References
- Adkins P.R.F., Middleton J.R.: Methods for diagnosing mastitis. Vet Clin North America Food Anim Pract 2018, 34, 479–491.
- Alsaweed M., Lai C.T., Hartmann P.E., Geddes D.T., Kakulas F.: Human milk miRNAs primarily originate from the mammary gland resulting in unique miRNA profiles of fractionated milk. Sci Rep 2016, 6, 20680–20686, doi: 10.1038/srep20680.
- Benjamini Y., Hochberg Y.: Controlling the false discovery rate: a practical and powerful approach to multiple testing. J Royal Stat Society 1995, 57, 289–300.
- Blowey R., Edmondson P.: Mastitis control in dairy herds. CABI. Book 2010.
- Chakraborty S., Dhama K., Tiwari R., Iqbal Y.M., Khurana S.K., Khandia R., Munjal A., Munuswamy P., Kumar M.A., Singh M., Singh R., Gupta V.K., Chaicumpa W.: Technological interventions and advances in the diagnosis of intramammary infections in animals with emphasis on bovine population-a review. Vet Q 2019, 39, 76–94, doi: 10.1080/01652176.2019.1642546.
- Chen L., Liu X., Li Z., Wang, H., Liu Y., He H., Yang J., Niu F., Wang L., Guo J.: Expression differences of miRNAs and genes on NF-kappaB pathway between the healthy and the mastitis Chinese Holstein cows. Gene 2014, 545, 117–125, doi: 10.1016/j.gene.2014.04.071.
- Dalen G.: The detection of intramammary infections using online somatic cell counts. J Dairy Sci 2019, 102, 5419–5429, doi: 10.3168/jds.2018-15295.
- Das K., Rao L.: The Role of microRNAs in Inflammation. Int J Mol Sci 2022, 23, 15479–15484, doi: 10.3390/ijms232415479.
- Dingwell R.T., Leslie K.E., Schukken Y.H., Sargeant J.M., Timms L.L.: Evaluation of the California mastitis test to detect an intramammary infection with a major pathogen in early lactation dairy cows. Can Vet J 2003, 44, 413–415.
- Dong H., Gao Q., Peng X., Sun Y., Han T., Zhao B.: Circulating MicroRNAs as potential biomarkers for veterinary infectious diseases. Front Vet Sci 2017, 4, 1–7, doi: 10.3389/fvets.2017. 00186.
- Ebrahimie E., Ebrahimi F., Ebrahimi M., Tomlinson S., Petrovski K.: A large-scale study of indicators of sub-clinical mastitis in dairy cattle by attribute weighting analysis of milk composition features: Highlighting the predictive power of lactose and electrical conductivity. J Dairy Res 2018, 85, 193–200, doi: 10.1017/S0022029918000249.
- Fernandes L., Guimaraes I., Noyes N.R., Caixeta L.S., Machado V.S.: Effect of subclinical mastitis detected in the first month of lactation on somatic cell count linear scores, milk yield, fertility, and culling of dairy cows in certified organic herds. J Dairy Sci 2021, 2, 2140–2150, doi: 10.3168/jds.2020-19153.
- Halasa T., Huijps K., Østerås O., Hogeveen H.: Economic effects of bovine mastitis and mastitis management: A review. Vet Quarterly 2007, 29, 18–31, doi: 10.1080/01652176.2007.9695224.
- Johnnidis J.B., Harris M.H., Wheeler R.T., Stehling-Sun S., Lam M.H., Kirak O., Brummelkamp T.R., Fleming M.D., Camargo F.D.: Regulation of progenitor cell proliferation and granulocyte function by microRNA-223. Nature 2008, 451, 1125–1129, doi: 10.1038/NATURE06607.
- Kaczorek-Łukowska E., Małaczewska J., Wójcik R.: Streptococci as the new dominant aetiological factors of mastitis in dairy cows in north-eastern Poland: analysis of the results obtained in 2013–2019. Ir Vet J 2021, 74, 2, doi: 10.1186/s13620-020-00181-z.
- Kuhn M.: Caret: Classification and Regression Training. R package version 6.0-93, https://CRAN.R-project.org/package=caret, 2022.
- Lai Y.C., Fujikawa T., Ando T., Kitahara G., Koiwa M., Kubota C., Miura N.: Rapid Communication: MiR-92a as a housekeeping gene for analysis of bovine mastitis-related microRNA in milk. J Anim Sci 2017, 95, 2732–2735, doi: 10.2527/jas.2017.1384.
- Lai Y.C., Fujikawa T., Maemura T., Ando T., Kitahara G., Endo Y., Yamato O., Koiwa M., Kubota Ch., Miura N.: Inflammation-related microRNA expression level in the bovine milk is affected by mastitis. PLoS One 2017, 12, e0177182, doi: 10.1371/journal.pone.0177182.
- Lai Y.C., Lai Y.T., Rahman M.M., Chen H.W., Husna A.A., Kubota Ch., Miura N.: Bovine milk transcriptome analysis reveals microRNAs and RNU2 involved in mastitis. FEBS J 2020, 287, 1899–1918, doi: 10.1111/febs.15114.
- Li. R., Zhang Ch.L., Liao X.X., Chen D., Wang W., Zhu Y., Geng X., Ji D., Mao Y., Gong Y., Yang Z.-P.: Transcriptome MicroRNA Profiling of Bovine Mammary Glands Infected with Staphylococcus aureus. Int J Mol Sci 2015, 16, 4997–5013, doi: 10.3390/ijms16034997.
- Moyes K.M., Sorensen P., Bionaz M.: The impact of intramammary Escherichia coli challenge on liver and mammary transcriptome and cross-talk in dairy cows during early lactation using RNAseq. PLoS One 2016, 11, e0157480, doi: 10.1371/journal.pone.0157480.
- Naeem K., Zhong S.J., Moisá J.K., Drackley K.M., Moyes J.J.: Bioinformatics analysis of microRNA and putative target genes in bovine mammary tissue infected with Streptococcus uberis. J Dairy Sci 2012, 95, 6397-6408, doi: 10.3168/jds.2011-5173.
- National Mastitis Council: Laboratory Handbook on Bovine Mastitis, National Mastitis Council, New Prague, MN, 2017.
- Oyelami F.O., Usman T., Suravajhala P., Ali N., Do D.N.: Emerging Roles of Noncoding RNAs in Bovine Mastitis Diseases. Pathogens 2022, 11, 1009, doi: 10.3390/pathogens11091009.
- Precazzini F., Detassis S., Imperatori A.S., Denti M.A., Campomenosi P.: Measurements Methods for the Development of MicroRNA-Based Tests for Cancer Diagnosis. Int J Mol Sci 2021, 22, 1176, doi: 10.3390/ijms22031176.
- Qi M., Geng H., Geng N., Cui Y., Qi C., Cheng G., Song K., Hu L., Liu Y., Lui J., Han B.: Streptococcus agalactiae-induced autophagy of bovine mammary epithelial cell via PI3K/AKT/mTOR pathway. J Dairy Res 2022, 7, 1–7, doi: 10.1017/S0022029922000243.
- R Core Team: R: A language and environment for statistical computing. R Foundation for Statistical Computing 2022, Vienna, Austria. https://www.R-project.org/.
- Romero J., Benavides E., Meza C.: Assessing Financial Impacts of Subclinical Mastitis on Colombian Dairy Farms. Front Vet Sci 2018, 5, 273, doi: 10.3389/fvets.2018.00273.
- Schepers A.J., Lam T.J., Schukken Y.H., Wilmink J.B., Hanekamp W.J.: Estimation of variance components for somatic cell counts to determine thresholds for uninfected quarters. J Dairy Sci 1997, 80, 1833–1840, doi: 10.3168/jds.S0022-0302(97)76118-6.
- Seegers H., Fourichon C., Beaudeau F.: Production effects related to mastitis and mastitis economics in dairy cattle herds. Vet Res 2003, 34, 475–491, doi: 10.1051/vetres:2003027.
- Sharma N., Singh N., Bhadwal M.: Relationship of Somatic Cell Count and Mastitis: An Overview. Anim Biosci 2011, 24, 429–438, doi: 10.5713/ajas.2011.10233.
- Smulski S., Gehrke M., Libera K., Cieślak A., Huang H., Patra K.A., Szumacher-Strabel M.: Effects of various mastitis treatments on the reproductive performance of cows. BMC Vet Res 2020, 16, 99, doi: 10.1186/s12917-020-02305-7.
- Sohel M.M.H.: Circulating microRNAs as biomarkers in cancer diagnosis. Life Sci 2020, 248, 117473, doi: 10.1016/j.lfs.2020.117473.
- Srikok S., Patchanee P., Boonyayatra S., Chuammitri P.: Potential role of MicroRNA as a diagnostic tool in the detection of bovine mastitis. Prevent Vet Med 2020, 182, 105101, doi: 10.1016/j.prevetmed.2020.105101.
- Taganov K.D., Boldin M.P., Chang K.J., Baltimore D.: NF-kappaB-dependent induction of microRNA miR-146, an inhibitor targeted to signaling proteins of innate immune responses. Proc Natl Acad Sci 2006, 103, 12481–12486, doi: 10.1073/pnas.0605298103.
- Tam W., Dahlberg J.E.: miR-155/BIC as an oncogenic microRNA. Genes Chromosomes Cancer 2006, 45, 211–212, doi: 10.1038/s41388-018-0571-y.
- Tzelos T., Ho W., Charmana V.I.: MiRNAs in milk can be used towards early prediction of mammary gland inflammation in cattle. Sci Rep 2022, 12, 5131, doi: 10.1038/s41598-022-09214-9.
- Wall S.K., Wellnitz O., Bruckmaier R.M., Schwarz D.: Differential somatic cell count in milk before, during, and after lipopolysaccharide- and lipoteichoic-acid-induced mastitis in dairy cows. J Dairy Sci 2018, 101, 5362–5373, doi: 10.3168/jds.2017-14152.
- Wellnitz O., Bruckmaier R.M.: The innate immune response of the bovine mammary gland to bacterial infection. Vet J 2012, 192, 148–152, doi: 10.1016/j.jinf.2006.06.010.