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Inflammatory biomarkers on an LPS-induced RAW 264.7 cell model: a systematic review and meta-analysis

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Abstract

Introduction

Several experimental models have been designed to promote the development of new anti-inflammatory drugs. The in vitro model using RAW 264.7 cells has been widely used. However, there is still no consensus on which inflammatory mediators should initially be measured to screen for possible anti-inflammatory effects. To determine the rationality of measuring inflammatory mediators together with NO, such as the levels of tumor necrosis factor (TNF)-α, and interleukins (IL) 1β and 6, we carried out this systematic review (SR) and meta-analysis (MA).

Methodology

We conducted this SR and MA in accordance with the Preferred Reporting of Systematic Reviews and Meta-Analysis and the Cochrane Handbook for Systematic Reviews of Intervention. This review was registered in the Open Science Framework (https://doi.org/10.17605/OSF.IO/8C3HT).

Results

LPS-induced cells produced high NO levels compared to non-LPS induced, and this production was not related to cell density. TNF-α, IL-1β, and IL-6, also showed high levels after cells had been stimulated with LPS. Though with some restrictions, all studies were reliable, as the risk of bias was detected in the test compounds and systems.

Conclusion

Measurement of NO levels may be sufficient to screen for possible anti-inflammatory action in the context of LPS-induced RAW 264.7 cells.

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References

  1. Fullerton JN, Gilroy DW. Resolution of inflammation: a new therapeutic frontier. Nat Rev Drug Discov [Internet]. 2016;15(8):551–67. Available from: http://www.nature.com/articles/nrd.2016.39

  2. Medzhitov R. Inflammation 2010: New Adventures of an Old Flame. Cell [Internet]. 2010;140(6):771–6. Available from: https://www.ncbi.nlm.nih.gov/pubmed/20303867

  3. Nathan C, Ding A. Nonresolving Inflammation. Cell. 2010;140(6):871–82.

    Article  CAS  Google Scholar 

  4. Zarrin AA, Bao K, Lupardus P, Vucic D. Kinase inhibition in autoimmunity and inflammation. Nat Rev Drug Discov [Internet]. 2021;20(1):39–63. Available from: http://www.nature.com/articles/s41573-020-0082-8

  5. Sugimoto MA, Sousa LP, Pinho V, Perretti M, Teixeira MM. Resolution of inflammation: What controls its onset? Front Immunol [Internet]. 2016;7(APR). Available from: http://journal.frontiersin.org/Article/https://doi.org/10.3389/fimmu.2016.00160/abstract

  6. Cicchitti L, Martelli M, Cerritelli F. Chronic inflammatory disease and osteopathy: A systematic review. PLoS One. 2015;10(3):1–18.

    Article  Google Scholar 

  7. Furman D, Campisi J, Verdin E, Carrera-Bastos P, Targ S, Franceschi C, et al. Chronic inflammation in the etiology of disease across the life span. Nat Med [Internet]. 2019;25(12):1822–32. Available from: http://www.nature.com/articles/s41591-019-0675-0

  8. Kabir I, Ansari I. a Review on in vivo and in vitro experimental models to investigate the anti-inflammatory activity of herbal extracts. Asian J Pharm Clin Res. 2018;11(11):29.

    Article  CAS  Google Scholar 

  9. Patil KR, Mahajan UB, Unger BS, Goyal SN, Belemkar S, Surana SJ, et al. Animal models of inflammation for screening of anti-inflammatory drugs: implications for the discovery and development of phytopharmaceuticals. Int J Mol Sci [Internet]. 2019;20(18):4367. Available from: https://www.mdpi.com/1422-0067/20/18/4367

  10. Benam KH, Dauth S, Hassell B, Herland A, Jain A, Jang K-J, et al. Engineered In Vitro Disease Models. Annu Rev Pathol Mech Dis [Internet]. 2015;10(1):195–262. Available from: http://www.annualreviews.org/doi/https://doi.org/10.1146/annurev-pathol-012414-040418

  11. Taciak B, Białasek M, Braniewska A, Sas Z, Sawicka P, Kiraga Ł, et al. Evaluation of phenotypic and functional stability of RAW 264.7 cell line through serial passages. Roberts DD, editor. PLoS One [Internet]. 2018;13(6):e0198943. Available from: https://dx.plos.org/https://doi.org/10.1371/journal.pone.0198943

  12. Elisia I, Pae HB, Lam V, Cederberg R, Hofs E, Krystal G. Comparison of RAW264.7, human whole blood and PBMC assays to screen for immunomodulators. J Immunol Methods [Internet]. 2018;452:26–31. Available from: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85031664464&doi=10.1016%2Fj.jim.2017.10.004&partnerID=40&md5=00ac5160d32a7297d3898e2807a5bb86

  13. Lawrence T. The nuclear factor NF-kappaB pathway in inflammation. Cold Spring Harb Perspect Biol. 2009;1(6):1–10.

    Article  Google Scholar 

  14. Lind M, Hayes A, Caprnda M, Petrovic D, Rodrigo L, Kruzliak P, et al. Inducible nitric oxide synthase: Good or bad? Biomed Pharmacother [Internet]. 2017;93:370–5. Available from: https://linkinghub.elsevier.com/retrieve/pii/S0753332217313562

  15. Saha BK, Burns SL. The story of nitric oxide, sepsis and methylene blue: a comprehensive pathophysiologic review. Am J Med Sci. 2020;360(4):329–37. https://doi.org/10.1016/j.amjms.2020.06.007.

    Article  PubMed  Google Scholar 

  16. Page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD, et al. The PRISMA statement: an updated guideline for reporting systematic reviews. BMJ. 2020. https://doi.org/10.1136/bmj.n71.

    Article  PubMed  Google Scholar 

  17. Higgins JPT, Thomas J, Chandler J, Cumpston M, Li T, Page MJ, et al. Cochrane Handbook for Systematic Reviews of Intervention. 2nd edition. Chichester (UK); 2019. 694

  18. American type culture collection (ATCC). ATCC Raw 264.7 (ATCC® TIB71™) product sheet. American Type Collection Culture. EUA, 2018.

  19. Biluca FC, da Silva B, Caon T, Mohr ETB, Vieira GN, Gonzaga L V, et al. Investigation of phenolic compounds, antioxidant and anti-inflammatory activities in stingless bee honey (Meliponinae). Food Res Int [Internet]. 2020;129. Available from: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85076113286&doi=10.1016%2Fj.foodres.2019.108756&partnerID=40&md5=532c50458a5140f6e7c1726ebc103f8f

  20. Duarte LJ, Chaves VC, Nascimento MVP dos S, Calvete E, Li M, Ciraolo E, et al. Molecular mechanism of action of Pelargonidin-3- O -glucoside, the main anthocyanin responsible for the anti-inflammatory effect of strawberry fruits. Food Chem [Internet]. 2018;247:56–65. Available from: https://linkinghub.elsevier.com/retrieve/pii/S0308814617319544

  21. Mohr ETB, dos Santos Nascimento MVP, da Rosa JS, Vieira GN, Kretzer IF, Sandjo LP, et al. Evidence that the anti-inflammatory effect of rubiadin-1-methyl ether has an immunomodulatory context. Mediators Inflamm [Internet]. 2019;2019:1–12. Available from: https://www.hindawi.com/journals/mi/2019/6474168/

  22. Altan A, Yuce H, Karataş O, Taşkan M, Gevrek F, Çolak S, et al. Free and liposome form of gallic acid improves calvarial bone wound healing in Wistar rats. Asian Pac J Trop Biomed [Internet]. 2020;10(4):156–63. Available from: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85082821467&doi=10.4103%2F2221-1691.280297&partnerID=40&md5=276ab92945ffa8b6630ea0ccc384717d

  23. Jung HA, Jin SE, Ahn BR, Lee CM, Choi JS. Anti-inflammatory activity of edible brown alga Eisenia bicyclis and its constituents fucosterol and phlorotannins in LPS-stimulated RAW264.7 macrophages. Food Chem Toxicol [Internet]. 2013;59:199–206. Available from: https://linkinghub.elsevier.com/retrieve/pii/S027869151300375X

  24. Yoon S-B, Lee Y-J, Park SK, Kim H-C, Bae H, Kim HM, et al. Anti-inflammatory effects of Scutellaria baicalensis water extract on LPS-activated RAW 264.7 macrophages. J Ethnopharmacol [Internet]. 2009;125(2):286–90. Available from: https://linkinghub.elsevier.com/retrieve/pii/S0378874109004024

  25. Urbaniak GC, Plous S. Research Randomizer (Version 4.0). 2013.

  26. Hozo SP, Djulbegovic B, Hozo I. Estimating the mean and variance from the median, range, and the size of a sample. BMC Med Res Methodol [Internet]. 2005;5(1):13. Available from: http://bmcmedresmethodol.biomedcentral.com/articles/https://doi.org/10.1186/1471-2288-5-13

  27. Beronius A, Molander L, Zilliacus J, Rudén C, Hanberg A. Testing and refining the Science in Risk Assessment and Policy (SciRAP) web-based platform for evaluating the reliability and relevance of in vivo toxicity studies. J Appl Toxicol [Internet]. 2018;38(12):1460–70. Available from: http://doi.wiley.com/https://doi.org/10.1002/jat.3648

  28. Ouzzani M, Hammady H, Fedorowicz Z, Elmagarmid A. Rayyan—a web and mobile app for systematic reviews. Syst Rev [Internet]. 2016;5(1):210. Available from: http://systematicreviewsjournal.biomedcentral.com/articles/https://doi.org/10.1186/s13643-016-0384-4

  29. Meram C, Wu J. Anti-inflammatory effects of egg yolk livetins (α, β, and γ-livetin) fraction and its enzymatic hydrolysates in lipopolysaccharide-induced RAW 264.7 macrophages. Food Res Int [Internet]. 2017;100:449–59. Available from: https://linkinghub.elsevier.com/retrieve/pii/S0963996917303563

  30. Pang Y, Gan L, Wang X, Su Q, Liang C, He P. Celecoxib aggravates atherogenesis and upregulates leukotrienes in ApoE mice and lipopolysaccharide-stimulated RAW264.7 macrophages. Atherosclerosis [Internet]. 2019;284:50–8. Available from: https://linkinghub.elsevier.com/retrieve/pii/S0021915019301030

  31. Guo Z, Xu H-Y, Xu L, Wang S-S, Zhang X-M. In vivo and in vitro immunomodulatory and anti-inflammatory effects of total flavonoids of Astragalus. Africa J Tradit Complement Altern Med [Internet]. 2016;13(4):60–73. Available from: http://journals.sfu.ca/africanem/index.php/ajtcam/article/view/3461/pdf

  32. He C, Lin H, Wang C, Zhang M, Lin Y, Huang F, et al. Exopolysaccharide from Paecilomyces lilacinus modulates macrophage activities through the TLR4/NF‑κB/MAPK pathway. Mol Med Rep [Internet]. 2019;20:4943–52. Available from: http://www.spandidos-publications.com/https://doi.org/10.3892/mmr.2019.10746

  33. Sun H, Cai W, Wang X, Liu Y, Hou B, Zhu X, et al. Vaccaria hypaphorine alleviates lipopolysaccharide-induced inflammation via inactivation of NFκB and ERK pathways in Raw 264.7 cells. BMC Complement Altern Med [Internet]. 2017;17(1):120. Available from: http://bmccomplementalternmed.biomedcentral.com/articles/https://doi.org/10.1186/s12906-017-1635-1

  34. Zhang Y, Yan R, Hu Y. Oxymatrine inhibits lipopolysaccharide-induced inflammation by down-regulating Toll-like receptor 4/nuclear factor-kappa B in macrophages. Can J Physiol Pharmacol [Internet]. 2015;93(4):253–60. Available from: http://www.nrcresearchpress.com/doi/https://doi.org/10.1139/cjpp-2014-0362

  35. Kim M-J, Jeong S-M, Kang B-K, Kim K-B-W-R, Ahn D-H. Anti-Inflammatory Effects of Grasshopper Ketone from Sargassum fulvellum Ethanol Extract on Lipopolysaccharide-Induced Inflammatory Responses in RAW 264.7 Cells. J Microbiol Biotechnol [Internet]. 2019;29(5):820–6. Available from: http://www.jmb.or.kr/journal/view.html?doi=https://doi.org/10.4014/jmb.1901.01027

  36. Kim YS, Ahn CB, Je JY. Anti-inflammatory action of high molecular weight Mytilus edulis hydrolysates fraction in LPS-induced RAW264.7 macrophage via NF-kappa B and MAPK pathways. Food Chem. 2016;202:9–14.

  37. Lee HA, Koh EK, Sung JE, Kim JE, Song SH, Kim DS, et al. Ethyl acetate extract from Asparagus cochinchinensis exerts anti-inflammatory effects in LPS-stimulated RAW264.7 macrophage cells by regulating COX-2/iNOS, inflammatory cytokine expression, MAP kinase pathways, the cell cycle and anti-oxidant activity. Mol Med Rep [Internet]. 2017;15(4):1613–23. Available from: https://www.spandidos-publications.com/https://doi.org/10.3892/mmr.2017.6166

  38. Lee S-B, Lee WS, Shin J-S, Jang DS, Lee KT. Xanthotoxin suppresses LPS-induced expression of iNOS, COX-2, TNF-α, and IL-6 via AP-1, NF-κB, and JAK-STAT inactivation in RAW 264.7 macrophages. Int Immunopharmacol [Internet]. 2017;49:21–9. Available from: https://linkinghub.elsevier.com/retrieve/pii/S1567576917301947

  39. Lim D, Kim MK, Jang Y-P, Kim J. Sceptridium ternatum attenuates allergic contact dermatitis-like skin lesions by inhibiting T helper 2-type immune responses and inflammatory responses in a mouse model. J Dermatol Sci [Internet]. 2015;79(3):288–97. Available from: https://linkinghub.elsevier.com/retrieve/pii/S0923181115300165

  40. Lim D, Lee E, Jeong E, Jang Y-P, Kim J. Stemona tuberosa prevented inflammation by suppressing the recruitment and the activation of macrophages in vivo and in vitro. J Ethnopharmacol [Internet]. 2015;160:41–51. Available from: https://linkinghub.elsevier.com/retrieve/pii/S0378874114008162

  41. Ghate NB, Chaudhuri D, Panja S, Singh SS, Gupta G, Lee CY, et al. In Vitro Mechanistic Study of the Anti-inflammatory Activity of a Quinoline Isolated from Spondias pinnata Bark. J Nat Prod [Internet]. 2018;81(9):1956–61. Available from: https://pubs.acs.org/doi/https://doi.org/10.1021/acs.jnatprod.8b00036

  42. Laksmitawati DR, Prasanti AP, Larasinta N, Syauta GA, Hilda R, Ramadaniati HU, et al. Anti-Inflammatory Potential of Gandarusa (<I>Gendarussa vulgaris</I> Nees) and Soursop (<I>Annona muricata</I> L) Extracts in LPS Stimulated-Macrophage Cell (RAW264.7). J Nat Remedies [Internet]. 2016;16(2):73. Available from: http://www.informaticsjournals.com/index.php/jnr/article/view/5367

  43. Da Silva LAL, Sandjo LP, Fratoni E, Kinoshita Moon YJ, Dalmarco EM, Biavatti MW. A single-step isolation by centrifugal partition chromatography of the potential anti-inflammatory glaucolide B from Lepidaploa chamissonis. J Chromatogr A [Internet]. 2019;1605:460362. Available from: https://linkinghub.elsevier.com/retrieve/pii/S0021967319307460

  44. Karatoprak GS, Pasayeva L, Safak EK, Göger F, Tugay O, Kosar M. Chemical composition and anti-inflammatory activity of Kitaibelia balansae BOISS. Farmacia [Internet]. 2019;67(6):1054–9. Available from: http://farmaciajournal.com/issue-articles/chemical-composition-and-anti-inflammatory-activity-of-kitaibelia-balansae-boiss/

  45. Kim Y-S, Ahn C-B, Je J-Y. Anti-inflammatory action of high molecular weight Mytilus edulis hydrolysates fraction in LPS-induced RAW264.7 macrophage via NF-κB and MAPK pathways. Food Chem [Internet]. 2016;202:9–14. Available from: https://linkinghub.elsevier.com/retrieve/pii/S0308814616301121

  46. Hunter RA, Storm WL, Coneski PN, Schoenfisch MH. Inaccuracies of Nitric Oxide Measurement Methods in Biological Media. Anal Chem [Internet]. 2013;85(3):1957–63. Available from: http://www.tandfonline.com/doi/full/https://doi.org/10.1080/10715760400017327

  47. Giustarini D, Dalle-Donne I, Colombo R, Milzani A, Rossi R. Adaptation of the Griess Reaction for Detection of Nitrite in Human Plasma. Free Radic Res [Internet]. 2004;38(11):1235–40. Available from: http://www.tandfonline.com/doi/full/https://doi.org/10.1080/10715760400017327

  48. Green LC, Wagner D a, Glogowski J, Skipper PL, Wishnok JS, Tannenbaum SR. Analysis of nitrate, nitrite, and [15N]nitrate in biological fluids. Anal Biochem. 1982;126:131–8.

  49. Romerio A, Peri F. Increasing the Chemical Variety of Small-Molecule-Based TLR4 Modulators: An Overview. Front Immunol [Internet]. 2020;11. Available from: https://www.frontiersin.org/article/https://doi.org/10.3389/fimmu.2020.01210/full

  50. Ciesielska A, Matyjek M, Kwiatkowska K. TLR4 and CD14 trafficking and its influence on LPS-induced pro-inflammatory signaling. Cell Mol Life Sci. 2021;78(4):1233–61. https://doi.org/10.1007/s00018-020-03656-y.

    Article  CAS  PubMed  Google Scholar 

  51. Poltorak A, Smirnova I, He X, Liu M-Y, Van HC, Birdwell D, et al. Genetic and physical mapping of the Lps Locus: identification of the toll-4 receptor as a candidate gene in the critical region. Blood Cells, Mol Dis. 1998;240(170):340–55.

    Article  Google Scholar 

  52. Dai B, Wei D, Zheng N, Chi Z, Xin N, Ma T, et al. Coccomyxa Gloeobotrydiformis Polysaccharide Inhibits Lipopolysaccharide-Induced Inflammation in RAW 264.7 Macrophages. Cell Physiol Biochem [Internet]. 2018 [cited 2020 Apr 1];51(6):2523–35. Available from: https://www.karger.com/Article/FullText/495922

  53. Hobbs S, Reynoso M, Geddis A V, Mitrophanov AY, Matheny RW. LPS-stimulated NF-kappa B p65 dynamic response marks the initiation of TNF expression and transition to IL-10 expression in RAW 264.7 macrophages. Physiol Rep. 2018;6(21).

  54. Rahman MM, McFadden G. Modulation of NF-κB signalling by microbial pathogens. Nat Rev Microbiol [Internet]. 2011;9(4):291–306. Available from: http://www.nature.com/articles/nrmicro2539

  55. Liu T, Zhang L, Joo D, Sun S-C. NF-κB signaling in inflammation. Signal Transduct Target Ther [Internet]. 2017;2(1):17023. Available from: http://www.nature.com/articles/sigtrans201723

  56. Moore K, Howard L, Brownmiller C, Gu I, Lee S-O, Mauromoustakos A. Inhibitory effects of cranberry polyphenol and volatile extracts on nitric oxide production in LPS activated RAW 264.7 macrophages. Food Funct [Internet]. 2019 [cited 2020 Apr 1];10(11):7091–102. Available from: http://xlink.rsc.org/?DOI=C9FO01500K

  57. Ranaweera SS, Dissanayake CY, Natraj P, Lee YJ, Han C-H. Anti-inflammatory effect of sulforaphane on LPS-stimulated RAW 264.7 cells and ob/ob mice. J Vet Sci [Internet]. 2020;21(6). Available from: http://xlink.rsc.org/?DOI=C9FO01500K

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Funding

This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior [Coordination for the Improvement of Higher Education Personnel] – Brazil (CAPES) – Finance Code 001.

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Authors and Affiliations

  1. Programa de Pós-Graduação Em Farmácia (PPGFar), Universidade Federal de Santa Catarina, Florianópolis, SC, Brazil

    Bruno Matheus Facchin, Gustavo Oliveira dos Reis, Guilherme Nicácio Vieira, Eduarda Talita Bramorski Mohr, Júlia Salvan da Rosa, Izabel Galhardo Demarchi & Eduardo Monguilhott Dalmarco

  2. Departamento de Análises Clínicas–CCS, Universidade Federal de Santa Catarina, Campus Universitário–Trindade, Florianópolis, SC, 88040-970, Brazil

    Júlia Salvan da Rosa, Iara Fabricia Kretzer, Izabel Galhardo Demarchi & Eduardo Monguilhott Dalmarco

  3. Programa de Pós-Graduação Em Ciências da Saúde, Universidade Estadual de Maringá, Maringá, PR, Brazil

    Izabel Galhardo Demarchi

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  1. Bruno Matheus Facchin
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Contributions

BMF, GOR, ETBM and GNV performed the literature research, designed the data extraction form, and performed the data extraction and data analysis. IGD, IFK, EMD critically reviewed the analyzed the data. BMF wrote the paper. IGD, IFK, EMD critically reviewed subsequent drafts. All authors approved the final version of the manuscript for submission. All authors had full access to all of the data in the study and took responsibility for the integrity of the data and the accuracy of the data analysis.

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Correspondence to Eduardo Monguilhott Dalmarco.

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Supplementary Information

Below is the link to the electronic supplementary material.

11_2022_1584_MOESM1_ESM.pdf

Supplementary file1 (PDF 114 KB) Figure S1 - Forest plot of studies considered as outliers, mean differences of cytokines production between the control group and the intervention group (LPS). A) Tumor Necrosis Factor alpha (TNF-α), B) Interleukin-6 (IL-6) and C) Interleukin-1-beta (IL-1β). 95-%-CI, 95-% confidence interval; Tau2, Kendall’s Tau correlation coefficient; I2, I2 statistic; df, degrees of freedom

11_2022_1584_MOESM2_ESM.pdf

Supplementary file2 (PDF 140 KB) Figure S2 - Forest plot of studies considered as outliers, mean differences in cytokine production between the control group and the intervention group (LPS) of the studies by cell density subgroups (1 - 2.5 and 3 – 5 x 105 cells/mL). A) Tumor Necrosis Factor alpha (TNF-α), B) Interleukin-6 (IL-6), C) Interleukin-1-beta (IL-1β). 95-%-CI, 95-% confidence interval; Tau2, Kendall’s Tau correlation coefficient; I2, I2 statistic; df, degrees of freedom

11_2022_1584_MOESM3_ESM.pdf

Supplementary file3 (PDF 139 KB) Figure S3 - Forest plot of studies considered as outliers, mean differences in cytokine production between the control group and the intervention group (LPS) of the studies included by NO production subgroups (20-50µM and >50µM). A) Tumor Necrosis Factor alpha (TNF-α), B) Interleukin-6 (IL-6), C) Interleukin-1-beta (IL-1β). 95-%-CI, 95-% confidence interval; Tau2, Kendall’s Tau correlation coefficient; I2, I2 statistic; df, degrees of freedom

11_2022_1584_MOESM4_ESM.pdf

Supplementary file4 (PDF 71 KB) Figure S4 - Funnel plots showing the mean difference (MD) between results of the LPS-induced and the control cells, by the standard error [SE (MD)]. A) Nitric Oxide (NO); B) Tumor Necrosis Factor-alpha (TNF-α); C) Interleukin-6 (IL-6); D) Interleukin-1-beta (IL-1β)

11_2022_1584_MOESM5_ESM.pdf

Supplementary file5 (PDF 727 KB) Figure S5 – Checklist for use of the inflammatory model using LPS in Raw 264.7 macrophages

Supplementary file6 (PDF 93 KB) Table S1 - Checklist PRISMA

Supplementary file7 (DOCX 11 KB) Table S2 - Database search strategy

Supplementary file8 (XLSX 92 KB) Table S3 - Data Extraction

Supplementary file9 (DOCX 9 KB) Table S4 - Checklist of thirty items - Inflammation model in RAW 264.7

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Facchin, B.M., dos Reis, G.O., Vieira, G.N. et al. Inflammatory biomarkers on an LPS-induced RAW 264.7 cell model: a systematic review and meta-analysis. Inflamm. Res. 71, 741–758 (2022). https://doi.org/10.1007/s00011-022-01584-0

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