Quantitative Evaluation of Links between Inflammatory Markers and Alzheimer’s Disease
Objectives. To obtain quantitative assessment of the link between Alzheimer’s disease (AD) and inflammatory markers such as the enzymatic activity of leukocyte elastase (LE) and the functional activity of α1-proteinase inhibitor ($ α_{1} $-PI) using a logistical regression model, which was then appl...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Simonov, A. N. [verfasserIn] Klyushnik, T. P. [verfasserIn] Androsova, L. V. [verfasserIn] Mikhaylova, N. M. [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2019 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: Neuroscience and behavioral physiology - New York, NY : Consultants Bureau, 1967, 49(2019), 7 vom: 13. Aug., Seite 910-915 |
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| Übergeordnetes Werk: |
volume:49 ; year:2019 ; number:7 ; day:13 ; month:08 ; pages:910-915 |
| Links: |
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| DOI / URN: |
10.1007/s11055-019-00818-9 |
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| Katalog-ID: |
SPR016128044 |
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| 100 | 1 | |a Simonov, A. N. |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Quantitative Evaluation of Links between Inflammatory Markers and Alzheimer’s Disease |
| 264 | 1 | |c 2019 | |
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| 520 | |a Objectives. To obtain quantitative assessment of the link between Alzheimer’s disease (AD) and inflammatory markers such as the enzymatic activity of leukocyte elastase (LE) and the functional activity of α1-proteinase inhibitor ($ α_{1} $-PI) using a logistical regression model, which was then applied to predicting the probability of AD in patients with mild cognitive impairment syndrome (MCI). Materials and methods. Mathematical analysis was run using a database containing the results of measurements of these immunological parameters (enzymatic activity of LE and functional activity of $ α_{1} $-PI) in the plasma of 91 patients with verified diagnoses of AD on in-patient or out-patient treatment and 37 healthy subjects of comparable age without clinical indications of somatic or mental pathology. Results and conclusions. A logistical regression model was constructed linking LE and $ α_{1} $-PI with the probability that a patient has AD. The model has favorable statistical properties and high predictive effectiveness. These results allow individual patients’ LE and $ α_{1} $-PI values to be used to generate quantitative probabilities that patients have AD. | ||
| 650 | 4 | |a Alzheimer’s disease |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a mild cognitive impairment |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a leukocyte elastase |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a α |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a -proteinase inhibitor |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a logistical regression |7 (dpeaa)DE-He213 | |
| 700 | 1 | |a Klyushnik, T. P. |e verfasserin |4 aut | |
| 700 | 1 | |a Androsova, L. V. |e verfasserin |4 aut | |
| 700 | 1 | |a Mikhaylova, N. M. |e verfasserin |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t Neuroscience and behavioral physiology |d New York, NY : Consultants Bureau, 1967 |g 49(2019), 7 vom: 13. Aug., Seite 910-915 |w (DE-627)325573425 |w (DE-600)2037678-9 |x 1573-899X |7 nnns |
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10.1007/s11055-019-00818-9 doi (DE-627)SPR016128044 (SPR)s11055-019-00818-9-e DE-627 ger DE-627 rakwb eng 570 610 ASE 44.90 bkl Simonov, A. N. verfasserin aut Quantitative Evaluation of Links between Inflammatory Markers and Alzheimer’s Disease 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Objectives. To obtain quantitative assessment of the link between Alzheimer’s disease (AD) and inflammatory markers such as the enzymatic activity of leukocyte elastase (LE) and the functional activity of α1-proteinase inhibitor ($ α_{1} $-PI) using a logistical regression model, which was then applied to predicting the probability of AD in patients with mild cognitive impairment syndrome (MCI). Materials and methods. Mathematical analysis was run using a database containing the results of measurements of these immunological parameters (enzymatic activity of LE and functional activity of $ α_{1} $-PI) in the plasma of 91 patients with verified diagnoses of AD on in-patient or out-patient treatment and 37 healthy subjects of comparable age without clinical indications of somatic or mental pathology. Results and conclusions. A logistical regression model was constructed linking LE and $ α_{1} $-PI with the probability that a patient has AD. The model has favorable statistical properties and high predictive effectiveness. These results allow individual patients’ LE and $ α_{1} $-PI values to be used to generate quantitative probabilities that patients have AD. Alzheimer’s disease (dpeaa)DE-He213 mild cognitive impairment (dpeaa)DE-He213 leukocyte elastase (dpeaa)DE-He213 α (dpeaa)DE-He213 -proteinase inhibitor (dpeaa)DE-He213 logistical regression (dpeaa)DE-He213 Klyushnik, T. P. verfasserin aut Androsova, L. V. verfasserin aut Mikhaylova, N. M. verfasserin aut Enthalten in Neuroscience and behavioral physiology New York, NY : Consultants Bureau, 1967 49(2019), 7 vom: 13. Aug., Seite 910-915 (DE-627)325573425 (DE-600)2037678-9 1573-899X nnns volume:49 year:2019 number:7 day:13 month:08 pages:910-915 https://dx.doi.org/10.1007/s11055-019-00818-9 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_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 44.90 ASE AR 49 2019 7 13 08 910-915 |
| spelling |
10.1007/s11055-019-00818-9 doi (DE-627)SPR016128044 (SPR)s11055-019-00818-9-e DE-627 ger DE-627 rakwb eng 570 610 ASE 44.90 bkl Simonov, A. N. verfasserin aut Quantitative Evaluation of Links between Inflammatory Markers and Alzheimer’s Disease 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Objectives. To obtain quantitative assessment of the link between Alzheimer’s disease (AD) and inflammatory markers such as the enzymatic activity of leukocyte elastase (LE) and the functional activity of α1-proteinase inhibitor ($ α_{1} $-PI) using a logistical regression model, which was then applied to predicting the probability of AD in patients with mild cognitive impairment syndrome (MCI). Materials and methods. Mathematical analysis was run using a database containing the results of measurements of these immunological parameters (enzymatic activity of LE and functional activity of $ α_{1} $-PI) in the plasma of 91 patients with verified diagnoses of AD on in-patient or out-patient treatment and 37 healthy subjects of comparable age without clinical indications of somatic or mental pathology. Results and conclusions. A logistical regression model was constructed linking LE and $ α_{1} $-PI with the probability that a patient has AD. The model has favorable statistical properties and high predictive effectiveness. These results allow individual patients’ LE and $ α_{1} $-PI values to be used to generate quantitative probabilities that patients have AD. Alzheimer’s disease (dpeaa)DE-He213 mild cognitive impairment (dpeaa)DE-He213 leukocyte elastase (dpeaa)DE-He213 α (dpeaa)DE-He213 -proteinase inhibitor (dpeaa)DE-He213 logistical regression (dpeaa)DE-He213 Klyushnik, T. P. verfasserin aut Androsova, L. V. verfasserin aut Mikhaylova, N. M. verfasserin aut Enthalten in Neuroscience and behavioral physiology New York, NY : Consultants Bureau, 1967 49(2019), 7 vom: 13. Aug., Seite 910-915 (DE-627)325573425 (DE-600)2037678-9 1573-899X nnns volume:49 year:2019 number:7 day:13 month:08 pages:910-915 https://dx.doi.org/10.1007/s11055-019-00818-9 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_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 44.90 ASE AR 49 2019 7 13 08 910-915 |
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10.1007/s11055-019-00818-9 doi (DE-627)SPR016128044 (SPR)s11055-019-00818-9-e DE-627 ger DE-627 rakwb eng 570 610 ASE 44.90 bkl Simonov, A. N. verfasserin aut Quantitative Evaluation of Links between Inflammatory Markers and Alzheimer’s Disease 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Objectives. To obtain quantitative assessment of the link between Alzheimer’s disease (AD) and inflammatory markers such as the enzymatic activity of leukocyte elastase (LE) and the functional activity of α1-proteinase inhibitor ($ α_{1} $-PI) using a logistical regression model, which was then applied to predicting the probability of AD in patients with mild cognitive impairment syndrome (MCI). Materials and methods. Mathematical analysis was run using a database containing the results of measurements of these immunological parameters (enzymatic activity of LE and functional activity of $ α_{1} $-PI) in the plasma of 91 patients with verified diagnoses of AD on in-patient or out-patient treatment and 37 healthy subjects of comparable age without clinical indications of somatic or mental pathology. Results and conclusions. A logistical regression model was constructed linking LE and $ α_{1} $-PI with the probability that a patient has AD. The model has favorable statistical properties and high predictive effectiveness. These results allow individual patients’ LE and $ α_{1} $-PI values to be used to generate quantitative probabilities that patients have AD. Alzheimer’s disease (dpeaa)DE-He213 mild cognitive impairment (dpeaa)DE-He213 leukocyte elastase (dpeaa)DE-He213 α (dpeaa)DE-He213 -proteinase inhibitor (dpeaa)DE-He213 logistical regression (dpeaa)DE-He213 Klyushnik, T. P. verfasserin aut Androsova, L. V. verfasserin aut Mikhaylova, N. M. verfasserin aut Enthalten in Neuroscience and behavioral physiology New York, NY : Consultants Bureau, 1967 49(2019), 7 vom: 13. Aug., Seite 910-915 (DE-627)325573425 (DE-600)2037678-9 1573-899X nnns volume:49 year:2019 number:7 day:13 month:08 pages:910-915 https://dx.doi.org/10.1007/s11055-019-00818-9 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_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 44.90 ASE AR 49 2019 7 13 08 910-915 |
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10.1007/s11055-019-00818-9 doi (DE-627)SPR016128044 (SPR)s11055-019-00818-9-e DE-627 ger DE-627 rakwb eng 570 610 ASE 44.90 bkl Simonov, A. N. verfasserin aut Quantitative Evaluation of Links between Inflammatory Markers and Alzheimer’s Disease 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Objectives. To obtain quantitative assessment of the link between Alzheimer’s disease (AD) and inflammatory markers such as the enzymatic activity of leukocyte elastase (LE) and the functional activity of α1-proteinase inhibitor ($ α_{1} $-PI) using a logistical regression model, which was then applied to predicting the probability of AD in patients with mild cognitive impairment syndrome (MCI). Materials and methods. Mathematical analysis was run using a database containing the results of measurements of these immunological parameters (enzymatic activity of LE and functional activity of $ α_{1} $-PI) in the plasma of 91 patients with verified diagnoses of AD on in-patient or out-patient treatment and 37 healthy subjects of comparable age without clinical indications of somatic or mental pathology. Results and conclusions. A logistical regression model was constructed linking LE and $ α_{1} $-PI with the probability that a patient has AD. The model has favorable statistical properties and high predictive effectiveness. These results allow individual patients’ LE and $ α_{1} $-PI values to be used to generate quantitative probabilities that patients have AD. Alzheimer’s disease (dpeaa)DE-He213 mild cognitive impairment (dpeaa)DE-He213 leukocyte elastase (dpeaa)DE-He213 α (dpeaa)DE-He213 -proteinase inhibitor (dpeaa)DE-He213 logistical regression (dpeaa)DE-He213 Klyushnik, T. P. verfasserin aut Androsova, L. V. verfasserin aut Mikhaylova, N. M. verfasserin aut Enthalten in Neuroscience and behavioral physiology New York, NY : Consultants Bureau, 1967 49(2019), 7 vom: 13. Aug., Seite 910-915 (DE-627)325573425 (DE-600)2037678-9 1573-899X nnns volume:49 year:2019 number:7 day:13 month:08 pages:910-915 https://dx.doi.org/10.1007/s11055-019-00818-9 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_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 44.90 ASE AR 49 2019 7 13 08 910-915 |
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10.1007/s11055-019-00818-9 doi (DE-627)SPR016128044 (SPR)s11055-019-00818-9-e DE-627 ger DE-627 rakwb eng 570 610 ASE 44.90 bkl Simonov, A. N. verfasserin aut Quantitative Evaluation of Links between Inflammatory Markers and Alzheimer’s Disease 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Objectives. To obtain quantitative assessment of the link between Alzheimer’s disease (AD) and inflammatory markers such as the enzymatic activity of leukocyte elastase (LE) and the functional activity of α1-proteinase inhibitor ($ α_{1} $-PI) using a logistical regression model, which was then applied to predicting the probability of AD in patients with mild cognitive impairment syndrome (MCI). Materials and methods. Mathematical analysis was run using a database containing the results of measurements of these immunological parameters (enzymatic activity of LE and functional activity of $ α_{1} $-PI) in the plasma of 91 patients with verified diagnoses of AD on in-patient or out-patient treatment and 37 healthy subjects of comparable age without clinical indications of somatic or mental pathology. Results and conclusions. A logistical regression model was constructed linking LE and $ α_{1} $-PI with the probability that a patient has AD. The model has favorable statistical properties and high predictive effectiveness. These results allow individual patients’ LE and $ α_{1} $-PI values to be used to generate quantitative probabilities that patients have AD. Alzheimer’s disease (dpeaa)DE-He213 mild cognitive impairment (dpeaa)DE-He213 leukocyte elastase (dpeaa)DE-He213 α (dpeaa)DE-He213 -proteinase inhibitor (dpeaa)DE-He213 logistical regression (dpeaa)DE-He213 Klyushnik, T. P. verfasserin aut Androsova, L. V. verfasserin aut Mikhaylova, N. M. verfasserin aut Enthalten in Neuroscience and behavioral physiology New York, NY : Consultants Bureau, 1967 49(2019), 7 vom: 13. Aug., Seite 910-915 (DE-627)325573425 (DE-600)2037678-9 1573-899X nnns volume:49 year:2019 number:7 day:13 month:08 pages:910-915 https://dx.doi.org/10.1007/s11055-019-00818-9 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_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 44.90 ASE AR 49 2019 7 13 08 910-915 |
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Enthalten in Neuroscience and behavioral physiology 49(2019), 7 vom: 13. Aug., Seite 910-915 volume:49 year:2019 number:7 day:13 month:08 pages:910-915 |
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Enthalten in Neuroscience and behavioral physiology 49(2019), 7 vom: 13. Aug., Seite 910-915 volume:49 year:2019 number:7 day:13 month:08 pages:910-915 |
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Alzheimer’s disease mild cognitive impairment leukocyte elastase α -proteinase inhibitor logistical regression |
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Neuroscience and behavioral physiology |
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Simonov, A. N. @@aut@@ Klyushnik, T. P. @@aut@@ Androsova, L. V. @@aut@@ Mikhaylova, N. M. @@aut@@ |
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To obtain quantitative assessment of the link between Alzheimer’s disease (AD) and inflammatory markers such as the enzymatic activity of leukocyte elastase (LE) and the functional activity of α1-proteinase inhibitor ($ α_{1} $-PI) using a logistical regression model, which was then applied to predicting the probability of AD in patients with mild cognitive impairment syndrome (MCI). Materials and methods. Mathematical analysis was run using a database containing the results of measurements of these immunological parameters (enzymatic activity of LE and functional activity of $ α_{1} $-PI) in the plasma of 91 patients with verified diagnoses of AD on in-patient or out-patient treatment and 37 healthy subjects of comparable age without clinical indications of somatic or mental pathology. Results and conclusions. A logistical regression model was constructed linking LE and $ α_{1} $-PI with the probability that a patient has AD. 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| author |
Simonov, A. N. |
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Simonov, A. N. ddc 570 bkl 44.90 misc Alzheimer’s disease misc mild cognitive impairment misc leukocyte elastase misc α misc -proteinase inhibitor misc logistical regression Quantitative Evaluation of Links between Inflammatory Markers and Alzheimer’s Disease |
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570 610 ASE 44.90 bkl Quantitative Evaluation of Links between Inflammatory Markers and Alzheimer’s Disease Alzheimer’s disease (dpeaa)DE-He213 mild cognitive impairment (dpeaa)DE-He213 leukocyte elastase (dpeaa)DE-He213 α (dpeaa)DE-He213 -proteinase inhibitor (dpeaa)DE-He213 logistical regression (dpeaa)DE-He213 |
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ddc 570 bkl 44.90 misc Alzheimer’s disease misc mild cognitive impairment misc leukocyte elastase misc α misc -proteinase inhibitor misc logistical regression |
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ddc 570 bkl 44.90 misc Alzheimer’s disease misc mild cognitive impairment misc leukocyte elastase misc α misc -proteinase inhibitor misc logistical regression |
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ddc 570 bkl 44.90 misc Alzheimer’s disease misc mild cognitive impairment misc leukocyte elastase misc α misc -proteinase inhibitor misc logistical regression |
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Quantitative Evaluation of Links between Inflammatory Markers and Alzheimer’s Disease |
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Quantitative Evaluation of Links between Inflammatory Markers and Alzheimer’s Disease |
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Simonov, A. N. |
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Neuroscience and behavioral physiology |
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Simonov, A. N. Klyushnik, T. P. Androsova, L. V. Mikhaylova, N. M. |
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verfasserin |
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quantitative evaluation of links between inflammatory markers and alzheimer’s disease |
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Quantitative Evaluation of Links between Inflammatory Markers and Alzheimer’s Disease |
| abstract |
Objectives. To obtain quantitative assessment of the link between Alzheimer’s disease (AD) and inflammatory markers such as the enzymatic activity of leukocyte elastase (LE) and the functional activity of α1-proteinase inhibitor ($ α_{1} $-PI) using a logistical regression model, which was then applied to predicting the probability of AD in patients with mild cognitive impairment syndrome (MCI). Materials and methods. Mathematical analysis was run using a database containing the results of measurements of these immunological parameters (enzymatic activity of LE and functional activity of $ α_{1} $-PI) in the plasma of 91 patients with verified diagnoses of AD on in-patient or out-patient treatment and 37 healthy subjects of comparable age without clinical indications of somatic or mental pathology. Results and conclusions. A logistical regression model was constructed linking LE and $ α_{1} $-PI with the probability that a patient has AD. The model has favorable statistical properties and high predictive effectiveness. These results allow individual patients’ LE and $ α_{1} $-PI values to be used to generate quantitative probabilities that patients have AD. |
| abstractGer |
Objectives. To obtain quantitative assessment of the link between Alzheimer’s disease (AD) and inflammatory markers such as the enzymatic activity of leukocyte elastase (LE) and the functional activity of α1-proteinase inhibitor ($ α_{1} $-PI) using a logistical regression model, which was then applied to predicting the probability of AD in patients with mild cognitive impairment syndrome (MCI). Materials and methods. Mathematical analysis was run using a database containing the results of measurements of these immunological parameters (enzymatic activity of LE and functional activity of $ α_{1} $-PI) in the plasma of 91 patients with verified diagnoses of AD on in-patient or out-patient treatment and 37 healthy subjects of comparable age without clinical indications of somatic or mental pathology. Results and conclusions. A logistical regression model was constructed linking LE and $ α_{1} $-PI with the probability that a patient has AD. The model has favorable statistical properties and high predictive effectiveness. These results allow individual patients’ LE and $ α_{1} $-PI values to be used to generate quantitative probabilities that patients have AD. |
| abstract_unstemmed |
Objectives. To obtain quantitative assessment of the link between Alzheimer’s disease (AD) and inflammatory markers such as the enzymatic activity of leukocyte elastase (LE) and the functional activity of α1-proteinase inhibitor ($ α_{1} $-PI) using a logistical regression model, which was then applied to predicting the probability of AD in patients with mild cognitive impairment syndrome (MCI). Materials and methods. Mathematical analysis was run using a database containing the results of measurements of these immunological parameters (enzymatic activity of LE and functional activity of $ α_{1} $-PI) in the plasma of 91 patients with verified diagnoses of AD on in-patient or out-patient treatment and 37 healthy subjects of comparable age without clinical indications of somatic or mental pathology. Results and conclusions. A logistical regression model was constructed linking LE and $ α_{1} $-PI with the probability that a patient has AD. The model has favorable statistical properties and high predictive effectiveness. These results allow individual patients’ LE and $ α_{1} $-PI values to be used to generate quantitative probabilities that patients have AD. |
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Quantitative Evaluation of Links between Inflammatory Markers and Alzheimer’s Disease |
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Klyushnik, T. P. Androsova, L. V. Mikhaylova, N. M. |
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2024-07-03T20:59:53.661Z |
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| score |
7.400736 |