiveness of Movement Restriction Policies, 2021, 10983015, 10.1016/j.jval.2020.12.009 | |
18. | S. Namilae, A. Srinivasan, A. Mubayi, M. Scotch, R. Pahle, Self-propelled pedestrian dynamics model: Application to passenger movement and infection propagation in airplanes, 2017, 465, 03784371, 248, 10.1016/j.physa.2016.08.028 | |
19. | Bechir Amdouni, Marlio Paredes, Christopher Kribs, Anuj Mubayi, Why do students quit school? Implications from a dynamical modelling study, 2017, 473, 1364-5021, 20160204, 10.1098/rspa.2016.0204 | |
20. | Syed Azhar Ali Shah, Muhammad Altaf Khan, Muhammad Farooq, Saif Ullah, Ebraheem O. Alzahrani, A fractional order model for Hepatitis B virus with treatment via Atangana–Baleanu derivative, 2020, 538, 03784371, 122636, 10.1016/j.physa.2019.122636 | |
21. | Baojun Song, Zhilan Feng, Gerardo Chowell, From the guest editors, 2013, 10, 1551-0018, 10.3934/mbe.2013.10.5i | |
22. | Mohammad A. Safi, Abba B. Gumel, Dynamics analysis of a quarantine model in two patches, 2015, 38, 01704214, 349, 10.1002/mma.3072 | |
23. | Necibe Tuncer, Chindu Mohanakumar, Samuel Swanson, Maia Martcheva, Efficacy of control measures in the control of Ebola, Liberia 2014–2015, 2018, 12, 1751-3758, 913, 10.1080/17513758.2018.1535095 | |
24. | Mohammad A. Safi, Abba B. Gumel, Dynamics of a model with quarantine-adjusted incidence and quarantine of susceptible individuals, 2013, 399, 0022247X, 565, 10.1016/j.jmaa.2012.10.015 | |
25. | Joseph Y. T. Mugisha, Joseph Ssebuliba, Juliet N. Nakakawa, Cliff R. Kikawa, Amos Ssematimba, Siew Ann Cheong, Mathematical modeling of COVID-19 transmission dynamics in Uganda: Implications of complacency and early easing of lockdown, 2021, 16, 1932-6203, e0247456, 10.1371/journal.pone.0247456 | |
26. | Fangyuan Chen, Jing'an Cui, 2016, Cross-species epidemic dynamic model of influenza, 978-1-5090-3710-0, 1567, 10.1109/CISP-BMEI.2016.7852965 | |
27. | Barbara Nussbaumer-Streit, Verena Mayr, Andreea Iulia Dobrescu, Andrea Chapman, Emma Persad, Irma Klerings, Gernot Wagner, Uwe Siebert, Dominic Ledinger, Casey Zachariah, Gerald Gartlehner, Quarantine alone or in combination with other public health measures to control COVID-19: a rapid review, 2020, 14651858, 10.1002/14651858.CD013574.pub2 | |
28. | Caroline Orset, People’s perception and cost-effectiveness of home confinement during an influenza pandemic: evidence from the French case, 2018, 19, 1618-7598, 1335, 10.1007/s10198-018-0978-y | |
29. | Anuj Mubayi, Christopher Kribs-Zaleta, Priscilla Greenwood, Steve Szymanowski, Rasheed Hameed, Ridouan Bani, Influence of environmental factors on college alcohol drinking patterns, 2013, 10, 1551-0018, 1281, 10.3934/mbe.2013.10.1281 | |
30. | Phontita Thiuthad, Valipuram S. Manoranjan, Yongwimon Lenbury, Analytical Solutions for an Avian Influenza Epidemic Model incorporating Spatial Spread as a Diffusive Process, 2015, 5, 2079-7362, 150, 10.4208/eajam.201114.080415a | |
31. | Fred Brauer, Carlos Castillo-Chavez, Zhilan Feng, 2019, Chapter 9, 978-1-4939-9826-5, 311, 10.1007/978-1-4939-9828-9_9 | |
32. | Amit Kumar Gupta, Vijander Singh, Priya Mathur, Carlos M. Travieso-Gonzalez, Prediction of COVID-19 pandemic measuring criteria using support vector machine, prophet and linear regression models in Indian scenario, 2021, 24, 0972-0502, 89, 10.1080/09720502.2020.1833458 | |
33. | Julio A. Benavides, Cristina Caparrós, Ramiro Monã da Silva, Tiziana Lembo, Philip Tem Dia, Katie Hampson, Feliciano Dos Santos, The Power of Music to Prevent and Control Emerging Infectious Diseases, 2021, 8, 2296-858X, 10.3389/fmed.2021.756152 | |
34. | Anuj Mubayi, Abhishek Pandey, Christine Brasic, Anamika Mubayi, Parijat Ghosh, Aditi Ghosh, Analytical Estimation of Data-Motivated Time-Dependent Disease Transmission Rate: An Application to Ebola and Selected Public Health Problems, 2021, 6, 2414-6366, 141, 10.3390/tropicalmed6030141 | |
35. | Cecilia Ajowho Adenusi, Olufunke Rebecca Vincent, Abiodun Folurera Ajayi, Bukola Taibat Adebiyi, 2022, Chapter 9, 978-3-030-87018-8, 151, 10.1007/978-3-030-87019-5_9 | |
36. | Maria M. Martignoni, Josh Renault, Joseph Baafi, Amy Hurford, Maria Vittoria Barbarossa, Downsizing of COVID-19 contact tracing in highly immune populations, 2022, 17, 1932-6203, e0268586, 10.1371/journal.pone.0268586 | |
37. | Anwarud Din, Asad Khan, Yassine Sabbar, Long-Term Bifurcation and Stochastic Optimal Control of a Triple-Delayed Ebola Virus Model with Vaccination and Quarantine Strategies, 2022, 6, 2504-3110, 578, 10.3390/fractalfract6100578 | |
38. | Janetta E. Skarp, Laura E. Downey, Julius W. E. Ohrnberger, Lucia Cilloni, Alexandra B. Hogan, Abagael L. Sykes, Susannah S. Wang, Hiral Anil Shah, Mimi Xiao, Katharina Hauck, A Systematic Review of the Costs Relating to Non-pharmaceutical Interventions Against Infectious Disease Outbreaks, 2021, 19, 1175-5652, 673, 10.1007/s40258-021-00659-z | |
39. | Zhihong Pang, Burçin Becerik-Gerber, Simi Hoque, Zheng O’Neill, Giulia Pedrielli, Jin Wen, Teresa Wu, How Work From Home Has Affected the Occupant's Well-Being in the Residential Built Environment: An International Survey Amid the Covid-19 Pandemic, 2021, 2, 2642-6641, 10.1115/1.4052640 | |
40. | Pankaj Bhardwaj, Nitin Kumar Joshi, Manoj Kumar Gupta, Akhil Dhanesh Goel, Suman Saurabh, Jaykaran Charan, Prakash Rajpurohit, Suresh Ola, Pritam Singh, Sunil Bisht, NR Bishnoi, Balwant Manda, Kuldeep Singh, Sanjeev Misra, Analysis of Facility and Home Isolation Strategies in COVID 19 Pandemic: Evidences from Jodhpur, India, 2021, Volume 14, 1178-6973, 2233, 10.2147/IDR.S309909 | |
41. | Marcin Piotr Walkowiak, Dariusz Walkowiak, Jarosław Walkowiak, To vaccinate or to isolate? Establishing which intervention leads to measurable mortality reduction during the COVID-19 Delta wave in Poland, 2023, 11, 2296-2565, 10.3389/fpubh.2023.1221964 | |
42. | Saumen Barua, Bornali Das, Attila Denes, A compartmental model for COVID-19 to assess effects of non-pharmaceutical interventions with emphasis on contact-based quarantine, 2023, 68, 02521938, 679, 10.24193/subbmath.2023.3.15 | |
43. | M. Pitchaimani, U. Aswini, Long-term effect of SARS-CoV-2 variant : Challenging issues and controlling strategies, 2024, 03781119, 148554, 10.1016/j.gene.2024.148554 | |
44. | M. Pitchaimani, U. Aswini, Delving a stochastic SEQAIJR COVID-19 model with hexa delayed and copious control strategies, 2024, 03781119, 148560, 10.1016/j.gene.2024.148560 | |
45. | Ye Xia, An individual-level probabilistic model and solution for control of infectious diseases, 2024, 21, 1551-0018, 7253, 10.3934/mbe.2024320 | |
46. | Shania Rossiter, Samantha Howe, Joshua Szanyi, James M. Trauer, Tim Wilson, Tony Blakely, The role of economic evaluation in modelling public health and social measures for pandemic policy: a systematic review, 2024, 22, 1478-7547, 10.1186/s12962-024-00585-6 |