CN110849855A - Artificial intelligence-based living bacterium quantitative detection method - Google Patents
Artificial intelligence-based living bacterium quantitative detection method Download PDFInfo
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- CN110849855A CN110849855A CN201911211790.7A CN201911211790A CN110849855A CN 110849855 A CN110849855 A CN 110849855A CN 201911211790 A CN201911211790 A CN 201911211790A CN 110849855 A CN110849855 A CN 110849855A
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- 241000894006 Bacteria Species 0.000 title claims abstract description 54
- 238000013473 artificial intelligence Methods 0.000 title claims abstract description 9
- 238000001514 detection method Methods 0.000 title abstract description 6
- 239000006228 supernatant Substances 0.000 claims abstract description 17
- 239000007853 buffer solution Substances 0.000 claims abstract description 11
- 239000002504 physiological saline solution Substances 0.000 claims abstract description 11
- 238000010186 staining Methods 0.000 claims abstract description 11
- 230000001580 bacterial effect Effects 0.000 claims abstract description 10
- 239000003086 colorant Substances 0.000 claims abstract description 10
- 238000000034 method Methods 0.000 claims description 17
- 238000005119 centrifugation Methods 0.000 claims description 15
- DPKHZNPWBDQZCN-UHFFFAOYSA-N acridine orange free base Chemical compound C1=CC(N(C)C)=CC2=NC3=CC(N(C)C)=CC=C3C=C21 DPKHZNPWBDQZCN-UHFFFAOYSA-N 0.000 claims description 9
- DZBUGLKDJFMEHC-UHFFFAOYSA-N benzoquinolinylidene Natural products C1=CC=CC2=CC3=CC=CC=C3N=C21 DZBUGLKDJFMEHC-UHFFFAOYSA-N 0.000 claims description 9
- 238000000799 fluorescence microscopy Methods 0.000 abstract 1
- 239000000872 buffer Substances 0.000 description 6
- 239000003795 chemical substances by application Substances 0.000 description 5
- 150000007523 nucleic acids Chemical class 0.000 description 4
- 102000039446 nucleic acids Human genes 0.000 description 4
- 108020004707 nucleic acids Proteins 0.000 description 4
- 230000003287 optical effect Effects 0.000 description 4
- 239000000243 solution Substances 0.000 description 4
- 239000000126 substance Substances 0.000 description 4
- 241000588724 Escherichia coli Species 0.000 description 1
- 241000607142 Salmonella Species 0.000 description 1
- 241000607272 Vibrio parahaemolyticus Species 0.000 description 1
- 244000052616 bacterial pathogen Species 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 230000014670 detection of bacterium Effects 0.000 description 1
- 201000010099 disease Diseases 0.000 description 1
- 208000037265 diseases, disorders, signs and symptoms Diseases 0.000 description 1
- 230000004907 flux Effects 0.000 description 1
- 238000003018 immunoassay Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000004445 quantitative analysis Methods 0.000 description 1
- 238000012113 quantitative test Methods 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 201000008827 tuberculosis Diseases 0.000 description 1
Images
Classifications
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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
- G01N21/62—Systems in which the material investigated is excited whereby it emits light or causes a change in wavelength of the incident light
- G01N21/63—Systems in which the material investigated is excited whereby it emits light or causes a change in wavelength of the incident light optically excited
- G01N21/64—Fluorescence; Phosphorescence
- G01N21/6402—Atomic fluorescence; Laser induced fluorescence
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N1/00—Sampling; Preparing specimens for investigation
- G01N1/28—Preparing specimens for investigation including physical details of (bio-)chemical methods covered elsewhere, e.g. G01N33/50, C12Q
- G01N1/30—Staining; Impregnating ; Fixation; Dehydration; Multistep processes for preparing samples of tissue, cell or nucleic acid material and the like for analysis
Abstract
The invention discloses a living bacteria quantitative detection method based on artificial intelligence, relating to the technical field of bacteria quantitative detection, and comprising the following steps: s1, bacterial staining: dissolving a sample to be tested in physiological saline under an aseptic condition, uniformly mixing, standing, taking a mixed sample, centrifuging for the first time, removing supernatant, adding a buffer solution to adjust pH, centrifuging for the second time, removing part of supernatant, mixing the rest, adding a coloring agent, and standing in a dark place to obtain a dyed sample; s2, image acquisition (fluorescence microscopy on live bacteria): collecting images under a microscope; s3, uploading the image to a cloud server and processing the image; s4, image recognition: and the cloud server transmits the data into image recognition software to measure the number of the living bacteria.
Description
Technical Field
The invention relates to the technical field of quantitative detection of bacteria, in particular to a living bacteria quantitative detection method based on artificial intelligence.
Background
Bacteria are widely distributed in nature and have important influence on human life, some bacteria threaten human health, and many diseases such as anthracnose, tuberculosis and the like are caused by the bacteria.
China has specific requirements and specifications for detecting most bacteria, particularly pathogenic bacteria. The national standard is mainly used for detecting the quantity of bacteria qualitatively by a flat plate counting method, and the method is long in time consumption, consumes manpower and is difficult to detect quantitatively and quickly. Other methods such as PCR, immunoassays, do not distinguish between dead and live bacteria, nor do they allow quantitative analysis of bacteria.
Disclosure of Invention
Aiming at the defects in the prior art, the invention provides a method for quantitatively detecting live bacteria based on artificial intelligence, which comprises the following steps: s1, bacterial staining: dissolving a sample to be tested in physiological saline under an aseptic condition, uniformly mixing, standing, taking a mixed sample, centrifuging for the first time, removing supernatant, adding a buffer solution to adjust pH, centrifuging for the second time, removing part of supernatant, mixing the rest part, adding a coloring agent to dye a specific fluorescent group on a nucleic acid substance in living bacteria, selecting different coloring agents such as acridine orange, PI, ISTC and the like according to different bacterial types, standing in a dark place, and obtaining a dyed sample; s2, image acquisition: the stained bacteria emit fluorescence under the action of laser, living bacteria and dead bacteria are further distinguished, image acquisition is carried out under a microscope, and images of the living bacteria are recorded; s3, uploading the image to a cloud server and processing the image; s4, image recognition: the cloud server transmits the data into image recognition software, converts optical signals emitted by bacteria fluorescence into digital signals, and measures the number of living bacteria.
The volume of the sample to be tested in S1 is 25ml, the volume of the physiological saline is 225ml, the volume of the mixed sample is 1ml, the volume of the added buffer solution is 1ml, the volume of the removed part of supernatant is 900 μ l, and the volume of the added stain is 10 μ l.
The pH was adjusted to 6.8-7.4 as described in S1.
The standing time in S1 is 10min, and the light-shielding standing time is 5 min.
The coloring agent in S1 is one or more selected from acridine orange, PI and ISTC.
Conditions for two centrifugations in S1: first centrifugation: 3000rpm, 3 min; and (3) second centrifugation: 3000rpm, 3 min.
In the step S2, the microscope is a fluorescence microscope, and the image acquisition is performed by using laser.
Preferably, the laser wavelength is 480 nm.
The image recognition software described in S4 is MATLAB recognition software.
The invention has the beneficial effects that:
1. the method realizes the identification and quantitative detection of the target living bacteria with high flux, full automation and ready use.
2. Can specifically stain and quantitatively identify living bacteria, and eliminates the influence of dead bacteria on results.
3. The test time is short, the quantitative test is accurately completed within 30min, the result is obtained, and the time and the fund are saved.
Drawings
In order to more clearly illustrate the detailed description of the invention or the technical solutions in the prior art, the drawings that are needed in the detailed description of the invention or the prior art will be briefly described below. Throughout the drawings, like elements or portions are generally identified by like reference numerals. In the drawings, elements or portions are not necessarily drawn to scale.
FIG. 1 is an image of a sample of example 1 of the present invention after being processed by MATLAB recognition software;
FIG. 2 is an image of a sample of example 2 of the present invention after being processed by MATLAB recognition software;
FIG. 3 is an image of a sample of example 3 of the present invention after being processed with MATLAB recognition software.
Detailed Description
Embodiments of the present invention will be described in detail below with reference to the accompanying drawings. The following examples are only for illustrating the technical solutions of the present invention more clearly, and therefore are only examples, and the protection scope of the present invention is not limited thereby.
It is to be noted that, unless otherwise specified, technical or scientific terms used herein shall have the ordinary meaning as understood by those skilled in the art to which the invention pertains.
Example 1
A method for quantitatively detecting living bacteria based on artificial intelligence comprises the following steps: s1, bacterial staining: dissolving a sample to be tested of vibrio parahaemolyticus in physiological saline under an aseptic condition, uniformly mixing, standing, taking a mixed sample, centrifuging for the first time, removing supernatant, adding a buffer solution to adjust PH, centrifuging for the second time, removing part of supernatant, mixing the rest, adding a staining agent to stain specific fluorescent groups on nucleic acid substances in living bacteria, selecting different staining agents such as acridine orange, PI, ISTC and the like according to different bacterial types, standing in a dark place, and obtaining a stained sample; s2, image acquisition: the stained bacteria emit fluorescence under the action of laser, living bacteria and dead bacteria are further distinguished, image acquisition is carried out under a microscope, and images of the living bacteria are recorded; s3, uploading the image to a cloud server and processing the image; s4, image recognition: the cloud server transmits the data into image recognition software, converts optical signals emitted by bacteria fluorescence into digital signals, and measures the number of living bacteria.
The volume of the sample to be tested in S1 is 25ml, the volume of the physiological saline is 225ml, the volume of the mixed sample is 1ml, the volume of the added buffer solution is 1ml, the volume of the removed part of supernatant is 900 μ l, and the volume of the added stain is 10 μ l.
The pH was adjusted to 6.9 as described in S1.
The standing time in S1 is 10min, and the light-shielding standing time is 5 min.
The staining agent described in S1 was acridine orange, and the buffer was PBS buffer (0.1mM, pH 6.8).
Conditions for two centrifugations in S1: first centrifugation: 3000rpm, 3 min; and (3) second centrifugation: 3000rpm, 3 min.
In the step S2, the microscope is a fluorescence microscope, and the image acquisition is performed by using laser.
Preferably, the laser wavelength is 480 nm.
The image recognition software described in S4 is MATLAB recognition software.
As shown in FIG. 1, the number of bacteria detected was 161.
Example 2
A method for quantitatively detecting living bacteria based on artificial intelligence comprises the following steps: s1, bacterial staining: dissolving a sample to be tested of escherichia coli into physiological saline under an aseptic condition, uniformly mixing, standing, taking a mixed sample, centrifuging for the first time, removing supernatant, adding a buffer solution to adjust pH, centrifuging for the second time, removing part of supernatant, mixing the rest, adding a coloring agent to dye specific fluorescent groups on nucleic acid substances in living bacteria, selecting different coloring agents such as acridine orange, PI, ISTC and the like according to different bacterial types, standing in a dark place, and obtaining a dyed sample; s2, image acquisition: the stained bacteria emit fluorescence under the action of laser, living bacteria and dead bacteria are further distinguished, image acquisition is carried out under a microscope, and images of the living bacteria are recorded; s3, uploading the image to a cloud server and processing the image; s4, image recognition: the cloud server transmits the data into image recognition software, converts optical signals emitted by bacteria fluorescence into digital signals, and measures the number of living bacteria.
The volume of the sample to be tested in S1 is 25ml, the volume of the physiological saline is 225ml, the volume of the mixed sample is 1ml, the volume of the added buffer solution is 1ml, the volume of the removed part of supernatant is 900 μ l, and the volume of the added stain is 10 μ l.
The pH was adjusted to 7.0 as described in S1.
The standing time in S1 is 10min, and the light-shielding standing time is 5 min.
The staining agent described in S1 was acridine orange, and the buffer was PBS buffer (0.1mM, pH 6.8).
Conditions for two centrifugations in S1: first centrifugation: 3000rpm, 3 min; and (3) second centrifugation: 3000rpm, 3 min.
In the step S2, the microscope is a fluorescence microscope, and the image acquisition is performed by using laser.
Preferably, the laser wavelength is 480 nm.
The image recognition software described in S4 is MATLAB recognition software.
As shown in fig. 2, the number of detected bacteria was 6.
Example 3
A method for quantitatively detecting living bacteria based on artificial intelligence comprises the following steps: s1, bacterial staining: taking a sample to be tested of salmonella, dissolving the sample into physiological saline under an aseptic condition, uniformly mixing, standing, taking a mixed sample, centrifuging for the first time, removing supernatant, adding buffer solution to adjust PH, centrifuging for the second time, removing part of supernatant, mixing the rest, adding a coloring agent to dye a specific fluorescent group on a nucleic acid substance in living bacteria, selecting different coloring agents such as acridine orange, PI, ISTC and the like according to different bacterial types, standing in a dark place, and obtaining a dyed sample; s2, image acquisition: the stained bacteria emit fluorescence under the action of laser, living bacteria and dead bacteria are further distinguished, image acquisition is carried out under a microscope, and images of the living bacteria are recorded; s3, uploading the image to a cloud server and processing the image; s4, image recognition: the cloud server transmits the data into image recognition software, converts optical signals emitted by bacteria fluorescence into digital signals, and measures the number of living bacteria.
The volume of the sample to be tested in S1 is 25ml, the volume of the physiological saline is 225ml, the volume of the mixed sample is 1ml, the volume of the added buffer solution is 1ml, the volume of the removed part of supernatant is 900 μ l, and the volume of the added stain is 10 μ l.
The pH was adjusted to 7.2 as described in S1.
The standing time in S1 is 10min, and the light-shielding standing time is 5 min.
The staining agent described in S1 was acridine orange, and the buffer was PBS buffer (0.1mM, pH 6.8).
Conditions for two centrifugations in S1: first centrifugation: 3000rpm, 3 min; and (3) second centrifugation: 3000rpm, 3 min.
In the step S2, the microscope is a fluorescence microscope, and the image acquisition is performed by using laser.
Preferably, the laser wavelength is 480 nm.
The image recognition software described in S4 is MATLAB recognition software.
As shown in fig. 3, the number of detected bacteria was 27.
Finally, it should be noted that: the above embodiments are only used to illustrate the technical solution of the present invention, and not to limit the same; while the invention has been described in detail and with reference to the foregoing embodiments, it will be understood by those skilled in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; such modifications and substitutions do not depart from the spirit and scope of the present invention, and they should be construed as being included in the following claims and description.
Claims (8)
1. A method for quantitatively detecting living bacteria based on artificial intelligence is characterized by comprising the following steps: the method comprises the following steps: s1, bacterial staining: dissolving a sample to be tested in physiological saline under an aseptic condition, uniformly mixing, standing, taking a mixed sample, centrifuging for the first time, removing supernatant, adding a buffer solution to adjust pH, centrifuging for the second time, removing part of supernatant, mixing the rest, adding a coloring agent, and standing in a dark place to obtain a dyed sample; s2, image acquisition: collecting images under a microscope; s3, uploading the image to a cloud server and processing the image; s4, image recognition: and the cloud server transmits the data into image recognition software to measure the number of the living bacteria.
2. The method of claim 1, wherein: the volume of the sample to be detected in S1 is 25ml, the volume of the physiological saline is 225ml, the volume of the mixed sample is 1ml, the volume of the added buffer solution is 1ml, the volume of the removed part of supernatant is 900 μ l, and the volume of the added stain is 10 μ l; the pH was adjusted to 6.8-7.4 as described in S1.
3. The method of claim 1, wherein: the standing time in S1 is 10min, and the light-shielding standing time is 5 min.
4. The method of claim 1, wherein: the coloring agent in S1 is one or more selected from acridine orange, PI and ISTC.
5. The method of claim 1, wherein: conditions for two centrifugations in S1: first centrifugation: 3000rpm, 3 min; and (3) second centrifugation: 3000rpm, 3 min.
6. The method of claim 1, wherein: in the step S2, the microscope is a fluorescence microscope, and the image acquisition is performed by using laser.
7. The method of claim 6, wherein: the laser wavelength is 480 nm.
8. The method of claim 1, wherein: the image recognition software described in S4 is MATLAB recognition software.
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Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20030022270A1 (en) * | 2001-07-30 | 2003-01-30 | Mark Seaver | Automated epifluorescence microscopy for detection of bacterial contamination in platelets |
| WO2007029821A1 (en) * | 2005-09-08 | 2007-03-15 | Matsushita Electric Industrial Co., Ltd. | Apparatus for counting the number of microorganisms |
| CN101788480A (en) * | 2010-01-29 | 2010-07-28 | 浙江大学 | Hepatoxic substance sieving and evaluating method based on fluorescence labeling |
| US20110104685A1 (en) * | 2009-10-30 | 2011-05-05 | Kei Takenaka | Method for testing microorganism or the like and testing apparatus therefor |
| CN106407984A (en) * | 2015-07-31 | 2017-02-15 | 腾讯科技（深圳）有限公司 | Target object recognition method and device |
| CN108279314A (en) * | 2017-12-30 | 2018-07-13 | 深圳市阿瑟医疗机器人有限公司 | Method, system and the laboratory of separation detection cancer cell |
| US20180274020A1 (en) * | 2014-11-05 | 2018-09-27 | California Institute Of Technology | Microfluidic measurements of the response of an organism to a drug |
| US20180291420A1 (en) * | 2015-04-30 | 2018-10-11 | Logos Biosystems, Inc. | Method for Detecting, Identifying, or Counting Microorganisms, and System Using Same |
| US20190086301A1 (en) * | 2016-03-17 | 2019-03-21 | Delta Instruments B.V. | Detection of cells in a liquid sample |
-
2019
- 2019-12-02 CN CN201911211790.7A patent/CN110849855A/en active Pending
Patent Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20030022270A1 (en) * | 2001-07-30 | 2003-01-30 | Mark Seaver | Automated epifluorescence microscopy for detection of bacterial contamination in platelets |
| WO2007029821A1 (en) * | 2005-09-08 | 2007-03-15 | Matsushita Electric Industrial Co., Ltd. | Apparatus for counting the number of microorganisms |
| US20110104685A1 (en) * | 2009-10-30 | 2011-05-05 | Kei Takenaka | Method for testing microorganism or the like and testing apparatus therefor |
| CN101788480A (en) * | 2010-01-29 | 2010-07-28 | 浙江大学 | Hepatoxic substance sieving and evaluating method based on fluorescence labeling |
| US20180274020A1 (en) * | 2014-11-05 | 2018-09-27 | California Institute Of Technology | Microfluidic measurements of the response of an organism to a drug |
| US20180291420A1 (en) * | 2015-04-30 | 2018-10-11 | Logos Biosystems, Inc. | Method for Detecting, Identifying, or Counting Microorganisms, and System Using Same |
| CN106407984A (en) * | 2015-07-31 | 2017-02-15 | 腾讯科技（深圳）有限公司 | Target object recognition method and device |
| US20190086301A1 (en) * | 2016-03-17 | 2019-03-21 | Delta Instruments B.V. | Detection of cells in a liquid sample |
| CN108279314A (en) * | 2017-12-30 | 2018-07-13 | 深圳市阿瑟医疗机器人有限公司 | Method, system and the laboratory of separation detection cancer cell |
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Application publication date: 20200228 |