Abstract

As a division of polymerase chain reaction (PCR), convective PCR (CPCR) is able to achieve highly efficient thermal cycling based on free thermal convection with pseudo-isothermal heating, which could be beneficial to point-of-care (POC) nucleic acid analysis. Similar to traditional PCR or isothermal amplification, due to a couple of issues, e.g., reagent, primer design, reactor, reaction dynamics, amplification status, temperature and heating condition, and other reasons, in some cases of CPCR tests, untypical real-time fluorescence curves with positive or negative tests will show up. Especially, when parts of the characteristics between untypical low-positive and negative tests are mixed together, it is difficult to discriminate between them using traditional cycle threshold (Ct) value method. To handle this issue which may occur in CPCR, traditional PCR or isothermal amplification, as an example, instead of using complicated mathematical modeling and signal processing strategy, an artificial intelligence (AI) classification method with artificial neural network (ANN) modeling is developed to improve the accuracy of nucleic acid detection. It has been proven that both the detection specificity and sensitivity can be significantly improved even with a simple ANN model. It can be estimated that the developed method based on AI modeling can be adopted to solve similar problem with PCR or isothermal amplification methods.

References

1.
Wiersinga
,
W. J.
,
Rhodes
,
A.
,
Cheng
,
A. C.
,
Peacock
,
S. J.
, and
Prescott
,
H. C.
,
2020
, “
Pathophysiology, Transmission, Diagnosis, and Treatment of Coronavirus Disease 2019 (COVID-19): A Review
,”
JAMA J. Am. Med. Assoc.
,
324
(
8
), pp.
782
793
.10.1001/jama.2020.12839
2.
Derwand
,
R.
,
Scholz
,
M.
, and
Zelenko
,
V.
,
2020
, “
COVID-19 Outpatients: Early Risk-Stratified Treatment With Zinc Plus Low-Dose Hydroxychloroquine and Azithromycin: A Retrospective Case Series Study
,”
Int. J. Antimicrob. Agents
,
56
(
6
), p.
106214
.10.1016/j.ijantimicag.2020.106214
3.
Iravani
,
S.
,
2020
, “
Nano- and Biosensors for the Detection of SARS-CoV-2: Challenges and Opportunities
,”
Mater. Adv.
,
1
(
9
), pp.
3092
3103
.10.1039/D0MA00702A
4.
Chen
,
J.
,
Di
,
C.
,
Yao
,
X.
,
Tao
,
Y.
, and
Xiang
,
C.
,
2013
, “
Progress of Microfluidics for Biology and Medicine
,”
Nano-Micro Lett.
,
5
(
1
), pp.
66
80
.10.1007/BF03354852
5.
Li
,
Y.
,
Zhao
,
H.
,
Yan
,
X.
,
Li
,
M.
,
Chen
,
P.
, and
Zhang
,
S.
,
2017
, “
A Universal Method for Direct PCR Amplification of Plant Tissues
,”
Anal. Methods
,
9
(
11
), pp.
1800
1805
.10.1039/C6AY03156K
6.
Battaglia
,
S.
,
Petralia
,
S.
,
Vicario
,
N.
,
Cirillo
,
D.
, and
Conoci
,
S.
,
2019
, “
An Innovative Silicon-Chip for Sensitive Real Time PCR Improvement in Pathogen Detection
,”
Analyst
,
144
(
7
), pp.
2353
2358
.10.1039/C9AN00006B
7.
Takahara
,
H.
,
Matsushita
,
H.
,
Inui
,
E.
,
Ochiai
,
M.
, and
Hashimoto
,
M.
,
2021
, “
Convenient Microfluidic Cartridge for Single-Molecule Droplet PCR Using Common Laboratory Equipment
,”
Anal. Methods
,
13
(
8
), pp.
974
985
.10.1039/D0AY01779E
8.
Pacocha
,
N.
,
Scheler
,
O.
,
Nowak
,
M. M.
,
Derzsi
,
L.
,
Cichy
,
J.
, and
Garstecki
,
P.
,
2019
, “
Direct Droplet Digital PCR (DddPCR) for Species Specific, Accurate and Precise Quantification of Bacteria in Mixed Samples
,”
Anal. Methods
,
11
(
44
), pp.
5730
5735
.10.1039/C9AY01874C
9.
Grover
,
J.
,
Juncosa
,
R. D.
,
Stoffel
,
N.
,
Boysel
,
M.
,
Brooks
,
A. I.
,
McLoughlin
,
M. P.
, and
Robbins
,
D. W.
,
2008
, “
Fast PCR Thermal Cycling Device
,”
IEEE Sens. J.
,
8
(
5
), pp.
476
487
.10.1109/JSEN.2008.918248
10.
Madhavi
,
K.
,
Ugaz
,
V. M.
, and
Burns
,
M. A.
,
2002
, “
PCR in a Rayleigh-Bénard Convection Cell
,”
Science
,
298
(
5594
), p.
793
.10.1126/science.298.5594.793
11.
Khnouf
,
R.
,
Jaradat
,
M. A. K.
,
Karasneh
,
D.
,
Al-Shami
,
F.
,
Sawaqed
,
L.
, and
Albiss
,
B. A.
,
2020
, “
Simulation and Optimization of a Single Heater Convective PCR Chip and Its Controller for Fast Salmonella Enteritidis Detection
,”
IEEE Sens. J.
,
20
(
22
), pp.
13186
13195
.10.1109/JSEN.2020.3004285
12.
Saito
,
M.
,
Kiriyama
,
Y.
,
Yamanaka
,
K.
, and
Tamiya
,
E.
,
2013
, “
Development of the POCT-Oriented PCR Device Driven by Centrifugation Assisted Thermal Convection
,”
17th International Conference on Miniaturized Systems for Chemistry and Life Sciences, MicroTAS 2013
, Vol.
2
, Freiburg, Germany, Oct. 27–31, pp.
1305
1307
.https://www.researchgate.net/publication/283135601_Development_of_the_poctoriented_PCR_device_driven_by_centrifugation_assisted_thermal_convection
13.
Rajendran
,
V. K.
,
Bakthavathsalam
,
P.
,
Bergquist
,
P. L.
, and
Sunna
,
A.
,
2019
, “
A Portable Nucleic Acid Detection System Using Natural Convection Combined With a Smartphone
,”
Biosens. Bioelectron.
,
134
(
134
), pp.
68
75
.10.1016/j.bios.2019.03.050
14.
Qiu
,
X.
,
Zhang
,
S.
,
Mei
,
L.
,
Wu
,
D.
,
Guo
,
Q.
,
Li
,
K.
,
Ge
,
S.
,
Ye
,
X.
,
Xia
,
N.
, and
Mauk
,
M. G.
,
2017
, “
Characterization and Analysis of Real-Time Capillary Convective PCR Toward Commercialization
,”
Biomicrofluidics
,
11
(
2
), p.
024103
.10.1063/1.4977841
15.
Li
,
Z.
,
Zhao
,
Y.
,
Zhang
,
D.
,
Zhuang
,
S.
, and
Yamaguchi
,
Y.
,
2016
, “
The Development of a Portable Buoyancy-Driven PCR System and Its Evaluation by Capillary Electrophoresis
,”
Sens. Actuators B Chem.
,
230
, pp.
779
784
.10.1016/j.snb.2016.02.143
16.
Hsieh
,
Y.
,
Yang
,
A.
,
Chen
,
P.
,
Yeh
,
S.
,
Chen
,
P.
,
Liao
,
S.
, and
Lee
,
D.
,
2013
, “
A Real-Time Convective PCR Machine in a Capillary Tube Instrumented With a CCD-Based Fluorometer
,”
Sens. Actuators B Chem.
,
183
(
20
), pp.
434
440
.10.1016/j.snb.2013.04.003
17.
Chen
,
Z.
,
Qian
,
S.
,
Abrams
,
W. R.
,
Daniel
,
M.
, and
Bau
,
H. H.
,
2004
, “
Thermosiphon-Based PCR Reactor: Experiment and Modeling
,”
Anal. Chem.
,
76
(
13
), pp.
3707
3715
.10.1021/ac049914k
18.
Chung
,
K. H.
,
Park
,
S. H.
, and
Choi
,
Y. H.
,
2010
, “
A Palmtop PCR System With a Disposable Polymer Chip Operated by the Thermosiphon Effect
,”
Lab Chip
,
10
(
2
), pp.
202
210
.10.1039/B915022F
19.
Kim
,
T. H.
,
Hwang
,
H. J.
, and
Kim
,
J. H.
,
2017
, “
Development of a Novel, Rapid Multiplex Polymerase Chain Reaction Assay for the Detection and Differentiation of Salmonella Enterica Serovars Enteritidis and Typhimurium Using Ultra-Fast Convection Polymerase Chain Reaction
,”
Foodborne Pathog. Dis.
,
14
(
10
), pp.
580
586
.10.1089/fpd.2017.2290
20.
Zhang
,
S.
,
Lin
,
Y.
,
Wang
,
J.
,
Wang
,
P.
,
Chen
,
J.
,
Xue
,
M.
,
He
,
S.
,
Zhou
,
W.
,
Xu
,
F.
,
Liu
,
P.
,
Chen
,
P.
,
Ge
,
S.
, and
Xia
,
N.
,
2014
, “
A Convenient Nucleic Acid Test on the Basis of the Capillary Convective PCR for the On-Site Detection of Enterovirus 71
,”
J. Mol. Diagn.
,
16
(
4
), pp.
452
458
.10.1016/j.jmoldx.2014.04.002
21.
Qiu
,
X.
,
Zhang
,
S.
,
Xiang
,
F.
,
Wu
,
D.
,
Guo
,
M.
,
Ge
,
S.
,
Li
,
K.
,
Ye
,
X.
,
Xia
,
N.
, and
Qian
,
S.
,
2017
, “
Instrument-Free Point-of-Care Molecular Diagnosis of H1N1 Based on Microfluidic Convective PCR
,”
Sens. Actuators B Chem.
,
243
, pp.
738
744
.10.1016/j.snb.2016.12.058
22.
Kim
,
T.-H.
,
Hwang
,
H. J.
, and
Kim
,
J. H.
,
2019
, “
Ultra-Fast on-Site Molecular Detection of Foodborne Pathogens Using a Combination of Convection Polymerase Chain Reaction and Nucleic Acid Lateral Flow Immunoassay
,”
Foodborne Pathog. Dis.
,
16
(
2
), pp.
144
151
.10.1089/fpd.2018.2500
23.
Rajendran
,
V. K.
,
Bakthavathsalam
,
P.
,
Bergquist
,
P. L.
, and
Sunna
,
A.
,
2019
, “
Smartphone Detection of Antibiotic Resistance Using Convective PCR and a Lateral Flow Assay
,”
Sens. Actuators B: Chem.
,
298
, p.
126849
.10.1016/j.snb.2019.126849
24.
Ana
,
A. D. D. C.
,
Fernandez-Molina
,
J. V.
,
Andoni
,
R. G.
,
Javier
,
S.
,
Hernando
,
F. L.
,
Javier
,
P.
,
Javier
,
G.
, and
Aitor
,
R.
,
2013
, “
The AspHS Gene as a New Target for Detecting Aspergillus Fumigatus During Infections by Quantitative Real-Time PCR
,”
Med. Mycol.
,
51
(
5
), pp.
545
554
.10.3109/13693786.2012.756989
25.
Rodriguez-Lazaro
,
D.
,
Gonzalez-García
,
P.
,
Delibato
,
E.
,
De Medici
,
D.
,
García-Gimeno
,
R. M.
,
Valero
,
A.
, and
Hernandez
,
M.
,
2014
, “
Next Day Salmonella Spp. Detection Method Based on Real-Time PCR for Meat, Dairy and Vegetable Food Products
,”
Int. J. Food Microbiol.
,
184
(
4
), pp.
113
120
.10.1016/j.ijfoodmicro.2014.03.021
26.
Ingham
,
D. J.
,
Beer
,
S.
,
Money
,
S.
, and
Hansen
,
G.
,
2001
, “
Quantitative Real-Time PCR Assay for Determining Transgene Copy Number in Transformed Plants
,”
Biotechniques
,
31
(
1
), pp.
132
140
.10.2144/01311rr04
27.
Rasmussen
,
T. B.
,
Uttenthal
,
A.
,
de Stricker
,
K.
,
BeláK
,
S.
, and
Storgaard
,
T.
,
2003
, “
Development of a Novel Quantitative Real-Time RT-PCR Assay for the Simultaneous Detection of All Serotypes of Foot-and-Mouth Disease Virus
,”
Arch. Virol.
,
148
(
10
), pp.
2005
2021
.10.1007/s00705-003-0145-2
28.
Han
,
N.
,
Shin
,
J. H.
, and
Han
,
K.-H.
,
2014
, “
An on-Chip RT-PCR Microfluidic Device, That Integrates MRNA Extraction, CDNA Synthesis, and Gene Amplification
,”
RSC Adv.
,
4
(
18
), pp.
9160
9165
.10.1039/c3ra47980c
29.
Zhang
,
L.-J.
,
Huang
,
X.-D.
,
Wang
,
Y.
,
Wang
,
C.-Y.
, and
Sun
,
Y.-Z.
,
2019
, “
Discussion on Dual–Tree Complex Wavelet Transform and Generalized Regression Neural Network Based Concentration-Resolved Fluorescence Spectroscopy for Oil Identification
,”
Anal. Methods
,
11
(
36
), pp.
4566
4574
.10.1039/C9AY01155B
30.
González-Durruthy
,
M.
,
Manske Nunes
,
S.
,
Ventura-Lima
,
J.
,
Gelesky
,
M. A.
,
González-Díaz
,
H.
,
Monserrat
,
J. M.
,
Concu
,
R.
, and
Cordeiro
,
M. N. D. S.
,
2019
, “
MitoTarget Modeling Using ANN-Classification Models Based on Fractal SEM Nano-Descriptors: Carbon Nanotubes as Mitochondrial F0F1-ATPase Inhibitors
,”
J. Chem. Inf. Model.
,
59
(
1
), pp.
86
97
.10.1021/acs.jcim.8b00631
31.
Yin
,
M.
,
Liu
,
X.
,
Liu
,
Y.
, and
Chen
,
X.
,
2019
, “
Medical Image Fusion With Parameter-Adaptive Pulse Coupled Neural Network in Nonsubsampled Shearlet Transform Domain
,”
IEEE Trans. Instrum. Meas.
,
68
(
1
), pp.
49
64
.10.1109/TIM.2018.2838778
32.
Beini
,
Z.
,
Xuee
,
C.
,
Bo
,
L.
, and
Weijia
,
W.
,
2021
, “
A New Few-Shot Learning Method of Digital PCR Image Detection
,”
IEEE Access
,
9
, pp.
74446
74453
.10.1109/ACCESS.2021.3081562
33.
Chowdhury
,
D.
, and
Chattopadhyay
,
M.
,
2021
, “
Study and Classification of Cell Bio-Impedance Signature for Identification of Malignancy Using Artificial Neural Network
,”
IEEE Trans. Instrum. Meas.
,
70
, pp.
1
8
.10.1109/TIM.2020.3046928
34.
Cho
,
S.-Y.
,
Lee
,
Y.
,
Lee
,
S.
,
Kang
,
H.
,
Kim
,
J.
,
Choi
,
J.
,
Ryu
,
J.
,
Joo
,
H.
,
Jung
,
H.-T.
, and
Kim
,
J.
,
2020
, “
Finding Hidden Signals in Chemical Sensors Using Deep Learning
,”
Anal. Chem.
,
92
(
9
), pp.
6529
6537
.10.1021/acs.analchem.0c00137
35.
Jacobs
,
B. K. M.
,
Goetghebeur
,
E.
,
Vandesompele
,
J.
,
De Ganck
,
A.
,
Nijs
,
N.
,
Beckers
,
A.
,
Papazova
,
N.
,
Roosens
,
N. H.
, and
Clement
,
L.
,
2017
, “
Model-Based Classification for Digital PCR: Your Umbrella for Rain
,”
Anal. Chem.
,
89
(
8
), pp.
4461
4467
.10.1021/acs.analchem.6b04208
36.
Qiu
,
X.
,
Li
,
K.
,
Yang
,
Y.
,
Zhang
,
S.
,
Ye
,
X.
,
Xia
,
N.
, and
Qian
,
S.
,
2017
, “
A Low-Cost and Fast Real-Time PCR System Based on Capillary Convection
,”
SLAS Technol., Translating Life Sciences Innovation
,
22
(
1
), pp.
13
17
.10.1177/2211068216652847
37.
Qiu
,
X.
,
Ge
,
S.
,
Gao
,
P.
,
Ke
,
L.
,
Yang
,
S.
,
Zhang
,
S.
,
Ye
,
X.
,
Xia
,
N.
,
Qian
,
S.
, and
Qiu
,
Z.
,
2017
, “
A Smartphone-Based Point-of-Care Diagnosis of H1N1 With Microfluidic Convection PCR
,”
Microsyst. Technol.
,
23
(
7
), pp.
2951
2956
.10.1007/s00542-016-2979-z
38.
Qiu
,
X.
,
Shu
,
J. I.
,
Baysal
,
O.
,
Wu
,
J.
,
Qian
,
S.
,
Ge
,
S.
,
Li
,
K.
,
Ye
,
X.
,
Xia
,
N.
, and
Yu
,
D.
,
2019
, “
Real-Time Capillary Convective PCR Based on Horizontal Thermal Convection
,”
Microfluid. Nanofluid.
,
23
(
3
), p.
39
.10.1007/s10404-019-2207-0
39.
Qiu
,
X.
,
Ye
,
X.
,
Ge
,
S.
,
Zhang
,
S.
,
Gao
,
P.
,
Ji
,
S.
,
Yang
,
Y.
,
Qiu
,
Z.
, and
Xia
,
N.
,
2017
, “
The Detection Mechanism for Polymerase Chain Reaction and Polymerase Chain Reaction Device
,” WO 2017/080358 A1.
40.
Song
,
K. Y.
,
Hwang
,
H. J.
, and
Kim
,
J. H.
,
2017
, “
Ultra-Fast DNA-Based Multiplex Convection PCR Method for Meat Species Identification With Possible on-Site Applications
,”
Food Chem.
,
229
, pp.
341
346
.10.1016/j.foodchem.2017.02.085
41.
Yang
,
Y.
,
Li
,
C.
,
Liu
,
S.
,
Min
,
H.
,
Yan
,
C.
,
Yang
,
M.
, and
Yu
,
J.
,
2020
, “
Classification and Identification of Brands of Iron Ores Using Laser-Induced Breakdown Spectroscopy Combined With Principal Component Analysis and Artificial Neural Networks
,”
Anal. Methods
,
12
(
10
), pp.
1316
1323
.10.1039/C9AY02443C
42.
Morais
,
C. L. M.
,
Costa
,
F. S. L.
, and
Lima
,
K. M. G.
,
2017
, “
Variable Selection With a Support Vector Machine for Discriminating Cryptococcus Fungal Species Based on ATR-FTIR Spectroscopy
,”
Anal. Methods
,
9
(
20
), pp.
2964
2970
.10.1039/C7AY00428A
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