Open Access
Med Sci (Paris)
Volume 35, Numéro 2, Février 2019
Page(s) 123 - 131
Section M/S Revues
Publié en ligne 18 février 2019
  1. Williams H, Pembroke A. Sniffer dogs in the melanoma clinic ?. Lancet 1989 ; 1 : 734. [Google Scholar]
  2. Pomerantz A, Blachman-Braun R, Galnares-Olalde JA, et al. The possibility of inventing new technologies in the detection of cancer by applying elements of the canine olfactory apparatus. Med Hypotheses 2015 ; 85 : 160–172. [Google Scholar]
  3. Wilson AD. Advances in electronic-nose technologies for the detection of volatile biomarker metabolites in the human breath. Metabolites 2015 ; 5 : 140–163. [CrossRef] [PubMed] [Google Scholar]
  4. Das S, Pal S, Mitra M. Significance of exhaled breath test in clinical diagnosis: a special focus on the detection of diabetes mellitus. J Med Biol Eng 2016 ; 36 : 605–624. [Google Scholar]
  5. Pauwels EKJ, Foray N, Bourguignon MH. Breast cancer induced by X-Ray mammography screening? A review based on recent understanding of low-dose radiobiology. Med Princ Pract 2016 ; 25 : 101–109. [CrossRef] [PubMed] [Google Scholar]
  6. Pirrone F, Albertini M. Olfactory detection of cancer by trained sniffer dogs: a systematic review of the literature. J Vet Behav Clinical Applications and Research 2017 ; 19 : 105–117. [CrossRef] [Google Scholar]
  7. Ehmann R, Boedeker E, Friedrich U, et al. Canine scent detection in the diagnosis of lung cancer: revisiting a puzzling phenomenon. Eur Respir J 2012 ; 39 : 669–676. [CrossRef] [PubMed] [Google Scholar]
  8. Mgode GF, Cox CL, Mwimanzi S, et al. Pediatric tuberculosis detection using trained African giant pouched rats. Pediatr Res 2018 ; 84 : 99–103. [CrossRef] [PubMed] [Google Scholar]
  9. Poling A, Valverde E, Beyene Negussie P, et al. Active tuberculosis detection by pouched rats in, 2014: More than 2,000 new patients found in two countries. JABA 2017 : 50 165–169. [Google Scholar]
  10. Hackner K, Errhalt P, Mueller MR, et al. Canine scent detection for the diagnosis of lung cancer in a screening-like situation. J Breath Res 2016 ; 10 : 046003. [CrossRef] [PubMed] [Google Scholar]
  11. Bijland LR, Bomers MK, Smulders YM. Smelling the diagnosis: a review on the use of scent in diagnosing disease. Neth J Med 2013 ; 71 : 300–307. [Google Scholar]
  12. Wilson A, Baietto M. Applications and advances in electronic-nose technologies. Sensors 2009 ; 9 : 5099. [CrossRef] [Google Scholar]
  13. Mansurova M, Ebert BE, Blank LM, et al. A breath of information: the volatilome. Curr Genet 2018 ; 64 : 959–964. [CrossRef] [PubMed] [Google Scholar]
  14. Davis MD, Fowler SJ, Montpetit AJ. Exhaled breath testing. A tool for the clinician and researcher. Paediatr Resp Rev 2018; doi: 10.1016/j.prrv.2018.05.002. [Google Scholar]
  15. Doran S, Romano A, Hanna G. Optimisation of sampling parameters for standardised exhaled breath sampling. J Breath Res 2018 ; 12 : 01600. [Google Scholar]
  16. Lawal O, Ahmed WM, Nijsen TME, et al. Exhaled breath analysis: a review of breath-taking methods for off-line analysis. Metabolomics 2017 ; 13 : 110. [CrossRef] [PubMed] [Google Scholar]
  17. Konvalina G, Haick H. Effect of humidity on nanoparticle-based chemiresistors: a comparison between synthetic and real-world samples. ACS Appl Mater Interfaces 2012 ; 4 : 317–325. [Google Scholar]
  18. Blanchet L, Smolinska A, Baranska A, et al. Factors that influence the volatile organic compound content in human breath. J Breath Res 2017 ; 11 : 016013. [CrossRef] [PubMed] [Google Scholar]
  19. Coronel Teixeira R, Rodríguez M, Jiménez de Romero N, et al. The potential of a portable, point-of-care electronic nose to diagnose tuberculosis. J Infect 2017; 75 : 441–7. [CrossRef] [PubMed] [Google Scholar]
  20. Tisch U, Haick H. Arrays of chemisensitive monolayer-capped metallic nanoparticles for diagnostic breath testing. Rev Chem Eng 2010 ; 26 : 171–179. [CrossRef] [Google Scholar]
  21. Zhou J, Huang ZA, Kumar U, et al. Review of recent developments in determining volatile organic compounds in exhaled breath as biomarkers for lung cancer diagnosis. Anal Chim Acta 2017 ; 996 : 1–9. [CrossRef] [PubMed] [Google Scholar]
  22. Phillips M, Cataneo RN, Cruz-Ramos JA, et al. Prediction of breast cancer risk with volatile biomarkers in breath. Breast Cancer Res Treat 2018 ; 170 : 343–350. [CrossRef] [PubMed] [Google Scholar]
  23. Phillips M, Beatty JD, Cataneo RN, et al. Rapid point-of-care breath test for biomarkers of breast cancer and abnormal mammograms. PLoS One 2014 ; 9 : e90226. [CrossRef] [PubMed] [Google Scholar]
  24. Tisch U, Schlesinger I, Ionescu R, et al. Detection of Alzheimer’s and Parkinson’s disease from exhaled breath using nanomaterial-based sensors. Nanomedicine 2013 ; 8 : 43–56. [Google Scholar]
  25. Bach JP, Gold M, Menge D, et al. Measuring compounds in exhaled air to detect Alzheimer’s disease and Parkinson’s disease. PLoS One 2015 ; 10 : e0132227. [CrossRef] [PubMed] [Google Scholar]
  26. Mazzatenta A, Pokorski M, Sartucci F, et al. Volatile organic compounds (VOCs) fingerprint of Alzheimer’s disease. Respir Physiol Neurobiol 2015 ; 209 : 81–84. [CrossRef] [PubMed] [Google Scholar]
  27. Broza YY, Har-Shai L, Jeries R, et al. Exhaled breath markers for nonimaging and noninvasive measures for detection of multiple sclerosis. ACS Chem Neurosci 2017 ; 8 : 2402–2413. [CrossRef] [PubMed] [Google Scholar]
  28. Amy Scott-Thomas, Syhre M, Pattemore P, et al. 2-Aminoacetophenone as a potential breath biomarker for Pseudomonas aeruginosa in the cystic fibrosis lung. BMC Pulm Med 2010 ; 10 : 56. [CrossRef] [PubMed] [Google Scholar]
  29. Metters JP, Kampouris DK, Banks CE. Electrochemistry provides a point-of-care approach for the marker indicative of Pseudomonas aeruginosa infection of cystic fibrosis patients. Analyst 2014 ; 139 : 3999–4004. [CrossRef] [PubMed] [Google Scholar]
  30. Nakhleh MK, Amal H, Jeries R, et al. Diagnosis and classification of 17 diseases from 1404 subjects via pattern analysis of exhaled molecules. ACS Nano 2017 ; 11 : 112–125. [Google Scholar]
  31. Marenco L, Wang R, McDougal R, et al. ORDB, HORDE, ODORactor and other on-line knowledge resources of olfactory receptor-odorant interactions. Database (Oxford) 2016; 2016 : baw132. [CrossRef] [PubMed] [Google Scholar]
  32. Launay G, Teletchea S, Wade F, et al. Automatic modeling of mammalian olfactory receptors and docking of odorants. PEDS 2012 ; 25 : 377–386. [Google Scholar]
  33. Park TH. Bioelectronic nose. Integration of biotechnology and nanotechnology. Seoul : Springer, 2014 : 290 p. [Google Scholar]
  34. Dung TT, Oh Y, Choi SJ, et al. Applications and advances in bioelectronic noses for odour sensing. Sensors 2018 ; 18 : 103. [CrossRef] [Google Scholar]
  35. Benilova IV, Minic Vidic J, Pajot-Augy E, et al. Electrochemical study of human olfactory receptor OR 17–40 stimulation by odorants in solution. Mat Sci Eng C 2008 ; 28 : 633–639. [CrossRef] [Google Scholar]
  36. Manai R, Zadeh-Habchi M, Kamouni-Belghiti D, et al. Diamond micro-cantilevers as transducers for olfactory receptors-based biosensors: application to the receptors M71 and OR7D4. Sens Actuators B Chem 2017 ; 238 : 1199–1206. [Google Scholar]
  37. Lee SH, Jin HJ, Song HS, et al. Bioelectronic nose with high sensitivity and selectivity using chemically functionalized carbon nanotube combined with human olfactory receptor. J Biotechnol 2012 ; 157 : 467–472. [CrossRef] [PubMed] [Google Scholar]
  38. Lee SH, Kwon OS, Song HS, et al. Mimicking the human smell sensing mechanism with an artificial nose platform. Biomaterials 2012 ; 33 : 1722–1729. [CrossRef] [PubMed] [Google Scholar]
  39. Kwon OS, Song HS, Park SJ, et al. An ultrasensitive, selective, multiplexed superbioelectronic nose that mimics the human sense of smell. Nano Lett 2015 ; 15 : 6559–6567. [CrossRef] [PubMed] [Google Scholar]
  40. Park SJ, Kwon Oh S, Lee SH, et al. Ultrasensitive flexible graphene-based field-effect transistor (FET)-type bioelectronic nose. Nano Lett 2012 ; 12 : 5082–5090. [CrossRef] [PubMed] [Google Scholar]
  41. Mouret A, Lledo PM. Comment le nez se connecte au cerveau. Med Sci (Paris) 2007 ; 23 : 252–255. [CrossRef] [EDP Sciences] [PubMed] [Google Scholar]

Les statistiques affichées correspondent au cumul d'une part des vues des résumés de l'article et d'autre part des vues et téléchargements de l'article plein-texte (PDF, Full-HTML, ePub... selon les formats disponibles) sur la platefome Vision4Press.

Les statistiques sont disponibles avec un délai de 48 à 96 heures et sont mises à jour quotidiennement en semaine.

Le chargement des statistiques peut être long.