| 000 | 03594nlm1a2200481 4500 | ||
|---|---|---|---|
| 001 | 668625 | ||
| 005 | 20231030042154.0 | ||
| 035 | _a(RuTPU)RU\TPU\network\39862 | ||
| 035 | _aRU\TPU\network\38737 | ||
| 090 | _a668625 | ||
| 100 | _a20230112a2022 k y0engy50 ba | ||
| 101 | 1 | _aeng | |
| 102 | _aNL | ||
| 135 | _aarcn ---uucaa | ||
| 181 | 0 | _ai | |
| 182 | 0 | _ab | |
| 200 | 1 |
_aQuantitative detection of a1-acid glycoprotein (AGP) level in blood plasma using SERS and CNN transfer learning approach _fM. Erzina, A. Trelin, O. A. Guselnikova [et al.] |
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| 203 |
_aText _celectronic |
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| 300 | _aTitle screen | ||
| 320 | _a[References: 48 tit.] | ||
| 330 | _aSurface-enhanced Raman spectroscopy (SERS) is a highly sensitive tool in medical diagnostics and bioanalysis fields, aimed at the qualitative detection of relevant biomolecules. However, quantitative SERS analysis of complex (bio)samples is a more challenging and, in many cases, almost impossible task, requiring functional SERS substrates or advanced spectral data analysis. In this work, we propose the combination of a functional SERS substrate, capable of trapping target biomolecules, with CNN transfer learning for quantitative detection of the relevant a1-acid glycoprotein (AGP, also known as orosomucoid) in human serum. As a SERS substrate, the plasmonic gold grating was functionalized with boronic acid moieties to entrap target AGP. The functionality of the substrate was tested on two model solutions: a solution containing saccharides as competing molecules and human serum with added AGP, which is close to real samples. The convolution neural network (CNN) was previously trained on a huge number of (bio)samples. Then CNN transfer learning was used to quantify AGP concentration in model samples, as well as in human serum. Developed strategy is able to identify the alarming increase of AGP concentration in an express and medically decentralized way, on short time and under lack of spectral data. Generally, the proposed combination of SERS and machine transfer learning could be expanded to a range of alternative cases, where the collection of real samples is restricted and can be substituted by the measurements of similar model systems, without loss of analysis reliability. | ||
| 338 |
_bРоссийский научный фонд _d19-73-00238 |
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| 461 | _tSensors and Actuators B: Chemical | ||
| 463 |
_tVol. 367 _v[132057, 8 p.] _d2022 |
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| 610 | 1 | _aтруды учёных ТПУ | |
| 610 | 1 | _aэлектронный ресурс | |
| 610 | 1 | _aSERS | |
| 610 | 1 | _afunctional surface | |
| 610 | 1 | _abiomolecules entrapping | |
| 610 | 1 | _aa1-acid glycoprotein | |
| 610 | 1 | _aserum | |
| 610 | 1 | _aCNN transfer learning | |
| 701 | 1 |
_aErzina _bM. _gMariia |
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| 701 | 1 |
_aTrelin _bA. |
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| 701 | 1 |
_aGuselnikova _bO. A. _cchemist _cengineer of Tomsk Polytechnic University _f1992- _gOlga Andreevna _2stltpush _3(RuTPU)RU\TPU\pers\34478 |
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| 701 | 1 |
_aSkvortsova _bA. |
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| 701 | 1 |
_aStrnadova _bK. |
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| 701 | 1 |
_aSvorcik _bV. |
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| 701 | 1 |
_aLyutakov _bO. _cchemist-technologist _cAssociate Scientist of Tomsk Polytechnic University _f1982- _gOleksy _2stltpush _3(RuTPU)RU\TPU\pers\36875 |
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| 712 | 0 | 2 |
_aНациональный исследовательский Томский политехнический университет _bИсследовательская школа химических и биомедицинских технологий _c(2017- ) _h8120 _2stltpush _3(RuTPU)RU\TPU\col\23537 |
| 801 | 2 |
_aRU _b63413507 _c20230112 _gRCR |
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| 856 | 4 | _uhttps://doi.org/10.1016/j.snb.2022.132057 | |
| 942 | _cCF | ||