CN111876479A - Application of noninvasive differential diagnosis of diabetic nephropathy/non-diabetic nephropathy based on urinary sediment specific micro RNA - Google Patents
Application of noninvasive differential diagnosis of diabetic nephropathy/non-diabetic nephropathy based on urinary sediment specific micro RNA Download PDFInfo
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Abstract
The invention provides application of noninvasive differential diagnosis of diabetic nephropathy/non-diabetic nephropathy based on urinary sediment specific micro RNA. The specific micro RNA is divided into an up-regulation group and a down-regulation group; wherein: the up-regulation group comprises at least one of microRNA-95-3p and microRNA-631; the down regulation group comprises at least one of microRNA-185-5p and microRNA-1246; if the micro RNA expression levels of the up-regulation group and the down-regulation group are respectively obviously up-regulated and obviously down-regulated, the nephropathy is particularly Diabetic Nephropathy (DN); otherwise, it is non-diabetic Nephropathy (NDRD); in addition, the following detection models were constructed:
Description
Technical Field
The invention relates to a marker for noninvasive differential diagnosis of diabetic nephropathy and non-diabetic nephropathy based on urinary sediment microRNA, and a related product and application thereof.
Background
With the development of socioeconomic and the improvement of living standard of people, the incidence rate of diabetes mellitus is continuously increased, and the number of people suffering from diabetes mellitus may break through 6.93 hundred million by 2045 years. Diabetic Nephropathy (DN) occurs in up to 20-40% of diabetic patients. Studies have shown that diabetic patients are not necessarily diabetic nephropathy when they develop renal disease. Many studies have found that some patients with diabetes complicated with kidney disease have different sensitivities to different treatment regimens compared with diabetic nephropathy patients, and in order to distinguish them, the disease is called non-diabetic renal disease (NDRD), i.e. patients with primary kidney disease have diabetes but do not cause secondary diabetic kidney damage. Since Diabetic Nephropathy (DN) is not the same type of disease as non-diabetic Nephropathy (NDRD), and its pathological features, clinical manifestations, treatment response, disease progression rate and prognosis are different, it is important to make accurate differential diagnosis (there are few cases where DN and NDRD coexist, but they are rare, and the treatment scheme has no significant difference compared with DN and NDRD alone, so this study is not considered temporarily). The gold standard for identifying DN and NDRD is kidney biopsy puncture, but the gold standard cannot be developed conventionally due to the limitations of being invasive, high in cost, high in technical condition requirement and the like, so that a simple, easy, stable and reliable identification method is urgently needed in clinical work.
microRNA (microRNA, miRNA) is endogenous non-coding single-stranded RNA consisting of 21-25 nucleotides, the expression of the microRNA has tissue specificity and timeliness, the post-transcriptional regulation and control function is achieved by inducing two modes of messenger RNA degradation and blocking protein translation, and the microRNA plays a key role in various physiological and pathological processes, such as: the early development of embryo, the differentiation of cell, the apoptosis, etc. also have important regulation and control function in the occurrence and development of kidney diseases. Urine is from glomerular filtration and renal tubule reabsorption, urinary sediment contains renal tissue cast-off cells, micro RNA can be detected in the urinary sediment, and urine specimens also have the characteristics of simple and convenient collection way, repeated acquisition, no wound, low cost and the like, and can be used as an ideal source of renal disease biomarkers. With the development of medical technology, the development trend of non-invasive detection is to find a non-invasive detection method. In recent years, microRNA has been widely used in noninvasive diagnosis, drug level monitoring, disease monitoring, and efficacy evaluation of various diseases such as tumors, renal diseases, cystic fibrosis, diabetes, cardiovascular diseases, caries, and the like. And a new technology and a new method based on urinary sediment micro RNA detection will gradually become a direction for the development of noninvasive diagnosis in the future.
Disclosure of Invention
The invention aims to provide a scheme for differential diagnosis of diabetic nephropathy/non-diabetic nephropathy based on urinary sediment microRNA.
The following application scheme is specifically obtained:
in a first aspect, the application of a unique probe based on urinary sediment specific micro RNA in constructing a test tool for non-invasive identification of diabetic nephropathy/non-diabetic nephropathy, wherein the specific micro RNA is divided into an up-regulation group and a down-regulation group; wherein:
the up-regulation group is at least one of microRNA-95-3p and microRNA-631;
down-regulating to at least one of microRNA-185-5p and microRNA-1246;
the identification basis is as follows: for a tested urinary sediment sample of a nephropathy patient, if the micro RNA expression of the up-regulation group is obviously up-regulated, and the micro RNA expression of the down-regulation group is obviously down-regulated, the nephropathy is specifically Diabetic Nephropathy (DN); otherwise, it is non-diabetic Nephropathy (NDRD).
Here, the specific form in which the test tool gives "authentication basis" is not limited, for example: the above conditions are described in the attached product specifications; if software is involved, the basis can also be embodied by a corresponding algorithm.
For selected microRNAs, their corresponding unique probes are known in the art.
Further, the testing tool is a micro RNA chip, a kit or an intelligent terminal taking a micro RNA detection result as input.
For microRNA chips, more comprehensive sampling of microRNA chips can be used in practice, but for the discrimination of diabetic nephropathy/non-diabetic nephropathy, only the specific microRNA is focused. The preparation and detection of the micro RNA chip are conventional methods, and the steps generally comprise urinary sediment collection, urinary sediment treatment and fluorescent labeling, chip detection and data analysis.
In a second aspect, a kit for differential diagnosis of diabetic nephropathy/non-diabetic nephropathy based on urinary sediment specific microRNA, wherein a unique probe of the specific microRNA is configured in the kit; the microRNA is the specific microRNA; the instructions for use of the kit give the following identification: for a detected urinary sediment sample of a nephropathy patient, if the micro RNA expression of an up-regulation group is obviously up-regulated and the micro RNA expression of a down-regulation group is obviously down-regulated, the nephropathy is specifically Diabetic Nephropathy (DN); otherwise, it is non-diabetic Nephropathy (NDRD).
The instruction book of the kit can also directly give the expression level of the specific micro RNA in the control group. The control group herein may be obtained from diagnosed non-diabetic Nephropathy (NDRD), or from diagnosed Diabetic Nephropathy (DN), or both control groups may be established.
In a third aspect, an intelligent terminal includes a processor and a program memory, and when a program stored in the program memory is loaded by the processor, the following steps are implemented:
obtaining a micro RNA test result of a detected urinary sediment sample of a nephropathy patient, wherein the micro RNA test result shows the expression level of the micro RNA;
obtaining the micro RNA expression level and identification basis of a corresponding control group (which can be recorded in an intelligent terminal in advance, or can be obtained from the outside, such as through networking and the like); the identification basis is as follows: if the micro RNA expression of the up-regulation group is obviously up-regulated and the micro RNA expression of the down-regulation group is obviously down-regulated, the nephropathy is specifically Diabetic Nephropathy (DN); otherwise, it is non-diabetic Nephropathy (NDRD);
and comparing the expression level of the micro RNA of the detected urinary sediment sample with that of a control group, and outputting an identification conclusion according to the identification basis.
Accordingly, a computer-readable storage medium storing a computer program characterized in that: the computer program, when loaded by a processor, performs the steps set out above.
In a fourth aspect, the application also combines binary Logistic stepwise regression analysis to utilize specific micro RNA to construct a diagnosis model of diabetic nephropathy/non-diabetic nephropathy, and finally, the differentiation capability of the diagnosis model is evaluated through ROC curve analysis (receiver operating characterization curve).
An intelligent terminal comprises a processor and a program memory, wherein when a program stored in the program memory is loaded by the processor, the following steps are realized:
obtaining a micro RNA test result of a detected urinary sediment sample of a nephropathy patient, wherein the micro RNA test result shows the expression level of the micro RNA;
calculating the detection values of the following models according to the micro RNA test result;
outputting and displaying the calculated detection value, and prompting an identification conclusion; if the detection value is less than or equal to 0.419, the Diabetic Nephropathy (DN) is determined; otherwise, if the detected value is greater than 0.419, the disease is non-diabetic Nephropathy (NDRD).
Accordingly, a computer readable storage medium stores a computer program which, when loaded by a processor, performs the steps listed above.
The above-mentioned "prompt/output discrimination result" may be a result of directly outputting whether or not the result is Diabetic Nephropathy (DN) or non-diabetic Nephropathy (NDRD), or may be a result of only providing a detection value, a reference value and a discrimination basis, or both.
The specific form of the intelligent terminal can be a special self-service terminal device, and can also be a mobile phone, a tablet personal computer and the like which are commonly used by common users.
In a fifth aspect, a system for non-invasively identifying diabetic nephropathy/non-diabetic nephropathy based on urinary sediment microrna, comprising:
A. the device is used for obtaining the expression level of the specific micro RNA of the urinary sediment sample, and the specific micro RNA is specifically identified by micro RNAmicroRNA-95-3p, micro RNA-631 and micro RNA-185-5p respectively;
B. the intelligent terminal (calculating the detection value of the model) is described above.
In the system, the device for acquiring the expression level of the specific microRNA of the urinary sediment sample and the intelligent terminal for identifying diabetic nephropathy/non-diabetic nephropathy based on the urinary sediment microRNA can be integrated medical diagnosis comprehensive devices or can be separated devices, and even no signal connection exists (for example, the microRNA test result of the urinary sediment sample can be automatically taken and sent by medical staff, patients and the like).
The device for obtaining the expression level of the specific micro RNA of the urinary sediment sample comprises a micro RNA chip, an incubation box and a biochip scanning system, wherein the micro RNA chip is at least provided with micro RNAmicroRNA-95-3p, micro RNA-631 and micro RNA-185-5 p.
The specific operations of the application schemes of the above aspects can be referred and combined mutually.
The application has the following beneficial effects:
the method can quickly and accurately identify whether the specific type of the renal disease patient to be tested belongs to diabetic nephropathy or non-diabetic nephropathy by detecting the expression level difference of specific micro RNA in the urinary sediment of the patient to be tested.
Drawings
FIG. 1 is a scattergram analysis of 83 differential microRNA results from urine sediment samples (diabetic nephropathy DN, n-41; non-diabetic nephropathy NDRD, n-42). In the figure, the ordinate represents the normalized fluorescence intensity NFI corresponding to the microrna on the microrna chip, the horizontal line in the figure represents the comparison between the two groups shown at the two ends, P is P-Value obtained according to one-way variance analysis, wherein P is less than 0.05, which represents significant difference; p <0.01, indicating that the difference was very significant; p <0.001, indicating very significant difference; DN: diabetic nephropathy patients, NDRD: patients with non-diabetic nephropathy.
FIG. 2 shows the ROC curve analysis results of 4 different kinds of selected microRNAs on the microRNA chip; AUC is area under line.
FIG. 3 is a DN/NDRD identification model constructed based on the micro RNA chip results, and the diagnostic ability of the model is evaluated by ROC curve analysis.
Detailed Description
The following describes the relevant experiments and conclusions of the present application to disclose the scientificity and feasibility of the technical solution of the present application. It should be understood that the development process and effort of the applicant is more than this.
Screening of differential microRNA (ribonucleic acid) of urinary sediment of diabetic nephropathy and non-diabetic nephropathy patients
The research method comprises the following steps:
1.1 Collection and pretreatment of urinary sediment samples
Urine sediment samples from Diabetic Nephropathy (DN) and non-diabetic Nephropathy (NDRD) patients used in this experiment were strictly approved by the general hospital of the liberation force (Human Research Ethics Committees (HRECs)). All volunteers who donated the urinary sediment samples, along with clinicians assisting in the sampling guidance, were informed, consented and highly coordinated to the study work, completing the sample collection under uniform sampling requirements. The concrete requirements are as follows: the organs of the sample donor except for the kidney disease should have no chronic diseases such as inflammation and tumor, and the donor needs to determine that the patient does not eat within 3 hours before collecting the urinary sediment and does not take the medicine within 24 hours during sampling, and then 150-200mL of morning urine of the patient is collected by a 50-mL sterile centrifugal tube and put into an ice box to be taken back to a laboratory. A total of 83 urine sediment samples were collected under clinician guidance: the specific sample information is shown in table 1, wherein 41 diabetic nephropathy patients (DN, n ═ 41) and 42 non-diabetic nephropathy patients (NDRD, n ═ 42) are listed.
The collected urine specimen was centrifuged at 3000 g.times.30 min in a 4 ℃ centrifuge, the supernatant was discarded, and the urine sediment was transferred with a pipette into a sterilized and previously labeled 1.5mL Eppendorf tube. Placing the Eppendorf tube in a 4 ℃ centrifuge for centrifugation of 13000g multiplied by 10min, discarding the supernatant, adding 1mL Trizol, blowing, beating and mixing evenly, and immediately placing in a refrigerator at-80 ℃ for storage.
TABLE 1 micro RNA chip for distinguishing diabetic nephropathy from non-diabetic nephropathy and construction of urinary sediment sample information by diagnostic model
Age (mean + -SD)
1.2 extraction and quality inspection of urinary sediment RNA
Taking out the frozen urine sediment specimen from a refrigerator at the temperature of minus 80 ℃, melting the frozen urine sediment specimen on ice, blowing and beating the frozen urine sediment specimen after the urine sediment specimen is completely melted, and uniformly mixing the frozen urine sediment specimen; adding 200uL chloroform into each sample, turning upside down and shaking vigorously for about 15s until the mixed liquid turns pink, and standing on ice for 3 min; the Eppendorf tube was placed in a 4 ℃ centrifuge and centrifuged at 12000rpm 10min, the liquid was separated into 3 layers after centrifugation, the uppermost layer of the transparent aqueous phase was gently transferred to a sterile enzyme-free 1.5mL Eppendorf tube, and the volume was recorded, the total volume was about 400-600 uL. Isopropanol with the same volume as the transferred upper aqueous phase was added to an Eppendorf tube, mixed by inversion, left to stand on ice for 20min and then centrifuged (4 ℃, 12000rpm, 15min), and the supernatant was discarded. 1mL of 75% ethanol in Diethylpyrocarbonate (DEPC) water was added to an Eppendorf tube, centrifuged at 4 ℃ to 12000rpm for 5min, the supernatant removed, and the washing step repeated 1 time again and the supernatant removed. Fastening the Eppendorf tube, placing the Eppendorf tube into a 4 ℃ centrifuge for instantaneous separation, sucking the liquid in the tube as dry as possible by using a pipette gun, and drying in a fume hood. And adding sterile and enzyme-free water to the Eppendorf tube by about 20uL, carrying out ice bath for 30min to 1h, and immediately carrying out RNA quality inspection after the precipitate is completely dissolved. And (3) blowing and stirring the uniformly mixed RNA solution, performing instantaneous separation, taking 1uL of the solution, and determining the concentration and purity of the total RNA on a NanoDrop 2000c spectrophotometer, wherein the RNA purity is better if the A260/280 range is 1.8-2.2.
1.3 labeling and chip detection of micro RNA of patient urinary sediment
Diluting the RNA sample concentration to 50ng/uL by using sterile and non-enzymatic water, and sucking 2uL of the diluted sample to 1.5mL of clean Eppendorf tubes for later use on ice; adding 2uL dephosphorizing solution into the sample tube, mixing the total 4uL dephosphorizing solution with the gun head, placing the mixture into a PCR instrument, and preserving the temperature at 37 ℃ for 30 min; after dephosphorylation of the sample is completed, 2.8uL of 100% dimethyl sulfoxide is added into each sample for denaturation, the mixed solution is heated at the temperature of 100 ℃ for 5-10min, and then is immediately transferred into ice water for cooling after being heated, and the next step of ligation reaction is immediately carried out. Sucking 4.5uL of reaction liquid into a sample tube, blowing and uniformly mixing, incubating for 2h at 16 ℃ after instantaneous separation, and completely pumping out the sample for later use.
Re-dissolving the drained sample in 17uL of enzyme-free water to prepare a hybridization reaction solution, slightly and uniformly mixing the prepared reaction solution in a vortex manner, heating the mixture in a metal bath at 100 ℃ for 5min, and quickly transferring the mixture into an ice-water bath to cool the mixture for 5min after the reaction is finished; the cover plate was placed on a base, 45uL of the reaction solution was pipetted onto the cover plate, the sample application side of the chip was placed face down on the cover plate, and then the chip was placed on a shelf of a Hybridization Oven and hybridized in the Hybridization Oven at 55 ℃ for 20 hours at a rotation speed of 20 rpm.
The instrument required for washing the chip is a wash tank Slide-standing dish, and the Kit used is a GeneExpression Wash Kit. Filling a Gene Expression Wash Buffer 1 into a No. 1 washing cylinder, and keeping the washing cylinder at room temperature for later use; placing the slide rack in No. 2 washing jar, injecting Gene Expression Wash Buffer 2 submerging the slide rack, and then placing dish on a magnetic stirrer; placing No. 3 washing jar preheated at 37 ℃ overnight on a magnetic stirrer, injecting Gene Expression Wash Buffer 2 preheated overnight, and continuously heating to maintain the temperature at 37 ℃; taking down the chip from the center of the hybridization oven, with the chip facing upwards, immersing in Gene Expression Wash Buffer 1, gently taking down the cover plate, and then quickly putting the chip into a slide holder placed in No. 2 Wash tank; repeating the operation, properly placing all the chips, starting the magnetic stirrer, and stirring at a medium speed for 5 minutes; after the end, taking out the slide rack with the chip, removing the contaminated liquid on the absorbent paper by gentle control, then putting the slide rack into a No. 3 washing cylinder, and stirring the slide rack for 5 minutes at 37 ℃ by using a magnetic stirrer at medium speed; after stirring, slightly removing the liquid stained on the slide holder on the absorbent paper, and immediately scanning the chip.
1.4 micro RNA chip data analysis
The process of digitizing the fluorescence signals of the micro RNA chips is completed by GenePix Pro (4000B) software, and data obtained by extracting data of each array comprises the following steps: the net difference FI (fluorescence intensity) obtained by subtracting the background signal from the probe signal, the standard deviation SD (Standard development) of the background, and the like. In the analysis process, firstly, judging the validity of the point data according to the FI/SD of each point, taking the point with the FI/SD being more than or equal to 1.5 as valid data, calculating the standard normalized fluorescence signal value NFI (normalized fluorescence intensity) of each probe, namely dividing the Median FI value of each probe by the sum of the FI values of the detection probes, and expressing the sum as follows by using a formula: NFIx＝Median FIx/(MedianFI1+Median FI2+Median FI3+…+Median FI37) From this, the NFI corresponding to the microRNA probe on each array can be obtained and used for statistical analysis.
The statistical analysis is mainly to perform ratio analysis on DN and NDRD group data by using GraphPad Prism 6.0, and screening microRNAs with NFI significant difference by combining with t test (the ratio of any microRNA between two groups is more than 1.50 or less than 0.67, and p is less than 0.05).
The research results are as follows:
2.1 micro RNA chip comparative analysis of expression level of DN and NDRD patients urinary sediment micro RNA
The microRNA expression level of urine sediment samples of 41 DN patients and 42 NDRD patients is comparatively analyzed by using a microRNA chip. The comparison result is shown in fig. 1, and a series of statistical data can visually reflect the significance and the dispersion degree of the difference compared among the groups on the graph. According to the results of the difference analysis among the groups: the expression level of microRNA-95-3p and microRNA-631 in DN patients is obviously higher than that of NDRD patients, and the expression level of microRNA-185-5p and microRNA-1246 in DN patients is obviously lower than that of NDRD patients.
From this, the following clinical applications can be derived: for a patient with unknown specific type of nephropathy, by detecting the urinary sediment microRNA expression level, if the expression of the microRNAmicroRNA-95-3 p and the expression of the microRNA-631 are obviously up-regulated and the expression of the microRNAmicroRNA-185-5 p and the expression of the microRNA-1246 are obviously down-regulated, the nephropathy is specifically Diabetic Nephropathy (DN); otherwise, it is non-diabetic Nephropathy (NDRD).
Constructing DN noninvasive diagnosis model by utilizing specific micro RNA
Accurate distinction between DN and NDRD patients is achieved by analyzing chip data of urinary sediment microRNA. Firstly, ROC curve analysis is carried out on the 4 kinds of micro RNAmicroRNA-95-3p, microRNA-631, microRNA-185-5p and microRNA-1246 with significant differences so as to estimate the diagnosis and identification capacity of the micro RNAmicroRNA-95-3p, microRNA-631, microRNA-185-5p and microRNA-1246 on the sample. As shown in FIG. 2, the area under the curve (AUC) of microRNAmicroRNA-185-5 p was 0.839, the specificity was 69.05%, the sensitivity was 85.37%, and the AUC of the remaining 3 microRNAs was not 0.8. Indicating that a single microRNA has certain disadvantages for distinguishing DN from NDRD patients.
The application builds a diagnostic model by combining several micro RNAs with reference to a binary stepwise Logistic regression method so as to improve the discrimination capability.
And (3) constructing a DN/NDRD identification model by using SPSS software and combining DN and NDRD patient urinary sediment micro RNA chip data by using a binary stepwise Logistic regression method, wherein the DN/NDRD identification model is used for distinguishing DN from NDRD patients. The final optimized model formula is as follows:
the AUC of modelDN/NDRD is 0.943, cut-off value is 0.419 (DN patient is detected as being equal to or less than 0.419, NDRD patient is detected as being greater than 0.419), the sensitivity is 90.48%, the specificity is 90.24%, and the ROC curve is shown in figure 3.
The model only relates to three kinds of micro RNA, and can identify diabetic nephropathy/non-diabetic nephropathy more simply, conveniently and accurately at low cost.
Claims (9)
1. The application of the unique probe based on the urinary sediment specific micro RNA in constructing a test tool for noninvasive identification of diabetic nephropathy/non-diabetic nephropathy is characterized in that: the specific micro RNA is divided into an up-regulation group and a down-regulation group; wherein:
the up-regulation group is at least one of microRNA-95-3p and microRNA-631;
down-regulating to at least one of microRNA-185-5p and microRNA-1246;
the identification basis is as follows: for a detected urinary sediment sample of a nephropathy patient with a history of diabetes, if the micro RNA expression of the up-regulation group is obviously up-regulated and the micro RNA expression of the down-regulation group is obviously down-regulated, the nephropathy is specifically Diabetic Nephropathy (DN); otherwise, it is non-diabetic Nephropathy (NDRD).
2. Use according to claim 1, characterized in that: the testing tool is a micro RNA chip, a kit or an intelligent terminal taking a micro RNA detection result as input.
3. A kit for identifying diabetic nephropathy/non-diabetic nephropathy based on urinary sediment microRNA, wherein a unique probe of specific microRNA is configured in the kit; the method is characterized in that: the microRNA is the specific microRNA described in claim 1; the instructions for use of the kit give the following identification: for a detected urinary sediment sample of a nephropathy patient, if the micro RNA expression of an up-regulation group is obviously up-regulated and the micro RNA expression of a down-regulation group is obviously down-regulated, the nephropathy is specifically Diabetic Nephropathy (DN); otherwise, it is non-diabetic Nephropathy (NDRD).
4. An intelligent terminal comprising a processor and a program memory, characterized in that: the program stored in the program memory realizes the following steps when being loaded by the processor:
obtaining microRNA test results of a tested urinary sediment sample of a nephropathy patient, wherein the microRNA test results represent the specific microRNA expression level in claim 1;
obtaining the micro RNA expression level and identification basis of a corresponding control group; the identification basis is as follows: if the expression level of the up-regulated microRNA is obviously up-regulated and the expression level of the down-regulated microRNA is obviously down-regulated, the nephropathy is particularly Diabetic Nephropathy (DN); otherwise, it is non-diabetic Nephropathy (NDRD);
and comparing the expression level of the micro RNA of the detected urinary sediment sample with that of a control group, and outputting an identification conclusion according to the identification basis.
5. A computer-readable storage medium storing a computer program, characterized in that: which when loaded by a processor carries out the steps as set forth in claim 4.
6. An intelligent terminal comprising a processor and a program memory, characterized in that: when the program stored in the program memory is loaded by the processor, the following steps are realized:
obtaining a micro RNA test result of a detected urinary sediment sample of a nephropathy patient, wherein the micro RNA test result shows the expression level of the micro RNA;
calculating the detection values of the following models according to the micro RNA test result;
outputting and displaying the calculated detection value, and prompting an identification conclusion; if the detection value is less than or equal to 0.419, the Diabetic Nephropathy (DN) is determined; otherwise, if the detected value is greater than 0.419, the disease is non-diabetic Nephropathy (NDRD).
7. A computer-readable storage medium storing a computer program, characterized in that: which when loaded by a processor carries out the steps as set forth in claim 6.
8. A system for discriminating diabetic nephropathy/non-diabetic nephropathy based on urinary sediment microRNA, comprising:
A. the device is used for detecting the expression level of specific microRNA of the urinary sediment sample, wherein the specific microRNA comprises microRNA-95-3p, microRNA-631 and microRNA-185-5 p;
B. an intelligent terminal as claimed in claim 6.
9. The system for identifying diabetic nephropathy/non-diabetic nephropathy based on urinary sediment microrna as claimed in claim 8, wherein: the device for obtaining the expression level of the specific micro RNA of the urinary sediment sample comprises a micro RNA chip, an incubation box and a biochip scanning system, wherein the micro RNA chip is at least provided with micro RNAmicroRNA-95-3p, micro RNA-631 and micro RNA-185-5 p.
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