CN117770793A - Gas signal molecule expiration detection method, system and electronic equipment - Google Patents
Gas signal molecule expiration detection method, system and electronic equipment Download PDFInfo
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Abstract
The invention discloses a method, a system and electronic equipment for detecting gas signal molecules by exhaling, and relates to the field of gas detection, wherein the method comprises the steps of obtaining the concentration of the gas signal molecules exhaled by an abnormal subject and a normal subject; the gas signal molecule comprises: NO, CO, H2S, H2, CH4, NH3, O2 and CO2; training a predictive model according to the concentration of gas signal molecules exhaled by the abnormal subjects and the normal subjects; and detecting the concentration of the gas signal molecules exhaled by the subject by using the trained predictive model. The invention can detect the accuracy of the expiration of the gas signal molecules.
Description
Technical Field
The present invention relates to the field of gas detection, and in particular, to a method, a system, and an electronic device for detecting expiration of gas signal molecules.
Background
Gas signaling molecules are endogenous molecules that have biological indication regulating effects on physiological and pathological changes of the body, typically produced by cells or/and bacteria in the body. By measuring the concentration change of the gas signal molecules in the exhaled body, the pathological processes of inflammation of the respiratory tract and the digestive tract, overgrowth of bacteria, unbalance of flora and the like can be monitored. At present, the detection of NO and CO in expired breath has been widely used for diagnosis and treatment and evaluation of inflammation of respiratory diseases such as asthma, chronic obstructive pulmonary disease and the like, while H 2 And CH (CH) 4 Has been used for the detection of excessive growth or loss of flora in the intestinal tract of irritable bowel syndrome and inflammatory bowel diseaseDiagnosis and treatment evaluation of balance.
Although NO elevation is also used for lung cancer detection, CH 4 Raising reports for intestinal cancer detection, such as the study of methane exhalations for colorectal cancer in journal of Lancet 1977, and the study of NO exhalations for lung cancer in UK respiratory medicine 2003, have been reported, but the specificity and sensitivity of the detection lack clinical diagnostic value, mainly because of NO and CH 4 Not only the lung cancer and the intestinal cancer are respectively increased, but also the pneumonia, the enteritis or other diseases are respectively increased, and the increase of the inflammation is more remarkable than the increase of the cancer, so that the specificity and the sensitivity of the cancer detection are lacking.
How to obtain more accurate prediction results according to gas signal molecule detection is a problem to be solved at present.
Disclosure of Invention
The invention aims to provide a method, a system and electronic equipment for detecting gas signal molecules by exhaling, which can detect the accuracy of the gas signal molecules by exhaling.
In order to achieve the above object, the present invention provides the following solutions:
a method of breath detection of a gas signaling molecule, comprising:
acquiring the concentration of gas signal molecules exhaled by the abnormal subjects and the normal subjects; the gas signal molecule comprises: NO, CO, H 2 S、H 2 、CH 4 、NH 3 、O 2 With CO 2 ；
Training a predictive model according to the concentration of gas signal molecules exhaled by the abnormal subjects and the normal subjects;
and detecting the concentration of the gas signal molecules exhaled by the subject by using the trained predictive model.
Optionally, the method for acquiring the concentration of the gas signal molecules exhaled by the abnormal subject and the normal subject specifically comprises the following steps:
the concentration of gas signal molecules exhaled by the abnormal subjects and the normal subjects is obtained by using a metabolic gas analyzer.
Optionally, the prediction model is Logistic regression, support vector machine, random forest or Xgboost.
A gas signaling molecule breath detection system comprising:
the concentration acquisition module is used for acquiring the concentration of the gas signal molecules exhaled by the abnormal subjects and the normal subjects; the gas signal molecule comprises: NO, CO, H 2 S、H 2 、CH 4 、NH 3 、O 2 With CO 2 ；
The prediction model training module is used for training a prediction model according to the concentration of gas signal molecules exhaled by the abnormal subjects and the normal subjects;
and the detection module is used for detecting the concentration of the gas signal molecules exhaled by the subject by using the trained prediction model.
An electronic device comprising a memory for storing a computer program and a processor that runs the computer program to cause the electronic device to perform the one gas signaling molecule exhalation detection method.
Optionally, the memory is a computer readable storage medium.
According to the specific embodiment provided by the invention, the invention discloses the following technical effects:
the invention provides a method, a system and electronic equipment for detecting expiration of gas signal molecules, which are realized by acquiring NO, CO and H 2 S、H 2 、CH 4 、NH 3 、O 2 With CO 2 According to the concentration of the gas, the concentration is detected by using a trained prediction model, and the accuracy of the detection result is improved by the concentration of various gases.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings that are needed in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic flow chart of a method for detecting expiration of gas signal molecules according to the present invention;
FIG. 2 is a schematic diagram showing the structure of a metabolic gas analyzer Sunvou-DA7349 in example 1;
FIG. 3 is a schematic view showing the effect of example 1;
FIG. 4 is a schematic structural diagram of the metabolic gas analyzer Sunvou-DA8000 in example 3.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The invention aims to provide a method, a system and electronic equipment for detecting gas signal molecules by exhaling, which can detect the accuracy of the gas signal molecules by exhaling.
In order that the above-recited objects, features and advantages of the present invention will become more readily apparent, a more particular description of the invention will be rendered by reference to the appended drawings and appended detailed description.
As shown in fig. 1, the present invention further provides a method for detecting expiration of gas signal molecules, including:
s101, acquiring the concentration of gas signal molecules exhaled by abnormal subjects and normal subjects; the gas signal molecule comprises: NO, CO, H 2 S、H 2 、CH 4 、NH 3 、O 2 With CO 2 The method comprises the steps of carrying out a first treatment on the surface of the The concentration of gas signal molecules exhaled by the abnormal subjects and the normal subjects is obtained by using a metabolic gas analyzer.
S102, training a prediction model according to the concentration of gas signal molecules exhaled by abnormal subjects and normal subjects; the prediction model is Logistic regression, support vector machine, random forest or Xgboost.
S103, detecting the concentration of gas signal molecules exhaled by the subject by using the trained prediction model.
Example 1
The present embodiment is an end-tidal sampling analysis method for intestinal cancer breath detection. A standardized scheme is preferably established to ensure reliability and repeatability of breath sampling. Before each test, we will use standard gas for daily calibration to ensure the accuracy of the test results. All subjects were prohibited from eating, smoking, and exercising for 12 hours prior to testing. Each subject was tested for 3 exhalations, each:
1) End-tidal NO (avoiding the effects of high nasal concentration NO): the breath hold time, the expiration duration, the flow rate and the expiration pressure are respectively set to 8s, 3-10s, 200mL/s and 10cmH 2 O；
2)H 2 S nose end-expiration (avoid high concentration H in oral cavity) 2 Effect of S): the breath hold time, the expiration duration, the flow rate and the expiration pressure are respectively set to 8s, 6-15s, 50mL/s and 10cmH 2 O；
3) Oral or nasal end-tidal: the breath hold time, the expiration duration, the flow rate and the expiration pressure are respectively set to 8s, 6-10s, 30-200mL/s and 10cmH 2 O。
The device in the embodiment is a metabolic gas analyzer Sunvou-DA7349, and comprises a sampling module 100 and an analysis module 200. The sampling module 100 is formed by connecting a first two-way valve 101, a buffer chamber 103 and a second two-way valve 102 in series, a pressure sensor 105 is connected in a gas circuit, and the rear end of the buffer chamber 103 is connected with a sampling pump 104; the analysis module 200 comprises a first three-way valve 201, a second three-way valve 202, an analysis pump 203, a filter 204, an oxygen sensor 205, a CO2 sensor 206 and H 2 S sensor 207, NO sensor 208, CO sensor 209, H 2 Sensor 210, and CH 4 The sensor 211 is composed of a first three-way valve 301 for switching the analysis sample gas or the zero gas filtered by the filter 204, and a second three-way valve 202 for switching the analysis gas into the H2S sensor detection channel or the CO sensor detection channel.
During the breath sample collection phase: the breath filter or one nostril of the subject is connected with the sampling module through the nose filter. After the subject begins to exhale, the pressure sensor 105 monitors the pressure of the exhale in real time and feeds back the flow rate of the exhale, when a pressure signal is detected, the first two-way valve 101 and the second two-way valve 102 are opened, the exhale enters the buffer chamber 103, the exhale which needs to be emptied is discharged from the rear end of the second two-way valve 102, and a sample of the exhale which meets the requirement is reserved in the buffer chamber 103; when the breath sample meets the requirement, the two-way valve one 101 and the two-way valve two 102 are closed.
During the breath sample analysis phase: after the sampling is completed, the device automatically turns on the analysis pump 203, and the expired air sample in the buffer chamber 103 is driven through the three-way valve two 202. If the concentration of H2S or NO is to be tested, the three-way valve II 202 switches the analysis gas to enter the detection channel of the H2S sensor, and if the concentration of CO, H2 or CH4 is to be tested, the three-way valve II 202 switches the analysis gas to enter the detection channel of the CO sensor, so that a sample signal value I1 of signal analysis is obtained; after the sample gas analysis is completed, the three-way valve I201 switches and analyzes the zero gas filtered by the filter 204, and the zero signal value I0 is reached, and the concentration C of each signal molecule of the end of the oral and nasal breathing can be obtained by calculating two sections of current values and the sensitivity S of each detector. The calculation formula is as follows:
C＝(I1-I0)/S；
test data of 80 subjects were collected, data analysis was performed using SPSS statistical software, and concentration values of each signal molecule were normalized (O 2 And CO 2 To assist in analyzing the gas, do not participate in statistical analysis). Comparing the concentration levels of the two groups of signal molecules using the Mann-WhitneyU test, the analysis showed that the colorectal cancer groups were CO, H compared to the control group 2 、CH 4 、H 2 The S and NO concentrations rise significantly, H in these 5 exhaled gas components 2 S showed the greatest concentration increase and is shown in table 1.
TABLE 1
Utilization of CO, H by binary logistic regression 2 、CH 4 、H 2 The concentrations of the S and NO gas components establish a variable optimization model to maximize sensitivity and specificity. The variable optimization model is described by the following formula:
HCHSN Score＝0.32*CO+0.21*H 2 +0.73*CH 4 +0.19*H 2 S+0.036*NO-11.63；
the AUC value for the HCHSN scoring model was 0.962, the associated sensitivity and specificity was 92.5% and 97.5% (determined by the optimal threshold = 0.9), respectively, compared to the univariate model, as shown in figure 3. Constructing the model facilitates diagnosis of colorectal cancer and provides important advice for the clinician to assess prognosis and survival time of colorectal cancer patients.
Example 2:
the present embodiment is an breath sampling analysis method for lung cancer breath detection. A standardized scheme is also established to ensure reliability and repeatability of breath sampling. All subjects were prohibited from eating, smoking, and exercising for 1 hour prior to testing. Each subject was tested for 3 exhalations, each:
1) NO port exhales (avoiding the effect of high concentration NO in nasal cavity): expiration duration, flow rate and expiration pressure were set to 6-10s, 50mL/s and 10cmH, respectively 2 O；
2)H 2 S-nose exhalations (avoiding the effects of high oral concentration H2S): the breath hold time, the expiration duration, the flow rate and the expiration pressure are respectively set to 6-10s, 30mL/s and 10cmH 2 O；
3) Oral or nasal exhalations of other molecules: the expiration duration, flow rate and expiration pressure were set to 6-10s, 30-100mL/s and 10cmH, respectively 2 O。
Breath samples were also collected and analyzed using the apparatus of example 1. Test data of 60 subjects were collected, data analysis was performed using SPSS statistical software, and concentration values of each signal molecule were normalized (O 2 And CO 2 To assist in analyzing the gas, do not participate in statistical analysis). The concentration levels of the two groups of signal molecules were compared using the Mann-Whitney U test, and analysis showed that the colorectal cancer groups were CO, H compared to the control group 2 、CH 4 、H 2 Both the S and NO concentrations were elevated, with NO exhibiting the greatest concentration increase among the 5 exhaled breath components, as shown in table 2.
TABLE 2
| Group number (n=60) | Lung cancer (n=30) | Control (n=30) | p |
| People number (Male/female) | 30(16/14) | 30(18/12) | 0.103 |
| Age of | 60.10±12.29 | 58.32±14.17 | 0.535 |
| CO | 9.90±3.48 | 3.20±1.62 | <0.001 |
| H 2 | 15.63±9.44 | 10.93±4.83 | 0.003 |
| CH 4 | 8.20±3.65 | 5.85±1.74 | 0.002 |
| H 2 S | 35.65±20.44 | 19.31±6.15 | <0.001 |
| NO | 28.83±7.23 | 10.02±3.42 | <0.001 |
Utilization of CO, H by binary logistic regression 2 、CH 4 、H 2 The concentrations of the S and NO gas components establish a variable optimization model to maximize sensitivity and specificity. The variable optimization model is described by the following formula:
HCHSN Score＝0.72*CO+0.14*H 2 +0.23*CH 4 +0.048*H 2 S+0.196*NO-9.89；
the AUC value for the HCHSN scoring model was 0.947, and the associated sensitivity and specificity was 93.3% and 96.4%, respectively (determined by the optimal threshold = 0.9), compared to the univariate model. Constructing the model facilitates diagnosis of lung cancer and provides important advice for the clinician to assess prognosis and survival time of lung patients.
Example 3
The embodiment is a method for sampling and analyzing the air extraction for detecting the oral cancer and nasopharyngeal cancer. The method uses a suitable sampling device to connect the oral cavity or nasal cavity toAnd (5) pumping and sampling at a certain flow rate for 10-20 seconds, and analyzing. The nose is required to breathe normally during oral cavity sampling, and the mouth is required to be kept to breathe during nasal cavity sampling, so that high concentration H in the oral cavity is avoided 2 Influence of S. The expired gas directly enters the sensor array for detection and analysis.
The device in this embodiment is a metabolic gas analyzer Sunvou-DA8000, as shown in FIG. 4, and comprises a sampling module 100 and an analyzing module 200. The sampling module 100 is formed by connecting a first two-way valve 101, a buffer chamber 103 and a second two-way valve 102 in series, a pressure sensor 105 is connected in a gas circuit, and the rear end of the buffer chamber 103 is connected with a sampling pump 104; the analysis module 200 is composed of a three-way valve I201, an analysis pump 203, a filter 204, an oxygen sensor 205 and CO 2 Sensor 206, NH3 sensor 212, H 2 S sensor 207, NO sensor 208, CO sensor 209, H 2 Sensor 210, and CH 4 Sensor 211, wherein three-way valve one 301 is used to switch the analysis sample gas or the ambient zero gas after filtration through filter 204.
During the breath sample collection phase: the oral filter or one nostril of the subject, which is well connected with the sampling module, is well connected with the sampling module through the nasal breathing filter. After the click test, the sampling pump 104 will collect oral or nasal gas into the buffer chamber 103 at a flow rate of 5/10 ml/s. When the breath sample is met, the two-way valve 101 and the sampling pump 104 are closed.
During the breath sample analysis phase: after the sampling is finished, the device automatically starts the analysis pump 203), and the expired air sample in the buffer chamber 103 is driven to pass through each sensor; after the sample gas analysis is completed, the three-way valve one 201 switches and analyzes zero gas filtered by the filter 204, and the concentration of each signal molecule of the oral cavity and the nasal cavity can be obtained by calculating two sections of current values.
Test data from subjects were also collected and analyzed, and out of these 6 exhaled gas components, NO from the oral cancer group showed the greatest concentration increase compared to the control group. H of nasopharyngeal carcinoma group 2 S showed the greatest increase in concentration.
Corresponding to the method, the invention also provides a gas signal molecule expiration detection system, which comprises:
the concentration acquisition module is used for acquiring the concentration of the gas signal molecules exhaled by the abnormal subjects and the normal subjects; the gas signal molecule comprises: NO, CO, H2S, H2, CH4, NH3, O2 and CO2;
the prediction model training module is used for training a prediction model according to the concentration of gas signal molecules exhaled by the abnormal subjects and the normal subjects;
and the detection module is used for detecting the concentration of the gas signal molecules exhaled by the subject by using the trained prediction model.
In order to execute the method corresponding to the embodiment to achieve the corresponding functions and technical effects, the invention also provides an electronic device, which comprises a memory and a processor, wherein the memory is used for storing a computer program, and the processor runs the computer program to enable the electronic device to execute the gas signal molecule expiration detection method.
The memory is a computer-readable storage medium.
Based on the above description, the technical solution of the present invention may be embodied in essence or a part contributing to the prior art or a part of the technical solution in the form of a software product stored in a storage medium, including several instructions for causing a computer device (which may be a personal computer, a server or a network device, etc.) to perform all or part of the steps of the method of the embodiments of the present invention. And the aforementioned computer storage medium includes: various media capable of storing program codes, such as a U disk, a mobile hard disk, a read-only memory, a random access memory, a magnetic disk or an optical disk.
In the present specification, each embodiment is described in a progressive manner, and each embodiment is mainly described in a different point from other embodiments, and identical and similar parts between the embodiments are all enough to refer to each other. For the system disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant points refer to the description of the method section.
The principles and embodiments of the present invention have been described herein with reference to specific examples, the description of which is intended only to assist in understanding the methods of the present invention and the core ideas thereof; also, it is within the scope of the present invention to be modified by those of ordinary skill in the art in light of the present teachings. In view of the foregoing, this description should not be construed as limiting the invention.
Claims (6)
1. A method for detecting expiration of a gas signal molecule, comprising:
acquiring the concentration of gas signal molecules exhaled by the abnormal subjects and the normal subjects; the gas signal molecule comprises: NO, CO, H 2 S、H 2 、CH 4 、NH 3 、O 2 With CO 2 ；
Training a predictive model according to the concentration of gas signal molecules exhaled by the abnormal subjects and the normal subjects;
and detecting the concentration of the gas signal molecules exhaled by the subject by using the trained predictive model.
2. The method for detecting the expiration of gas signal molecules according to claim 1, wherein the step of obtaining the concentration of the gas signal molecules expired by the abnormal subject and the normal subject specifically comprises the steps of:
the concentration of gas signal molecules exhaled by the abnormal subjects and the normal subjects is obtained by using a metabolic gas analyzer.
3. The method of claim 1, wherein the predictive model is Logistic regression, support vector machine, random forest, or Xgboost.
4. A gas signaling molecule exhalation detection system, comprising:
the concentration acquisition module is used for acquiring the concentration of the gas signal molecules exhaled by the abnormal subjects and the normal subjects; by a means ofThe gas signal molecule comprises: NO, CO, H 2 S、H 2 、CH 4 、NH 3 、O 2 With CO 2 ；
The prediction model training module is used for training a prediction model according to the concentration of gas signal molecules exhaled by the abnormal subjects and the normal subjects;
and the detection module is used for detecting the concentration of the gas signal molecules exhaled by the subject by using the trained prediction model.
5. An electronic device comprising a memory for storing a computer program and a processor that runs the computer program to cause the electronic device to perform a gas signal molecule exhalation detection method according to any one of claims 1 to 3.
6. The electronic device of claim 5, wherein the memory is a computer readable storage medium.
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