PRODUCT, METHOD AND SMARTPHONE IMAGING ANALYSIS SYSTEM FOR MERCURY ION DETECTION

PRODUIT, METHODE ET SYSTEME D'ANALYSE D'IMAGERIE PAR TELEPHONE INTELLIGENT POUR LA POUR LA DETECTION D'IONS DE MERCURE

English Abstract

A product, a method and a smartphone imaging analysis system for mercury ion
detection
are provided. A side flow tomography sensor based on nucleic acid base
mismatch is provided,
comprising at least one nucleotide sequence shown by sequence 1-3, which has a
detection
specificity and sensitivity. A smartphone imaging analysis system used in
conjunction with the
side flow tomography sensor is provided, which transfers the detection result
displayed in the side
flow tomography sensor into a concentration of mercury ion through a mobile
phone, to realize a
quantitative determination of the mercury ion in the sample. The above
product, method and
smartphone imaging analysis system for mercury ion detection are suitable for
untrained
personnel to carry out on-site test, and provide convenience for on-site
detection such as food
safety and environmental safety.

Claims

What is claimed is
1. A composition, comprising at least one of the following 1)-3),
1) a nucleotide sequence shown by SEQ ID N~: 1 in a Sequence Listing; or a
nucleotide
sequence which is obtained by substitution and/or deletion and/or addition of
one or several
nucleotides in the nucleotide sequence shown by SEQ ID N~: 1 in the Sequence
Listing and has
the same functions as the nucleotide sequence shown by SEQ ID N~: 1 in the
Sequence Listing;
2) a nucleotide sequence shown by SEQ ID N~: 2 in the Sequence Listing; or a
nucleotide
sequence which is obtained by substitution and/or deletion and/or addition of
one or several
nucleotides in the nucleotide sequence shown by SEQ ID N~: 2 in the Sequence
Listing and has
the same functions as the nucleotide sequence shown by SEQ ID N~: 2 in the
Sequence Listing;
and
3) a nucleotide sequence shown by SEQ ID N~: 3 in the Sequence Listing; or a
nucleotide
sequence which is obtained by substitution and/or deletion and/or addition of
one or several
nucleotides in the nucleotide sequence shown by SEQ ID N~: 3 in the Sequence
Listing and has
the same functions as the nucleotide sequence shown by SEQ ID N~: 3 in the
Sequence Listing.
2. The composition according to claim 1, further comprising at least one of
the following
1)-3):
1) 5' end of the nucleotide sequence shown by SEQ ID N~: 1 in the Sequence
Listing is
modified with a mercapto group;
2) 5' end of the nucleotide sequence shown by SEQ ID N~: 2 in the Sequence
Listing is
labeled with a biotin; and
3) 5' end of the nucleotide sequence shown by SEQ ID N~: 3 in the Sequence
Listing is
labeled with a biotin.
3. A side flow tomography sensor, comprising the composition according to any
one of
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claims 1 and/or 2.
4. The side flow tomography sensor according to claim 3, wherein the sensor
comprises a
quality control line and a test line, and the side flow tomography sensor
further comprises at least
one of the following 1)-4):
1) the nucleotide sequence shown by SEQ ID N~: 2 in the Sequence Listing is
located on the
test line;
2) the side flow tomography sensor further comprises a bonding pad, and on the
bonding pad
the nucleotide sequence shown by SEQ ID N~: 1 in the Sequence Listing is
coupled with gold
nanoparticles;
3) the nucleotide sequence shown by SEQ ID N~: 3 in the Sequence Listing is
located on the
quality control line; and
4) the side flow tomography sensor further comprises a substrate, an NC
membrane, a
bonding pad, an absorbent pad or paper and a sample pad; the NC membrane is
located in the
middle of the substrate; the bonding pad partially overlaps one end of the NC
membrane, and the
absorbent pad or paper partially overlaps the other end of the NC membrane;
the sample pad
partially overlaps an end of the bonding pad away from the NC membrane; and
the quality
control line and the test line are both located on the NC membrane, wherein
the quality control
line is located at an end close to the absorbent pad or paper, and the test
line is located at an end
close to the bonding pad.
5. A method for detecting mercury and/or mercury ion, comprising using the
composition
according to claims 1 and/or 2, or using the side flow tomography sensor
according to claims 3
and/or 4 to carry out detection.
6. A method for obtaining a concentration of an analyte from the side flow
tomography
sensor according to claims 3 and/or 4, comprising dropping the sample to be
detected into a
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sample pad area of the side flow tomography sensor, and carrying out a
quantitative analysis to
obtain the concentration of the analyte after the detection result is
displayed on the test line of the
side flow tomography sensor, wherein the method further comprises:
1) obtaining and/or displaying a detection image of the detection result of
the side flow
tomography sensor by a mobile phone;
2) calculating and/or inputting a gray scale intensity value and peak area S
formed in the test
line area of the side flow tomography sensor in the detection image;
3) manually inputting the quantitative detection standard curve of the side
flow tomography
sensor, S=693.711gC-1360.4, R2=0.9868, into the mobile phone software, wherein
1gC is the
logarithm value of the concentration of the analyte, and S is the peak area in
step 2);
4) inputting the peak area S obtained in step 2) into the quantitative
detection standard curve
in step 3), and calculating and outputting the concentration of the analyte in
the sample to be
detected to complete the detection work;
wherein the method for calculating the gray scale intensity value and the peak
area S formed
in the test line area of the side flow tomography sensor in the detection
image comprises:
taking the flow direction of the sample to be detected in the side flow
tomography sensor in
the detection image as the direction of abscissa, the ordinate being
perpendicular to the abscissa,
recording mean value of the gray scale value Y at all the ordinates having the
same abscissa x in
the detection image as a gray scale intensity value y of the column, and
establishing a gray scale
intensity P(X) function curve using the obtained gray scale intensity value y
of the column and
the abscissa value x;
the method for calculating the gray scale value Y being
Y=Q.299R+0.587G+0.114B, wherein
R, G and B are R, G and B values of pixel points;
the gray scale intensity function curve of the detection image having a
resolution of m×n
being P(x) =~~Y(x,y),x = 1,...,m ,
wherein in the gray scale intensity function curve, Y is a gray scale value, x
is an abscissa
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value, y is an ordinate value, and m and n are resolutions of the detection
image; and
selecting a peak surface of the gray scale intensity function curve in the
test line area,
calculating the peak area by integrating to obtain the peak area S.
7. A storage medium, comprising a stored program, wherein the method of claim
6 is
executed by a processor while the program is running.
8. A quantitative detection analysis system, comprising:
an image acquisition module, an image capture module, an area image processing
module
and a standard curve module, wherein the image acquisition module is
configured to invoke a
camera to perform image acquisition or read an image from a mobile phone
storage device, the
image capture module is configured to capture a portion of the image to be
detected, the area
image processing module is configured to calculate the pixel gray scale value
of the portion;
establishing a gray scale intensity function, selecting a peak surface
according to the gray scale
intensity function and calculating the peak area S; and the standard curve
module is configured to
input the standard curve and calculate and/or output the concentration of the
analyte;
wherein the method for calculating the gray scale intensity function and the
peak area S
comprises:
taking the flow direction of the sample to be detected in the side flow paper-
based
tomography sensor in the image as a direction of abscissa, the ordinate being
perpendicular to the
abscissa, recording mean value of the gray scale value Y at all the ordinates
having the same
abscissa x in the image as a gray scale intensity value y of the column, and
establishing a gray
scale intensity function curve using the obtained gray scale intensity value y
of the column and
the abscissa value x;
the method for calculating the gray scale value Y being
Y=0.299R+0.587G+0.114B, wherein
R, G and B are R, G and B values of pixel points;
the gray scale intensity function curve of the image having a resolution of
m×n being
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<IMG>
wherein in the gray scale intensity function curve, Y is a gray scale value, x
is an abscissa
value, y is an ordinate value, and m and n are resolutions of the image; and
selecting a peak surface of the gray scale intensity function curve in the
test line area in the
side flow paper-based tomography sensor in the image, calculating the peak
area by integrating to
obtain the peak area S.
9. The system according to claim 8, wherein the method for obtaining the
standard curve
comprises:
1) providing a various of standard samples, wherein the concentration of the
analyte in the
various of standard samples is diluted by the same multiple;
2) respectively detecting the various of standard samples by using the side
flow paper-based
tomography sensor, and respectively acquiring and/or displaying a detection
image of the
detection results of the side flow paper-based tomography sensor through a
mobile phone;
3) calculating and/or outputting a various of peak area S formed by the test
line area of the
side flow paper-based tomography sensor in the detection image of the various
of standard
samples; and
4) taking a concentration value C of the analyte or a logarithm value 1gC of
the
concentration value C of the analyte in the various of standard samples as an
abscissa, taking the
various of peak area S value corresponding to different concentrations of
analytes obtained in step
3) as an ordinate, to draw a picture and give a various of discrete points,
connecting the various of
discrete points to a straight line, wherein the slope of the straight line is
the slope value a in the
standard curve S=a×C+b or S=a×1gC+b, the intercept of the straight
line and the abscissa axis is
the intercept value b, wherein C is the concentration of the analyte, and S is
the peak area S.
10. Use of the composition according to claims 1 and/or 2, the side flow
tomography sensor
according to claims 3 and/or 4, the method according to claims 5 or 6, the
storage medium
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according to claim 7 and the system according to claim 8 and/or 9.
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Description

PRODUCT, METHOD AND SMARTPHONE IMAGING ANALYSIS SYSTEM FOR
MERCURY ION DETECTION
CROSS REFERENCE TO RELATED APPLICATIONS
[0001] This application claims the priority of Chinese Patent Application No.
201810688361.8,
filed on June 28, 2018, and titled with "PRODUCT, METHOD AND SMARTPHONE
IMAGING ANALYSIS SYSTEM FOR MERCURY ION DETECTION", and the disclosures of
which are hereby incorporated by reference.
FIELD
[0002] The present disclosure relates to the field of biological detection
technology, specifically
to a product, a method and a smartphone imaging analysis system for mercury
ion detection.
BACKGROUND
[0003] Water-soluble divalent mercury ion is a relatively common heavy metal
risk factor in
food safety and drinking water safety. Mercury ion has strong bioaccumulation
and is harmful to
the human body, and would damage the nervous system, digestive system, brain
tissue and kidney
tissue even at very low concentrations. Many countries and organizations have
adjusted the
maximum allowable upper limit for mercury ions in drinking water samples. For
example, the
World Health Organization (WHO) specifies that the maximum allowable limit for
mercury ions
in drinking water is not more than 6 ng mL-1 (30 nM), the US Environmental
Protection Agency
(EPA) specifies that the acceptable limit for mercury ions in drinking water
is 2 ng mL-1 (10 nM),
and both the European Union (EU) Drinking Water Standard and the Chinese
Ministry of Health
stipulate that the maximum allowable limit for mercury ions is not more than 1
ng mL-I (5 nM).
Therefore, the detection of trace amount of mercury ions is a common concern
worldwide. At
present, establishing a sensor for detecting mercury ion based on the "nucleic
acid base mismatch"
recognition system is a mainstream research trend, which means that two
thymine bases of DNA
can mismatch with a mercury ion to form a stable "T-Hg(II) -T" structure.
However, most of
these sensors face the dilemma of a complex system and being difficult to
detect quantitatively.
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[0004] As an emerging fast detection platform, side flow tomography sensor has
characteristics
of fast, simple, specific, accurate, sensitive, etc. However, conventional
side flow tomography
sensor can only achieve qualitative or semi-quantitative determination, and
additional specialized
instruments are required for quantitative testing. In addition, there is no
simple and convenient
technology or portable instrument that can directly read quantitative
detection data from side flow
tomography sensor at present.
SUMMARY
[0005] The present disclosure provides a product, method and smartphone
imaging analysis
system for mercury ion detection, which may transfer the detection result
displayed in the side
flow tomography sensor into a concentration of mercury ion through a mobile
phone, so as to at
least realize the simple and effective quantitative determination of the
concentration of mercury
ion in the sample. The side flow tomography sensor based on nucleic acid base
mismatch and/or
the smartphone imaging analysis system used in conjunction provided in the
present disclosure is
very suitable for untrained personnel to carry out on-site test, and provides
great convenience for
on-site detection such as food safety and environmental safety.
[0006] An object of the present disclosure is to provide a composition,
comprising at least one
of the following 1)-3),
1) a nucleotide sequence shown by SEQ ID N.2: 1 in a Sequence Listing; or a
nucleotide
sequence which is obtained by substitution and/or deletion and/or addition of
one or several
nucleotides in the nucleotide sequence shown by SEQ ID N2: 1 in the Sequence
Listing and has
the same functions as the nucleotide sequence shown by SEQ ID N2: 1 in the
Sequence Listing;
specifically, the function comprises at least one of the following (1)-(4):
(1) being able to
specifically recognize or bind the nucleotide sequence shown by SEQ ID N2: 3
in the Sequence
Listing; (2) being able to specifically recognize or bind a nucleotide
sequence which is obtained
by substitution and/or deletion and/or addition of one or several nucleotides
in the nucleotide
sequence shown by SEQ ID N2: 3 in the Sequence Listing; (3) being able to form
a T-Hg(II)-T
structure with mercury and the nucleotide sequence shown by SEQ ID N2: 2 in
the Sequence
Listing, when there is mercury; and (4) being able to form a T-Hg(II)-T
structure with mercury
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and a nucleotide sequence which is obtained by substitution and/or deletion
and/or addition of
one or several nucleotides in the nucleotide sequence shown by SEQ ID N2: 2 in
the Sequence
Listing, when there is mercury;
2) a nucleotide sequence shown by SEQ ID N2: 2 in the Sequence Listing; or a
nucleotide sequence which is obtained by substitution and/or deletion and/or
addition of one or
several nucleotides in the nucleotide sequence shown by SEQ ID N2: 2 in the
Sequence Listing
and has the same functions as the nucleotide sequence shown by SEQ ID N2: 2 in
the Sequence
Listing; specifically, the functions comprises at least one of the following
(1)-(2): (1) being able
to form a T-Hg(II)-T structure with mercury and the nucleotide sequence shown
by SEQ ID N2: 1
in the Sequence Listing, when there is mercury; and (2) being able to form a T-
Hg(II)-T structure
with mercury and a nucleotide sequence which is obtained by substitution
and/or deletion and/or
addition of one or several nucleotides in the nucleotide sequence shown by SEQ
ID N2: 1 in the
Sequence Listing, when there is mercury; and
3) a nucleotide sequence shown by SEQ ID N2: 3 in the Sequence Listing; or a
.. nucleotide sequence which is obtained by substitution and/or deletion
and/or addition of one or
several nucleotides in the nucleotide sequence shown by SEQ ID N2: 3 in the
Sequence Listing
and has the same functions as the nucleotide sequence shown by SEQ ID N.2: 3
in the Sequence
Listing. Specifically, the function comprises at least one of the following
(1)-(2): (1) being able to
specifically recognize or bind the nucleotide sequence shown by SEQ ID N2: 1
in the Sequence
.. Listing; (2) being able to specifically recognize or bind a nucleotide
sequence which is obtained
by substitution and/or deletion and/or addition of one or several nucleotides
in the nucleotide
sequence shown by SEQ ID N2: 1 in the Sequence Listing.
[0007] Another object of the present disclosure is to provide a side flow
tomography sensor,
comprising the composition according to any one of the above.
[0008] Another object of the present disclosure is to provide a method for
detecting mercury
and/or mercury ion, comprising using the above composition or the side flow
tomography sensor
to carry out the detection.
Another object of the present disclosure is to provide a method for obtaining
a concentration
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of an analyte from the side flow tomography sensor according to any embodiment
of the present
disclosure, comprising dropping the sample to be detected into a sample pad
area of the side flow
tomography sensor, and carrying out a quantitative analysis to obtain the
concentration of the
analyte after the detection result is displayed on the test line of the side
flow tomography sensor,
wherein the method further comprises:
1) obtaining and/or displaying a detection image of the detection result of
the side flow
tomography sensor by a mobile phone;
2) calculating and/or inputting a gray scale intensity value and peak area S
formed in the test
line area of the side flow tomography sensor in the detection image;
3) manually inputting the quantitative detection standard curve of the side
flow tomography
sensor, S=693.711gC-1360.4, R2=0.9868, into the mobile phone software, wherein
1gC is the
logarithm value of the concentration of the analyte, and S is the peak area in
step 2);
4) inputting the peak area S obtained in step 2) into the quantitative
detection standard curve
in step 3), and calculating and outputting the concentration of the analyte in
the sample to be
detected to complete the detection work;
wherein the method for calculating the gray scale intensity value and the peak
area S formed
in the test line area of the side flow tomography sensor in the detection
image comprises:
taking the flow direction of the sample to be detected in the side flow
tomography sensor in
the detection image as the direction of abscissa, the ordinate being
perpendicular to the abscissa,
recording mean value of the gray scale value Y at all the ordinates having the
same abscissa x in
the detection image as a gray scale intensity value y of the column, and
establishing a gray scale
intensity P(X) function curve using the obtained gray scale intensity value y
of the column and
the abscissa value x;
the method for calculating the gray scale value Y being
Y=0.299R+0.587G+0.114B, wherein
R, G and B are R, G and B values of pixel points;
the gray scale intensity function curve of the detection image having a
resolution of mxn
P(x)
being y=1
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wherein in the gray scale intensity function curve, Y is a gray scale value, x
is an abscissa
value, y is an ordinate value, and m and n are resolutions of the detection
image; and
selecting a peak surface of the gray scale intensity function curve in the
test line area,
calculating the peak area by integrating to obtain the peak area S.
[0009] Another object of the present disclosure is to provide a storage
medium, comprising a
stored program, wherein the method in the present disclosure is executed by a
processor while the
program is running.
[0010] Another object of the present disclosure is to provide a quantitative
determination
analysis system, comprising:
an image acquisition module, an image capture module, an area image processing
module
and a standard curve module, wherein the image acquisition module is
configured to invoke a
camera to perform image acquisition or read an image from a mobile phone
storage device, the
image capture module is configured to capture a portion of the image to be
detected, the area
image processing module is configured to calculate the pixel gray scale value
of the portion;
establishing a gray scale intensity function, selecting a peak surface
according to the gray scale
intensity function and calculating the peak area S; and the standard curve
module is configured to
input the standard curve and calculate and/or output the concentration of the
analyte;
wherein the method for calculating the gray scale intensity function and the
peak area S
comprises:
taking the flow direction of the sample to be detected in the side flow paper-
based
tomography sensor in the image as a direction of abscissa, the ordinate being
perpendicular to the
abscissa, recording mean value of the gray scale value Y at all the ordinates
having the same
abscissa x in the image as a gray scale intensity value y of the column, and
establishing a gray
scale intensity function curve using the obtained gray scale intensity value y
of the column and
the abscissa value x;
the method for calculating the gray scale value Y being
Y=0.299R+0.587G+0.114B, wherein
R, G and B are R, G and B values of pixel points;
the gray scale intensity function curve of the image having a resolution of
mxn being
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"
P(x)=-1 EY(x,y),x =1. in
n y=1
wherein in the gray scale intensity function curve, Y is a gray scale value, x
is an abscissa
value, y is an ordinate value, and m and n are resolutions of the image; and
selecting a peak surface of the gray scale intensity function curve in the
test line area in the
side flow paper-based tomography sensor in the image, calculating the peak
area by integrating to
obtain the peak area S.
[0011] Specifically, the method for obtaining the standard curve comprises:
1) providing a various of standard samples, wherein the concentration of the
analyte in the
various of standard samples is diluted by the same multiple;
2) respectively detecting the various of standard samples by using the side
flow paper-based
tomography sensor, and respectively acquiring and/or displaying a detection
image of the
detection results of the side flow paper-based tomography sensor through a
mobile phone;
3) calculating and/or outputting a various of peak area S formed by the test
line area of the
side flow paper-based tomography sensor in the detection image of the various
of standard
samples; and
4) taking a concentration value C of the analyte or a logarithm value 1gC of
the
concentration value C of the analyte in the various of standard samples as an
abscissa, taking the
various of peak area S value corresponding to different concentrations of
analytes obtained in step
3) as an ordinate, to draw a picture and give a various of discrete points,
connecting the various of
discrete points to a straight line, wherein the slope of the straight line is
the slope value a in the
standard curve S=axC+b or S=ax1gC+b, the intercept of the straight line and
the abscissa axis is
the intercept value b, wherein C is the concentration of the analyte, and S is
the peak area S.
[0012] Another object of the present disclosure is to provide use of any
composition of the
present disclosure, any side flow tomography sensor of the present disclosure,
any method of the
present disclosure, any storage medium of the present disclosure and any
system of the present
disclosure.
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[0013] Specifically, the use comprises qualitatively detection or
quantitatively detection of
mercury ion.
[0014] Compared with other detecting techniques, the detection method in the
present
disclosure has at least the following advantages:
(1) Nucleic acid base mismatched side flow tomography sensor: the sensor uses
gold
nanoparticle as signal, and uses a nucleic acid sequence that is rich in
thymine base (T) and is
able to specifically recognize mercury ion, and the mercury ion in the water
sample to be tested to
form a "T-Hg(II)-T" structure. A naked eye cognizable red line is shown on the
test line. The
depth of the line color is positively correlated with the concentration of
mercury ions. This at
least solves the problem of rapid recognizing the mercury ion in water and
transferring its
concentration into reliable optical signal.
(2) Smartphone imaging analysis system: the system is developed basing on
Android
system, comprising two parts of a human-computer interaction interface and an
image processing
algorithm design, to achieve rapid quantitative determination of side flow
tomography sensor.
The users may directly read the concentration of the analyte to be detected by
the side flow
tomography sensor, which at least solves the problem that the conventional
quantitative method
requires additionally equipment having large volume, expensive prices and
being incapable of
moving.
(3) The side flow tomography sensor based on nucleic acid base mismatch and/or
the
smartphone imaging analysis system used in conjunction provided in the present
disclosure have
signal response only to mercury ion, and have a good specificity of detection.
The system can
realize a lowest mercury ion test line of 1 OnM, and can quantitatively detect
mercury ions in the
liquid in a linear range of 10 nM to 1 mM, having a high sensitivity of
detection.
(4) The side flow tomography sensor based on nucleic acid base mismatch and/or
the
smartphone imaging analysis system used in conjunction provided in the present
disclosure are
very suitable for untrained personnel to carry out on-site test, and provide
great convenience for
on-site detection such as food safety and environmental safety.
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BRIEF DESCRIPTION OF DRAWINGS
[0015] The drawings described herein are to provide a further understanding of
the present
application, and constitute a part of the present application, but do not
constitute a limitation of
the present application. In the figures:
[0016] Figure 1 is a schematic diagram of the side flow tomography sensor
based on the
nucleic acid base mismatch, wherein No. 1-5 respectively represent for a
plastic substrate, an NC
membrane, a bonding pad, an absorbent pad (paper) and a sample pad.
[0017] Figure 2 is a graph showing the specific experimental results of the
side flow
tomography sensor based on the nucleic acid base mismatch, wherein the No. 1-
13 respectively
.. represent for the detection results of the solutions of Hg(II), Zn(II),
Mg(II), Pb(II), Fe(III), Fe(II),
Cu(II), K(I), Ca(II), Mn(II), Ag(I), Au(III) and Ni(II).
[0018] Figure 3 is a photograph showing the detection results displayed by the
lateral flow
tomography sensor.
[0019] Figure 4 is a graph of optical density distribution curve.
.. [0020] Figure 5 is a photograph showing the detection results displayed by
the lateral flow
tomography sensor, wherein No. 0-9 respectively represent for the detection
results having a
mercury ion concentration of negative, 1nM, lOnM, 100nM, 1 M, 101.1M, 100 M,
1mM, 10mM
and 100mM.
[0021] Figure 6 is a graph of optical density distribution curve, wherein No.
0-9 respectively
represents for the graph of optical density distribution curve having a
mercury concentration of
negative, 1nM, lOnM, 100nM, liaM, 101.1M, 100 M, 1mM, 10mM and 100mM.
[0022] Figure 7 is a graph showing the relationship between the peak area and
the mercury ion
concentration in the mercury standard solution.
[0023] Figure 8 is a standard curve of peak area and mercury ion
concentration.
[0024] Figure 9 is the structural representation of a quantitative
determination analysis system.
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DETAILED DESCRIPTION
[0025] The experimental methods used in the following examples are
conventional methods
unless otherwise specified.
[0026] The molecular biology experimental methods without specific description
in the
following examples are all carried out according to the specific methods
listed in the book
"Molecular Cloning Experiment Guide" (the 3rd edition) by J. Sambrook, or
according to the
specifications of kit and product.
[0027] The materials, reagents and the like used in the following examples are
commercially
available unless otherwise specified.
[0028] The following examples and specific descriptions are to be construed as
illustrative and
not restrictive.
Example 1 Preparation of side flow tomography sensor based on nucleic acid
base
mismatch
[0029] (I) Design of nucleotide sequence for detection
[0030] Sequence 1 (nucleotide sequence on the gold nanoparticles ): 5' -
ThioMC6
-GGTGGTGGTGGTGG-3'
[0031] Sequence 2 (nucleotide sequence on the test
line): 5 ' -Biotin-
CCCCCCCTCCTCCTCCTCC-3'
[0032] Sequence 3 (nucleotide sequence on the quality control line): 5'-Biotin-
CCCCCCCACCACCACCACC-3'
[0033] All the nucleotide sequences in the above design were obtained by
artificial synthesis.
Therein, the sequence I was obtained by modifying the 5' end of the nucleotide
sequence shown
by SEQ ID N2: 1 in the Sequence Listing with ThioMC6 mercapto group; the
sequence 2 was
obtained by labelling the 5' end of the nucleotide sequence shown by SEQ ID
.N2: 2 in the
Sequence Listing with biotin; and the sequence 3 was obtained by labelling the
5' end of the
nucleotide sequence shown by SEQ ID N2: 3 in the Sequence Listing with biotin.
[0034] (II) Preparation of side flow tomography sensor based on nucleic acid
base mismatch
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[0035] 1. The nucleic acid sequence enriched in thymine base named sequence 2
in the above
design was fixed on the test line (T line) of the NC membrane. The
specifically fixing process
referred to the document Nan Cheng, Yuancong Xu, Kunlun Huang, Yuting Chen,
Zhanshen Yang,
Yunbo Luo, Wentao Xu. One-step competitive lateral flow biosensor running on
an independent
quantification system for smart phones based in-situ detection of trace Hg(II)
in water. Food
Chemistry, 2017, 214: 169-175.
[0036] 2. The nucleic acid sequence enriched in thymine base named sequence 1
in the above
design was coupled with gold nanoparticle; both the preparation of gold
nanoparticles and the
coupling process can refer to the document in the above step 1.
[0037] 3. The nucleic acid sequence coupled with gold nanoparticles was fixed
on the bonding
pad; the specific fixing process can refer to the document in the above step
1.
[0038] 4. The nucleic acid sequence enriched in adenine base named sequence 3
in the above
design was fixed on the quality control line (C line) of the NC membrane; the
specific fixing
process also can refer to the document in the above step 1.
[0039] 5. The prepared NC membrane and the bonding pad were made into a side
flow
tomography sensor by a conventional method. Specifically, as shown in Figure
1, the prepared
NC membrane was fixed in the middle of the plastic pad 1; the prepared bonding
pad was
covered on one end of the NC membrane 2, so that the bonding pad 3 partially
overlapped the NC
membrane 2; the absorbent pad (paper) 4 was covered on the other end of the NC
membrane 2, so
that the NC membrane 2 partially overlapped the absorbent pad (paper) 4; the
sample pad 5 was
covered on the end of the bonding pad 3 away from the NC membrane 2, so that
the bonding pad
3 partially overlapped the sample pad 5; and finally the protective membrane
was covered, to
prepare a side flow tomography sensor.
[0040] The material of the NC membrane 2, bonding pad 3, sample pad 4 and
absorbent pad
(paper) 4 were respectively nitrocellulose membrane, glass fiber membrane,
glass fiber
membrane and absorbent paper.
[0041] (III) Detection principles and process of side flow tomography sensor
based on nucleic
acid base mismatch
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[0042] The detection principle of side flow tomography sensor based on nucleic
acid base
mismatch was based on a sandwich structure (nucleic acid sequence enriched in
thymine
base-mercury ion-nucleic acid sequence enriched in thymine base). As shown in
Figure 1, a
section of nucleic acid sequence enriched in thymine base was fixed on the
test line. Another
section of nucleic acid sequence enriched in thymine base was coupled with
gold nanoparticle,
and fixed on the bonding pad. A section of nucleic acid sequence enriched in
adenine base was
fixed on the quality control line. In a standard detection, a sample
containing a certain
concentration of mercury ions was firstly dropped onto the sample pad, and the
solution moved
upwards to the bonding pad along the direction of the tomography sensor due to
capillary force
(i.e., the suction of the absorbent pad or paper). The solution, together with
the complex of the
nucleic acid sequence enriched in thymine base coupled with the gold
nanoparticle on the
bonding pad, continuously moved upward to the test line along the direction of
the tomography
sensor. On the test line, the mercury ions of a certain concentration in the
sample combined with
the two sections of nucleic acid sequence enriched in thymine base, to form a
"T-Hg(II)-T"
structure, making the gold nanoparticle captured and accumulated on the test
line. A naked eye
recognizable red line showed on the test line. The larger the concentration of
the mercury ions in
the sample was, the darker the red was. The excessive amount of the complex of
the nucleic acid
sequence enriched in thymine base coupled with the gold nanoparticle
continuously moved
upward to the quality control line. Through the complementary pairing of
thymine and adenine
bases, the gold nanoparticles were captured and accumulated on the quality
control line, and a
naked eye recognizable red line showed on the quality control line. If the
sample did not contain a
certain concentration of mercury ions, the "T-Hg(II)-T" structure cannot be
formed on the
detection line, and there was no accumulation of gold nanoparticles, and thus
there was no naked
eye recognizable red line.
Example 2 Specificity test of side flow tomography sensor based on nucleic
acid base
mismatch
[0043] The side flow tomography sensor based on nucleic acid base mismatch
prepared in
Example 1 was used to detect the solutions of different metal ions, to test
the specificity of the
sensor, wherein the concentration of Hg(II) was 1 M, and the concentrations of
other metal ions
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were 1Mm.
[0044] The sample to be detected was dropped on the sample pad. About 5min
later, the side
flow tomography sensor displayed the detection results. The results of the
specificity test was
shown in Figure 2, in which the side flow tomography sensor based on nucleic
acid base
mismatch had signal response only to mercury ions, indicating that the method
had a good
specificity.
Example 3 Quantitative detection of the side flow tomography sensor realized
through a
smartphone imaging analysis system
[0045] When carrying out the detection of mercury ions with the side flow
tomography sensor
according to the present disclosure, i.e., the Example 1, a red line displayed
on the test line and/or
quality control line. Therefore, by using an image containing red lines, by
establishing a standard
curve corresponding to the concentrations of mercury ions in the mobile phone,
it was possible to
utilize the captured image containing the red line (or the image stored in
advance in the mobile
phone or downloaded via a mobile phone) to quantitatively detect the
concentrations of mercury
ions.
[0046] (I) Establishment of the standard curve
[0047] A series of mercury standard solutions of known concentration diluted
in multiples were
prepared. The mercury ion concentrations in different mercury standard
solutions were
respectively 0, 1nM, lOnM, 100nM, 11.1M, 10 M, 10011M, 1mM, 10mM andlOOmM.
[0048] The prepared mercury standard solutions of different concentrations
were respectively
dropped on 10 sample pads of side flow tomography sensors based on nucleic
acid base mismatch
prepared in Example 1, and about 5min later, the side flow tomography sensor
displayed the
detection results.
[0049] By taking a picture of the respective detection results displayed on
the side flow
tomography sensor with a mobile phone camera, an image showing a red line on
the quality
control line and/or the actual measurement line as shown in HG. 3 might be
obtained. The picture
might also be a picture stored in advance in the mobile phone or downloaded
via a mobile phone.
[0050] Thereafter, taking the longitudinal extension direction of the side
flow tomography
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sensor as an abscissa, and the mean gray scale value of the column
corresponding to each
abscissa as an ordinate, the curve as shown in Figure 4 (i.e., the optical
density distribution curve)
was obtained.
[0051] Selecting the curve, in which position the test line was, the peak area
of the curve in this
position was calculated (i.e., the curve was integrated), to obtain the peak
area value
corresponding to the mercury ions of a certain concentration.
[0052] In the above manner, the optical density distribution curve as shown in
Figure 6 was
obtained according to the photo shown in Figure 5. According to the obtained
curve, the peak
area values corresponding to mercury ions of different concentrations were
calculated (for
example, 0, 1nM, 1 OnM, 100nM, 111M, 10 M, 10011M, 1mM, 10mM and 100mM). As
shown in
Figure 7, a curve of the concentration of mercury ions in a known mercury
standard solution was
made using the obtained peak area. Finally, the standard curve shown in Figure
8,
S=693.711gC-1360.4, R2=0.9868, was obtained by Excel artificial fitting
calculation, wherein 1gC
was the logarithm value of the mercury ion concentration of the analyte, and S
was the peak area.
[0053] (II) Establishment of the smartphone imaging analysis system
[0054] The standard curve S=693.711gC-1360.4 (R2=0.9868) obtained by fitting
calculation
was built in the smartphone imaging analysis system.
[0055] The sample to be detected was dropped on the sample pad, and about 5min
later, the
side flow tomography sensor displayed the detection results. By taking a
picture of the detection
results with a mobile phone camera, the detected image was obtained. The
photographed result
can be stored in the mobile phone, or be directly used.
[0056] The method for calculating the gray scale intensity value and the peak
area S the formed
in the detection image comprised:
taking the flow direction of the sample to be detected in the side flow
tomography sensor in
the detection image as the direction of abscissa, the ordinate being
perpendicular to the abscissa,
recording mean value of the gray scale value Y at all the ordinates having the
same abscissa x in
the detection image as a gray scale intensity value y of the column, and
establishing a gray scale
intensity P(X) function curve using the obtained gray scale intensity value y
of the column and
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the abscissa value x;
the method for calculating the gray scale value Y being
Y=0.299R+0.587G+0.114B, wherein
R, G and B are R, G and B values of pixel points;
the gray scale intensity function curve of the detection image having a
resolution of mxn
P(x) = Y(x,y), x =1,...,m
being r=1
wherein in the gray scale intensity function curve, Y was a gray scale value,
x was an
abscissa value, y was an ordinate value, and m and n were resolutions of the
detection image; and
selecting a peak surface of the gray scale intensity function curve in the
test line area,
calculating the peak area by integrating to obtain the peak area S.
[0057] The obtained peak area S was input into the built-in standard curve, to
calculate and
output the concentration value C, i.e., the concentration value corresponding
to the test line in the
photograph of the detection result was output.
[0058] In a specific embodiment, the smartphone imaging analysis system in the
present
example can be developed and designed based on the Android system.
(III) Sensitivity of mercury ion quantitative detection by the side flow
tomography sensor
based on nucleic acid base mismatch and the smartphone imaging analysis system
[0059] As shown in Figure 7 and Figure 8, the peak area obtained by the
smartphone imaging
analysis system of the present example had a good correlation with the
concentration of mercury
ions. The side flow tomography sensor based on nucleic acid base mismatch
prepared in example
1 and the smartphone imaging analysis system in this example can realize a
lowest mercury ion
test line of lOnM, having a high sensitivity of detection, and can
quantitatively detect mercury
ions in water in a linear range of 10 nM to 1 mM.
[0060] In addition, Figure 9 showed a structural representation of a
quantitative determination
analysis system, comprising:
an image acquisition module, an image capture module, an area image processing
module
and a standard curve module, wherein the image acquisition module was
configured to invoke a
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camera to perform image acquisition or read an image from a mobile phone
storage device, the
image capture module was configured to capture a portion of the image to be
detected, the area
image processing module was configured to calculate the pixel gray scale value
of the portion;
establishing a gray scale intensity function, selecting a peak surface
according to the gray scale
intensity function and calculating the peak area S; and the standard curve
module was configured
to input the standard curve and calculate and/or output the concentration of
the analyte;
wherein the method for calculating the gray scale intensity function and the
peak area S
comprised:
taking the flow direction of the sample to be detected in the side flow paper-
based
tomography sensor in the image as a direction of abscissa, the ordinate being
perpendicular to the
abscissa, recording mean value of the gray scale value Y at all the ordinates
having the same
abscissa x in the image as a gray scale intensity value y of the column, and
establishing a gray
scale intensity function curve using the obtained gray scale intensity value y
of the column and
the abscissa value x;
the method for calculating the gray scale value Y being
Y=0.299R+0.587G+0.114B, wherein
R, G and B are R, G and B values of pixel points;
the gray scale intensity function curve of the image having a resolution of
mxn being
"
P(x) = _y Y(x, y), x m
n
wherein in the gray scale intensity function curve, Y was a gray scale value,
x was an
abscissa value, y was an ordinate value, and m and n were resolutions of the
image; and
selecting a peak surface of the gray scale intensity function curve in the
test line area in the
side flow paper-based tomography sensor in the image, calculating the peak
area by integrating to
obtain the peak area S.
[0061] The method for obtaining the standard curve comprised:
1) providing a various of standard samples, wherein the concentration of the
analyte in the
various of standard samples was diluted by the same multiple;
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2) respectively detecting the various of standard samples by using the side
flow paper-based
tomography sensor, and respectively acquiring and/or displaying a detection
image of the
detection results of the side flow paper-based tomography sensor through a
mobile phone;
3) calculating and/or outputting a various of peak area S formed by the test
line area of the
side flow paper-based tomography sensor in the detection image of the various
of standard
samples; and
4) taking a concentration value C of the analyte or a logarithm value 1gC of
the
concentration value C of the analyte in the various of standard samples as an
abscissa, taking the
various of peak area S value corresponding to concentrations of different
analytes obtained in step
3) as an ordinate, to draw a picture and give a various of discrete points,
connecting the various of
discrete points to a straight line, wherein the slope of the straight line was
the slope value a in the
standard curve S=axC+b or S=ax1gC+b, the intercept of the straight line and
the abscissa axis
was the intercept value b, wherein C was the concentration of the analyte, and
S was the peak
area S.
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Representative Drawing