Correlation analysis on data sets to detect infectious agents in the airways by ion mobility spectrometry of exhaled breath
Abstract A correlation analysis of peaks found in IMS-Chromatograms was carried out to show the potential of the method in clinical applications. As an example, the data of exhaled breath of patients suffering infections of Pseudomonas were compared to healthy non-smokers. Using a rank sum calculati...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Maddula, Sasidhar [verfasserIn] Rabis, Thomas [verfasserIn] Sommerwerck, Urte [verfasserIn] Anhenn, Olaf [verfasserIn] Darwiche, Kaid [verfasserIn] Freitag, Lutz [verfasserIn] Teschler, Helmut [verfasserIn] Baumbach, Jörg Ingo [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2011 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: International journal for ion mobility spectrometry - Berlin : Springer, 2008, 14(2011), 4 vom: 11. Aug., Seite 197-206 |
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| Übergeordnetes Werk: |
volume:14 ; year:2011 ; number:4 ; day:11 ; month:08 ; pages:197-206 |
| Links: |
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| DOI / URN: |
10.1007/s12127-011-0076-7 |
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| Katalog-ID: |
SPR024421960 |
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| 520 | |a Abstract A correlation analysis of peaks found in IMS-Chromatograms was carried out to show the potential of the method in clinical applications. As an example, the data of exhaled breath of patients suffering infections of Pseudomonas were compared to healthy non-smokers. Using a rank sum calculation and providing a correlation table of all peaks found, delivers the basis for visualisation of highest ranked analytes. In addition, a consideration of positive and negative correlated peaks could support sub-grouping, if present. A set of signals could be found for discriminating the two groups of patients using MCC-IMS. Investigations of exhaled breath using ion mobility spectrometry seems to provide a promising means for the non-invasive identification of patients which are colonized or infected with bacteria such as Pseudomonas aeruginosa. | ||
| 650 | 4 | |a Correlation analysis |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a MCC/IMS |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Breath analysis |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Exhale |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Data mining |7 (dpeaa)DE-He213 | |
| 700 | 1 | |a Rabis, Thomas |e verfasserin |4 aut | |
| 700 | 1 | |a Sommerwerck, Urte |e verfasserin |4 aut | |
| 700 | 1 | |a Anhenn, Olaf |e verfasserin |4 aut | |
| 700 | 1 | |a Darwiche, Kaid |e verfasserin |4 aut | |
| 700 | 1 | |a Freitag, Lutz |e verfasserin |4 aut | |
| 700 | 1 | |a Teschler, Helmut |e verfasserin |4 aut | |
| 700 | 1 | |a Baumbach, Jörg Ingo |e verfasserin |4 aut | |
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10.1007/s12127-011-0076-7 doi (DE-627)SPR024421960 (SPR)s12127-011-0076-7-e DE-627 ger DE-627 rakwb eng 530 ASE 33.00 bkl Maddula, Sasidhar verfasserin aut Correlation analysis on data sets to detect infectious agents in the airways by ion mobility spectrometry of exhaled breath 2011 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract A correlation analysis of peaks found in IMS-Chromatograms was carried out to show the potential of the method in clinical applications. As an example, the data of exhaled breath of patients suffering infections of Pseudomonas were compared to healthy non-smokers. Using a rank sum calculation and providing a correlation table of all peaks found, delivers the basis for visualisation of highest ranked analytes. In addition, a consideration of positive and negative correlated peaks could support sub-grouping, if present. A set of signals could be found for discriminating the two groups of patients using MCC-IMS. Investigations of exhaled breath using ion mobility spectrometry seems to provide a promising means for the non-invasive identification of patients which are colonized or infected with bacteria such as Pseudomonas aeruginosa. Correlation analysis (dpeaa)DE-He213 MCC/IMS (dpeaa)DE-He213 Breath analysis (dpeaa)DE-He213 Exhale (dpeaa)DE-He213 Data mining (dpeaa)DE-He213 Rabis, Thomas verfasserin aut Sommerwerck, Urte verfasserin aut Anhenn, Olaf verfasserin aut Darwiche, Kaid verfasserin aut Freitag, Lutz verfasserin aut Teschler, Helmut verfasserin aut Baumbach, Jörg Ingo verfasserin aut Enthalten in International journal for ion mobility spectrometry Berlin : Springer, 2008 14(2011), 4 vom: 11. Aug., Seite 197-206 (DE-627)564758841 (DE-600)2423554-4 1865-4584 nnns volume:14 year:2011 number:4 day:11 month:08 pages:197-206 https://dx.doi.org/10.1007/s12127-011-0076-7 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 33.00 ASE AR 14 2011 4 11 08 197-206 |
| spelling |
10.1007/s12127-011-0076-7 doi (DE-627)SPR024421960 (SPR)s12127-011-0076-7-e DE-627 ger DE-627 rakwb eng 530 ASE 33.00 bkl Maddula, Sasidhar verfasserin aut Correlation analysis on data sets to detect infectious agents in the airways by ion mobility spectrometry of exhaled breath 2011 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract A correlation analysis of peaks found in IMS-Chromatograms was carried out to show the potential of the method in clinical applications. As an example, the data of exhaled breath of patients suffering infections of Pseudomonas were compared to healthy non-smokers. Using a rank sum calculation and providing a correlation table of all peaks found, delivers the basis for visualisation of highest ranked analytes. In addition, a consideration of positive and negative correlated peaks could support sub-grouping, if present. A set of signals could be found for discriminating the two groups of patients using MCC-IMS. Investigations of exhaled breath using ion mobility spectrometry seems to provide a promising means for the non-invasive identification of patients which are colonized or infected with bacteria such as Pseudomonas aeruginosa. Correlation analysis (dpeaa)DE-He213 MCC/IMS (dpeaa)DE-He213 Breath analysis (dpeaa)DE-He213 Exhale (dpeaa)DE-He213 Data mining (dpeaa)DE-He213 Rabis, Thomas verfasserin aut Sommerwerck, Urte verfasserin aut Anhenn, Olaf verfasserin aut Darwiche, Kaid verfasserin aut Freitag, Lutz verfasserin aut Teschler, Helmut verfasserin aut Baumbach, Jörg Ingo verfasserin aut Enthalten in International journal for ion mobility spectrometry Berlin : Springer, 2008 14(2011), 4 vom: 11. Aug., Seite 197-206 (DE-627)564758841 (DE-600)2423554-4 1865-4584 nnns volume:14 year:2011 number:4 day:11 month:08 pages:197-206 https://dx.doi.org/10.1007/s12127-011-0076-7 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 33.00 ASE AR 14 2011 4 11 08 197-206 |
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10.1007/s12127-011-0076-7 doi (DE-627)SPR024421960 (SPR)s12127-011-0076-7-e DE-627 ger DE-627 rakwb eng 530 ASE 33.00 bkl Maddula, Sasidhar verfasserin aut Correlation analysis on data sets to detect infectious agents in the airways by ion mobility spectrometry of exhaled breath 2011 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract A correlation analysis of peaks found in IMS-Chromatograms was carried out to show the potential of the method in clinical applications. As an example, the data of exhaled breath of patients suffering infections of Pseudomonas were compared to healthy non-smokers. Using a rank sum calculation and providing a correlation table of all peaks found, delivers the basis for visualisation of highest ranked analytes. In addition, a consideration of positive and negative correlated peaks could support sub-grouping, if present. A set of signals could be found for discriminating the two groups of patients using MCC-IMS. Investigations of exhaled breath using ion mobility spectrometry seems to provide a promising means for the non-invasive identification of patients which are colonized or infected with bacteria such as Pseudomonas aeruginosa. Correlation analysis (dpeaa)DE-He213 MCC/IMS (dpeaa)DE-He213 Breath analysis (dpeaa)DE-He213 Exhale (dpeaa)DE-He213 Data mining (dpeaa)DE-He213 Rabis, Thomas verfasserin aut Sommerwerck, Urte verfasserin aut Anhenn, Olaf verfasserin aut Darwiche, Kaid verfasserin aut Freitag, Lutz verfasserin aut Teschler, Helmut verfasserin aut Baumbach, Jörg Ingo verfasserin aut Enthalten in International journal for ion mobility spectrometry Berlin : Springer, 2008 14(2011), 4 vom: 11. Aug., Seite 197-206 (DE-627)564758841 (DE-600)2423554-4 1865-4584 nnns volume:14 year:2011 number:4 day:11 month:08 pages:197-206 https://dx.doi.org/10.1007/s12127-011-0076-7 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 33.00 ASE AR 14 2011 4 11 08 197-206 |
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10.1007/s12127-011-0076-7 doi (DE-627)SPR024421960 (SPR)s12127-011-0076-7-e DE-627 ger DE-627 rakwb eng 530 ASE 33.00 bkl Maddula, Sasidhar verfasserin aut Correlation analysis on data sets to detect infectious agents in the airways by ion mobility spectrometry of exhaled breath 2011 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract A correlation analysis of peaks found in IMS-Chromatograms was carried out to show the potential of the method in clinical applications. As an example, the data of exhaled breath of patients suffering infections of Pseudomonas were compared to healthy non-smokers. Using a rank sum calculation and providing a correlation table of all peaks found, delivers the basis for visualisation of highest ranked analytes. In addition, a consideration of positive and negative correlated peaks could support sub-grouping, if present. A set of signals could be found for discriminating the two groups of patients using MCC-IMS. Investigations of exhaled breath using ion mobility spectrometry seems to provide a promising means for the non-invasive identification of patients which are colonized or infected with bacteria such as Pseudomonas aeruginosa. Correlation analysis (dpeaa)DE-He213 MCC/IMS (dpeaa)DE-He213 Breath analysis (dpeaa)DE-He213 Exhale (dpeaa)DE-He213 Data mining (dpeaa)DE-He213 Rabis, Thomas verfasserin aut Sommerwerck, Urte verfasserin aut Anhenn, Olaf verfasserin aut Darwiche, Kaid verfasserin aut Freitag, Lutz verfasserin aut Teschler, Helmut verfasserin aut Baumbach, Jörg Ingo verfasserin aut Enthalten in International journal for ion mobility spectrometry Berlin : Springer, 2008 14(2011), 4 vom: 11. Aug., Seite 197-206 (DE-627)564758841 (DE-600)2423554-4 1865-4584 nnns volume:14 year:2011 number:4 day:11 month:08 pages:197-206 https://dx.doi.org/10.1007/s12127-011-0076-7 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 33.00 ASE AR 14 2011 4 11 08 197-206 |
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10.1007/s12127-011-0076-7 doi (DE-627)SPR024421960 (SPR)s12127-011-0076-7-e DE-627 ger DE-627 rakwb eng 530 ASE 33.00 bkl Maddula, Sasidhar verfasserin aut Correlation analysis on data sets to detect infectious agents in the airways by ion mobility spectrometry of exhaled breath 2011 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract A correlation analysis of peaks found in IMS-Chromatograms was carried out to show the potential of the method in clinical applications. As an example, the data of exhaled breath of patients suffering infections of Pseudomonas were compared to healthy non-smokers. Using a rank sum calculation and providing a correlation table of all peaks found, delivers the basis for visualisation of highest ranked analytes. In addition, a consideration of positive and negative correlated peaks could support sub-grouping, if present. A set of signals could be found for discriminating the two groups of patients using MCC-IMS. Investigations of exhaled breath using ion mobility spectrometry seems to provide a promising means for the non-invasive identification of patients which are colonized or infected with bacteria such as Pseudomonas aeruginosa. Correlation analysis (dpeaa)DE-He213 MCC/IMS (dpeaa)DE-He213 Breath analysis (dpeaa)DE-He213 Exhale (dpeaa)DE-He213 Data mining (dpeaa)DE-He213 Rabis, Thomas verfasserin aut Sommerwerck, Urte verfasserin aut Anhenn, Olaf verfasserin aut Darwiche, Kaid verfasserin aut Freitag, Lutz verfasserin aut Teschler, Helmut verfasserin aut Baumbach, Jörg Ingo verfasserin aut Enthalten in International journal for ion mobility spectrometry Berlin : Springer, 2008 14(2011), 4 vom: 11. Aug., Seite 197-206 (DE-627)564758841 (DE-600)2423554-4 1865-4584 nnns volume:14 year:2011 number:4 day:11 month:08 pages:197-206 https://dx.doi.org/10.1007/s12127-011-0076-7 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 33.00 ASE AR 14 2011 4 11 08 197-206 |
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English |
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Enthalten in International journal for ion mobility spectrometry 14(2011), 4 vom: 11. Aug., Seite 197-206 volume:14 year:2011 number:4 day:11 month:08 pages:197-206 |
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Enthalten in International journal for ion mobility spectrometry 14(2011), 4 vom: 11. Aug., Seite 197-206 volume:14 year:2011 number:4 day:11 month:08 pages:197-206 |
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Correlation analysis MCC/IMS Breath analysis Exhale Data mining |
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International journal for ion mobility spectrometry |
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Maddula, Sasidhar @@aut@@ Rabis, Thomas @@aut@@ Sommerwerck, Urte @@aut@@ Anhenn, Olaf @@aut@@ Darwiche, Kaid @@aut@@ Freitag, Lutz @@aut@@ Teschler, Helmut @@aut@@ Baumbach, Jörg Ingo @@aut@@ |
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2011-08-11T00:00:00Z |
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Maddula, Sasidhar |
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Maddula, Sasidhar ddc 530 bkl 33.00 misc Correlation analysis misc MCC/IMS misc Breath analysis misc Exhale misc Data mining Correlation analysis on data sets to detect infectious agents in the airways by ion mobility spectrometry of exhaled breath |
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530 ASE 33.00 bkl Correlation analysis on data sets to detect infectious agents in the airways by ion mobility spectrometry of exhaled breath Correlation analysis (dpeaa)DE-He213 MCC/IMS (dpeaa)DE-He213 Breath analysis (dpeaa)DE-He213 Exhale (dpeaa)DE-He213 Data mining (dpeaa)DE-He213 |
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Maddula, Sasidhar Rabis, Thomas Sommerwerck, Urte Anhenn, Olaf Darwiche, Kaid Freitag, Lutz Teschler, Helmut Baumbach, Jörg Ingo |
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correlation analysis on data sets to detect infectious agents in the airways by ion mobility spectrometry of exhaled breath |
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Correlation analysis on data sets to detect infectious agents in the airways by ion mobility spectrometry of exhaled breath |
| abstract |
Abstract A correlation analysis of peaks found in IMS-Chromatograms was carried out to show the potential of the method in clinical applications. As an example, the data of exhaled breath of patients suffering infections of Pseudomonas were compared to healthy non-smokers. Using a rank sum calculation and providing a correlation table of all peaks found, delivers the basis for visualisation of highest ranked analytes. In addition, a consideration of positive and negative correlated peaks could support sub-grouping, if present. A set of signals could be found for discriminating the two groups of patients using MCC-IMS. Investigations of exhaled breath using ion mobility spectrometry seems to provide a promising means for the non-invasive identification of patients which are colonized or infected with bacteria such as Pseudomonas aeruginosa. |
| abstractGer |
Abstract A correlation analysis of peaks found in IMS-Chromatograms was carried out to show the potential of the method in clinical applications. As an example, the data of exhaled breath of patients suffering infections of Pseudomonas were compared to healthy non-smokers. Using a rank sum calculation and providing a correlation table of all peaks found, delivers the basis for visualisation of highest ranked analytes. In addition, a consideration of positive and negative correlated peaks could support sub-grouping, if present. A set of signals could be found for discriminating the two groups of patients using MCC-IMS. Investigations of exhaled breath using ion mobility spectrometry seems to provide a promising means for the non-invasive identification of patients which are colonized or infected with bacteria such as Pseudomonas aeruginosa. |
| abstract_unstemmed |
Abstract A correlation analysis of peaks found in IMS-Chromatograms was carried out to show the potential of the method in clinical applications. As an example, the data of exhaled breath of patients suffering infections of Pseudomonas were compared to healthy non-smokers. Using a rank sum calculation and providing a correlation table of all peaks found, delivers the basis for visualisation of highest ranked analytes. In addition, a consideration of positive and negative correlated peaks could support sub-grouping, if present. A set of signals could be found for discriminating the two groups of patients using MCC-IMS. Investigations of exhaled breath using ion mobility spectrometry seems to provide a promising means for the non-invasive identification of patients which are colonized or infected with bacteria such as Pseudomonas aeruginosa. |
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| container_issue |
4 |
| title_short |
Correlation analysis on data sets to detect infectious agents in the airways by ion mobility spectrometry of exhaled breath |
| url |
https://dx.doi.org/10.1007/s12127-011-0076-7 |
| remote_bool |
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| author2 |
Rabis, Thomas Sommerwerck, Urte Anhenn, Olaf Darwiche, Kaid Freitag, Lutz Teschler, Helmut Baumbach, Jörg Ingo |
| author2Str |
Rabis, Thomas Sommerwerck, Urte Anhenn, Olaf Darwiche, Kaid Freitag, Lutz Teschler, Helmut Baumbach, Jörg Ingo |
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| doi_str |
10.1007/s12127-011-0076-7 |
| up_date |
2024-07-04T00:52:43.198Z |
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| score |
7.3989286 |