CN114121147B - Neural cell adhesion molecule detection system - Google Patents
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Abstract
The invention relates to the technical field of nerve cell detection and discloses a nerve cell adhesion molecule detection system which comprises a sample acquisition module, a characteristic collection module, an analysis processing module and a central control module; the sample acquisition module is used for acquiring a blood sample of a detection target and forming a detection sample, and then sending the detection sample to the analysis processing module; the characteristic collection module is used for collecting characteristic information of the detection target and forming a first information set, and then sending the first information set to the analysis processing module; and the analysis processing module finds out the index with the strongest correlation with the liver cancer and establishes a liver cancer judgment model through the analysis processing of the detection sample, thereby judging whether the patient has the liver cancer. The invention has the beneficial effects of improving the accuracy of liver cancer judgment results, effectively helping to accurately judge early liver cancer patients and improving prognosis and survival rate of the patients.
Description
Technical Field
The invention relates to the technical field of nerve cell detection, in particular to a nerve cell adhesion molecule detection system.
Background
Hepatocellular carcinoma (Hepatocellular carcinoma, HCC) is one of the most common malignant tumors worldwide, and as a malignant tumor, the incidence rate is ranked third in the fifth world, and the mortality rate is seriously harmful to human health [1]. The major causative factors of hepatocellular carcinoma (hereinafter referred to as liver cancer or HCC) include hepatitis b virus (hepatitis B virus, HBV) infection, hepatitis c virus (hepatitis C virus, HCV) infection, and alcoholic liver disease.
With the progress of modern medical technology, survival rate and quality of life of liver cancer patients are improved, but due to lack of early judging measures and possible tumor metastasis, a large number of liver cancer patients lose early operation opportunities, so that death rate of liver cancer patients is high, and the reason is mainly that liver cancer judgment is often in late stage, and early liver cancer judgment lacks high-accuracy imaging means and serum markers. Liver biopsy is the gold standard for judging liver cancer at present, but the clinical use of liver biopsy is limited by the potential bleeding risk and the tumor spreading risk. Therefore, the development of serum markers related to liver cancer judgment has very important clinical significance.
Since the approval of the Food and Drug Administration (FDA) in the 80 s of the 20 th century, measurement of serum alpha-fetoprotein (AFP) content has been one of the conventional methods for liver cancer judgment, and although AFP is more sensitive to early and very early stage liver cancer judgment, AFP has been elevated in patients with chronic hepatitis, cirrhosis or other liver diseases, and its specificity has been unsatisfactory. Abnormal prothrombin (PIVKA-II), also known as des-gamma-carboxyprothrombin (DCP), has been internationally confirmed in its judgment value for liver cancer, although the mechanism of PIVKA-II production in liver cancer is not yet fully elucidated. Because the area under the curve (AUC), sensitivity and specificity of the serum PIVKA-II are superior to those of AFP when the serum PIVKA-II judges liver cancer, the detection of the PIVKA-II has been incorporated into screening management items of Japanese liver cancer high risk groups. However, according to different researches, the judging efficacy of AFP and PIVKA-II can only reach 70% -85%, and the accuracy is widely questioned. Therefore, searching new serological tumor markers has important significance for accurate analysis and judgment of liver cancer.
Disclosure of Invention
The invention aims to provide a nerve cell adhesion molecule detection system so as to solve the technical problem of low accuracy of early liver cancer judgment results.
In order to achieve the above purpose, the invention adopts the following technical scheme: the nerve cell adhesion molecule detection system comprises a sample acquisition module, a characteristic collection module, an analysis processing module and a central control module;
the sample acquisition module is used for acquiring a blood sample of a detection target and forming a detection sample, and then sending the detection sample to the analysis processing module;
the characteristic collection module is used for collecting characteristic information of the detection target and forming a first information set, and then sending the first information set to the analysis processing module;
the analysis processing module comprises a data storage unit and a data analysis unit, and the data processing unit is used for storing the detection sample and the first information set; the data analysis unit is used for carrying out analysis processing on the detection sample according to a preset analysis strategy and combining the first information set to obtain an analysis judgment result of the detection target on liver cancer;
the central control module comprises a control unit and a display unit, and the control unit is used for controlling the normal operation of the detection system; and the display unit is used for displaying the analysis and judgment result of the detection target on the liver cancer.
The principle and the advantages of the scheme are as follows: in practical application, a blood sample of a detection target is obtained through a sample obtaining module to serve as the detection sample, characteristic information of the detection target is collected through a characteristic collecting module, then an index with strong correlation with liver cancer is analyzed, a liver cancer judging model is built through the obtained index, analysis and judgment on the liver cancer are carried out on the detection target, and finally an accurate judging result is obtained. Compared with the prior art, the method has the advantages that the analysis model and the analysis means adopted in the process of carrying out liver cancer related indexes and specific judgment are more advanced, so that the liver cancer related indexes are more comprehensively analyzed, a liver cancer judgment model with higher accuracy is established, the liver cancer judgment capability is obviously improved, the accuracy of a liver cancer judgment result is effectively ensured, accurate judgment of early-stage liver cancer patients can be effectively facilitated, and the prognosis and survival rate of the patients are greatly improved.
Preferably, as a modification, the blood sample of the detection target is obtained by collecting venous blood in a fasting state of the detection target with a blood collector.
The chemical component content in the blood of normal people is influenced by age, sex and the like, and is also influenced by factors such as spirit, diet, activity, medication and the like, and the chemical component in the blood can change along with the change of the state, so that venous blood in a fasting state is collected, various substances in the body reach a relatively stable and balanced state at the moment, the physiological state of the human body can be clearly and clearly reflected, the relevant analysis of liver cancer is facilitated, and the accuracy of analysis results is ensured.
Preferably, as an improvement, the characteristic information includes abdominal pain, anorexia, emaciation, hypodynamia and jaundice.
According to actual case situation analysis, the symptoms of liver cancer may be abdominal pain, anorexia, emaciation, hypodynamia, jaundice and the like, so that the characteristics of easy viewing and judgment are collected as characteristic information, an auxiliary effect can be achieved on the side of judging the liver cancer, and the accuracy of judging the liver cancer is improved.
Preferably, as an improvement, the detection targets include liver cancer patients and non-liver cancer patients.
The detection targets are set as liver cancer patients and non-liver cancer patients, so that the collected analysis samples are more comprehensive, and the correlation indexes of liver cancer judgment are subjected to comparative analysis, the indexes most correlated with the liver cancer are obtained, and the accuracy of liver cancer analysis judgment results is improved.
Preferably, as a modification, the preset analysis strategy includes,
detecting the content of neural cell adhesion molecules in the detection sample by using a double-antibody sandwich ELISA detection sample kit;
analyzing the correlation between the clinical basic characteristic information of the detection target and the protein level of the neural cell adhesion molecule, and simultaneously analyzing the relation between the expression level of the neural cell adhesion molecule and liver cancer;
screening indexes directly related to liver cancer accurate judgment in clinical basic characteristic information by using a logistic regression model, and establishing a liver cancer judgment model according to screening results output by the logistic regression model;
and analyzing and judging the detection sample by using a liver cancer judgment model to obtain an accurate judgment result of liver cancer.
Through the preset analysis strategy, not only the characteristic information related to the liver cancer can be analyzed, but also the index with the strongest correlation with the liver cancer judgment can be screened out through the logistic regression model, and the liver cancer judgment model is built on the basis, so that the accurate judgment of the liver cancer is realized, the accuracy of the liver cancer judgment result is improved, the early-stage liver cancer patients can be subjected to corresponding intervention treatment, and the survival rate of the liver cancer patients is greatly improved.
Preferably, as an improvement, the clinical essential characteristic information includes age, sex, history of hepatitis B, liver cirrhosis, albumin content, bilirubin content, glutamic pyruvic transaminase content, glutamic oxaloacetic transaminase content, platelet content, alpha fetoprotein content, and abnormal prothrombin content.
The clinical basic characteristic information is listed through detection analysis of a detection sample, and factors possibly related to liver cancer are considered according to clinical test experience and medical common knowledge, so that the analysis of the correlation with the level of the neural cell adhesion molecule protein can be comprehensively carried out, the correlation between the factors and the liver cancer is obtained, and a reliable basis is provided for the follow-up accurate judgment of the liver cancer.
Preferably, as an improvement, the index directly related to the accurate judgment of the liver cancer in the clinical basic characteristic information is screened by using a logistic regression model, wherein the index is a first index which has strong correlation with the liver cancer and is analyzed by using a single factor regression model, and a second index which has statistical significance in the first index is incorporated into a multi-factor regression model to carry out mutual correction, so that a third index which has highest correlation with the liver cancer and has statistical significance is obtained.
Through rescreening of the single-factor regression model and the multi-factor regression model, interference factors can be completely eliminated, and an index with the highest correlation with liver cancer and statistical significance is obtained, so that the accuracy of judging liver cancer is improved, and accurate analysis and judgment of early liver cancer are realized.
Preferably, as an improvement, the first indicator includes history of hepatitis B, positive hepatitis B virus load, cirrhosis status, glutamic pyruvic transaminase content, glutamic oxaloacetic transaminase content, platelet content, alpha fetoprotein content, and neural cell adhesion molecule content.
The index with strong correlation acquired by the single factor regression model can laterally verify the factors related to the liver cancer on one hand, and select the most representative part by statistical screening of the factors on the other hand, so as to realize accurate interpretation of the liver cancer.
Preferably, as an improvement, the third index includes history of hepatitis B and neural cell adhesion molecule content.
Through analysis, two indexes with the most representative and statistical significance of the history of hepatitis B and the content of neural cell adhesion molecules are selected to realize accurate analysis and judgment of liver cancer, so that the accuracy of the liver cancer judgment result is greatly improved and the survival rate of liver cancer patients is improved on the basis of ensuring the function of early liver cancer judgment.
Preferably, as a modification, the second statistically significant index is an index having a reference value of less than 0.05.
In the analysis of medical data, a plurality of relevant index factors are obtained, but the influence of some index factors on the whole is not great, so that representative indexes are screened out to realize comprehensive and accurate analysis, and indexes with reference values smaller than 0.05 are taken into consideration, so that the index most relevant to liver cancer can be found, and accurate judgment of liver cancer is realized.
Drawings
FIG. 1 is a schematic diagram of a system for detecting neural cell adhesion molecules according to an embodiment of the present invention.
FIG. 2 is a schematic diagram showing the expression level of a serum neural cell adhesion molecule in a liver cancer patient and a non-liver cancer patient according to an embodiment of the neural cell adhesion molecule detection system of the present invention.
Detailed Description
The following is a further detailed description of the embodiments:
the labels in the drawings of this specification include: the device comprises a sample acquisition module 1, a characteristic collection module 2, an analysis processing module 3, a central control module 4, a data storage unit 5, a data analysis unit 6, a control unit 7 and a display unit 8.
Embodiment one:
this embodiment is basically as shown in fig. 1: the nerve cell adhesion molecule detection system comprises a sample acquisition module 1, a characteristic collection module 2, an analysis processing module 3 and a central control module 4;
the sample acquisition module 1 is used for acquiring venous blood of liver cancer patients and non-liver cancer patients in a fasting state and forming a detection sample, and then sending the detection sample to the analysis processing module 3;
the feature collection module 2 is used for collecting feature information of a detection target, including abdominal pain, anorexia, emaciation, hypodynamia and jaundice, forming a first information set, and then sending the first information set to the analysis processing module 3;
the analysis processing module 3 comprises a data storage unit 5 and a data analysis unit 6, and the data processing unit is used for storing the detection sample and the first information set; the data analysis unit 6 is used for carrying out analysis processing on the detection sample according to a preset analysis strategy and combining the first information set, and obtaining an analysis judgment result of the detection target on liver cancer;
the central control module 4 comprises a control unit 7 and a display unit 8, wherein the control unit 7 is used for controlling the normal operation of the detection system; and a display unit 8 for displaying the analysis and judgment result of the detection target on liver cancer.
When the analysis processing module 3 is used for analyzing and processing a detection sample, the content of the neural cell adhesion molecules in the detection sample is detected by using a double-antibody sandwich ELISA detection sample kit, so that the relation between the serum neural cell adhesion molecule protein level and clinical characteristics of liver cancer patients is analyzed, and as shown in the table 1, the ages, sexes, hepatitis B medical history, liver cirrhosis conditions, albumin content, bilirubin content, glutamic-pyruvic transaminase content (ALT), glutamic-oxaloacetic transaminase content (AST), platelet content, alpha fetoprotein content (AFP), abnormal prothrombin content (PIVKA-II) and neural cell adhesion molecule (NRCAM) content of 84 liver cancer patients and 29 non-liver cancer patients are listed.
Table 1: condition table for liver cancer patients and non-liver cancer patients
As shown in FIG. 2, the lowest content of NRCAM in 84 liver cancer patients is 432.291pg/ml, the highest content is 4784.173 pg/ml, the content distribution of NRCAM in 29 non-liver cancer patients is 409.341-1785.389 pg/ml, the NRCAM expression level in liver cancer patients is obviously higher than that in non-liver cancer patients (1820.03 +/-793.123 vs. 920.10 +/-398.756 pg/ml, P < 0.001), and the correlation between the content of neural cell adhesion molecules (NRCAM) in serum and whether liver cancer exists or not is very high.
Further, the clinical feature variables were screened for indicators that could accurately determine liver cancer using logistic regression model, as shown in table 2, the single factor regression model found that the history of hepatitis b (P < 0.001), positive hepatitis b viral load (p=0.001), cirrhosis status (P < 0.001), platelet content (p=0.008), ALT (p=0.014), AST (p=0.003), clotting time (p=0.002), AFP (p=0.029) and NRCAM (P < 0.001) correlated strongly with liver cancer. The index with statistical significance (P < 0.05) in the indexes is included in a multi-factor model for mutual correction, the index directly related to liver cancer is judged and found, and the two indexes with statistical significance, namely the history of hepatitis B (P < 0.001) and the content of neural cell adhesion molecules (P=0.004) are obtained through analysis, wherein the indexes have the highest correlation with the liver cancer.
Table 2: single factor and multiple factor regression analysis screening liver cancer judgement index
According to the result obtained by multi-factor regression model analysis, the highest correlation between the hepatitis B infection condition and the neural cell adhesion molecule content and liver cancer judgment is achieved, and a liver cancer judgment model is established by utilizing the two indexes:
hepatitis b infection (with hepatitis b=1, no hepatitis b=0) 3.317997+ neuronal adhesion molecule content 0.003184
The established liver cancer judgment model is compared with the existing Alpha Fetoprotein (AFP), abnormal prothrombin (PIVKA-II) and nerve cell adhesion molecule (NRCAM) by using the method of the area under the curve (ROC) to independently judge the liver cancer. The liver cancer judging model has judging capacity (ROC 0.95, 95% CI 0.90-0.99, P < 0.001) obviously higher than that of single index, including nerve cell adhesion molecule (ROC 0.87, 95% CI 0.79-0.94, P < 0.001), alpha fetoprotein (ROC 0.87, 95% CI 0.80-0.93, P < 0.001) and abnormal prothrombin (ROC 0.84, 95% CI 0.77-0.92, P < 0.001).
And (3) analyzing and judging a detection sample of the patient by using a liver cancer judgment model, if the calculated model calculation value is greater than 6.14, judging the patient as a liver cancer patient, otherwise judging the patient as a non-liver cancer patient, and obtaining that the judgment sensitivity of the liver cancer judgment model is 89.29 percent, namely the probability of non-omission is 89.29 percent, the specificity is 96.55 percent, namely the probability of correct judgment is 96.55 percent through multiple test analysis.
The specific implementation process of this embodiment is as follows:
firstly, collecting venous blood of a liver cancer patient and a non-liver cancer patient in a fasting state by using a blood collector as a detection sample.
And secondly, collecting characteristic information of liver cancer patients and non-liver cancer patients, wherein the characteristic information comprises abdominal pain, anorexia, emaciation, hypodynamia and jaundice.
Thirdly, analyzing and processing the detection sample by utilizing the analysis and processing module 3 and combining the acquired characteristic information, firstly detecting the content of the neural cell adhesion molecules in the detection sample by utilizing a double-antibody sandwich ELISA detection kit, listing the ages, sexes, hepatitis B medical history, cirrhosis conditions, albumin content, bilirubin content, glutamic pyruvic transaminase content (ALT), glutamic oxaloacetic transaminase content (AST), platelet content, alpha fetoprotein content (AFP), abnormal prothrombin content (PIVKA-II) and neural cell adhesion molecule (NRCAM) of 84 liver cancer patients and 29 non-liver cancer patients, and analyzing the relation between the serum neural cell adhesion molecule protein level and the clinical characteristics of the liver cancer patients, wherein the result shows that the correlation between the content of the neural cell adhesion molecules (NRCAM) in serum and whether liver cancer exists is very high.
Fourth, the index with strong correlation with liver cancer metastasis is detected by utilizing a single factor regression model, then the index with statistical significance in the index is included into a multi-factor model to carry out mutual correction, and the index directly related to liver cancer is judged and found to be the history of hepatitis B (P < 0.001) and the content of neural cell adhesion molecules (P=0.004).
Fifthly, establishing a liver cancer judgment model by using two indexes of the history of hepatitis B (P < 0.001) and the content of neural cell adhesion molecules (P=0.004), analyzing a detection sample of a patient by the liver cancer judgment model, and judging the patient as a liver cancer patient if the calculated model calculation value is greater than 6.14, otherwise judging the patient as a non-liver cancer patient.
And step six, the display unit 8 of the central control module 4 displays the analysis and judgment result about liver cancer.
Liver cancer is one of the most common malignant tumors worldwide, and in recent years, the incidence of liver cancer is gradually increasing. With the progress of modern medical technology, survival rate and quality of life of liver cancer patients are improved, but due to lack of early judging measures and possible tumor metastasis, a large number of liver cancer patients lose early operation opportunities, so that death rate of liver cancer patients is high, and the reason is mainly that liver cancer judgment is often in late stage, and early liver cancer judgment lacks high-accuracy imaging means and serum markers. Liver biopsy is the gold standard for judging liver cancer at present, but the clinical use of liver biopsy is limited by the potential bleeding risk and the tumor spreading risk. Therefore, the development of serum markers related to liver cancer judgment has very important clinical significance. In the field of liver cancer detection, due to the advanced reasons such as the prior detection equipment and analysis method, the detection of liver cancer is performed based on a certain characteristic index, for example, the content of serum Alpha Fetoprotein (AFP), the content of abnormal prothrombin (PIVKA-II) and the content of neural cell adhesion molecule (NRCAM) are used for judging liver cancer independently, but the judging factor is too single, although the accuracy of the judging result is basically guaranteed, along with the development of the era, the accuracy of the liver cancer can not meet the detection requirement of people, and if early liver cancer patients can not be found timely, the life and health of the patients can be endangered. However, it is very difficult to combine different factors to detect liver cancer, because the activity indexes of different detection factors are different, and therefore, the different detection factors have different judging and analyzing means for liver cancer, so that the detection and judgment of liver cancer have no breakthrough progress, and the old set of detection methods are still being prolonged.
In the scheme, venous blood of a liver cancer patient and a non-liver cancer patient in a fasting state is collected to serve as a detection sample, a double-antibody sandwich ELISA detection sample kit is used for detecting the content of neural cell adhesion molecules in serum, and the relationship between the expression level of each index in the serum of the patient and clinical characteristics of the patient is analyzed to obtain a conclusion that the higher the expression level of the neural cell adhesion molecules is, the higher the possibility of early liver cancer is; and then analyzing the index with highest correlation with liver cancer analysis and judgment through a single factor regression model and a multi-factor regression model, establishing a liver cancer judgment model according to the analyzed index, accurately analyzing and judging the early liver cancer condition of a patient, accurately analyzing and judging the early liver cancer according to the combination of a plurality of indexes, improving the judgment capability of the early liver cancer, effectively helping the accurate judgment of the early liver cancer patient, enabling the early liver cancer patient to be correspondingly treated, and improving the prognosis and survival rate of the liver cancer patient.
Embodiment two:
this embodiment is basically the same as embodiment one, except that: the neural cell adhesion molecule detection system also comprises an intelligent terminal module, wherein the intelligent terminal module comprises a user side, the user side downloads an installed intelligent APP on a mobile phone or a tablet personal computer for a user, private information of the user is bound through the APP, and a detection result corresponding to the user can be seen and checked on the APP; if the detection result shows that the user is a non-liver cancer patient currently, reminding the user to review in a specified time according to the detection item index of the user; if the result shows that the user is a liver cancer patient, the user is reminded to go to the hospital as soon as possible for a diagnosis check. Meanwhile, the intelligent APP is further provided with an emergency call function, and a user can conduct one-key emergency call through the intelligent APP in any emergency.
The implementation process of this embodiment is the same as that of the first embodiment, except that:
a sixth step, the display unit 8 of the central control module 4 displays the analysis and judgment result about liver cancer, and simultaneously sends the analysis and judgment result to the user side of the intelligent terminal, the user checks the detection result of the user on the intelligent APP, if the detection result shows that the user is a non-liver cancer patient currently, the user is reminded to detect again within 15 days according to the detection item index of the user; if the result shows that the user is a liver cancer patient, the user is reminded to go to the hospital as soon as possible for a diagnosis check. Meanwhile, the intelligent APP is further provided with an emergency call function, a user can conduct one-key emergency call through the intelligent APP at any emergency, and after the nearest emergency ambulances around the user receive the call, the user can arrive at first time to rescue the user.
Through the intelligent terminal module, the channel for receiving the detection result by the user is increased, the user can know the detection result faster, and the detection result is displayed to the user, and meanwhile, the professional medical detection report is very strong in professional and can not be understood by the general user, so that popular and easily understood interpretation content is attached to the detection result, and the user can understand the detection result conveniently; if the user is detected to be a liver cancer patient, the user is reminded to go to a hospital for diagnosis and check as soon as possible so as to enter treatment as soon as possible and inhibit the development of liver cancer, and if the user is a non-liver cancer patient, the user is reminded to carry out recheck so as to achieve the aim of accurate judgment. In addition, this intelligent APP still through setting up "emergency call" function, when making the user meet any emergency, for example fall down, emergency such as sudden illness, can carry out a key emergency call through this intelligent APP, after the nearest ambulance around the user received the call, can arrive the rescue to the user very first time, ensure user's life safety.
Embodiment III:
this embodiment is basically the same as embodiment one, except that: the analysis processing module 3 of the above-mentioned neural cell adhesion molecule detection system further includes a prediction unit, if the content of neural cell adhesion molecule (NRCAM) in the detection sample is insufficient to determine that the patient is a liver cancer patient, but the content of neural cell adhesion molecule (NRCAM) is within the range of 70% -90% of the determination standard value, the prediction unit predicts the liver cancer suffering rate of the patient through analysis, and displays the liver cancer suffering rate through the display unit 8.
The implementation process of this embodiment is the same as that of the first embodiment, except that:
fifthly, establishing a liver cancer judgment model by using two indexes of the history of hepatitis B (P < 0.001) and the content of neural cell adhesion molecules (P=0.004), analyzing a detection sample of a patient by the liver cancer judgment model, and judging that the patient is a non-liver cancer patient if the calculated model calculation value is smaller than 6.14. However, the detection result shows that the content of the neural cell adhesion molecule (NRCAM) of the patient is in the range of 70% -90% of the judgment standard value, and the prediction unit predicts the liver cancer suffering rate of the patient by analysis, and then sends the suffering rate to the display unit 8.
And step six, the display unit 8 of the central control module 4 displays the analysis and judgment result on the liver cancer and the liver cancer suffering rate of the patient.
In the actual detection process, probably due to time reasons, the content of neural cell adhesion molecules (NRCAM) in the current detection sample is insufficient to judge that the patient is a liver cancer patient, but the detection result shows that the content of the neural cell adhesion molecules (NRCAM) of the patient is in the range of 70% -90% of the judgment standard value, the liver cancer suffering rate of the patient is predicted through analysis of the prediction unit and is displayed to be known by a user, so that the user can clearly know the situation of the user, and can go to a hospital to carry out diagnosis and examination by himself according to the actual situation of the user and combine with the predicted liver cancer suffering rate, and meanwhile, a plan for periodic review of the user is set according to the actual situation of the user, so that the importance degree of the user on the detection result can be improved, the accuracy of early liver cancer judgment can be further improved through periodic review, and the survival rate of the early liver cancer patient is ensured.
The foregoing is merely exemplary of the present invention, and specific technical solutions and/or features that are well known in the art have not been described in detail herein. It should be noted that, for those skilled in the art, several variations and modifications can be made without departing from the technical solution of the present invention, and these should also be regarded as the protection scope of the present invention, which does not affect the effect of the implementation of the present invention and the practical applicability of the patent. The protection scope of the present application shall be subject to the content of the claims, and the description of the specific embodiments and the like in the specification can be used for explaining the content of the claims.
Claims (7)
1. A neural cell adhesion molecule detection system, characterized in that: the device comprises a sample acquisition module, a characteristic collection module, an analysis processing module and a central control module;
the sample acquisition module is used for acquiring a blood sample of a detection target and forming a detection sample, and then sending the detection sample to the analysis processing module;
the characteristic collection module is used for collecting characteristic information of the detection target and forming a first information set, and then sending the first information set to the analysis processing module;
the analysis processing module comprises a data storage unit and a data analysis unit, wherein the data storage unit is used for storing the detection sample and the first information set; the data analysis unit is used for carrying out analysis processing on the detection sample according to a preset analysis strategy and combining the first information set to obtain an analysis judgment result of the detection target on liver cancer;
the central control module comprises a control unit and a display unit, and the control unit is used for controlling the normal operation of the detection system; the display unit is used for displaying the analysis and judgment result of the detection target on liver cancer;
the characteristic information comprises abdominal pain, anorexia, emaciation, hypodynamia and jaundice;
the preset analysis strategy includes the following,
detecting the content of neural cell adhesion molecules in the detection sample by using a double-antibody sandwich ELISA detection sample kit;
analyzing the correlation between the clinical basic characteristic information of the detection target and the protein level of the neural cell adhesion molecule, and simultaneously analyzing the relation between the expression level of the neural cell adhesion molecule and liver cancer;
screening indexes directly related to liver cancer accurate judgment in clinical basic characteristic information by using a logistic regression model, and establishing a liver cancer judgment model according to screening results output by the logistic regression model;
and analyzing and judging the detection sample by using a liver cancer judging model.
2. The neural cell adhesion molecule detection system of claim 1, wherein: the blood sample of the detection target is obtained by utilizing a blood collector to collect venous blood in the fasting state of the detection target.
3. The neural cell adhesion molecule detection system of claim 1, wherein: the detection targets include liver cancer patients and non-liver cancer patients.
4. The neural cell adhesion molecule detection system of claim 1, wherein: the clinical basic characteristic information comprises age, sex, history of hepatitis B, cirrhosis condition, albumin content, bilirubin content, glutamic pyruvic transaminase content, glutamic oxaloacetic transaminase content, platelet content, alpha fetoprotein content and abnormal prothrombin content.
5. The neural cell adhesion molecule detection system of claim 1, wherein: the screening of the indexes directly related to the accurate judgment of the liver cancer in the clinical basic characteristic information by using the logistic regression model is to analyze a first index with strong correlation with the liver cancer by using the single factor regression model, and to incorporate a second index with statistical significance in the first index into the multi-factor regression model for mutual correction, so as to obtain a third index with highest correlation with the liver cancer and statistical significance.
6. The neural cell adhesion molecule detection system of claim 5, wherein: the first index comprises hepatitis B history, positive hepatitis B virus load, cirrhosis condition, glutamic-pyruvic transaminase content, glutamic-oxaloacetic transaminase content, platelet content, alpha fetoprotein content and neural cell adhesion molecule content.
7. The neural cell adhesion molecule detection system of claim 5, wherein: the second index with statistical significance is an index with a reference value smaller than 0.05.
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