Journal of Veterinary Science

The Korean Society of Veterinary Science (대한수의학회)
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Basic concepts, recent advances, and future perspectives in the diagnosis of bovine mastitis

  • Samah Attia Algharib (Engineering Laboratory for Tarim Animal Diseases Diagnosis and Control, College of Animal Science and Technology, Tarim University) ;
  • Ali Sobhy Dawood (The State Key Laboratory of Agricultural Microbiology, (HZAU)) ;
  • Lingli Huang (MOA Laboratory for Risk Assessment of Quality and Safety of Livestock and Poultry Products, Huazhong Agricultural University) ;
  • Aizhen Guo (The State Key Laboratory of Agricultural Microbiology, (HZAU)) ;
  • Gang Zhao (Key Laboratory of Ministry of Education for Conservation and Utilization of Special Biological Resources in the Western China, School of Life Sciences, Ningxia University) ;
  • Kaixiang Zhou (National Reference Laboratory of Veterinary Drug Residues (HZAU) and MAO Key Laboratory for Detection of Veterinary Drug Residues) ;
  • Chao Li (National Reference Laboratory of Veterinary Drug Residues (HZAU) and MAO Key Laboratory for Detection of Veterinary Drug Residues) ;
  • Jinhuan Liu (Engineering Laboratory for Tarim Animal Diseases Diagnosis and Control, College of Animal Science and Technology, Tarim University) ;
  • Xin Gao (College of Integrated Chinese and Western Medicine, Southwest Medical University) ;
  • Wanhe Luo (Engineering Laboratory for Tarim Animal Diseases Diagnosis and Control, College of Animal Science and Technology, Tarim University) ;
  • Shuyu Xie (National Reference Laboratory of Veterinary Drug Residues (HZAU) and MAO Key Laboratory for Detection of Veterinary Drug Residues)
  • Received : 2023.07.01
  • Accepted : 2023.10.23
  • Published : 2024.01.31
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Abstract

Mastitis is one of the most widespread infectious diseases that adversely affects the profitability of the dairy industry worldwide. Accurate diagnosis and identification of pathogens early to cull infected animals and minimize the spread of infection in herds is critical for improving treatment effects and dairy farm welfare. The major pathogens causing mastitis and pathogenesis are assessed first. The most recent and advanced strategies for detecting mastitis, including genomics and proteomics approaches, are then evaluated. Finally, the advantages and disadvantages of each technique, potential research directions, and future perspectives are reported. This review provides a theoretical basis to help veterinarians select the most sensitive, specific, and cost-effective approach for detecting bovine mastitis early.

Keywords

Acknowledgement

This work was supported by the President's fund of Tarim University (TDZKSS202144), the national key research and development program of China (2017 YFD 0501402), and the National Natural Science Foundation of China (grant No. 31772797).

References

  1. Food and Agriculture Organization of the United Nations. References - Scientific Research Publishing [Internet]. Rome: Food and Agriculture Organization of the United Nations; https://www.fao.org/home/en. Updated 2024. Accessed 2023 Dec 17.
  2. Sakwinska O, Morisset D, Madec JY, Waldvogel A, Moreillon P, Haenni M. Link between genotype and antimicrobial resistance in bovine mastitis-related Staphylococcus aureus strains, determined by comparing Swiss and French isolates from the Rhone Valley. Appl Environ Microbiol. 2011;77(10):3428-3432. https://doi.org/10.1128/AEM.02468-10
  3. Yang FL, Li XS, He BX, Du YL, Li GH, Yang B, et al. Bovine mastitis in subtropical dairy farms, 2005-2009. J Anim Vet Adv. 2011;10(1):68-72. https://doi.org/10.3923/javaa.2011.68.72
  4. Goulart DB, Mellata M. Escherichia coli mastitis in dairy cattle: etiology, diagnosis, and treatment challenges. Front Microbiol. 2022;13:928346.
  5. Ashraf A, Imran M. Diagnosis of bovine mastitis: from laboratory to farm. Trop Anim Health Prod. 2018;50(6):1193-1202. https://doi.org/10.1007/s11250-018-1629-0
  6. Seegers H, Fourichon C, Beaudeau F. Production effects related to mastitis and mastitis economics in dairy cattle herds. Vet Res. 2003;34(5):475-491. https://doi.org/10.1051/vetres:2003027
  7. Ajose DJ, Oluwarinde BO, Abolarinwa TO, Fri J, Montso KP, Fayemi OE, et al. Combating bovine mastitis in the dairy sector in an era of antimicrobial resistance: ethno-veterinary medicinal option as a viable alternative approach. Front Vet Sci. 2022;9:800322.
  8. Hogeveen H, Huijps K, Lam TJ. Economic aspects of mastitis: new developments. N Z Vet J. 2011;59(1):16-23. https://doi.org/10.1080/00480169.2011.547165
  9. Shome BR, Das Mitra S, Bhuvana M, Krithiga N, Velu D, Shome R, et al. Multiplex PCR assay for species identification of bovine mastitis pathogens. J Appl Microbiol. 2011;111(6):1349-1356. https://doi.org/10.1111/j.1365-2672.2011.05169.x
  10. Steele NM. Mastitis pathogen identification using polymerase chain reaction in New Zealand milk samples [thesis]. Palmerston North: Massey University; 2015.
  11. Medrano-Galarza C, Ahumada-Beltran DG, Romero-Zuniga JJ, Donado-Godoy P. Prevalence, incidence and risk factors of subclinical mastitis in specialized dairies in Colombia. Agron Mesoam. 2021;32(2):487-507. https://doi.org/10.15517/am.v32i2.43794
  12. Belay N, Mohammed N, Seyoum W. Bovine mastitis: prevalence, risk factors, and bacterial pathogens isolated in lactating cows in Gamo Zone, Southern Ethiopia. Vet Med (Auckl). 2022;13:9-19. https://doi.org/10.2147/VMRR.S344024
  13. Bortolami A, Fiore E, Gianesella M, Corro M, Catania S, Morgante M. Evaluation of the udder health status in subclinical mastitis affected dairy cows through bacteriological culture, somatic cell count and thermographic imaging. Pol J Vet Sci. 2015;18(4):799-805. https://doi.org/10.1515/pjvs-2015-0104
  14. Algharib SA, Dawood A, Xie S. Nanoparticles for treatment of bovine Staphylococcus aureus mastitis. Drug Deliv. 2020;27(1):292-308. https://doi.org/10.1080/10717544.2020.1724209
  15. Zeryehun T, Aya T, Bayecha R. Study on prevalence, bacterial pathogens and associated risk factors of bovine mastitis in small holder dairy farms in and around Addis Ababa, Ethiopia. J Anim Plant Sci. 2013;23(1):50-55.
  16. Mulugeta Y, Wassie M. Prevalence, risk factors and major bacterial causes of bovine mastitis in and around Wolaita Sodo, Southern Ethiopia. Afr J Microbiol Res. 2013;7(48):5400-5405. https://doi.org/10.5897/AJMR2013.6261
  17. Han G, Zhang B, Luo Z, Lu B, Luo Z, Zhang J, et al. Molecular typing and prevalence of antibiotic resistance and virulence genes in Streptococcus agalactiae isolated from Chinese dairy cows with clinical mastitis. PLoS One. 2022;17(5):e0268262.
  18. Dawood A, Algharib SA, Zhao G, Zhu T, Qi M, Delai K, et al. Mycoplasmas as host pantropic and specific pathogens: clinical implications, gene transfer, virulence factors, and future perspectives. Front Cell Infect Microbiol. 2022;12:1-40.
  19. Lopez-Benavides MG, Williamson JH, Pullinger GD, Lacy-Hulbert SJ, Cursons RT, Leigh JA. Field observations on the variation of Streptococcus uberis populations in a pasture-based dairy farm. J Dairy Sci. 2007;90(12):5558-5566. https://doi.org/10.3168/jds.2007-0194
  20. Bradley AJ, Leach KA, Breen JE, Green LE, Green MJ. Survey of the incidence and aetiology of mastitis on dairy farms in England and Wales. Vet Rec. 2007;160(8):253-257. https://doi.org/10.1136/vr.160.8.253
  21. Maity S, Ambatipudi K. Mammary microbial dysbiosis leads to the zoonosis of bovine mastitis: a OneHealth perspective. FEMS Microbiol Ecol. 2020;97(1):fiaa241.
  22. Taponen S, Simojoki H, Haveri M, Larsen HD, Pyorala S. Clinical characteristics and persistence of bovine mastitis caused by different species of coagulase-negative staphylococci identified with API or AFLP. Vet Microbiol. 2006;115(1-3):199-207. https://doi.org/10.1016/j.vetmic.2006.02.001
  23. Thorberg BM, Kuhn I, Aarestrup FM, Brandstrom B, Jonsson P, Danielsson-Tham ML. Pheno- and genotyping of Staphylococcus epidermidis isolated from bovine milk and human skin. Vet Microbiol. 2006;115(1-3):163-172. https://doi.org/10.1016/j.vetmic.2006.01.013
  24. Dufour S, Dohoo IR, Barkema HW, Descoteaux L, Devries TJ, Reyher KK, et al. Epidemiology of coagulase-negative staphylococci intramammary infection in dairy cattle and the effect of bacteriological culture misclassification. J Dairy Sci. 2012;95(6):3110-3124. https://doi.org/10.3168/jds.2011-5164
  25. Pyorala S, Taponen S. Coagulase-negative staphylococci-emerging mastitis pathogens. Vet Microbiol. 2009;134(1-2):3-8. https://doi.org/10.1016/j.vetmic.2008.09.015
  26. Pyorala S. Indicators of inflammation in the diagnosis of mastitis. Vet Res. 2003;34(5):565-578. https://doi.org/10.1051/vetres:2003026
  27. Paulrud CO. Basic concepts of the bovine teat canal. Vet Res Commun. 2005;29(3):215-245. https://doi.org/10.1023/B:VERC.0000047496.47571.41
  28. Hota A, Jambagi K, Swain S. Bovine mastitis: pathogenesis and susceptibility. Agro Econ Int J. 2020;7:107-110.
  29. Zhao X, Lacasse P. Mammary tissue damage during bovine mastitis: causes and control. J Anim Sci. 2008;86(13 Suppl):57-65. https://doi.org/10.2527/jas.2007-0302
  30. Bank U, Ansorge S. More than destructive: neutrophil-derived serine proteases in cytokine bioactivity control. J Leukoc Biol. 2001;69(2):197-206. https://doi.org/10.1189/jlb.69.2.197
  31. Paape MJ, Bannerman DD, Zhao X, Lee JW. The bovine neutrophil: Structure and function in blood and milk. Vet Res. 2003;34(5):597-627. https://doi.org/10.1051/vetres:2003024
  32. Hyde RM, Down PM, Bradley AJ, Breen JE, Hudson C, Leach KA, et al. Automated prediction of mastitis infection patterns in dairy herds using machine learning. Sci Rep. 2020;10(1):4289.
  33. Holmes B, Costas M, Ganner M, On SL, Stevens M. Evaluation of Biolog system for identification of some gram-negative bacteria of clinical importance. J Clin Microbiol. 1994;32(8):1970-1975. https://doi.org/10.1128/jcm.32.8.1970-1975.1994
  34. Funke G, Funke-Kissling P. Performance of the new VITEK 2 GP card for identification of medically relevant gram-positive cocci in a routine clinical laboratory. J Clin Microbiol. 2005;43(1):84-88. https://doi.org/10.1128/JCM.43.1.84-88.2005
  35. Nyberg SD, Meurman O, Jalava J, Rantakokko-Jalava K. Evaluation of detection of extended-spectrum beta-lactamases among Escherichia coli and Klebsiella spp. isolates by VITEK 2 AST-N029 compared to the agar dilution and disk diffusion methods. Scand J Infect Dis. 2008;40(5):355-362. https://doi.org/10.1080/00365540701704706
  36. Quirino A, Marascio N, Scarlata GG, Cicino C, Pavia G, Pantanella M, et al. Orthopedic device-related infections due to emerging pathogens diagnosed by a combination of microbiological approaches: case series and literature review. Diagnostics (Basel). 2022;12(12):3224.
  37. Chamchoy T, Okello E, Williams DR, Tonooka K, Glenn K, Maehana K, et al. Bayesian estimation of sensitivity and specificity of a rapid mastitis test kit, bacterial culture, and PCR for detection of Staphylococcus aureus, Streptococcus species, and coliforms in bovine milk samples. J Dairy Sci. 2022;105(7):6240-6250. https://doi.org/10.3168/jds.2021-20940
  38. Galfi A, Radinovic M, Davidov I, Erdeljan M, Kovacevic Z. Detection of subclinical mastitis in dairy cows using California and Draminski mastitis test. Biotechnol Anim Husb. 2017;33(4):465-473. https://doi.org/10.2298/BAH1704465G
  39. Sharma N, Pandey V, Sudhan N. Comparison of some indirect screening tests for detection of subclinical mastitis in dairy cows. Bulg J Vet Med. 2010;13(2):98-103.
  40. Sztachanska M, Baranski W, Janowski T, Pogorzelska J, Zdunczyk S. Prevalence and etiological agents of subclinical mastitis at the end of lactation in nine dairy herds in North-East Poland. Pol J Vet Sci. 2016;19(1):119-124. https://doi.org/10.1515/pjvs-2016-0015
  41. Schukken YH, Wilson DJ, Welcome F, Garrison-Tikofsky L, Gonzalez RN. Monitoring udder health and milk quality using somatic cell counts. Vet Res. 2003;34(5):579-596. https://doi.org/10.1051/vetres:2003028
  42. Elhaig MM, Selim A. Molecular and bacteriological investigation of subclinical mastitis caused by Staphylococcus aureus and Streptococcus agalactiae in domestic bovids from Ismailia, Egypt. Trop Anim Health Prod. 2015;47(2):271-276. https://doi.org/10.1007/s11250-014-0715-1
  43. Petzer IM, Karzis J, Donkin EF, Webb EC, Etter EM. Somatic cell count thresholds in composite and quarter milk samples as indicator of bovine intramammary infection status. Onderstepoort J Vet Res. 2017;84(1):e1-e10. https://doi.org/10.4102/ojvr.v84i1.1269
  44. Duarte CM, Freitas PP, Bexiga R. Technological advances in bovine mastitis diagnosis: an overview. J Vet Diagn Invest. 2015;27(6):665-672. https://doi.org/10.1177/1040638715603087
  45. Ruegg PL. Investigation of mastitis problems on farms. Vet Clin North Am Food Anim Pract. 2003;19(1):47-73. https://doi.org/10.1016/S0749-0720(02)00078-6
  46. Moon JS, Koo HC, Joo YS, Jeon SH, Hur DS, Chung CI, et al. Application of a new portable microscopic somatic cell counter with disposable plastic chip for milk analysis. J Dairy Sci. 2007;90(5):2253-2259. https://doi.org/10.3168/jds.2006-622
  47. Rossi RS, Amarante AF, Correia LB, Guerra ST, Nobrega DB, Latosinski GS, et al. Diagnostic accuracy of Somaticell, California Mastitis Test, and microbiological examination of composite milk to detect Streptococcus agalactiae intramammary infections. J Dairy Sci. 2018;101(11):10220-10229. https://doi.org/10.3168/jds.2018-14753
  48. Mishra G, Goswami SC, Jhirwal AK, Paliwal S. Effect of breed, season and stage of lactation on different milk parameters at organized farm. Asian J Dairy Food Res. 2022. Epub ahead of print. doi: 10.18805/ajdfr.DR-1937.
  49. Soberon F, Lukas JL, Van Amburgh ME, Capuco AV, Galton DM, Overton TR. Effects of increased milking frequency on metabolism and mammary cell proliferation in Holstein dairy cows. J Dairy Sci. 2010;93(2):565-573. https://doi.org/10.3168/jds.2009-2345
  50. VanKlompenberg MK, McMicking HF, Hovey RC. Technical note: A vacuum-assisted approach for biopsying the mammary glands of various species. J Dairy Sci. 2012;95(1):243-246. https://doi.org/10.3168/jds.2011-4565
  51. Histology, Staining - StatPearls - NCBI Bookshelf [Internet]. Bethesda: NLM; https://www.ncbi.nlm.nih.gov/books/NBK557663/. Updated 2023. Accessed 2023 Jan 7.
  52. Mahmmod Y. The future of PCR technologies in diagnosis of bovine mastitis pathogens. Adv Dairy Res. 2015;2(1):e106.
  53. Riffon R, Sayasith K, Khalil H, Dubreuil P, Drolet M, Lagace J. Development of a rapid and sensitive test for identification of major pathogens in bovine mastitis by PCR. J Clin Microbiol. 2001;39(7):2584-2589. https://doi.org/10.1128/JCM.39.7.2584-2589.2001
  54. Phuektes P, Mansell PD, Browning GF. Multiplex polymerase chain reaction assay for simultaneous detection of Staphylococcus aureus and streptococcal causes of bovine mastitis. J Dairy Sci. 2001;84(5):1140-1148. https://doi.org/10.3168/jds.S0022-0302(01)74574-2
  55. Gillespie BE, Oliver SP. Simultaneous detection of mastitis pathogens, Staphylococcus aureus, Streptococcus uberis, and Streptococcus agalactiae by multiplex real-time polymerase chain reaction. J Dairy Sci. 2005;88(10):3510-3518. https://doi.org/10.3168/jds.S0022-0302(05)73036-8
  56. Ashraf A, Imran M, Yaqub T, Tayyab M, Shehzad W, Thomson PC. A novel multiplex PCR assay for simultaneous detection of nine clinically significant bacterial pathogens associated with bovine mastitis. Mol Cell Probes. 2017;33:57-64. https://doi.org/10.1016/j.mcp.2017.03.004
  57. Koskinen MT, Holopainen J, Pyorala S, Bredbacka P, Pitkala A, Barkema HW, et al. Analytical specificity and sensitivity of a real-time polymerase chain reaction assay for identification of bovine mastitis pathogens. J Dairy Sci. 2009;92(3):952-959. https://doi.org/10.3168/jds.2008-1549
  58. Graber HU, Casey MG, Naskova J, Steiner A, Schaeren W. Development of a highly sensitive and specific assay to detect Staphylococcus aureus in bovine mastitic milk. J Dairy Sci. 2007;90(10):4661-4669. https://doi.org/10.3168/jds.2006-902
  59. Gurjar A, Gioia G, Schukken Y, Welcome F, Zadoks R, Moroni P. Molecular diagnostics applied to mastitis problems on dairy farms. Vet Clin North Am Food Anim Pract. 2012;28(3):565-576. https://doi.org/10.1016/j.cvfa.2012.07.011
  60. Studer E, Schaeren W, Naskova J, Pfaeffli H, Kaufmann T, Kirchhofer M, et al. A longitudinal field study to evaluate the diagnostic properties of a quantitative real-time polymerase chain reaction-based assay to detect Staphylococcus aureus in milk. J Dairy Sci. 2008;91(5):1893-1902. https://doi.org/10.3168/jds.2007-0485
  61. Ghorbanpoor M, Seifiabad Shapouri M, Motamedi H, Jamshidian M, Gooraninejad S. Comoarison of PCR and bacterial culture methods for diagnosis of dairy cattles subclinical submastitis caused by Staphylocuccus aureus. Iran J Vet Med. 2007;1(1):87-91.
  62. Gey A, Werckenthin C, Poppert S, Straubinger RK. Identification of pathogens in mastitis milk samples with fluorescent in situ hybridization. J Vet Diagn Invest. 2013;25(3):386-394. https://doi.org/10.1177/1040638713486113
  63. Choudhary S, Diwakar Bhati T, , Kataria AK. Molecular typing of virulence associated gene (spa) of S. aureus isolated from cattle clinical mastitis. J Entomol Zool Stud. 2022;6(1):1057-1060.
  64. Hakimi Alni R, Mohammadzadeh A, Mahmoodi P. Molecular typing of Staphylococcus aureus of different origins based on the polymorphism of the spa gene: characterization of a novel spa type. 3 Biotech. 2018;8(1):58.
  65. Dendani Chadi Z, Dib L, Zeroual F, Benakhla A. Usefulness of molecular typing methods for epidemiological and evolutionary studies of Staphylococcus aureus isolated from bovine intramammary infections. Saudi J Biol Sci. 2022;29(8):103338.
  66. Chakraborty S, Dhama K, Tiwari R, Iqbal Yatoo M, Khurana SK, Khandia R, et al. Technological interventions and advances in the diagnosis of intramammary infections in animals with emphasis on bovine population-a review. Vet Q. 2019;39(1):76-94. https://doi.org/10.1080/01652176.2019.1642546
  67. Srednik ME, Usongo V, Lepine S, Janvier X, Archambault M, Gentilini ER. Characterisation of Staphylococcus aureus strains isolated from mastitis bovine milk in Argentina. J Dairy Res. 2018;85(1):57-63. https://doi.org/10.1017/S0022029917000851
  68. Heikens E, Fleer A, Paauw A, Florijn A, Fluit AC. Comparison of genotypic and phenotypic methods for species-level identification of clinical isolates of coagulase-negative staphylococci. J Clin Microbiol. 2005;43(5):2286-2290. https://doi.org/10.1128/JCM.43.5.2286-2290.2005
  69. Hu H, Fang Z, Mu T, Wang Z, Ma Y, Ma Y. Application of metabolomics in diagnosis of cow mastitis: a review. Front Vet Sci. 2021;8:747519.
  70. Zhang H, Jiang H, Fan Y, Chen Z, Li M, Mao Y, et al. Transcriptomics and iTRAQ-proteomics analyses of bovine mammary tissue with streptococcus agalactiae-induced mastitis. J Agric Food Chem. 2018;66(42):11188-11196. https://doi.org/10.1021/acs.jafc.8b02386
  71. Zhu T, Liu H, Su L, Dawood A, Hu C, Chen X, et al. Identification of unique key miRNAs, TFs, and mRNAs in virulent MTB infection macrophages by network analysis. Int J Mol Sci. 2021;23(1):382.
  72. Zhou Q, Li M, Wang X, Li Q, Wang T, Zhu Q, et al. Immune-related microRNAs are abundant in breast milk exosomes. Int J Biol Sci. 2012;8(1):118-123. https://doi.org/10.7150/ijbs.8.118
  73. Sun J, Aswath K, Schroeder SG, Lippolis JD, Reinhardt TA, Sonstegard TS. MicroRNA expression profiles of bovine milk exosomes in response to Staphylococcus aureus infection. BMC Genomics. 2015;16(1):806.
  74. Lawless N, Reinhardt TA, Bryan K, Baker M, Pesch B, Zimmerman D, et al. MicroRNA regulation of bovine monocyte inflammatory and metabolic networks in an in vivo infection model. G3 (Bethesda). 2014;4(6):957-971. https://doi.org/10.1534/g3.113.009936
  75. Li R, Zhang CL, Liao XX, Chen D, Wang WQ, Zhu YH, et al. Transcriptome microRNA profiling of bovine mammary glands infected with Staphylococcus aureus. Int J Mol Sci. 2015;16(3):4997-5013. https://doi.org/10.3390/ijms16034997
  76. Lai YC, Fujikawa T, Maemura T, Ando T, Kitahara G, Endo Y, et al. Inflammation-related microRNA expression level in the bovine milk is affected by mastitis. PLoS One. 2017;12(5):e0177182.
  77. Lai YC, Lai YT, Rahman MM, Chen HW, Husna AA, Fujikawa T, et al. Bovine milk transcriptome analysis reveals microRNAs and RNU2 involved in mastitis. FEBS J. 2020;287(9):1899-1918. https://doi.org/10.1111/febs.15114
  78. Tzelos T, Ho W, Charmana VI, Lee S, Donadeu FX. MiRNAs in milk can be used towards early prediction of mammary gland inflammation in cattle. Sci Rep. 2022;12(1):5131.
  79. Lin H. Article commentary: microarray data analysis of gene expression evolution. Gene Regul Syst Bio. 2009. Epub ahead of print. doi: 10.4137/GRSB.S2997.
  80. Vidic J, Manzano M, Chang CM, Jaffrezic-Renault N. Advanced biosensors for detection of pathogens related to livestock and poultry. Vet Res. 2017;48(1):11.
  81. Lee KH, Lee JW, Wang SW, Liu LY, Lee MF, Chuang ST, et al. Development of a novel biochip for rapid multiplex detection of seven mastitis-causing pathogens in bovine milk samples. J Vet Diagn Invest. 2008;20(4):463-471. https://doi.org/10.1177/104063870802000408
  82. Hoque MN, Istiaq A, Rahman MS, Islam MR, Anwar A, Siddiki AM, et al. Microbiome dynamics and genomic determinants of bovine mastitis. Genomics. 2020;112(6):5188-5203. https://doi.org/10.1016/j.ygeno.2020.09.039
  83. Turk R, Rosic N, Kules J, Horvatic A, Gelemanovic A, Galen A, et al. Milk and serum proteomes in subclinical and clinical mastitis in Simmental cows. J Proteomics. 2021;244:104277.
  84. Smolenski G, Haines S, Kwan FY, Bond J, Farr V, Davis SR, et al. Characterisation of host defence proteins in milk using a proteomic approach. J Proteome Res. 2007;6(1):207-215. https://doi.org/10.1021/pr0603405
  85. Garrels JI. The QUEST system for quantitative analysis of two-dimensional gels. J Biol Chem. 1989;264(9):5269-5282. https://doi.org/10.1016/S0021-9258(18)83728-0
  86. Roegener J, Lutter P, Reinhardt R, Bluggel M, Meyer HE, Anselmetti D. Ultrasensitive detection of unstained proteins in acrylamide gels by native UV fluorescence. Anal Chem. 2003;75(1):157-159. https://doi.org/10.1021/ac020517o
  87. Vincent D, Mertens D, Rochfort S. Optimisation of milk protein top-down sequencing using in-source collision-induced dissociation in the maxis quadrupole time-of-flight mass spectrometer. Molecules. 2018;23(11):2777.
  88. Xu MF, Dai JF, Xiang MX, Cheng XF, Zeng XC, Wan CQ, et al. Study on differences of milk proteins by liquid chromatography-mass spectrometer. Chin J Anal Chem. 2014;42(4):501-506. https://doi.org/10.1016/S1872-2040(13)60723-9
  89. Chen Z, Xia H, Shen H, Xu X, Arbab AA, Li M, et al. Pathological features of Staphylococcus aureus induced mastitis in dairy cows and isobaric-tags-for-relative-and-absolute-quantitation proteomic analyses. J Agric Food Chem. 2018;66(15):3880-3890. https://doi.org/10.1021/acs.jafc.7b05461
  90. Raemy A, Meylan M, Casati S, Gaia V, Berchtold B, Boss R, et al. Phenotypic and genotypic identification of streptococci and related bacteria isolated from bovine intramammary infections. Acta Vet Scand. 2013;55(1):53.
  91. Bohme K, Morandi S, Cremonesi P, Fernandez No IC, Barros-Velazquez J, Castiglioni B, et al. Characterization of Staphylococcus aureus strains isolated from Italian dairy products by MALDI-TOF mass fingerprinting. Electrophoresis. 2012;33(15):2355-2364. https://doi.org/10.1002/elps.201100480
  92. Khan MZ, Belhan S, Cetin N, Ayan A, Khan A, Irshad A, et al. Mining for the association of bovine mastitis linked genes to pathological signatures and pathways. Ann Anim Sci. 2022;22(2):583-591. https://doi.org/10.2478/aoas-2021-0049
  93. Roncada P, Piras C, Soggiu A, Turk R, Urbani A, Bonizzi L. Farm animal milk proteomics. J Proteomics. 2012;75(14):4259-4274. https://doi.org/10.1016/j.jprot.2012.05.028
  94. Paulina J, Tadeusz S. Acute phase proteins in cattle. In: Acute Phase Proteins as Early Non-Specific Biomarkers of Human and Veterinary Diseases. London: IntechOpen; 2011.
  95. Georgieva TM, Vasilev N, Karadaev M, Fasulkov I, Ceciliani F. Field study on plasma haptoglobin concentrations and total milk somatic cell counts in cows with untreated and treated mastitis. Bulg J Vet Med. 2018;21(2):160-168. https://doi.org/10.15547/bjvm.1052
  96. Hussein HA, El-Razik KA, Gomaa AM, Elbayoumy MK, Abdelrahman KA, Hosein HI. Milk amyloid A as a biomarker for diagnosis of subclinical mastitis in cattle. Vet World. 2018;11(1):34-41. https://doi.org/10.14202/vetworld.2018.34-41
  97. Eckersall PD, Young FJ, McComb C, Hogarth CJ, Safi S, Weber A, et al. Acute phase proteins in serum and milk from dairy cows with clinical mastitis. Vet Rec. 2001;148(2):35-41. https://doi.org/10.1136/vr.148.2.35
  98. Pyorala S, Hovinen M, Simojoki H, Fitzpatrick J, Eckersall PD, Orro T. Acute phase proteins in milk in naturally acquired bovine mastitis caused by different pathogens. Vet Rec. 2011;168(20):535.
  99. Kovac G, Tothova C, Nagy O, Seidel H. Milk amyloid a and selected serum proteins in cows suffering from mastitis. Acta Vet Brno. 2011;80(1):3-9. https://doi.org/10.2754/avb201180010003
  100. Sandholm M, Honkanen-Buzalski T, Kaartinen L. Detection of inflammatory changes in the milk. In: Sandholm M, Honkanen-Busalsaki T, Kaartinen L, Pyyorala S, editors. The Bovine Udder and Mastitis. Jyvaskyla: Gummerus Press; 1995, 89-104.
  101. Bakken G, Thorburn M. Seriousness and stability of subclinical mastitis assessed by quarter milk serum albumin. Acta Vet Scand. 1985;26(2):273-285. https://doi.org/10.1186/BF03546557
  102. Addis MF, Tedde V, Dore S, Pisanu S, Puggioni GM, Roggio AM, et al. Evaluation of milk cathelicidin for detection of dairy sheep mastitis. J Dairy Sci. 2016;99(8):6446-6456. https://doi.org/10.3168/jds.2015-10293
  103. Le MN, Kawada-Matsuo M, Komatsuzawa H. Efficiency of antimicrobial peptides against multidrug-resistant staphylococcal pathogens. Front Microbiol. 2022;13:930629.
  104. Kalantari A, Safi S, Foroushani A. Milk lactate dehydrogenase and alkaline phosphatase as biomarkers in detection of bovine subclinical mastitis. Ann Biol Res. 2013;4:302-307.
  105. Babaei H, Mansouri-Najand L, Molaei MM, Kheradmand A, Sharifan M. Assessment of lactate dehydrogenase, alkaline phosphatase and aspartate aminotransferase activities in cow's milk as an indicator of subclinical mastitis. Vet Res Commun. 2007;31(4):419-425. https://doi.org/10.1007/s11259-007-3539-x
  106. Batavani RA, Asri S, Naebzadeh H. The effect of subclinical mastitis on milk composition in dairy cows. Iranian J Vet Res. 2007;8(3):205-211.
  107. Chagunda MG, Friggens NC, Rasmussen MD, Larsen T. A model for detection of individual cow mastitis based on an indicator measured in milk. J Dairy Sci. 2006;89(8):2980-2998. https://doi.org/10.3168/jds.S0022-0302(06)72571-1
  108. Mansfeld R, Mansfeld S, Santl B, Hoedemaker M. New aspects regarding the use of the milk electrical conductivity as a parameter for routine diagnostics in dairy production medicine programs. 2nd International Symposium on Bovine Mastitis and Milk Quality; 2001 Sep 13-15; Vancouver, Canada. New Prague: National Mastitis Council; 2011, 488-489.
  109. Chagund M, Larsen T, Bjerring M, Ingvartsen K. Changes in the LDH, NAG-ase, and SCC in relation to the development of mastitis in dairy cows. Proc of the 4th IDF International Mastitis Conference Mastitis in dairy production Current knowledge and future solutions; 2005 Jun 12-15; Maastricht, Netherlands. Maastricht: Wageningen Academic Publishers; 2005, 445-448.
  110. Kandemir FM, Yuksel M, Ozdemir N, Deveci H. A different approach to diagnosis of subclinical mastitis: milk arginase activity. Vet Arh. 2013;83(6):603-610.
  111. Antanaitis R, Juozaitiene V, Jonike V, Baumgartner W, Paulauskas A. Milk lactose as a biomarker of subclinical mastitis in dairy cows. Animals (Basel). 2021;11(6):1736.
  112. Kawecka-Grochocka E, Zalewska M, Rzewuska M, Kosciuczuk E, Zabek T, Sakowski T, et al. Expression of cytokines in dairy cattle mammary gland parenchyma during chronic staphylococcal infection. Vet Res. 2021;52(1):132.
  113. Vitenberga-Verza Z, Pilmane M, Serstnova K, Melderis I, Gontar L, Kochanski M, et al. Identification of inflammatory and regulatory cytokines IL-1α-, IL-4-, IL-6-, IL-12-, IL-13-, IL-17A-, TNF-α-, and IFN-γ-producing cells in the milk of dairy cows with subclinical and clinical mastitis. Pathogens. 2022;11(3):372.
  114. Potapow A, Sauter-Louis C, Schmauder S, Friker J, Nautrup CP, Mehne D, et al. Investigation of mammary blood flow changes by transrectal colour Doppler sonography in an Escherichia coli mastitis model. J Dairy Res. 2010;77(2):205-212.
  115. Fasulkov IR. Ultrasonography of the mammary gland in ruminants: a review. Bulg J Vet Med. 2012;15(1):1-12.
  116. Risvanli A, Dogan H, Safak T, Kilic MA, Seker I. The relationship between mastitis and the B-mode, colour Doppler ultrasonography measurements of supramammary lymph nodes in cows. J Dairy Res. 2019;86(3):315-318. https://doi.org/10.1017/S0022029919000530
  117. Dawood AS, Elrashedy A, Nayel M, Salama A, Guo A, Zhao G, et al. Brucellae as resilient intracellular pathogens: epidemiology, host-pathogen interaction, recent genomics and proteomics approaches, and future perspectives. Front Vet Sci. 2023;10:1255239.
  118. Xudong Z, Xi K, Ningning F, Gang L. Automatic recognition of dairy cow mastitis from thermal images by a deep learning detector. Comput Electron Agric. 2020;178:105754.
  119. McManus R, Boden LA, Weir W, Viora L, Barker R, Kim Y, et al. Thermography for disease detection in livestock: a scoping review. Front Vet Sci. 2022;9:965622.
  120. Velasco-Bolanos J, Ceballes-Serrano CC, Velasquez-Mejia D, Riano-Rojas JC, Giraldo CE, Carmona JU, et al. Application of udder surface temperature by infrared thermography for diagnosis of subclinical mastitis in Holstein cows located in tropical highlands. J Dairy Sci. 2021;104(9):10310-10323. https://doi.org/10.3168/jds.2020-19894
  121. Pezeshki A, Stordeur P, Wallemacq H, Schynts F, Stevens M, Boutet P, et al. Variation of inflammatory dynamics and mediators in primiparous cows after intramammary challenge with Escherichia coli. Vet Res. 2011;42(1):15.
  122. Metzner M, Sauter-Louis C, Seemueller A, Petzl W, Zerbe H. Infrared thermography of the udder after experimentally induced Escherichia coli mastitis in cows. Vet J. 2015;204(3):360-362. https://doi.org/10.1016/j.tvjl.2015.04.013
  123. Sathiyabarathi M, Jeyakumar S, Manimaran A, Pushpadass HA, Sivaram M, Ramesha KP, et al. Investigation of body and udder skin surface temperature differentials as an early indicator of mastitis in Holstein Friesian crossbred cows using digital infrared thermography technique. Vet World. 2016;9(12):1386-1391. https://doi.org/10.14202/vetworld.2016.1386-1391
  124. Digiovani DB, Borges MH, Galdioli VH, Matias BF, Bernardo GM, Silva TR, et al. Infrared thermography as diagnostic tool for bovine subclinical mastitis detection. Rev Bras Hig Sanid Anim. 2016;10(4):685-692. https://doi.org/10.5935/1981-2965.20160055
  125. Fuchs P, Adrion F, Shafiullah AZ, Bruckmaier RM, Umstatter C. Detecting ultra- and circadian activity rhythms of dairy cows in automatic milking systems using the degree of functional coupling-a pilot study. Front Anim Sci. 2022;3:839906.
  126. Deng Z, Hogeveen H, Lam TJ, van der Tol R, Koop G. Performance of online somatic cell count estimation in automatic milking systems. Front Vet Sci. 2020;7:221.
  127. Norberg E, Hogeveen H, Korsgaard IR, Friggens NC, Sloth KH, Lovendahl P. Electrical conductivity of milk: ability to predict mastitis status. J Dairy Sci. 2004;87(4):1099-1107. https://doi.org/10.3168/jds.S0022-0302(04)73256-7
  128. Martins SA, Martins VC, Cardoso FA, Germano J, Rodrigues M, Duarte C, et al. Biosensors for on-farm diagnosis of mastitis. Front Bioeng Biotechnol. 2019;7:186.
  129. Eriksson A, Waller KP, Svennersten-Sjaunja K, Haugen JE, Lundby F, Lind O. Detection of mastitic milk using a gas-sensor array system (electronic nose). Int Dairy J. 2005;15(12):1193-1201. https://doi.org/10.1016/j.idairyj.2004.12.012
  130. Hettinga KA, van Valenberg HJ, Lam TJ, van Hooijdonk AC. Detection of mastitis pathogens by analysis of volatile bacterial metabolites. J Dairy Sci. 2008;91(10):3834-3839. https://doi.org/10.3168/jds.2007-0941
  131. Kan X, Hu G, Liu Y, Xu P, Huang Y, Cai X, et al. Mammary fibrosis tendency and mitochondrial adaptability in dairy cows with mastitis. Metabolites. 2022;12(11):1035.
  132. Thiruvengadam M, Venkidasamy B, Selvaraj D, Samynathan R, Subramanian U. Sensitive screen-printed electrodes with the colorimetric zone for simultaneous determination of mastitis and ketosis in bovine milk samples. J Photochem Photobiol B. 2020;203:111746.
  133. Kim B, Lee YJ, Park JG, Yoo D, Hahn YK, Choi S. A portable somatic cell counter based on a multifunctional counting chamber and a miniaturized fluorescence microscope. Talanta. 2017;170:238-243. https://doi.org/10.1016/j.talanta.2017.04.014
  134. Garcia-Miranda Ferrari A, Rowley-Neale SJ, Banks CE. Screen-printed electrodes: Transitioning the laboratory in-to-the field. Talanta Open. 2021;3:100032.
  135. Rodriguez RR, Galanaugh CF. Microfluidic chamber assembly for mastitis assay, PCT. Patent no. WO/2007/112332. 2007, 115-9.
  136. Algharib SA, Dawood A, Zhou K, Chen D, Li C, Meng K, et al. Designing, structural determination and biological effects of rifaximin loaded chitosan- carboxymethyl chitosan nanogel. Carbohydr Polym. 2020;248:116782.
  137. Algharib SA, Dawood A, Zhou K, Chen D, Li C, Meng K, et al. Preparation of chitosan nanoparticles by ionotropic gelation technique: Effects of formulation parameters and in vitro characterization. J Mol Struct. 2022;1252:132129.
  138. Zhu X, Radovic-Moreno AF, Wu J, Langer R, Shi J. Nanomedicine in the management of microbial infection - overview and perspectives. Nano Today. 2014;9(4):478-498. https://doi.org/10.1016/j.nantod.2014.06.003
  139. Mortari A, Lorenzelli L. Recent sensing technologies for pathogen detection in milk: a review. Biosens Bioelectron. 2014;60:8-21. https://doi.org/10.1016/j.bios.2014.03.063
  140. Duarte C, Costa T, Carneiro C, Soares R, Jitariu A, Cardoso S, et al. Semi-quantitative method for streptococci magnetic detection in raw milk. Biosensors (Basel). 2016;6(2):19.
  141. Duarte CM, Fernandes AC, Cardoso FA, Bexiga R, Cardoso SF, Freitas PJ. Magnetic counter for Group B Streptococci detection in milk. IEEE Trans Magn. 2015;51(1):1-4. https://doi.org/10.1109/TMAG.2014.2359574
  142. Henriksen AD, Wang SX, Hansen MF. On the importance of sensor height variation for detection of magnetic labels by magnetoresistive sensors. Sci Rep. 2015;5(1):12282.
  143. Butler C, Matsumoto A, Rutherford C, Lima HK. Comparison of the effectiveness of four commercial DNA extraction kits on fresh and frozen human milk samples. Methods Protoc. 2022;5(4):63.
  144. Cheema AS, Stinson LF, Lai CT, Geddes DT, Payne MS. DNA extraction method influences human milk bacterial profiles. J Appl Microbiol. 2021;130(1):142-156. https://doi.org/10.1111/jam.14780
  145. Lima SF, Bicalho ML, Bicalho RC. Evaluation of milk sample fractions for characterization of milk microbiota from healthy and clinical mastitis cows. PLoS One. 2018;13(3):e0193671.
  146. Bosward KL, House JK, Deveridge A, Mathews K, Sheehy PA. Development of a loop-mediated isothermal amplification assay for the detection of Streptococcus agalactiae in bovine milk. J Dairy Sci. 2016;99(3):2142-2150. https://doi.org/10.3168/jds.2015-10073
  147. Cornelissen JB, De Greeff A, Heuvelink AE, Swarts M, Smith HE, Van der Wal FJ, et al. Rapid detection of Streptococcus uberis in raw milk by loop-mediated isothermal amplification. J Dairy Sci. 2016;99(6):4270-4281. https://doi.org/10.3168/jds.2015-10683
  148. Tie Z, Chunguang W, Xiaoyuan W, Xinghua Z, Xiuhui Z. Loop-mediated isothermal amplification for detection of Staphylococcus aureus in dairy cow suffering from mastitis. J Biomed Biotechnol. 2012;2012:435982.
  149. Pakbin B, Zolghadr L, Rafiei S, Bruck WM, Bruck TB. FTIR differentiation based on genomic DNA for species identification of Shigella isolates from stool samples. Sci Rep. 2022;12(1):2780.
  150. Krishnamoorthy P, Suresh KP, Jayamma KS, Shome BR, Patil SS, Amachawadi RG. An understanding of the global status of major bacterial pathogens of milk concerning bovine mastitis: a systematic review and meta-analysis (scientometrics). Pathogens. 2021;10(5):545.
  151. Krishnamoorthy P, Goudar AL, Suresh KP, Roy P. Global and countrywide prevalence of subclinical and clinical mastitis in dairy cattle and buffaloes by systematic review and meta-analysis. Res Vet Sci. 2021;136:561-586. https://doi.org/10.1016/j.rvsc.2021.04.021
  152. Olde Riekerink RG, Barkema HW, Scholl DT, Poole DE, Kelton DF. Management practices associated with the bulk-milk prevalence of Staphylococcus aureus in Canadian dairy farms. Prev Vet Med. 2010;97(1):20-28. https://doi.org/10.1016/j.prevetmed.2010.07.002
  153. Busanello M, Rossi RS, Cassoli LD, Pantoja JC, Machado PF. Estimation of prevalence and incidence of subclinical mastitis in a large population of Brazilian dairy herds. J Dairy Sci. 2017;100(8):6545-6553. https://doi.org/10.3168/jds.2016-12042
  154. Mureithi DK, Njuguna MN. Prevalence of subclinical mastitis and associated risk factors in dairy farms in urban and peri-urban areas of Thika Sub County, Kenya. Livest Res Rural Dev. 2016;28(2):13.
  155. Plozza K, Lievaart JJ, Potts G, Barkema HW. Subclinical mastitis and associated risk factors on dairy farms in New South Wales. Aust Vet J. 2011;89(1-2):41-46. https://doi.org/10.1111/j.1751-0813.2010.00649.x
  156. Hashemi M, Kafi M, Safdarian M. The prevalence of clinical and subclinical mastitis in dairy cows in the central region of Fars province, south of Iran. Iran J Vet Res. 2011;12(3):236-241.
  157. Hiitio H, Vakkamaki J, Simojoki H, Autio T, Junnila J, Pelkonen S, et al. Prevalence of subclinical mastitis in Finnish dairy cows: changes during recent decades and impact of cow and herd factors. Acta Vet Scand. 2017;59(1):22-22. https://doi.org/10.1186/s13028-017-0288-x
  158. Mekonnen SA, Koop G, Melkie ST, Getahun CD, Hogeveen H, Lam TJ. Prevalence of subclinical mastitis and associated risk factors at cow and herd level in dairy farms in North-West Ethiopia. Prev Vet Med. 2017;145:23-31. https://doi.org/10.1016/j.prevetmed.2017.06.009
  159. Ayano A, Hiriko F, Simyalew A, Yohannes A. Prevalence of subclinical mastitis in lactating cows in selected commercial dairy farms of Holeta district. J Vet Med Anim Health. 2013;5:67-72.
  160. Ndahetuye JB, Persson Y, Nyman AK, Tukei M, Ongol MP, Bage R. Aetiology and prevalence of subclinical mastitis in dairy herds in peri-urban areas of Kigali in Rwanda. Trop Anim Health Prod. 2019;51(7):2037-2044. https://doi.org/10.1007/s11250-019-01905-2
  161. Abrahmsen M, Persson Y, Kanyima BM, Bage R. Prevalence of subclinical mastitis in dairy farms in urban and peri-urban areas of Kampala, Uganda. Trop Anim Health Prod. 2014;46(1):99-105. https://doi.org/10.1007/s11250-013-0455-7
  162. Mpatswenumugabo JP, Bebora LC, Gitao GC, Mobegi VA, Iraguha B, Kamana O, et al. Prevalence of subclinical mastitis and distribution of pathogens in dairy farms of Rubavu and Nyabihu Districts, Rwanda. J Vet Med. 2017;2017:8456713.
  163. Ali T, Kamran , Raziq A, Wazir I, Ullah R, Shah P, et al. Prevalence of mastitis pathogens and antimicrobial susceptibility of isolates from cattle and buffaloes in Northwest of Pakistan. Front Vet Sci. 2021;8:746755.
  164. Sayeed MA, Rahman MA, Bari MS, Islam A, Rahman MM, Hoque MA. Prevalence of subclinical mastitis and associated risk factors at cow level in dairy farms in Jhenaidah, Bangladesh. Adv Anim Vet Sci. 2020;8(Suppl 2):112-121. https://doi.org/10.17582/journal.aavs/2020/8.s2.112.121
  165. Singh K, Mishra KK, Shrivastava N, Jha AK, Ranjan R. Prevalence of sub-clinical mastitis in dairy cow of Rewa District of Madhya Pradesh. J Anim Res. 2021;11(1):89-95. https://doi.org/10.30954/2277-940X.01.2021.11
  166. Gianneechini R, Concha C, Rivero R, Delucci I, Moreno Lopez J. Occurrence of clinical and sub-clinical mastitis in dairy herds in the West Littoral Region in Uruguay. Acta Vet Scand. 2002;43(4):221-230. https://doi.org/10.1186/1751-0147-43-221
  167. Madouasse A, Browne WJ, Huxley JN, Toni F, Bradley AJ, Green MJ. Risk factors for a high somatic cell count at the first milk recording in a large sample of UK dairy herds. J Dairy Sci. 2012;95(4):1873-1884. https://doi.org/10.3168/jds.2011-4801
  168. Nyman AK, Persson Waller K, Emanuelson U, Frossling J. Sensitivity and specificity of PCR analysis and bacteriological culture of milk samples for identification of intramammary infections in dairy cows using latent class analysis. Prev Vet Med. 2016;135:123-131. https://doi.org/10.1016/j.prevetmed.2016.11.009
  169. Hata T, Murakami K, Nakatani H, Yamamoto Y, Matsuda T, Aoki N. Isolation of bovine milk-derived microvesicles carrying mRNAs and microRNAs. Biochem Biophys Res Commun. 2010;396(2):528-533. https://doi.org/10.1016/j.bbrc.2010.04.135
  170. Croissant J, Chaix A, Mongin O, Wang M, Clement S, Raehm L, et al. Two-photon-triggered drug delivery via fluorescent nanovalves. Small. 2014;10(9):1752-1755. https://doi.org/10.1002/smll.201400042
  171. Ajmal M, Yunus U, Matin A, Haq NU. Synthesis, characterization and in vitro evaluation of methotrexate conjugated fluorescent carbon nanoparticles as drug delivery system for human lung cancer targeting. J Photochem Photobiol B. 2015;153:111-120. https://doi.org/10.1016/j.jphotobiol.2015.09.006
  172. Wellnitz O, Bruckmaier RM. The innate immune response of the bovine mammary gland to bacterial infection. Vet J. 2012;192(2):148-152. https://doi.org/10.1016/j.tvjl.2011.09.013
  173. Sharma N, Singh NK, Bhadwal MS. Relationship of somatic cell count and mastitis: an overview. Asian-Australas J Anim Sci. 2011;24(3):429-438. https://doi.org/10.5713/ajas.2011.10233
  174. Donadeu FX, Howes NL, Esteves CL, Howes MP, Byrne TJ, Macrae AI. Farmer and veterinary practices and opinions related to the diagnosis of mastitis and metabolic disease in UK Dairy Cows. Front Vet Sci. 2020;7:127.
  175. Gerardi G, Bernardini D, Azzurra Elia C, Ferrari V, Iob L, Segato S. Use of serum amyloid A and milk amyloid A in the diagnosis of subclinical mastitis in dairy cows. J Dairy Res. 2009;76(4):411-417. https://doi.org/10.1017/S0022029909990057
  176. Tothova C, Nagy O, Seidel H, Kovac G. Acute phase proteins in relation to various inflammatory diseases of calves. Comp Clin Pathol. 2012;21(5):1037-1042. https://doi.org/10.1007/s00580-011-1224-5
  177. Chassagne M, Barnouin J, Chacornac JP. Biological predictors for early clinical mastitis occurrence in Holstein cows under field conditions in France. Prev Vet Med. 1998;35(1):29-38. https://doi.org/10.1016/S0167-5877(97)00092-5
  178. Schrodl W, Kruger M, Hien TT, Fuldner M, Kunze R. C-reactive protein as a new parameter of mastitis. Tierarztl Prax 1995;23(4):337-341. PUBMED
  179. Pongphol Pongthaisong S, Katawatin CT. Cathelicidin responded to Streptococcus agalactiae and associated with the severity of subclinical mastitis. Wetchasan Sattawaphaet. 2015;45(4):651-655. https://doi.org/10.56808/2985-1130.2696
  180. Lacambra M, Thai TA, Lam CC, Yu AM, Pham HT, Tran PV, et al. Granulomatous mastitis: the histological differentials. J Clin Pathol. 2011;64(5):405-411. https://doi.org/10.1136/jcp.2011.089565
  181. Kano R, Sato A, Sobukawa H, Sato Y, Ito T, Suzuki K, et al. Short communication: ELISA system for screening of bovine mastitis caused by Prototheca zopfii. J Dairy Sci. 2016;99(8):6590-6593. https://doi.org/10.3168/jds.2016-11168
  182. Leng N, Ju M, Jiang Y, Guan D, Liu J, Chen W, et al. The therapeutic effect of florfenicol-loaded carboxymethyl chitosan-gelatin shell nanogels against Escherichia coli infection in mice. J Mol Struct. 2022;1269:133847.
  183. Iyer AP, Albaik M, Baghallab I. Mastitis in camels in African and Middle East countries. J Bacteriol Parasitol. 2014;5(3):1-11.