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For: Johansson H, Rydnert F, Kühnl J, Schepky A, Borrebaeck C, Lindstedt M. Genomic allergen rapid detection in-house validation--a proof of concept. Toxicol Sci 2014;139:362-70. [PMID: 24675087 DOI: 10.1093/toxsci/kfu046] [Cited by in Crossref: 25] [Cited by in F6Publishing: 25] [Article Influence: 2.8] [Reference Citation Analysis]
Number Citing Articles
1 Pemberton MA, Kimber I. Propylene glycol, skin sensitisation and allergic contact dermatitis: A scientific and regulatory conundrum. Regul Toxicol Pharmacol 2023;138:105341. [PMID: 36702195 DOI: 10.1016/j.yrtph.2023.105341] [Reference Citation Analysis]
2 Lustig M, Schwartz D, Bryant R, Gefen A. A machine learning algorithm for early detection of heel deep tissue injuries based on a daily history of sub-epidermal moisture measurements. Int Wound J 2022. [PMID: 35019208 DOI: 10.1111/iwj.13728] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
3 Masinja W, Elliott C, Modi S, Enoch SJ, Cronin MTD, McInnes EF, Currie RA. Comparison of the predictive nature of the Genomic Allergen Rapid Detection (GARD) assay with mammalian assays in determining the skin sensitisation potential of agrochemical active ingredients. Toxicol In Vitro 2021;70:105017. [PMID: 33038465 DOI: 10.1016/j.tiv.2020.105017] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 1.0] [Reference Citation Analysis]
4 Johansson H, Gradin R, Johansson A, Adriaens E, Edwards A, Zuckerstätter V, Jerre A, Burleson F, Gehrke H, Roggen EL. Validation of the GARD™skin Assay for Assessment of Chemical Skin Sensitizers: Ring Trial Results of Predictive Performance and Reproducibility. Toxicol Sci 2019;170:374-81. [PMID: 31099396 DOI: 10.1093/toxsci/kfz108] [Cited by in Crossref: 13] [Cited by in F6Publishing: 14] [Article Influence: 4.3] [Reference Citation Analysis]
5 Gomolin A, Netchiporouk E, Gniadecki R, Litvinov IV. Artificial Intelligence Applications in Dermatology: Where Do We Stand? Front Med (Lausanne) 2020;7:100. [PMID: 32296706 DOI: 10.3389/fmed.2020.00100] [Cited by in Crossref: 48] [Cited by in F6Publishing: 50] [Article Influence: 16.0] [Reference Citation Analysis]
6 Thomsen K, Iversen L, Titlestad TL, Winther O. Systematic review of machine learning for diagnosis and prognosis in dermatology. J Dermatolog Treat 2020;31:496-510. [PMID: 31625775 DOI: 10.1080/09546634.2019.1682500] [Cited by in Crossref: 32] [Cited by in F6Publishing: 21] [Article Influence: 8.0] [Reference Citation Analysis]
7 Hardwick RN, Betts CJ, Whritenour J, Sura R, Thamsen M, Kaufman EH, Fabre K. Drug-induced skin toxicity: gaps in preclinical testing cascade as opportunities for complex in vitro models and assays. Lab Chip 2020;20:199-214. [PMID: 31598618 DOI: 10.1039/c9lc00519f] [Cited by in Crossref: 17] [Cited by in F6Publishing: 19] [Article Influence: 4.3] [Reference Citation Analysis]
8 Forreryd A, Johansson H, Lindberg T, Zeller KS, Lindstedt M. Letter to the editor regarding the article “Is a combination of assays really needed for non-animal prediction of skin sensitization potential? Performance of the GARD™ (Genomic Allergen Rapid Detection) assay in comparison with OECD guideline assays alone and in combination.”. Regulatory Toxicology and Pharmacology 2019;107:104439. [DOI: 10.1016/j.yrtph.2019.104439] [Cited by in Crossref: 1] [Article Influence: 0.3] [Reference Citation Analysis]
9 Naumann BD, Arnold SF. Setting surface wipe limits for skin sensitizers. Toxicol Ind Health 2019;35:614-25. [PMID: 31547787 DOI: 10.1177/0748233719875365] [Cited by in Crossref: 4] [Cited by in F6Publishing: 4] [Article Influence: 1.0] [Reference Citation Analysis]
10 de Ávila RI, Lindstedt M, Valadares MC. The 21st Century movement within the area of skin sensitization assessment: From the animal context towards current human-relevant in vitro solutions. Regul Toxicol Pharmacol 2019;108:104445. [PMID: 31430506 DOI: 10.1016/j.yrtph.2019.104445] [Cited by in Crossref: 10] [Cited by in F6Publishing: 10] [Article Influence: 2.5] [Reference Citation Analysis]
11 de Ávila RI, Veloso DFMC, Teixeira GC, Rodrigues TL, Lindberg T, Lindstedt M, Fonseca SG, Lima EM, Valadares MC. Evaluation of in vitro testing strategies for hazard assessment of the skin sensitization potential of “real‐life” mixtures: The case of henna‐based hair‐colouring products containing p ‐phenylenediamine. Contact Dermatitis 2019;81:194-209. [DOI: 10.1111/cod.13294] [Cited by in Crossref: 7] [Cited by in F6Publishing: 8] [Article Influence: 1.8] [Reference Citation Analysis]
12 Lindberg T, Forreryd A, Bergendorff O, Lindstedt M, Zeller KS. In vitro assessment of mechanistic events induced by structurally related chemical rubber sensitizers. Toxicol In Vitro 2019;60:144-53. [PMID: 31082492 DOI: 10.1016/j.tiv.2019.05.006] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.3] [Reference Citation Analysis]
13 Wilm A, Kühnl J, Kirchmair J. Computational approaches for skin sensitization prediction. Critical Reviews in Toxicology 2018;48:738-60. [DOI: 10.1080/10408444.2018.1528207] [Cited by in Crossref: 22] [Cited by in F6Publishing: 20] [Article Influence: 4.4] [Reference Citation Analysis]
14 Roberts DW. Is a combination of assays really needed for non-animal prediction of skin sensitization potential? Performance of the GARD™ (Genomic Allergen Rapid Detection) assay in comparison with OECD guideline assays alone and in combination. Regulatory Toxicology and Pharmacology 2018;98:155-60. [DOI: 10.1016/j.yrtph.2018.07.014] [Cited by in Crossref: 14] [Cited by in F6Publishing: 14] [Article Influence: 2.8] [Reference Citation Analysis]
15 Forreryd A, Norinder U, Lindberg T, Lindstedt M. Predicting skin sensitizers with confidence - Using conformal prediction to determine applicability domain of GARD. Toxicol In Vitro 2018;48:179-87. [PMID: 29374571 DOI: 10.1016/j.tiv.2018.01.021] [Cited by in Crossref: 15] [Cited by in F6Publishing: 15] [Article Influence: 3.0] [Reference Citation Analysis]
16 Hirota M, Ashikaga T, Kouzuki H. Development of an artificial neural network model for risk assessment of skin sensitization using human cell line activation test, direct peptide reactivity assay, KeratinoSens™ and in silico structure alert parameter: ANN model for skin sensitization risk using h-CLAT/DPRA/KeratinoSens™. J Appl Toxicol 2018;38:514-26. [DOI: 10.1002/jat.3558] [Cited by in Crossref: 21] [Cited by in F6Publishing: 21] [Article Influence: 3.5] [Reference Citation Analysis]
17 Zeller KS, Johansson H, Lund TØ, Kristensen NN, Roggen EL, Lindstedt M. An alternative biomarker-based approach for the prediction of proteins known to sensitize the respiratory tract. Toxicol In Vitro 2018;46:155-62. [PMID: 29017774 DOI: 10.1016/j.tiv.2017.09.029] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 0.3] [Reference Citation Analysis]
18 Casati S. Contact hypersensitivity: Integrated approaches to testing and assessment. Current Opinion in Toxicology 2017;5:1-5. [DOI: 10.1016/j.cotox.2017.05.004] [Cited by in Crossref: 4] [Cited by in F6Publishing: 3] [Article Influence: 0.7] [Reference Citation Analysis]
19 Lindstedt M, Zeller KS, Johansson H, Borrebaeck C. GARD: Genomic Allergen Rapid Detection. Alternatives for Dermal Toxicity Testing 2017. [DOI: 10.1007/978-3-319-50353-0_27] [Reference Citation Analysis]
20 Forreryd A, Zeller KS, Lindberg T, Johansson H, Lindstedt M. From genome-wide arrays to tailor-made biomarker readout – Progress towards routine analysis of skin sensitizing chemicals with GARD. Toxicology in Vitro 2016;37:178-88. [DOI: 10.1016/j.tiv.2016.09.013] [Cited by in Crossref: 41] [Cited by in F6Publishing: 51] [Article Influence: 5.9] [Reference Citation Analysis]
21 Natsch A, Emter R. Reaction Chemistry to Characterize the Molecular Initiating Event in Skin Sensitization: A Journey to Be Continued. Chem Res Toxicol 2017;30:315-31. [DOI: 10.1021/acs.chemrestox.6b00365] [Cited by in Crossref: 15] [Cited by in F6Publishing: 15] [Article Influence: 2.1] [Reference Citation Analysis]
22 Ezendam J, Braakhuis HM, Vandebriel RJ. State of the art in non-animal approaches for skin sensitization testing: from individual test methods towards testing strategies. Arch Toxicol 2016;90:2861-83. [PMID: 27629427 DOI: 10.1007/s00204-016-1842-4] [Cited by in Crossref: 75] [Cited by in F6Publishing: 77] [Article Influence: 10.7] [Reference Citation Analysis]
23 Dumont C, Barroso J, Matys I, Worth A, Casati S. Analysis of the Local Lymph Node Assay (LLNA) variability for assessing the prediction of skin sensitisation potential and potency of chemicals with non-animal approaches. Toxicol In Vitro 2016;34:220-8. [PMID: 27085510 DOI: 10.1016/j.tiv.2016.04.008] [Cited by in Crossref: 37] [Cited by in F6Publishing: 37] [Article Influence: 5.3] [Reference Citation Analysis]
24 Pallocca G, Grinberg M, Henry M, Frickey T, Hengstler JG, Waldmann T, Sachinidis A, Rahnenführer J, Leist M. Identification of transcriptome signatures and biomarkers specific for potential developmental toxicants inhibiting human neural crest cell migration. Arch Toxicol 2016;90:159-80. [PMID: 26705709 DOI: 10.1007/s00204-015-1658-7] [Cited by in Crossref: 31] [Cited by in F6Publishing: 30] [Article Influence: 3.9] [Reference Citation Analysis]
25 Forreryd A, Johansson H, Albrekt AS, Borrebaeck CA, Lindstedt M. Prediction of chemical respiratory sensitizers using GARD, a novel in vitro assay based on a genomic biomarker signature. PLoS One 2015;10:e0118808. [PMID: 25760038 DOI: 10.1371/journal.pone.0118808] [Cited by in Crossref: 24] [Cited by in F6Publishing: 27] [Article Influence: 3.0] [Reference Citation Analysis]
26 Lee S, Dong DX, Jindal R, Maguire T, Mitra B, Schloss R, Yarmush M. Predicting full thickness skin sensitization using a support vector machine. Toxicol In Vitro 2014;28:1413-23. [PMID: 25025180 DOI: 10.1016/j.tiv.2014.07.002] [Cited by in Crossref: 7] [Cited by in F6Publishing: 7] [Article Influence: 0.8] [Reference Citation Analysis]