CA3053677A1 - Product, method and smartphone imaging analysis system for mercury ion detection - Google Patents
Product, method and smartphone imaging analysis system for mercury ion detectionInfo
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- CA3053677A1 CA3053677A1 CA3053677A CA3053677A CA3053677A1 CA 3053677 A1 CA3053677 A1 CA 3053677A1 CA 3053677 A CA3053677 A CA 3053677A CA 3053677 A CA3053677 A CA 3053677A CA 3053677 A1 CA3053677 A1 CA 3053677A1
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/75—Systems in which material is subjected to a chemical reaction, the progress or the result of the reaction being investigated
- G01N21/77—Systems in which material is subjected to a chemical reaction, the progress or the result of the reaction being investigated by observing the effect on a chemical indicator
- G01N21/78—Systems in which material is subjected to a chemical reaction, the progress or the result of the reaction being investigated by observing the effect on a chemical indicator producing a change of colour
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/84—Systems specially adapted for particular applications
- G01N21/8483—Investigating reagent band
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/84—Systems specially adapted for particular applications
- G01N21/8483—Investigating reagent band
- G01N2021/8494—Measuring or storing parameters of the band
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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.
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
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
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.
- -13560853.1
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.
- -13560853.1
[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
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 13560853.1 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.
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 13560853.1 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 13560853.1 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 13560853.1 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 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.
Another object of the present disclosure is to provide a method for obtaining a concentration 13560853.1 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 13560853.1 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 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 13560853.1 "
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.
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 13560853.1 "
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.
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.
13560853.1
13560853.1
[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.
13560853.1 BRIEF DESCRIPTION OF DRAWINGS
(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.
13560853.1 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.
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.
13560853.1 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 13560853.1 [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 13560853.1 [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 13560853.1 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 13560853.1 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 13560853.1 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 13560853.1 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;
13560853.1 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.
13560853.1
.. [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.
13560853.1 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 13560853.1 [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 13560853.1 [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 13560853.1 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 13560853.1 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 13560853.1 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 13560853.1 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;
13560853.1 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.
13560853.1
Claims (10)
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) 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.
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 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.
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 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 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.
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 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 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.
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 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.
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 according to claim 7 and the system according to claim 8 and/or 9.
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| CN201810688361.8 | 2018-06-28 | ||
| CN201810688361.8A CN108844951B (en) | 2018-06-28 | 2018-06-28 | Mercury ion detection product and method and smartphone imaging analysis system |
| PCT/CN2018/100985 WO2020000598A1 (en) | 2018-06-28 | 2018-08-17 | Mercury ion detection product and method and smart phone imaging analysis system |
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| CN112285078B (en) * | 2020-10-14 | 2023-04-25 | 安庆师范大学 | Novel method for detecting mercury ions on site based on intelligent DNA hydrogel |
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| CN1687752A (en) * | 2005-04-29 | 2005-10-26 | 农业部环境保护科研监测所 | Test paper for detecting heavy metal mercury rapidly, preparation method and application |
| CN102608108A (en) * | 2012-02-21 | 2012-07-25 | 温州医学院 | Nano biosensor method and kit for rapid detection of Hg ions |
| CN104076004A (en) * | 2014-07-16 | 2014-10-01 | 常熟理工学院 | Method for detecting mercury ionic concentration in sample |
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