Current uses of artificial intelligence in the analysis of biofluid markers involved in corneal and ocular surface diseases: a systematic review

von Thun und Hohenstein-Blaul N, Funke S, Grus FH. Tears as a source of biomarkers for ocular and systemic diseases. Exp Eye Res. 2013;117:126–37.

Google Scholar 

Zhou L, Beuerman RW. Tear analysis in ocular surface diseases. Progr Retinal Eye Res. 2012;31:527–50.

CAS  Google Scholar 

Wang MTM, Muntz A, Wolffsohn JS, Craig JP. Association between dry eye disease, self-perceived health status, and self-reported psychological stress burden. Clin Exp Optom. 2021;104:835–40.

PubMed  Google Scholar 

Yu J, Asche CV, Fairchild CJ. The economic burden of dry eye disease in the United States: A decision tree analysis. Cornea. 2011;30:379–87.

PubMed  Google Scholar 

Chan C, Ziai S, Myageri V, Burns JG, Prokopich CL. Economic burden and loss of quality of life from dry eye disease in Canada. BMJ Open Ophthalmol. 2021;6:e000709.

PubMed  PubMed Central  Google Scholar 

Yang W, Luo Y, Wu S, Niu X, Yan Y, Qiao C, et al. Estimated annual economic burden of dry eye disease based on a multi-center analysis in china: a retrospective study. Front Med (Lausanne). 2021;8:771352.

Google Scholar 

de Almeida Borges D, Alborghetti MR, Franco Paes Leme A, Ramos Domingues R, Duarte B, Veiga M, et al. Tear proteomic profile in three distinct ocular surface diseases: keratoconus, pterygium, and dry eye related to graft-versus-host disease. Clin Proteomics [Internet]. 2020;17:1–16.

Google Scholar 

Ji YW, Kim HM, Ryu SY, Oh JW, Yeo A, Choi CY, et al. Changes in human tear proteome following topical treatment of dry eye disease: Cyclosporine a versus diquafosol tetrasodium. Invest Ophthalmol Vis Sci. 2019;60:5035–44.

PubMed  CAS  Google Scholar 

Menegay M, Lee DM, Tabbara KF, Cafaro TA, Urrets-Zavalía JA, Serra HM, et al. Proteomic analysis of climatic keratopathy droplets. Invest Ophthalmol Vis Sci. 2008;49:2829–37.

PubMed  Google Scholar 

Jiang Y, Yang C, Zheng Y, Liu Y, Chen Y. A set of global metabolomic biomarker candidates to predict the risk of dry eye disease. Front Cell Dev Biol. 2020;8:344.

PubMed  PubMed Central  Google Scholar 

Wojakowska A, Pietrowska M, Widlak P, Dobrowolski D, Wylegała E, Tarnawska D. Metabolomic signature discriminates normal human cornea from Keratoconus—A pilot GC/MS study. Molecules. 2020;25:2933.

PubMed Central  CAS  Google Scholar 

González N, Iloro I, Soria J, Duran JA, Santamaría A, Elortza F, et al. Human tear peptide/protein profiling study of ocular surface diseases by SPE-MALDI-TOF mass spectrometry analyses. EuPA Open Proteom. 2014;3:206–15.

Google Scholar 

Soria J, Villarrubia A, Merayo-Lloves J, Elortza F, Azkargorta M, de Toledo JA, et al. Label-free LC–MS/MS quantitative analysis of aqueous humor from keratoconic and normal eyes. Mol Vis. 2015;21:451–60.

PubMed  PubMed Central  CAS  Google Scholar 

Keskinbora K, Güven F. Artificial intelligence and ophthalmology. Turk J Ophthalmol. 2020;50:37–43.

PubMed  PubMed Central  Google Scholar 

Schmidt-Erfurth U, Sadeghipour A, Gerendas BS, Waldstein SM, Bogunović H. Artificial intelligence in retina. Prog Retin Eye Res. 2018;67:1–29.

PubMed  Google Scholar 

Yu LR, Stewart NA, Veenstra TD. Chapter 8 - Proteomics: The Deciphering of the Functional Genome. In: Ginsburg GS, Willard HFBTE of G and PM, editors. San Diego: Academic Press; 2010:89–96

Larrañaga P, Calvo B, Santana R, Bielza C, Galdiano J, Inza I, et al. Machine learning in bioinformatics. Brief Bioinform. 2006;7:86–112.

PubMed  Google Scholar 

Cryan LM, O’Brien C. Proteomics as a research tool in clinical and experimental ophthalmology. Proteomics Clin Appl. 2008;2:762–75.

PubMed  CAS  Google Scholar 

Schmidt A, Forne I, Imhof A. Bioinformatic analysis of proteomics data. BMC Syst Biol. 2014;8(Suppl 2):S3–S3. 2014/03/13

PubMed  PubMed Central  Google Scholar 

Tan SZ, Begley P, Mullard G, Hollywood KA, Bishop PN. Introduction to metabolomics and its applications in ophthalmology. Eye (Basingstoke). 2016;30:773–83.

CAS  Google Scholar 

Hopkins JJ, Keane PA, Balaskas K. Delivering personalized medicine in retinal care: From artificial intelligence algorithms to clinical application. Curr Opin Ophthalmol. 2020;31:329–36.

Google Scholar 

Page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD, et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ. 2021;372:n71.

PubMed  PubMed Central  Google Scholar 

Munn Z, Moola S, Riitano D, Lisy K. The development of a critical appraisal tool for use in systematic reviews addressing questions of prevalence. Int J Health Policy Manag. 2014;3:123–8.

PubMed  PubMed Central  Google Scholar 

Valesan LF, Da-Cas CD, Réus JC, Denardin ACS, Garanhani RR, Bonotto D, et al. Prevalence of temporomandibular joint disorders: a systematic review and meta-analysis. Clin Oral Investig. 2021;25:441–53.

PubMed  Google Scholar 

Liang JT, Huang J, Chen TC, Hung JS. The Toldt fascia: A historic review and surgical implications in complete mesocolic excision for colon cancer. Asian J Surg. 2019;42:1–5.

PubMed  Google Scholar 

Srinivasan S, Thangavelu M, Zhang L, Green KB, Nichols KK. iTRAQ quantitative proteomics in the analysis of tears in dry eye patients. Invest Ophthalmol Vis Sci. 2012;53:5052–9.

PubMed  PubMed Central  CAS  Google Scholar 

Huang Z, Du CX, Pan XD. The use of in-strip digestion for fast proteomic analysis on tear fluid from dry eye patients. PLoS One. 2018;13:e0200702.

PubMed  PubMed Central  Google Scholar 

Yawata N, Awate S, Liu YC, Yuan S, Woon K, Siak J, et al. Kinetics of tear fluid proteins after endothelial keratoplasty and predictive factors for recovery from corneal haze. J Clin Med. 2020;9:1–14.

Google Scholar 

Linghu D, Guo L, Zhao Y, Liu Z, Zhao M, Huang L, et al. iTRAQ-based quantitative proteomic analysis and bioinformatics study of proteins in pterygia. 2018;1600094:7–8.

Aqrawi LA, Galtung HK, Vestad B, Øvstebø R, Thiede B, Rusthen S, et al. Identification of potential saliva and tear biomarkers in primary Sjögren’s syndrome, utilising the extraction of extracellular vesicles and proteomics analysis. Arthritis Res Ther. 2017;19:1–15.

Google Scholar 

Grus FH, Podust VN, Bruns K, Lackner K, Fu S, Dalmasso EA, et al. SELDI-TOF-MS ProteinChip array profiling of tears from patients with dry eye. Invest Ophthalmol Vis Sci. 2005;46:863–76.

PubMed  Google Scholar 

Tong L, Zhou L, Beuerman R, Simonyi S, Hollander DA, Stern ME. Effects of punctal occlusion on global tear proteins in patients with dry eye. Ocular Surface. 2017;15:736–41.

PubMed  Google Scholar 

Zou X, Wang S, Zhang P, Lu L, Zou H. Quantitative proteomics and weighted correlation network analysis of tear samples in adults and children with diabetes and dry eye. Transl Vis Sci Technol. 2020;9:1–15.

Google Scholar 

Soria J, Durán JA, Etxebarria J, Merayo J, González N, Reigada R, et al. Tear proteome and protein network analyses reveal a novel pentamarker panel for tear film characterization in dry eye and meibomian gland dysfunction. J Proteomics. 2013;78:94–112.

PubMed  CAS  Google Scholar 

Fodor M, Vitályos G, Losonczy G, Hassan Z, Pásztor D, Gogolák P, et al. Tear mediators NGF along with IL-13 predict keratoconus progression. Ocul Immunol Inflamm. 2021;29:1090–101.

PubMed  CAS  Google Scholar 

Fodor M, Gogolák P, Rajnavölgyi É, Berta A, Kardos L, Módis L, et al. Long-term kinetics of cytokine responses in human tears after penetrating keratoplasty. J Interferon Cytokine Res. 2009;29:375–9.

PubMed  CAS  Google Scholar 

Kim SW, Lee J, Lee B, Rhim T. Proteomic analysis in pterygium; upregulated protein expression of ALDH3A1, PDIA3, and PRDX2. Mol Vis. 2014;20:1192–202.

PubMed  PubMed Central  CAS  Google Scholar 

Sembler-Møller ML, Belstrøm D, Locht H, Pedersen AML. Proteomics of saliva, plasma, and salivary gland tissue in Sjögren’s syndrome and non-Sjögren patients identify novel biomarker candidates. J Proteomics. 2020;225:103877.

PubMed  Google Scholar 

O’leary OE, Schoetzau A, Amruthalingam L, Geber-Hollbach N, Plattner K, Jenoe P, et al. Tear proteomic predictive biomarker model for ocular graft versus host disease classification. Transl Vis Sci Technol. 2020;9:1–15.

Google Scholar 

Piyacomn Y, Kasetsuwan N, Reinprayoon U, Satitpitakul V, Tesapirat L. Efficacy and safety of intense pulsed light in patients with meibomian gland dysfunction-a randomized, double-masked, sham-controlled clinical trial. Cornea. 2020;39:325–32.

PubMed  Google Scholar 

Leonardi A, Palmigiano A, Mazzola EA, Messina A, Milazzo EMS, Bortolotti M, et al. Identification of human tear fluid biomarkers in vernal keratoconjunctivitis using iTRAQ quantitative proteomics. Allergy: Eur J Allergy Clin Immunol. 2014;69:254–60.

CAS  Google Scholar 

Sembler-Møller ML, Belstrøm D, Locht H, Pedersen AML. Proteomics of saliva, plasma, and salivary gland tissue in Sjögren’s syndrome and non-Sjögren patients identify novel biomarker candidates. J Proteomics. 2020;225:103877.

PubMed  Google Scholar 

Inamoto Y, Valdés-Sanz N, Ogawa Y, Alves M, Berchicci L, Galvin J, et al. Ocular graft-versus-host disease after hematopoietic cell transplantation: Expert review from the Late Effects and Quality of Life Working Committee of the CIBMTR and Transplant Complications Working Party of the EBMT. Bone Marrow Transpl. 2019;54:662–73.

Google Scholar 

Yam GHF, Fuest M, Zhou L, Liu YC, Deng L, Chan ASY, et al. Differential epithelial and stromal protein profiles in cone and non-cone regions of keratoconus corneas. Sci Rep. 2019;9:1–17.

CAS  Google Scholar 

Sharif R, Bak-Nielsen S, Sejersen H, Ding K, Hjortdal J, Karamichos D. Prolactin-induced protein is a novel biomarker for keratoconus. Exp Eye Res. 2019;179:55–63.

PubMed  CAS  Google Scholar 

Soria J, Acera A, Merayo-Lloves J, Durán JA, González N, Rodriguez S, et al. Tear proteome analysis in ocular surface diseases using label-free LC-MS/MS and multiplexed-microarray biomarker validation. Sci Rep. 2017;7:17478.

PubMed  PubMed Central  Google Scholar 

Karimpour-Fard A, Elaine Epperson L, Hunter LE. A survey of computational tools for downstream analysis of proteomic and other omic datasets. Hum Genomics. 2015;9:28.

PubMed  PubMed Central  Google Scholar 

Lancashire LJ, Lemetre C, Ball GR. An introduction to artificial neural networks in bioinformatics—Application to complex microarray and mass spectrometry datasets in cancer studies. Briefings in Bioinformatics. 2009;10:315–29.

PubMed  CAS  Google Scholar 

González N, Iloro I, Durán JA, Elortza F, Suárez T. Evaluation of inter-day and inter-individual variability of tear peptide/protein profiles by MALDI-TOF MS analyses. Mol Vis. 2012;18:1572–82.

PubMed  PubMed Central  Google Scholar 

Vizcaíno JA, Foster JM, Martens L. Proteomics data repositories: Providing a safe haven for your data and acting as a springboard for further research. J Proteomics. 2010;73:2136.

留言 (0)

沒有登入
gif