Colorimetric and Raman dual-mode lateral flow immunoassay detection of SARS-CoV-2 N protein antibody based on Ag nanoparticles with ultrathin Au shell assembled onto $ Fe_{3} %$ O_{4} $ nanoparticles
Serological antibody tests are useful complements of nuclei acid detection for SARS-CoV-2 diagnosis, which can significantly improve diagnostic accuracy. However, antibody detection in serum or plasma remains challenging to do with high sensitivity. In this study, Ag nanoparticles with ultra-thin Au...
Ausführliche Beschreibung
Autor*in: |
Li, Jingwen [verfasserIn] |
---|
Format: |
E-Artikel |
---|---|
Sprache: |
Englisch |
Erschienen: |
2022 |
---|
Schlagwörter: |
---|
Anmerkung: |
© Springer-Verlag GmbH Germany, part of Springer Nature 2022. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
---|
Übergeordnetes Werk: |
Enthalten in: Analytical and bioanalytical chemistry - Berlin : Springer, 2002, 415(2022), 4 vom: 22. Nov., Seite 545-554 |
---|---|
Übergeordnetes Werk: |
volume:415 ; year:2022 ; number:4 ; day:22 ; month:11 ; pages:545-554 |
Links: |
---|
DOI / URN: |
10.1007/s00216-022-04437-1 |
---|
Katalog-ID: |
SPR049419854 |
---|
LEADER | 01000caa a22002652 4500 | ||
---|---|---|---|
001 | SPR049419854 | ||
003 | DE-627 | ||
005 | 20230510060625.0 | ||
007 | cr uuu---uuuuu | ||
008 | 230227s2022 xx |||||o 00| ||eng c | ||
024 | 7 | |a 10.1007/s00216-022-04437-1 |2 doi | |
035 | |a (DE-627)SPR049419854 | ||
035 | |a (SPR)s00216-022-04437-1-e | ||
040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
041 | |a eng | ||
100 | 1 | |a Li, Jingwen |e verfasserin |4 aut | |
245 | 1 | 0 | |a Colorimetric and Raman dual-mode lateral flow immunoassay detection of SARS-CoV-2 N protein antibody based on Ag nanoparticles with ultrathin Au shell assembled onto $ Fe_{3} %$ O_{4} $ nanoparticles |
264 | 1 | |c 2022 | |
336 | |a Text |b txt |2 rdacontent | ||
337 | |a Computermedien |b c |2 rdamedia | ||
338 | |a Online-Ressource |b cr |2 rdacarrier | ||
500 | |a © Springer-Verlag GmbH Germany, part of Springer Nature 2022. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. | ||
520 | |a Serological antibody tests are useful complements of nuclei acid detection for SARS-CoV-2 diagnosis, which can significantly improve diagnostic accuracy. However, antibody detection in serum or plasma remains challenging to do with high sensitivity. In this study, Ag nanoparticles with ultra-thin Au shells embedded with 4-mercaptobenzoic acid (MBA) ($ Ag^{MBA} $Au) were manufactured and then assembled onto $ Fe_{3} %$ O_{4} $ surface by electrostatic interaction to construct the $ Fe_{3} %$ O_{4} $-$ Ag^{MBA} $@Au nanoparticles (NPs) with magnetic-Raman-colorimetric properties. Based on the composite nanoparticles, a colorimetric and Raman dual-mode lateral flow immunoassay (LFIA) for ultrasensitive identification of SARS-CoV-2 nucleocapsid (N) protein antibody was constructed. The magnetic nanoparticles ($ Fe_{3} %$ O_{4} $ NPs) were acted as the core and coated a layer of $ Ag^{MBA} $@Au particles on the surface by electrostatic interaction to prepare $ Fe_{3} %$ O_{4} $-$ Ag^{MBA} $@Au NPs, which can amplify the SERS signal due to multiple $ Ag^{MBA} $@Au particles concentrated on a single magnetic nanoparticle. Moreover, the $ Fe_{3} %$ O_{4} $-$ Ag^{MBA} $@Au NPs facilitated pre-purifying sample using magnetic separation, and complex matrix interference would be greatly decreased in the detection. The $ Fe_{3} %$ O_{4} $-$ Ag^{MBA} $@Au NPs modified with N protein recognized and bound with N protein antibodies, which were trapped on the T-line, forming color band for observing detection. Under optimal conditions, the N protein antibodies could be qualitatively detected in colorimetric mode with the visual limit of $ 10^{−8} $ mg/mL and quantitatively detected by SERS signals between $ 10^{−6} $ and $ 10^{−10} $ mg /mL with 0.08 pg/mL detection limit. The coefficients variations (CV) of intra-assay was 8.0%, whereas of inter-assay was 11.7%, confirming of good reproducibility. Finally, this approach was able to discriminate between positive, negative, and weakly positive samples when detecting 107 clinical serum samples. The process enables highly sensitive quantitative assays that are valuable for evaluating disease processes and guiding treatment. Graphical Abstract Colorimetric and Raman dual-mode LFIA detection of SARS-CoV-2 N protein antibody based on $ Fe_{3} %$ O_{4} $-$ Ag^{MBA} $@Au nanoparticles | ||
650 | 4 | |a SARS-CoV-2 N protein antibody |7 (dpeaa)DE-He213 | |
650 | 4 | |a Fe |7 (dpeaa)DE-He213 | |
650 | 4 | |a O |7 (dpeaa)DE-He213 | |
650 | 4 | |a -Ag |7 (dpeaa)DE-He213 | |
650 | 4 | |a @Au NPs |7 (dpeaa)DE-He213 | |
650 | 4 | |a LFIA |7 (dpeaa)DE-He213 | |
650 | 4 | |a SERS |7 (dpeaa)DE-He213 | |
650 | 4 | |a Colorimetry |7 (dpeaa)DE-He213 | |
700 | 1 | |a Liang, Penghui |4 aut | |
700 | 1 | |a Zhao, Tianyu |4 aut | |
700 | 1 | |a Guo, Gengchen |4 aut | |
700 | 1 | |a Zhu, Jinyue |4 aut | |
700 | 1 | |a Wen, Congying |4 aut | |
700 | 1 | |a Zeng, Jingbin |4 aut | |
773 | 0 | 8 | |i Enthalten in |t Analytical and bioanalytical chemistry |d Berlin : Springer, 2002 |g 415(2022), 4 vom: 22. Nov., Seite 545-554 |w (DE-627)25372337X |w (DE-600)1459122-4 |x 1618-2650 |7 nnns |
773 | 1 | 8 | |g volume:415 |g year:2022 |g number:4 |g day:22 |g month:11 |g pages:545-554 |
856 | 4 | 0 | |u https://dx.doi.org/10.1007/s00216-022-04437-1 |z lizenzpflichtig |3 Volltext |
912 | |a GBV_USEFLAG_A | ||
912 | |a SYSFLAG_A | ||
912 | |a GBV_SPRINGER | ||
912 | |a GBV_ILN_11 | ||
912 | |a GBV_ILN_20 | ||
912 | |a GBV_ILN_22 | ||
912 | |a GBV_ILN_23 | ||
912 | |a GBV_ILN_24 | ||
912 | |a GBV_ILN_31 | ||
912 | |a GBV_ILN_32 | ||
912 | |a GBV_ILN_39 | ||
912 | |a GBV_ILN_40 | ||
912 | |a GBV_ILN_60 | ||
912 | |a GBV_ILN_62 | ||
912 | |a GBV_ILN_63 | ||
912 | |a GBV_ILN_65 | ||
912 | |a GBV_ILN_69 | ||
912 | |a GBV_ILN_70 | ||
912 | |a GBV_ILN_73 | ||
912 | |a GBV_ILN_74 | ||
912 | |a GBV_ILN_90 | ||
912 | |a GBV_ILN_95 | ||
912 | |a GBV_ILN_100 | ||
912 | |a GBV_ILN_101 | ||
912 | |a GBV_ILN_105 | ||
912 | |a GBV_ILN_110 | ||
912 | |a GBV_ILN_120 | ||
912 | |a GBV_ILN_138 | ||
912 | |a GBV_ILN_150 | ||
912 | |a GBV_ILN_151 | ||
912 | |a GBV_ILN_152 | ||
912 | |a GBV_ILN_161 | ||
912 | |a GBV_ILN_170 | ||
912 | |a GBV_ILN_171 | ||
912 | |a GBV_ILN_187 | ||
912 | |a GBV_ILN_206 | ||
912 | |a GBV_ILN_213 | ||
912 | |a GBV_ILN_224 | ||
912 | |a GBV_ILN_230 | ||
912 | |a GBV_ILN_250 | ||
912 | |a GBV_ILN_281 | ||
912 | |a GBV_ILN_285 | ||
912 | |a GBV_ILN_293 | ||
912 | |a GBV_ILN_370 | ||
912 | |a GBV_ILN_602 | ||
912 | |a GBV_ILN_636 | ||
912 | |a GBV_ILN_702 | ||
912 | |a GBV_ILN_2001 | ||
912 | |a GBV_ILN_2003 | ||
912 | |a GBV_ILN_2004 | ||
912 | |a GBV_ILN_2005 | ||
912 | |a GBV_ILN_2006 | ||
912 | |a GBV_ILN_2007 | ||
912 | |a GBV_ILN_2008 | ||
912 | |a GBV_ILN_2009 | ||
912 | |a GBV_ILN_2010 | ||
912 | |a GBV_ILN_2011 | ||
912 | |a GBV_ILN_2014 | ||
912 | |a GBV_ILN_2015 | ||
912 | |a GBV_ILN_2020 | ||
912 | |a GBV_ILN_2021 | ||
912 | |a GBV_ILN_2025 | ||
912 | |a GBV_ILN_2026 | ||
912 | |a GBV_ILN_2027 | ||
912 | |a GBV_ILN_2031 | ||
912 | |a GBV_ILN_2034 | ||
912 | |a GBV_ILN_2037 | ||
912 | |a GBV_ILN_2038 | ||
912 | |a GBV_ILN_2039 | ||
912 | |a GBV_ILN_2044 | ||
912 | |a GBV_ILN_2048 | ||
912 | |a GBV_ILN_2049 | ||
912 | |a GBV_ILN_2050 | ||
912 | |a GBV_ILN_2055 | ||
912 | |a GBV_ILN_2056 | ||
912 | |a GBV_ILN_2057 | ||
912 | |a GBV_ILN_2059 | ||
912 | |a GBV_ILN_2061 | ||
912 | |a GBV_ILN_2064 | ||
912 | |a GBV_ILN_2065 | ||
912 | |a GBV_ILN_2068 | ||
912 | |a GBV_ILN_2088 | ||
912 | |a GBV_ILN_2093 | ||
912 | |a GBV_ILN_2106 | ||
912 | |a GBV_ILN_2107 | ||
912 | |a GBV_ILN_2108 | ||
912 | |a GBV_ILN_2110 | ||
912 | |a GBV_ILN_2111 | ||
912 | |a GBV_ILN_2112 | ||
912 | |a GBV_ILN_2113 | ||
912 | |a GBV_ILN_2118 | ||
912 | |a GBV_ILN_2122 | ||
912 | |a GBV_ILN_2129 | ||
912 | |a GBV_ILN_2143 | ||
912 | |a GBV_ILN_2144 | ||
912 | |a GBV_ILN_2147 | ||
912 | |a GBV_ILN_2148 | ||
912 | |a GBV_ILN_2152 | ||
912 | |a GBV_ILN_2153 | ||
912 | |a GBV_ILN_2188 | ||
912 | |a GBV_ILN_2190 | ||
912 | |a GBV_ILN_2232 | ||
912 | |a GBV_ILN_2336 | ||
912 | |a GBV_ILN_2360 | ||
912 | |a GBV_ILN_2446 | ||
912 | |a GBV_ILN_2470 | ||
912 | |a GBV_ILN_2472 | ||
912 | |a GBV_ILN_2507 | ||
912 | |a GBV_ILN_2522 | ||
912 | |a GBV_ILN_2548 | ||
912 | |a GBV_ILN_4035 | ||
912 | |a GBV_ILN_4037 | ||
912 | |a GBV_ILN_4046 | ||
912 | |a GBV_ILN_4112 | ||
912 | |a GBV_ILN_4125 | ||
912 | |a GBV_ILN_4126 | ||
912 | |a GBV_ILN_4242 | ||
912 | |a GBV_ILN_4246 | ||
912 | |a GBV_ILN_4249 | ||
912 | |a GBV_ILN_4251 | ||
912 | |a GBV_ILN_4277 | ||
912 | |a GBV_ILN_4305 | ||
912 | |a GBV_ILN_4306 | ||
912 | |a GBV_ILN_4307 | ||
912 | |a GBV_ILN_4313 | ||
912 | |a GBV_ILN_4322 | ||
912 | |a GBV_ILN_4323 | ||
912 | |a GBV_ILN_4324 | ||
912 | |a GBV_ILN_4325 | ||
912 | |a GBV_ILN_4326 | ||
912 | |a GBV_ILN_4328 | ||
912 | |a GBV_ILN_4333 | ||
912 | |a GBV_ILN_4334 | ||
912 | |a GBV_ILN_4335 | ||
912 | |a GBV_ILN_4336 | ||
912 | |a GBV_ILN_4338 | ||
912 | |a GBV_ILN_4393 | ||
912 | |a GBV_ILN_4700 | ||
951 | |a AR | ||
952 | |d 415 |j 2022 |e 4 |b 22 |c 11 |h 545-554 |
author_variant |
j l jl p l pl t z tz g g gg j z jz c w cw j z jz |
---|---|
matchkey_str |
article:16182650:2022----::ooiercnrmnuloeaeafoimnasyeetoosrcvnrtiatbdbsdngaoatcewtutahn |
hierarchy_sort_str |
2022 |
publishDate |
2022 |
allfields |
10.1007/s00216-022-04437-1 doi (DE-627)SPR049419854 (SPR)s00216-022-04437-1-e DE-627 ger DE-627 rakwb eng Li, Jingwen verfasserin aut Colorimetric and Raman dual-mode lateral flow immunoassay detection of SARS-CoV-2 N protein antibody based on Ag nanoparticles with ultrathin Au shell assembled onto $ Fe_{3} %$ O_{4} $ nanoparticles 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag GmbH Germany, part of Springer Nature 2022. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Serological antibody tests are useful complements of nuclei acid detection for SARS-CoV-2 diagnosis, which can significantly improve diagnostic accuracy. However, antibody detection in serum or plasma remains challenging to do with high sensitivity. In this study, Ag nanoparticles with ultra-thin Au shells embedded with 4-mercaptobenzoic acid (MBA) ($ Ag^{MBA} $Au) were manufactured and then assembled onto $ Fe_{3} %$ O_{4} $ surface by electrostatic interaction to construct the $ Fe_{3} %$ O_{4} $-$ Ag^{MBA} $@Au nanoparticles (NPs) with magnetic-Raman-colorimetric properties. Based on the composite nanoparticles, a colorimetric and Raman dual-mode lateral flow immunoassay (LFIA) for ultrasensitive identification of SARS-CoV-2 nucleocapsid (N) protein antibody was constructed. The magnetic nanoparticles ($ Fe_{3} %$ O_{4} $ NPs) were acted as the core and coated a layer of $ Ag^{MBA} $@Au particles on the surface by electrostatic interaction to prepare $ Fe_{3} %$ O_{4} $-$ Ag^{MBA} $@Au NPs, which can amplify the SERS signal due to multiple $ Ag^{MBA} $@Au particles concentrated on a single magnetic nanoparticle. Moreover, the $ Fe_{3} %$ O_{4} $-$ Ag^{MBA} $@Au NPs facilitated pre-purifying sample using magnetic separation, and complex matrix interference would be greatly decreased in the detection. The $ Fe_{3} %$ O_{4} $-$ Ag^{MBA} $@Au NPs modified with N protein recognized and bound with N protein antibodies, which were trapped on the T-line, forming color band for observing detection. Under optimal conditions, the N protein antibodies could be qualitatively detected in colorimetric mode with the visual limit of $ 10^{−8} $ mg/mL and quantitatively detected by SERS signals between $ 10^{−6} $ and $ 10^{−10} $ mg /mL with 0.08 pg/mL detection limit. The coefficients variations (CV) of intra-assay was 8.0%, whereas of inter-assay was 11.7%, confirming of good reproducibility. Finally, this approach was able to discriminate between positive, negative, and weakly positive samples when detecting 107 clinical serum samples. The process enables highly sensitive quantitative assays that are valuable for evaluating disease processes and guiding treatment. Graphical Abstract Colorimetric and Raman dual-mode LFIA detection of SARS-CoV-2 N protein antibody based on $ Fe_{3} %$ O_{4} $-$ Ag^{MBA} $@Au nanoparticles SARS-CoV-2 N protein antibody (dpeaa)DE-He213 Fe (dpeaa)DE-He213 O (dpeaa)DE-He213 -Ag (dpeaa)DE-He213 @Au NPs (dpeaa)DE-He213 LFIA (dpeaa)DE-He213 SERS (dpeaa)DE-He213 Colorimetry (dpeaa)DE-He213 Liang, Penghui aut Zhao, Tianyu aut Guo, Gengchen aut Zhu, Jinyue aut Wen, Congying aut Zeng, Jingbin aut Enthalten in Analytical and bioanalytical chemistry Berlin : Springer, 2002 415(2022), 4 vom: 22. Nov., Seite 545-554 (DE-627)25372337X (DE-600)1459122-4 1618-2650 nnns volume:415 year:2022 number:4 day:22 month:11 pages:545-554 https://dx.doi.org/10.1007/s00216-022-04437-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER 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_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 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_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 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_2118 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_2360 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_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4277 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 415 2022 4 22 11 545-554 |
spelling |
10.1007/s00216-022-04437-1 doi (DE-627)SPR049419854 (SPR)s00216-022-04437-1-e DE-627 ger DE-627 rakwb eng Li, Jingwen verfasserin aut Colorimetric and Raman dual-mode lateral flow immunoassay detection of SARS-CoV-2 N protein antibody based on Ag nanoparticles with ultrathin Au shell assembled onto $ Fe_{3} %$ O_{4} $ nanoparticles 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag GmbH Germany, part of Springer Nature 2022. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Serological antibody tests are useful complements of nuclei acid detection for SARS-CoV-2 diagnosis, which can significantly improve diagnostic accuracy. However, antibody detection in serum or plasma remains challenging to do with high sensitivity. In this study, Ag nanoparticles with ultra-thin Au shells embedded with 4-mercaptobenzoic acid (MBA) ($ Ag^{MBA} $Au) were manufactured and then assembled onto $ Fe_{3} %$ O_{4} $ surface by electrostatic interaction to construct the $ Fe_{3} %$ O_{4} $-$ Ag^{MBA} $@Au nanoparticles (NPs) with magnetic-Raman-colorimetric properties. Based on the composite nanoparticles, a colorimetric and Raman dual-mode lateral flow immunoassay (LFIA) for ultrasensitive identification of SARS-CoV-2 nucleocapsid (N) protein antibody was constructed. The magnetic nanoparticles ($ Fe_{3} %$ O_{4} $ NPs) were acted as the core and coated a layer of $ Ag^{MBA} $@Au particles on the surface by electrostatic interaction to prepare $ Fe_{3} %$ O_{4} $-$ Ag^{MBA} $@Au NPs, which can amplify the SERS signal due to multiple $ Ag^{MBA} $@Au particles concentrated on a single magnetic nanoparticle. Moreover, the $ Fe_{3} %$ O_{4} $-$ Ag^{MBA} $@Au NPs facilitated pre-purifying sample using magnetic separation, and complex matrix interference would be greatly decreased in the detection. The $ Fe_{3} %$ O_{4} $-$ Ag^{MBA} $@Au NPs modified with N protein recognized and bound with N protein antibodies, which were trapped on the T-line, forming color band for observing detection. Under optimal conditions, the N protein antibodies could be qualitatively detected in colorimetric mode with the visual limit of $ 10^{−8} $ mg/mL and quantitatively detected by SERS signals between $ 10^{−6} $ and $ 10^{−10} $ mg /mL with 0.08 pg/mL detection limit. The coefficients variations (CV) of intra-assay was 8.0%, whereas of inter-assay was 11.7%, confirming of good reproducibility. Finally, this approach was able to discriminate between positive, negative, and weakly positive samples when detecting 107 clinical serum samples. The process enables highly sensitive quantitative assays that are valuable for evaluating disease processes and guiding treatment. Graphical Abstract Colorimetric and Raman dual-mode LFIA detection of SARS-CoV-2 N protein antibody based on $ Fe_{3} %$ O_{4} $-$ Ag^{MBA} $@Au nanoparticles SARS-CoV-2 N protein antibody (dpeaa)DE-He213 Fe (dpeaa)DE-He213 O (dpeaa)DE-He213 -Ag (dpeaa)DE-He213 @Au NPs (dpeaa)DE-He213 LFIA (dpeaa)DE-He213 SERS (dpeaa)DE-He213 Colorimetry (dpeaa)DE-He213 Liang, Penghui aut Zhao, Tianyu aut Guo, Gengchen aut Zhu, Jinyue aut Wen, Congying aut Zeng, Jingbin aut Enthalten in Analytical and bioanalytical chemistry Berlin : Springer, 2002 415(2022), 4 vom: 22. Nov., Seite 545-554 (DE-627)25372337X (DE-600)1459122-4 1618-2650 nnns volume:415 year:2022 number:4 day:22 month:11 pages:545-554 https://dx.doi.org/10.1007/s00216-022-04437-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER 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_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 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_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 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_2118 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_2360 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_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4277 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 415 2022 4 22 11 545-554 |
allfields_unstemmed |
10.1007/s00216-022-04437-1 doi (DE-627)SPR049419854 (SPR)s00216-022-04437-1-e DE-627 ger DE-627 rakwb eng Li, Jingwen verfasserin aut Colorimetric and Raman dual-mode lateral flow immunoassay detection of SARS-CoV-2 N protein antibody based on Ag nanoparticles with ultrathin Au shell assembled onto $ Fe_{3} %$ O_{4} $ nanoparticles 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag GmbH Germany, part of Springer Nature 2022. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Serological antibody tests are useful complements of nuclei acid detection for SARS-CoV-2 diagnosis, which can significantly improve diagnostic accuracy. However, antibody detection in serum or plasma remains challenging to do with high sensitivity. In this study, Ag nanoparticles with ultra-thin Au shells embedded with 4-mercaptobenzoic acid (MBA) ($ Ag^{MBA} $Au) were manufactured and then assembled onto $ Fe_{3} %$ O_{4} $ surface by electrostatic interaction to construct the $ Fe_{3} %$ O_{4} $-$ Ag^{MBA} $@Au nanoparticles (NPs) with magnetic-Raman-colorimetric properties. Based on the composite nanoparticles, a colorimetric and Raman dual-mode lateral flow immunoassay (LFIA) for ultrasensitive identification of SARS-CoV-2 nucleocapsid (N) protein antibody was constructed. The magnetic nanoparticles ($ Fe_{3} %$ O_{4} $ NPs) were acted as the core and coated a layer of $ Ag^{MBA} $@Au particles on the surface by electrostatic interaction to prepare $ Fe_{3} %$ O_{4} $-$ Ag^{MBA} $@Au NPs, which can amplify the SERS signal due to multiple $ Ag^{MBA} $@Au particles concentrated on a single magnetic nanoparticle. Moreover, the $ Fe_{3} %$ O_{4} $-$ Ag^{MBA} $@Au NPs facilitated pre-purifying sample using magnetic separation, and complex matrix interference would be greatly decreased in the detection. The $ Fe_{3} %$ O_{4} $-$ Ag^{MBA} $@Au NPs modified with N protein recognized and bound with N protein antibodies, which were trapped on the T-line, forming color band for observing detection. Under optimal conditions, the N protein antibodies could be qualitatively detected in colorimetric mode with the visual limit of $ 10^{−8} $ mg/mL and quantitatively detected by SERS signals between $ 10^{−6} $ and $ 10^{−10} $ mg /mL with 0.08 pg/mL detection limit. The coefficients variations (CV) of intra-assay was 8.0%, whereas of inter-assay was 11.7%, confirming of good reproducibility. Finally, this approach was able to discriminate between positive, negative, and weakly positive samples when detecting 107 clinical serum samples. The process enables highly sensitive quantitative assays that are valuable for evaluating disease processes and guiding treatment. Graphical Abstract Colorimetric and Raman dual-mode LFIA detection of SARS-CoV-2 N protein antibody based on $ Fe_{3} %$ O_{4} $-$ Ag^{MBA} $@Au nanoparticles SARS-CoV-2 N protein antibody (dpeaa)DE-He213 Fe (dpeaa)DE-He213 O (dpeaa)DE-He213 -Ag (dpeaa)DE-He213 @Au NPs (dpeaa)DE-He213 LFIA (dpeaa)DE-He213 SERS (dpeaa)DE-He213 Colorimetry (dpeaa)DE-He213 Liang, Penghui aut Zhao, Tianyu aut Guo, Gengchen aut Zhu, Jinyue aut Wen, Congying aut Zeng, Jingbin aut Enthalten in Analytical and bioanalytical chemistry Berlin : Springer, 2002 415(2022), 4 vom: 22. Nov., Seite 545-554 (DE-627)25372337X (DE-600)1459122-4 1618-2650 nnns volume:415 year:2022 number:4 day:22 month:11 pages:545-554 https://dx.doi.org/10.1007/s00216-022-04437-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER 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_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 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_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 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_2118 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_2360 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_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4277 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 415 2022 4 22 11 545-554 |
allfieldsGer |
10.1007/s00216-022-04437-1 doi (DE-627)SPR049419854 (SPR)s00216-022-04437-1-e DE-627 ger DE-627 rakwb eng Li, Jingwen verfasserin aut Colorimetric and Raman dual-mode lateral flow immunoassay detection of SARS-CoV-2 N protein antibody based on Ag nanoparticles with ultrathin Au shell assembled onto $ Fe_{3} %$ O_{4} $ nanoparticles 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag GmbH Germany, part of Springer Nature 2022. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Serological antibody tests are useful complements of nuclei acid detection for SARS-CoV-2 diagnosis, which can significantly improve diagnostic accuracy. However, antibody detection in serum or plasma remains challenging to do with high sensitivity. In this study, Ag nanoparticles with ultra-thin Au shells embedded with 4-mercaptobenzoic acid (MBA) ($ Ag^{MBA} $Au) were manufactured and then assembled onto $ Fe_{3} %$ O_{4} $ surface by electrostatic interaction to construct the $ Fe_{3} %$ O_{4} $-$ Ag^{MBA} $@Au nanoparticles (NPs) with magnetic-Raman-colorimetric properties. Based on the composite nanoparticles, a colorimetric and Raman dual-mode lateral flow immunoassay (LFIA) for ultrasensitive identification of SARS-CoV-2 nucleocapsid (N) protein antibody was constructed. The magnetic nanoparticles ($ Fe_{3} %$ O_{4} $ NPs) were acted as the core and coated a layer of $ Ag^{MBA} $@Au particles on the surface by electrostatic interaction to prepare $ Fe_{3} %$ O_{4} $-$ Ag^{MBA} $@Au NPs, which can amplify the SERS signal due to multiple $ Ag^{MBA} $@Au particles concentrated on a single magnetic nanoparticle. Moreover, the $ Fe_{3} %$ O_{4} $-$ Ag^{MBA} $@Au NPs facilitated pre-purifying sample using magnetic separation, and complex matrix interference would be greatly decreased in the detection. The $ Fe_{3} %$ O_{4} $-$ Ag^{MBA} $@Au NPs modified with N protein recognized and bound with N protein antibodies, which were trapped on the T-line, forming color band for observing detection. Under optimal conditions, the N protein antibodies could be qualitatively detected in colorimetric mode with the visual limit of $ 10^{−8} $ mg/mL and quantitatively detected by SERS signals between $ 10^{−6} $ and $ 10^{−10} $ mg /mL with 0.08 pg/mL detection limit. The coefficients variations (CV) of intra-assay was 8.0%, whereas of inter-assay was 11.7%, confirming of good reproducibility. Finally, this approach was able to discriminate between positive, negative, and weakly positive samples when detecting 107 clinical serum samples. The process enables highly sensitive quantitative assays that are valuable for evaluating disease processes and guiding treatment. Graphical Abstract Colorimetric and Raman dual-mode LFIA detection of SARS-CoV-2 N protein antibody based on $ Fe_{3} %$ O_{4} $-$ Ag^{MBA} $@Au nanoparticles SARS-CoV-2 N protein antibody (dpeaa)DE-He213 Fe (dpeaa)DE-He213 O (dpeaa)DE-He213 -Ag (dpeaa)DE-He213 @Au NPs (dpeaa)DE-He213 LFIA (dpeaa)DE-He213 SERS (dpeaa)DE-He213 Colorimetry (dpeaa)DE-He213 Liang, Penghui aut Zhao, Tianyu aut Guo, Gengchen aut Zhu, Jinyue aut Wen, Congying aut Zeng, Jingbin aut Enthalten in Analytical and bioanalytical chemistry Berlin : Springer, 2002 415(2022), 4 vom: 22. Nov., Seite 545-554 (DE-627)25372337X (DE-600)1459122-4 1618-2650 nnns volume:415 year:2022 number:4 day:22 month:11 pages:545-554 https://dx.doi.org/10.1007/s00216-022-04437-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER 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_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 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_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 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_2118 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_2360 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_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4277 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 415 2022 4 22 11 545-554 |
allfieldsSound |
10.1007/s00216-022-04437-1 doi (DE-627)SPR049419854 (SPR)s00216-022-04437-1-e DE-627 ger DE-627 rakwb eng Li, Jingwen verfasserin aut Colorimetric and Raman dual-mode lateral flow immunoassay detection of SARS-CoV-2 N protein antibody based on Ag nanoparticles with ultrathin Au shell assembled onto $ Fe_{3} %$ O_{4} $ nanoparticles 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag GmbH Germany, part of Springer Nature 2022. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Serological antibody tests are useful complements of nuclei acid detection for SARS-CoV-2 diagnosis, which can significantly improve diagnostic accuracy. However, antibody detection in serum or plasma remains challenging to do with high sensitivity. In this study, Ag nanoparticles with ultra-thin Au shells embedded with 4-mercaptobenzoic acid (MBA) ($ Ag^{MBA} $Au) were manufactured and then assembled onto $ Fe_{3} %$ O_{4} $ surface by electrostatic interaction to construct the $ Fe_{3} %$ O_{4} $-$ Ag^{MBA} $@Au nanoparticles (NPs) with magnetic-Raman-colorimetric properties. Based on the composite nanoparticles, a colorimetric and Raman dual-mode lateral flow immunoassay (LFIA) for ultrasensitive identification of SARS-CoV-2 nucleocapsid (N) protein antibody was constructed. The magnetic nanoparticles ($ Fe_{3} %$ O_{4} $ NPs) were acted as the core and coated a layer of $ Ag^{MBA} $@Au particles on the surface by electrostatic interaction to prepare $ Fe_{3} %$ O_{4} $-$ Ag^{MBA} $@Au NPs, which can amplify the SERS signal due to multiple $ Ag^{MBA} $@Au particles concentrated on a single magnetic nanoparticle. Moreover, the $ Fe_{3} %$ O_{4} $-$ Ag^{MBA} $@Au NPs facilitated pre-purifying sample using magnetic separation, and complex matrix interference would be greatly decreased in the detection. The $ Fe_{3} %$ O_{4} $-$ Ag^{MBA} $@Au NPs modified with N protein recognized and bound with N protein antibodies, which were trapped on the T-line, forming color band for observing detection. Under optimal conditions, the N protein antibodies could be qualitatively detected in colorimetric mode with the visual limit of $ 10^{−8} $ mg/mL and quantitatively detected by SERS signals between $ 10^{−6} $ and $ 10^{−10} $ mg /mL with 0.08 pg/mL detection limit. The coefficients variations (CV) of intra-assay was 8.0%, whereas of inter-assay was 11.7%, confirming of good reproducibility. Finally, this approach was able to discriminate between positive, negative, and weakly positive samples when detecting 107 clinical serum samples. The process enables highly sensitive quantitative assays that are valuable for evaluating disease processes and guiding treatment. Graphical Abstract Colorimetric and Raman dual-mode LFIA detection of SARS-CoV-2 N protein antibody based on $ Fe_{3} %$ O_{4} $-$ Ag^{MBA} $@Au nanoparticles SARS-CoV-2 N protein antibody (dpeaa)DE-He213 Fe (dpeaa)DE-He213 O (dpeaa)DE-He213 -Ag (dpeaa)DE-He213 @Au NPs (dpeaa)DE-He213 LFIA (dpeaa)DE-He213 SERS (dpeaa)DE-He213 Colorimetry (dpeaa)DE-He213 Liang, Penghui aut Zhao, Tianyu aut Guo, Gengchen aut Zhu, Jinyue aut Wen, Congying aut Zeng, Jingbin aut Enthalten in Analytical and bioanalytical chemistry Berlin : Springer, 2002 415(2022), 4 vom: 22. Nov., Seite 545-554 (DE-627)25372337X (DE-600)1459122-4 1618-2650 nnns volume:415 year:2022 number:4 day:22 month:11 pages:545-554 https://dx.doi.org/10.1007/s00216-022-04437-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER 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_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 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_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 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_2118 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_2360 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_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4277 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 415 2022 4 22 11 545-554 |
language |
English |
source |
Enthalten in Analytical and bioanalytical chemistry 415(2022), 4 vom: 22. Nov., Seite 545-554 volume:415 year:2022 number:4 day:22 month:11 pages:545-554 |
sourceStr |
Enthalten in Analytical and bioanalytical chemistry 415(2022), 4 vom: 22. Nov., Seite 545-554 volume:415 year:2022 number:4 day:22 month:11 pages:545-554 |
format_phy_str_mv |
Article |
institution |
findex.gbv.de |
topic_facet |
SARS-CoV-2 N protein antibody Fe O -Ag @Au NPs LFIA SERS Colorimetry |
isfreeaccess_bool |
false |
container_title |
Analytical and bioanalytical chemistry |
authorswithroles_txt_mv |
Li, Jingwen @@aut@@ Liang, Penghui @@aut@@ Zhao, Tianyu @@aut@@ Guo, Gengchen @@aut@@ Zhu, Jinyue @@aut@@ Wen, Congying @@aut@@ Zeng, Jingbin @@aut@@ |
publishDateDaySort_date |
2022-11-22T00:00:00Z |
hierarchy_top_id |
25372337X |
id |
SPR049419854 |
language_de |
englisch |
fullrecord |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">SPR049419854</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230510060625.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">230227s2022 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s00216-022-04437-1</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)SPR049419854</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(SPR)s00216-022-04437-1-e</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Li, Jingwen</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Colorimetric and Raman dual-mode lateral flow immunoassay detection of SARS-CoV-2 N protein antibody based on Ag nanoparticles with ultrathin Au shell assembled onto $ Fe_{3} %$ O_{4} $ nanoparticles</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2022</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© Springer-Verlag GmbH Germany, part of Springer Nature 2022. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Serological antibody tests are useful complements of nuclei acid detection for SARS-CoV-2 diagnosis, which can significantly improve diagnostic accuracy. However, antibody detection in serum or plasma remains challenging to do with high sensitivity. In this study, Ag nanoparticles with ultra-thin Au shells embedded with 4-mercaptobenzoic acid (MBA) ($ Ag^{MBA} $Au) were manufactured and then assembled onto $ Fe_{3} %$ O_{4} $ surface by electrostatic interaction to construct the $ Fe_{3} %$ O_{4} $-$ Ag^{MBA} $@Au nanoparticles (NPs) with magnetic-Raman-colorimetric properties. Based on the composite nanoparticles, a colorimetric and Raman dual-mode lateral flow immunoassay (LFIA) for ultrasensitive identification of SARS-CoV-2 nucleocapsid (N) protein antibody was constructed. The magnetic nanoparticles ($ Fe_{3} %$ O_{4} $ NPs) were acted as the core and coated a layer of $ Ag^{MBA} $@Au particles on the surface by electrostatic interaction to prepare $ Fe_{3} %$ O_{4} $-$ Ag^{MBA} $@Au NPs, which can amplify the SERS signal due to multiple $ Ag^{MBA} $@Au particles concentrated on a single magnetic nanoparticle. Moreover, the $ Fe_{3} %$ O_{4} $-$ Ag^{MBA} $@Au NPs facilitated pre-purifying sample using magnetic separation, and complex matrix interference would be greatly decreased in the detection. The $ Fe_{3} %$ O_{4} $-$ Ag^{MBA} $@Au NPs modified with N protein recognized and bound with N protein antibodies, which were trapped on the T-line, forming color band for observing detection. Under optimal conditions, the N protein antibodies could be qualitatively detected in colorimetric mode with the visual limit of $ 10^{−8} $ mg/mL and quantitatively detected by SERS signals between $ 10^{−6} $ and $ 10^{−10} $ mg /mL with 0.08 pg/mL detection limit. The coefficients variations (CV) of intra-assay was 8.0%, whereas of inter-assay was 11.7%, confirming of good reproducibility. Finally, this approach was able to discriminate between positive, negative, and weakly positive samples when detecting 107 clinical serum samples. The process enables highly sensitive quantitative assays that are valuable for evaluating disease processes and guiding treatment. Graphical Abstract Colorimetric and Raman dual-mode LFIA detection of SARS-CoV-2 N protein antibody based on $ Fe_{3} %$ O_{4} $-$ Ag^{MBA} $@Au nanoparticles</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">SARS-CoV-2 N protein antibody</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Fe</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">O</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">-Ag</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">@Au NPs</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">LFIA</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">SERS</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Colorimetry</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Liang, Penghui</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Zhao, Tianyu</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Guo, Gengchen</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Zhu, Jinyue</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Wen, Congying</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Zeng, Jingbin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Analytical and bioanalytical chemistry</subfield><subfield code="d">Berlin : Springer, 2002</subfield><subfield code="g">415(2022), 4 vom: 22. Nov., Seite 545-554</subfield><subfield code="w">(DE-627)25372337X</subfield><subfield code="w">(DE-600)1459122-4</subfield><subfield code="x">1618-2650</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:415</subfield><subfield code="g">year:2022</subfield><subfield code="g">number:4</subfield><subfield code="g">day:22</subfield><subfield code="g">month:11</subfield><subfield code="g">pages:545-554</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://dx.doi.org/10.1007/s00216-022-04437-1</subfield><subfield code="z">lizenzpflichtig</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_SPRINGER</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_11</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_20</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_22</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_23</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_24</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_31</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_32</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_39</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_40</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_60</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_62</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_63</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_65</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_69</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_73</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_74</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_90</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_95</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_100</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_101</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_105</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_110</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_120</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_138</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_150</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_151</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_152</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_161</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_170</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_171</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_187</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_206</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_213</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_224</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_230</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_250</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_281</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_285</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_293</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_370</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_602</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_636</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_702</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2001</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2003</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2004</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2005</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2006</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2007</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2008</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2009</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2010</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2011</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2014</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2015</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2020</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2021</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2025</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2026</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2027</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2031</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2034</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2037</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2038</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2039</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2044</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2048</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2049</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2050</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2055</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2056</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2057</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2059</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2061</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2064</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2065</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2068</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2088</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2093</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2106</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2107</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2108</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2110</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2111</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2113</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2118</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2122</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2129</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2143</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2144</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2147</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2148</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2152</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2153</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2188</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2190</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2232</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2336</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2360</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2446</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2470</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2472</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2507</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2522</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2548</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4035</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4037</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4046</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4125</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4126</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4242</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4246</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4249</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4251</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4277</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4305</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4306</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4307</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4313</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4322</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4323</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4324</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4325</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4326</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4328</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4333</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4334</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4335</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4336</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4338</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4393</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4700</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">415</subfield><subfield code="j">2022</subfield><subfield code="e">4</subfield><subfield code="b">22</subfield><subfield code="c">11</subfield><subfield code="h">545-554</subfield></datafield></record></collection>
|
author |
Li, Jingwen |
spellingShingle |
Li, Jingwen misc SARS-CoV-2 N protein antibody misc Fe misc O misc -Ag misc @Au NPs misc LFIA misc SERS misc Colorimetry Colorimetric and Raman dual-mode lateral flow immunoassay detection of SARS-CoV-2 N protein antibody based on Ag nanoparticles with ultrathin Au shell assembled onto $ Fe_{3} %$ O_{4} $ nanoparticles |
authorStr |
Li, Jingwen |
ppnlink_with_tag_str_mv |
@@773@@(DE-627)25372337X |
format |
electronic Article |
delete_txt_mv |
keep |
author_role |
aut aut aut aut aut aut aut |
collection |
springer |
remote_str |
true |
illustrated |
Not Illustrated |
issn |
1618-2650 |
topic_title |
Colorimetric and Raman dual-mode lateral flow immunoassay detection of SARS-CoV-2 N protein antibody based on Ag nanoparticles with ultrathin Au shell assembled onto $ Fe_{3} %$ O_{4} $ nanoparticles SARS-CoV-2 N protein antibody (dpeaa)DE-He213 Fe (dpeaa)DE-He213 O (dpeaa)DE-He213 -Ag (dpeaa)DE-He213 @Au NPs (dpeaa)DE-He213 LFIA (dpeaa)DE-He213 SERS (dpeaa)DE-He213 Colorimetry (dpeaa)DE-He213 |
topic |
misc SARS-CoV-2 N protein antibody misc Fe misc O misc -Ag misc @Au NPs misc LFIA misc SERS misc Colorimetry |
topic_unstemmed |
misc SARS-CoV-2 N protein antibody misc Fe misc O misc -Ag misc @Au NPs misc LFIA misc SERS misc Colorimetry |
topic_browse |
misc SARS-CoV-2 N protein antibody misc Fe misc O misc -Ag misc @Au NPs misc LFIA misc SERS misc Colorimetry |
format_facet |
Elektronische Aufsätze Aufsätze Elektronische Ressource |
format_main_str_mv |
Text Zeitschrift/Artikel |
carriertype_str_mv |
cr |
hierarchy_parent_title |
Analytical and bioanalytical chemistry |
hierarchy_parent_id |
25372337X |
hierarchy_top_title |
Analytical and bioanalytical chemistry |
isfreeaccess_txt |
false |
familylinks_str_mv |
(DE-627)25372337X (DE-600)1459122-4 |
title |
Colorimetric and Raman dual-mode lateral flow immunoassay detection of SARS-CoV-2 N protein antibody based on Ag nanoparticles with ultrathin Au shell assembled onto $ Fe_{3} %$ O_{4} $ nanoparticles |
ctrlnum |
(DE-627)SPR049419854 (SPR)s00216-022-04437-1-e |
title_full |
Colorimetric and Raman dual-mode lateral flow immunoassay detection of SARS-CoV-2 N protein antibody based on Ag nanoparticles with ultrathin Au shell assembled onto $ Fe_{3} %$ O_{4} $ nanoparticles |
author_sort |
Li, Jingwen |
journal |
Analytical and bioanalytical chemistry |
journalStr |
Analytical and bioanalytical chemistry |
lang_code |
eng |
isOA_bool |
false |
recordtype |
marc |
publishDateSort |
2022 |
contenttype_str_mv |
txt |
container_start_page |
545 |
author_browse |
Li, Jingwen Liang, Penghui Zhao, Tianyu Guo, Gengchen Zhu, Jinyue Wen, Congying Zeng, Jingbin |
container_volume |
415 |
format_se |
Elektronische Aufsätze |
author-letter |
Li, Jingwen |
doi_str_mv |
10.1007/s00216-022-04437-1 |
title_sort |
colorimetric and raman dual-mode lateral flow immunoassay detection of sars-cov-2 n protein antibody based on ag nanoparticles with ultrathin au shell assembled onto $ fe_{3} %$ o_{4} $ nanoparticles |
title_auth |
Colorimetric and Raman dual-mode lateral flow immunoassay detection of SARS-CoV-2 N protein antibody based on Ag nanoparticles with ultrathin Au shell assembled onto $ Fe_{3} %$ O_{4} $ nanoparticles |
abstract |
Serological antibody tests are useful complements of nuclei acid detection for SARS-CoV-2 diagnosis, which can significantly improve diagnostic accuracy. However, antibody detection in serum or plasma remains challenging to do with high sensitivity. In this study, Ag nanoparticles with ultra-thin Au shells embedded with 4-mercaptobenzoic acid (MBA) ($ Ag^{MBA} $Au) were manufactured and then assembled onto $ Fe_{3} %$ O_{4} $ surface by electrostatic interaction to construct the $ Fe_{3} %$ O_{4} $-$ Ag^{MBA} $@Au nanoparticles (NPs) with magnetic-Raman-colorimetric properties. Based on the composite nanoparticles, a colorimetric and Raman dual-mode lateral flow immunoassay (LFIA) for ultrasensitive identification of SARS-CoV-2 nucleocapsid (N) protein antibody was constructed. The magnetic nanoparticles ($ Fe_{3} %$ O_{4} $ NPs) were acted as the core and coated a layer of $ Ag^{MBA} $@Au particles on the surface by electrostatic interaction to prepare $ Fe_{3} %$ O_{4} $-$ Ag^{MBA} $@Au NPs, which can amplify the SERS signal due to multiple $ Ag^{MBA} $@Au particles concentrated on a single magnetic nanoparticle. Moreover, the $ Fe_{3} %$ O_{4} $-$ Ag^{MBA} $@Au NPs facilitated pre-purifying sample using magnetic separation, and complex matrix interference would be greatly decreased in the detection. The $ Fe_{3} %$ O_{4} $-$ Ag^{MBA} $@Au NPs modified with N protein recognized and bound with N protein antibodies, which were trapped on the T-line, forming color band for observing detection. Under optimal conditions, the N protein antibodies could be qualitatively detected in colorimetric mode with the visual limit of $ 10^{−8} $ mg/mL and quantitatively detected by SERS signals between $ 10^{−6} $ and $ 10^{−10} $ mg /mL with 0.08 pg/mL detection limit. The coefficients variations (CV) of intra-assay was 8.0%, whereas of inter-assay was 11.7%, confirming of good reproducibility. Finally, this approach was able to discriminate between positive, negative, and weakly positive samples when detecting 107 clinical serum samples. The process enables highly sensitive quantitative assays that are valuable for evaluating disease processes and guiding treatment. Graphical Abstract Colorimetric and Raman dual-mode LFIA detection of SARS-CoV-2 N protein antibody based on $ Fe_{3} %$ O_{4} $-$ Ag^{MBA} $@Au nanoparticles © Springer-Verlag GmbH Germany, part of Springer Nature 2022. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
abstractGer |
Serological antibody tests are useful complements of nuclei acid detection for SARS-CoV-2 diagnosis, which can significantly improve diagnostic accuracy. However, antibody detection in serum or plasma remains challenging to do with high sensitivity. In this study, Ag nanoparticles with ultra-thin Au shells embedded with 4-mercaptobenzoic acid (MBA) ($ Ag^{MBA} $Au) were manufactured and then assembled onto $ Fe_{3} %$ O_{4} $ surface by electrostatic interaction to construct the $ Fe_{3} %$ O_{4} $-$ Ag^{MBA} $@Au nanoparticles (NPs) with magnetic-Raman-colorimetric properties. Based on the composite nanoparticles, a colorimetric and Raman dual-mode lateral flow immunoassay (LFIA) for ultrasensitive identification of SARS-CoV-2 nucleocapsid (N) protein antibody was constructed. The magnetic nanoparticles ($ Fe_{3} %$ O_{4} $ NPs) were acted as the core and coated a layer of $ Ag^{MBA} $@Au particles on the surface by electrostatic interaction to prepare $ Fe_{3} %$ O_{4} $-$ Ag^{MBA} $@Au NPs, which can amplify the SERS signal due to multiple $ Ag^{MBA} $@Au particles concentrated on a single magnetic nanoparticle. Moreover, the $ Fe_{3} %$ O_{4} $-$ Ag^{MBA} $@Au NPs facilitated pre-purifying sample using magnetic separation, and complex matrix interference would be greatly decreased in the detection. The $ Fe_{3} %$ O_{4} $-$ Ag^{MBA} $@Au NPs modified with N protein recognized and bound with N protein antibodies, which were trapped on the T-line, forming color band for observing detection. Under optimal conditions, the N protein antibodies could be qualitatively detected in colorimetric mode with the visual limit of $ 10^{−8} $ mg/mL and quantitatively detected by SERS signals between $ 10^{−6} $ and $ 10^{−10} $ mg /mL with 0.08 pg/mL detection limit. The coefficients variations (CV) of intra-assay was 8.0%, whereas of inter-assay was 11.7%, confirming of good reproducibility. Finally, this approach was able to discriminate between positive, negative, and weakly positive samples when detecting 107 clinical serum samples. The process enables highly sensitive quantitative assays that are valuable for evaluating disease processes and guiding treatment. Graphical Abstract Colorimetric and Raman dual-mode LFIA detection of SARS-CoV-2 N protein antibody based on $ Fe_{3} %$ O_{4} $-$ Ag^{MBA} $@Au nanoparticles © Springer-Verlag GmbH Germany, part of Springer Nature 2022. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
abstract_unstemmed |
Serological antibody tests are useful complements of nuclei acid detection for SARS-CoV-2 diagnosis, which can significantly improve diagnostic accuracy. However, antibody detection in serum or plasma remains challenging to do with high sensitivity. In this study, Ag nanoparticles with ultra-thin Au shells embedded with 4-mercaptobenzoic acid (MBA) ($ Ag^{MBA} $Au) were manufactured and then assembled onto $ Fe_{3} %$ O_{4} $ surface by electrostatic interaction to construct the $ Fe_{3} %$ O_{4} $-$ Ag^{MBA} $@Au nanoparticles (NPs) with magnetic-Raman-colorimetric properties. Based on the composite nanoparticles, a colorimetric and Raman dual-mode lateral flow immunoassay (LFIA) for ultrasensitive identification of SARS-CoV-2 nucleocapsid (N) protein antibody was constructed. The magnetic nanoparticles ($ Fe_{3} %$ O_{4} $ NPs) were acted as the core and coated a layer of $ Ag^{MBA} $@Au particles on the surface by electrostatic interaction to prepare $ Fe_{3} %$ O_{4} $-$ Ag^{MBA} $@Au NPs, which can amplify the SERS signal due to multiple $ Ag^{MBA} $@Au particles concentrated on a single magnetic nanoparticle. Moreover, the $ Fe_{3} %$ O_{4} $-$ Ag^{MBA} $@Au NPs facilitated pre-purifying sample using magnetic separation, and complex matrix interference would be greatly decreased in the detection. The $ Fe_{3} %$ O_{4} $-$ Ag^{MBA} $@Au NPs modified with N protein recognized and bound with N protein antibodies, which were trapped on the T-line, forming color band for observing detection. Under optimal conditions, the N protein antibodies could be qualitatively detected in colorimetric mode with the visual limit of $ 10^{−8} $ mg/mL and quantitatively detected by SERS signals between $ 10^{−6} $ and $ 10^{−10} $ mg /mL with 0.08 pg/mL detection limit. The coefficients variations (CV) of intra-assay was 8.0%, whereas of inter-assay was 11.7%, confirming of good reproducibility. Finally, this approach was able to discriminate between positive, negative, and weakly positive samples when detecting 107 clinical serum samples. The process enables highly sensitive quantitative assays that are valuable for evaluating disease processes and guiding treatment. Graphical Abstract Colorimetric and Raman dual-mode LFIA detection of SARS-CoV-2 N protein antibody based on $ Fe_{3} %$ O_{4} $-$ Ag^{MBA} $@Au nanoparticles © Springer-Verlag GmbH Germany, part of Springer Nature 2022. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
collection_details |
GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER 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_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 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_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 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_2118 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_2360 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_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4277 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 |
container_issue |
4 |
title_short |
Colorimetric and Raman dual-mode lateral flow immunoassay detection of SARS-CoV-2 N protein antibody based on Ag nanoparticles with ultrathin Au shell assembled onto $ Fe_{3} %$ O_{4} $ nanoparticles |
url |
https://dx.doi.org/10.1007/s00216-022-04437-1 |
remote_bool |
true |
author2 |
Liang, Penghui Zhao, Tianyu Guo, Gengchen Zhu, Jinyue Wen, Congying Zeng, Jingbin |
author2Str |
Liang, Penghui Zhao, Tianyu Guo, Gengchen Zhu, Jinyue Wen, Congying Zeng, Jingbin |
ppnlink |
25372337X |
mediatype_str_mv |
c |
isOA_txt |
false |
hochschulschrift_bool |
false |
doi_str |
10.1007/s00216-022-04437-1 |
up_date |
2024-07-04T00:43:36.461Z |
_version_ |
1803607150653603840 |
fullrecord_marcxml |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">SPR049419854</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230510060625.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">230227s2022 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s00216-022-04437-1</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)SPR049419854</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(SPR)s00216-022-04437-1-e</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Li, Jingwen</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Colorimetric and Raman dual-mode lateral flow immunoassay detection of SARS-CoV-2 N protein antibody based on Ag nanoparticles with ultrathin Au shell assembled onto $ Fe_{3} %$ O_{4} $ nanoparticles</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2022</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© Springer-Verlag GmbH Germany, part of Springer Nature 2022. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Serological antibody tests are useful complements of nuclei acid detection for SARS-CoV-2 diagnosis, which can significantly improve diagnostic accuracy. However, antibody detection in serum or plasma remains challenging to do with high sensitivity. In this study, Ag nanoparticles with ultra-thin Au shells embedded with 4-mercaptobenzoic acid (MBA) ($ Ag^{MBA} $Au) were manufactured and then assembled onto $ Fe_{3} %$ O_{4} $ surface by electrostatic interaction to construct the $ Fe_{3} %$ O_{4} $-$ Ag^{MBA} $@Au nanoparticles (NPs) with magnetic-Raman-colorimetric properties. Based on the composite nanoparticles, a colorimetric and Raman dual-mode lateral flow immunoassay (LFIA) for ultrasensitive identification of SARS-CoV-2 nucleocapsid (N) protein antibody was constructed. The magnetic nanoparticles ($ Fe_{3} %$ O_{4} $ NPs) were acted as the core and coated a layer of $ Ag^{MBA} $@Au particles on the surface by electrostatic interaction to prepare $ Fe_{3} %$ O_{4} $-$ Ag^{MBA} $@Au NPs, which can amplify the SERS signal due to multiple $ Ag^{MBA} $@Au particles concentrated on a single magnetic nanoparticle. Moreover, the $ Fe_{3} %$ O_{4} $-$ Ag^{MBA} $@Au NPs facilitated pre-purifying sample using magnetic separation, and complex matrix interference would be greatly decreased in the detection. The $ Fe_{3} %$ O_{4} $-$ Ag^{MBA} $@Au NPs modified with N protein recognized and bound with N protein antibodies, which were trapped on the T-line, forming color band for observing detection. Under optimal conditions, the N protein antibodies could be qualitatively detected in colorimetric mode with the visual limit of $ 10^{−8} $ mg/mL and quantitatively detected by SERS signals between $ 10^{−6} $ and $ 10^{−10} $ mg /mL with 0.08 pg/mL detection limit. The coefficients variations (CV) of intra-assay was 8.0%, whereas of inter-assay was 11.7%, confirming of good reproducibility. Finally, this approach was able to discriminate between positive, negative, and weakly positive samples when detecting 107 clinical serum samples. The process enables highly sensitive quantitative assays that are valuable for evaluating disease processes and guiding treatment. Graphical Abstract Colorimetric and Raman dual-mode LFIA detection of SARS-CoV-2 N protein antibody based on $ Fe_{3} %$ O_{4} $-$ Ag^{MBA} $@Au nanoparticles</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">SARS-CoV-2 N protein antibody</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Fe</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">O</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">-Ag</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">@Au NPs</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">LFIA</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">SERS</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Colorimetry</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Liang, Penghui</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Zhao, Tianyu</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Guo, Gengchen</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Zhu, Jinyue</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Wen, Congying</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Zeng, Jingbin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Analytical and bioanalytical chemistry</subfield><subfield code="d">Berlin : Springer, 2002</subfield><subfield code="g">415(2022), 4 vom: 22. Nov., Seite 545-554</subfield><subfield code="w">(DE-627)25372337X</subfield><subfield code="w">(DE-600)1459122-4</subfield><subfield code="x">1618-2650</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:415</subfield><subfield code="g">year:2022</subfield><subfield code="g">number:4</subfield><subfield code="g">day:22</subfield><subfield code="g">month:11</subfield><subfield code="g">pages:545-554</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://dx.doi.org/10.1007/s00216-022-04437-1</subfield><subfield code="z">lizenzpflichtig</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_SPRINGER</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_11</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_20</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_22</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_23</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_24</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_31</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_32</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_39</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_40</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_60</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_62</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_63</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_65</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_69</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_73</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_74</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_90</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_95</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_100</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_101</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_105</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_110</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_120</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_138</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_150</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_151</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_152</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_161</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_170</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_171</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_187</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_206</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_213</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_224</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_230</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_250</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_281</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_285</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_293</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_370</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_602</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_636</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_702</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2001</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2003</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2004</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2005</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2006</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2007</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2008</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2009</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2010</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2011</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2014</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2015</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2020</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2021</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2025</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2026</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2027</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2031</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2034</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2037</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2038</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2039</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2044</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2048</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2049</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2050</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2055</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2056</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2057</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2059</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2061</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2064</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2065</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2068</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2088</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2093</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2106</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2107</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2108</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2110</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2111</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2113</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2118</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2122</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2129</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2143</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2144</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2147</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2148</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2152</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2153</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2188</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2190</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2232</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2336</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2360</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2446</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2470</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2472</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2507</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2522</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2548</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4035</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4037</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4046</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4125</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4126</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4242</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4246</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4249</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4251</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4277</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4305</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4306</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4307</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4313</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4322</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4323</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4324</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4325</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4326</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4328</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4333</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4334</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4335</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4336</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4338</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4393</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4700</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">415</subfield><subfield code="j">2022</subfield><subfield code="e">4</subfield><subfield code="b">22</subfield><subfield code="c">11</subfield><subfield code="h">545-554</subfield></datafield></record></collection>
|
score |
7.4006405 |