Composition, kit and application thereof, and application of metabolite

Abstract

The invention discloses a composition, a kit containing the composition and application of vaginal metabolites in construction of a diagnostic model of dinoprostone suppository labor induction sensitivity, the composition comprises 400 microliters of an extracting solution and L-2-chlorophenylalanine serving as an internal standard, and the extracting solution is prepared from methanol and water according to the ratio of 4: 1 (V: V). Therefore, the prediction model of the dinoprostone labor induction sensitivity can be constructed based on the vaginal metabolite, so that decision and help are provided for clinicians to judge the dinoprostone labor induction sensitivity of pregnant women, and the method has important significance in reducing pain of patients.

Description

Technical Field

[0001] The present invention belongs to the technical field of biomarkers, and specifically relates to a composition, a kit comprising the composition, and applications of the composition and the kit, as well as applications of metabolites. Background Art

[0002] Induction of labor is a medical intervention before spontaneous labor begins. It is a common but challenging procedure in obstetrics. Current guidelines primarily select different induction methods based on the modified Bishop score. For women with a cervical Bishop score ≥7, the optimal induction method is oral PGE1 solution or artificial rupture of membranes combined with intravenous oxytocin. For women with a cervical Bishop score below 7, medication or mechanical methods are needed to soften and dilate the cervix before induction to increase the chances of a successful vaginal delivery.

[0003] Dinoprostone suppositories are widely used clinically as vaginal medications for inducing labor with an immature cervix. These controlled-release vaginal suppositories contain 10 mg of prostaglandin E2 and consist of a woven bag containing dinoprostone and a recovery tape. They release the active ingredient at a rate of approximately 0.3 mg / h. Their primary mechanisms for promoting cervical ripening include: ① Activating collagenase and elastase, which disrupts cervical collagen fibers, altering mucopolysaccharide concentrations and increasing hyaluronic acid levels, thereby softening the cervix; ② Directly acting on contractile proteins in uterine smooth muscle, increasing free calcium ion concentrations in uterine body myocytes and promoting uterine smooth muscle contraction; and ③ Promoting the formation of gap junctions in uterine smooth muscle cells, increasing the number of oxytocin receptors in the myometrium, and enhancing uterine sensitivity to oxytocin.

[0004] However, in actual clinical practice, we have observed that some women can initiate labor or achieve cervical ripening within 24 hours after using dinoprostone, while others still fail to achieve cervical ripening or enter labor after 24 hours. Based on these different scenarios, it can be assumed that some women are more sensitive to the drug and have a higher chance of vaginal birth using this method alone, while others are less sensitive to the drug and have a lower chance of vaginal birth using this method alone. Although the success of induction is closely related to parity, gestational age, fetal size, gestational age, pelvic condition, cervical Bishop score, and maternal body mass index (BMI), other studies have shown that the success rate of induction increases with gestational age. The success rate of induction in late preterm pregnancies (34 to 36 + 6 weeks of gestation) is similar to that of full-term pregnancies, while the success rate of induction in preterm pregnancies (considering only live births) between 24 and 33 + 6 weeks of gestation is 56.9% to 66.7%. Therefore, the clinical use of this drug for induction of labor is still somewhat unpredictable, but there is currently no reliable indicator to predict the sensitivity of dinoprostone to induction of labor.

[0005] The vaginal microbiome, a crucial component of the vaginal microbiome, is closely linked to women's reproductive health. Numerous studies have linked vaginal microbiome associations with bacterial vaginosis, sexually transmitted infections, cervical inflammatory disease, pelvic inflammatory disease, miscarriage, and preterm birth. Increased richness and diversity of vaginal microbes, as well as specific vaginal microbial compositions, are widely associated with an increased risk of spontaneous preterm birth. Recent studies have also revealed significant differences in vaginal microbial diversity between pregnant women with mature and immature cervixes at comparable gestational ages. Furthermore, several studies have suggested that vaginal microbes may influence cervical remodeling by affecting the cervical epithelial barrier, secreting proteases, and inducing inflammatory responses. To maintain its internal balance, the vaginal microbiome produces a variety of metabolites, including lactate, hydrogen peroxide, short-chain fatty acids, and bacteriocins, which play a crucial role in maintaining vaginal health. When the vaginal microbiome is dysregulated, the balance of vaginal metabolites is disrupted, and changes in specific vaginal metabolites can even serve as diagnostic indicators for vaginal diseases. However, the specific mechanisms by which vaginal microbes influence cervical ripening and sensitivity to exogenous prostaglandin E2 remain unclear. Therefore, mining metabolites with predictive value from the vaginal microenvironment and constructing a predictive model for dinoprostone-induced labor sensitivity are of great significance in providing decision-making and assistance to clinicians and reducing patients' suffering. Summary of the Invention

[0006] In view of the above situation, the present invention provides a composition and a kit containing the composition, which are used to construct a diagnostic model for sensitivity to dinoprostone suppositories for induction of labor based on vaginal metabolites. This model can accurately determine a parturient's sensitivity to dinoprostone for induction of labor.

[0007] The present invention solves the above technical problems through the following technical solutions.

[0008] Scheme 1. A composition comprising 400 μL of an extract and L-2-chlorophenylalanine as an internal standard, wherein the extract is methanol:water=4:1 (V:V).

[0009] Scheme 2: The composition described in Scheme 1 above is used to prepare a kit for constructing a diagnostic model for sensitivity to dinoprostone suppositories for induced labor through vaginal metabolites.

[0010] Scheme 3: The composition described in Scheme 2 above, wherein the method for constructing a diagnostic model for sensitivity of dinoprostone suppositories to induced labor using vaginal metabolites comprises the following steps: Step 1: Collect and preserve vaginal secretion samples; Step 2: sample processing; Step 3: Liquid chromatography-mass spectrometry detection; Step 4: data processing and interpretation; Step 5: Feature selection and model building.

[0011] Scheme 4: The composition described in Scheme 2 above, wherein the vaginal metabolites are: Floionolic acid, Mycolactone, Glucose aspartate, Calcitroic acid, Erythronic acid, Tanacetol B, and Leucyl-Threonine.

[0012] Scheme 5. The composition described in Scheme 3 above, wherein step 1 comprises: using a disposable sterile cotton swab to rotate at the posterior vaginal fornix to obtain a sample, provided that the pregnant woman has vaginal bleeding and has not undergone vaginal operation within 24 hours; breaking the swab after collection and placing it in a sterile freezing box; labeling the box and quickly freezing it in liquid nitrogen; and then transporting it to the laboratory and freezing it in a -80°C refrigerator for long-term storage.

[0013] Option 6: The composition described in Option 3 above, wherein the step 2 comprises: The entire sample was transferred to a 2 mL centrifuge tube and a 6 mm diameter grinding bead was added; the above composition, i.e., 400 μL of the extract (methanol: water = 4:1 (v:v)) containing L-2-chlorophenylalanine as an internal standard, was added; Grind in a frozen tissue grinder at -10°C, 50 Hz for 6 min; Low-temperature ultrasonic extraction was performed at 5°C and 40 KHz for 30 min; The sample was placed at -20°C for 30 min; Centrifuge at 13000G for 15 min at 4°C, transfer the supernatant into an injection vial with an inner cannula and analyze on an analyzer; 20 μL of supernatant was taken from each sample and mixed to serve as quality control samples.

[0014] Scheme 7: The composition described in Scheme 3 above, wherein in step 3, Chromatographic conditions were as follows: ACQUITY UPLC HSS T3 column, 100 mm × 2.1 mm id, 1.8 μm; Waters, Milford, USA; mobile phase A was 95% water + 5% acetonitrile containing 0.1% formic acid; mobile phase B was 47.5% acetonitrile + 47.5% isopropanol + 5% water containing 0.1% formic acid; injection volume was 3 μL; column temperature was 40°C; Mass spectrometry conditions: The samples were ionized by electrospray ionization, and the mass spectrometry signals were collected in positive and negative ion scanning modes, respectively.

[0015] Scheme 8. The composition described in Scheme 3 above, wherein step 4 comprises: importing the raw data into the metabolomics processing software ProgenesisQI for baseline filtering, peak identification, integration, retention time correction, and peak alignment, ultimately obtaining a data matrix including retention time, mass-to-charge ratio, and peak intensity information; then using the software to perform characteristic peak library search and identification, matching the MS and MS / MS mass spectrometry information with the metabolic database, setting the MS mass error to less than 10 ppm, and identifying metabolites based on the secondary mass spectrometry matching score.

[0016] Option 9: The composition described in Option 3 above, wherein the step five comprises: The characteristic ions of each substance are screened out by the triple quadrupole, and the signal intensity (CPS) of the characteristic ions is obtained in the detector. The mass spectrum file of the sample is opened and the chromatographic peaks are integrated and corrected. The peak area (Area) of each chromatographic peak represents the relative content of the corresponding substance. Finally, all the chromatographic peak area integration data are exported and saved; The top seven vaginal metabolites with variable importance projection greater than 1 and p-value less than 0.05 obtained based on the OPLS-DA model and upregulated in dinoprostone-induced labor insensitivity were screened; Method for constructing ROC curves and calculating cutoff values: handle missing values ​​and outliers, and divide the data into training and test sets, usually in a ratio of 7:3; use the pROC package in the R software to draw an ROC curve with 1-specificity as the horizontal axis and sensitivity as the vertical axis; the larger the AUC value, the better the model performance; calculate the Youden index at each possible threshold, the formula is Youden index = sensitivity + specificity - 1, and the threshold corresponding to the maximum Youden index is the optimal cutoff value.

[0017] Option 10. A kit comprising the test composition described in Option 1 or 2 and grinding beads.

[0018] Scheme 11: The kit described in Scheme 10 above, wherein a diagnostic model for sensitivity to dinoprostone suppositories for induced labor is constructed using vaginal metabolites according to the following method: The method comprises: Step 1: Collect and preserve vaginal secretion samples; Step 2: sample processing; Step 3: Liquid chromatography-mass spectrometry detection; Step 4: data processing and interpretation; Step 5: Feature selection and model building.

[0019] Scheme 12: Use of vaginal metabolites in constructing a diagnostic model for sensitivity to dinoprostone suppositories for induced labor, wherein the composition described in Scheme 1 or 2 above, or the kit described in Scheme 10 or 11 above is used.

[0020] Solution 13: The use described in Solution 12 above, wherein the method for constructing a diagnostic model for sensitivity to dinoprostone suppositories for induced labor comprises the following steps: Step 1: Collect and preserve vaginal secretion samples; Step 2: sample processing; Step 3: Liquid chromatography-mass spectrometry detection; Step 4: data processing and interpretation; Step 5: Feature selection and model building.

[0021] Scheme 14. The use described in Scheme 12 above, wherein the vaginal metabolites are: Floionolic acid, Mycolactone, Glucose aspartate, Calcitroic acid, Erythronic acid, Tanacetol B, and Leucyl-Threonine.

[0022] Technical effects of the present invention The above technical solution can construct a prediction model for dinoprostone-induced labor sensitivity based on vaginal metabolites, thereby providing decision-making and assistance for clinicians to judge the dinoprostone-induced labor sensitivity of pregnant women, which is of great significance for reducing patients' pain. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 ROC curves for predicting sensitivity of dinoprostone to induction of labor for 7 vaginal metabolites; Figure 2 Receiver operating characteristic (ROC) curves for seven vaginal metabolites predicting sensitivity to dinoprostone for labor induction in an external dataset. DETAILED DESCRIPTION

[0024] In order to more clearly understand the above-mentioned objects, features and advantages of the present application, the embodiments of the present application will be further described below. It should be noted that the embodiments of the present application and the features therein can be combined with each other in the absence of conflict.

[0025] The following description sets forth many specific details to facilitate a full understanding of the present application, but the present application may also be implemented in other ways than those described herein. Obviously, the embodiments in the specification are only some of the embodiments of the present application, not all of them.

[0026] In response to the problems existing in the prior art, the present invention provides a composition comprising 400 μL of an extract and L-2-chlorophenylalanine as an internal standard, wherein the extract is methanol:water=4:1 (V:V).

[0027] By using the above composition to process vaginal metabolite samples of pregnant women, a precise construction of a dinoprostone-induced labor sensitivity diagnostic model can be achieved, thereby enabling clinical physicians to accurately determine the dinoprostone-induced labor sensitivity of pregnant women.

[0028] In the composition, the concentration of L-2-chlorophenylalanine can be 0.005-0.10 mg / mL, preferably 0.01-0.10 mg / mL, preferably 0.01-0.08 mg / mL, particularly preferably 0.01-0.05 mg / mL, specifically preferably 0.01 mg / mL, 0.02 mg / mL, 0.03 mg / mL, 0.04 mg / mL, 0.05 mg / mL, etc.

[0029] In actual operation, the above composition is used to process vaginal metabolite samples and detect them by liquid chromatography-mass spectrometry to obtain relevant data. After these data are processed and interpreted, characteristic ions are selected to construct the model.

[0030] While the mechanism by which vaginal metabolite samples processed using the aforementioned composition enable accurate model construction remains unclear, the present inventors speculate as follows: The composition incorporates methanol and water in a specific ratio, achieving a balanced solubility system for water-soluble and fat-soluble substances. Furthermore, the use of L-2-chlorophenylalanine as an internal standard enables more accurate vaginal metabolite concentration data to be obtained compared to other internal standards.

[0031] The composition can be in various forms, for example, a single component form, a two-component form, and a kit form. From the perspective of ease of use and convenient operation, the kit form is preferred.

[0032] The composition can be used to prepare a kit for constructing a diagnostic model of dinoprostone suppository-induced labor sensitivity through vaginal metabolites.

[0033] In one embodiment, a kit is provided, comprising the above-described composition. In actual operation, the kit is contacted with a vaginal metabolite sample, the vaginal metabolite sample is processed, and relevant data is obtained by liquid chromatography-mass spectrometry. The data is then processed and interpreted, and characteristic ions are selected for model construction.

[0034] The vaginal metabolites are: Floionolic acid, Mycolactone, Glucose aspartate, Calcitroic acid, Erythronic acid, Tanacetol B, Leucyl-Threonine.

[0035] The reason for using the above vaginal metabolites as indicators of sensitivity to dinoprostone suppositories for induction of labor is that, first, these seven metabolites are widely distributed in the vaginas of various pregnant women. In addition, there are significant differences in the concentration distribution of these seven metabolites in the vaginas of pregnant women who are sensitive to dinoprostone suppositories for induction of labor and those who are not sensitive to dinoprostone suppositories for induction of labor.

[0036] The specific method for constructing a dinoprostone-induced labor sensitivity diagnostic model using the composition and the kit of the present invention through vaginal metabolites comprises the following steps: Step 1: Collect and preserve vaginal secretion samples; Step 2: sample processing; Step 3: Liquid chromatography-mass spectrometry detection; Step 4: data processing and interpretation; Step 5: Feature selection and model building; The model finally established by the present invention contains the following metabolites: Floionolic acid, Mycolactone, Glucose aspartate, Calcitroic acid, Erythronic acid, Tanacetol B, and Leucyl-Threonine.

[0037] 1. Collection and storage of vaginal secretion samples Pregnant women undergoing induction of labor using dinoprostone were randomly assigned to a sensitive or insensitive group based on their response to the drug (sensitive group: labor or cervical ripening within 24 hours after single-dose administration; insensitive group: no labor or cervical ripening within 24 hours after single-dose administration). 2. Sample Processing 1. Transfer the entire sample to a 2 mL centrifuge tube and add a 6 mm diameter grinding bead. 2. Add the above-mentioned composition of the present invention, namely 400 µL of the extract (methanol:water = 4:1 (v:v)) and L-2-chlorophenylalanine (0.02 mg / mL) as an internal standard. 3. Grind in a cryo-tissue grinder for 6 minutes (-10°C, 50 Hz). 4. Extract by low-temperature ultrasound for 30 minutes (5°C, 40 kHz). 5. Incubate the sample at -20°C for 30 minutes. 6. Centrifuge for 15 minutes (13,000 g, 4°C), transfer the supernatant to an injection vial with an inner cannula, and analyze the sample. 7. In addition, transfer 20 µL of the supernatant from each sample and mix them together to serve as a quality control sample.

[0038] or, 1. Transfer all samples to the above-mentioned kit of the present invention; 2. Grind in a cryo-tissue grinder for 6 minutes (-10°C, 50 Hz); 3. Perform low-temperature ultrasonic extraction for 30 minutes (5°C, 40 kHz); 4. Incubate the samples at -20°C for 30 minutes; 5. Centrifuge for 15 minutes (13,000 g, 4°C), transfer the supernatant to an intubated vial for analysis; 6. Separately, transfer 20 µL of the supernatant from each sample and mix them together to serve as a quality control sample.

[0039] 3. Liquid chromatography-mass spectrometry detection Chromatographic conditions: The chromatographic column was an ACQUITY UPLC HSS T3 (100 mm × 2.1 mm id, 1.8 µm; Waters, Milford, USA); the mobile phase A was 95% water + 5% acetonitrile (containing 0.1% formic acid), and the mobile phase B was 47.5% acetonitrile + 47.5% isopropanol + 5% water (containing 0.1% formic acid). The injection volume was 3 μL, and the column temperature was 40°C.

[0040] Mass spectrometry conditions: Samples were ionized by electrospray ionization, and mass spectrometry signals were acquired in both positive and negative ion scan modes. Specific parameters: Scan type (m / z) 70-1050, Sheath gas flow rate (arb) 60, Auxiliary gas flow rate (arb) 20, Heater temperature (°C) 350, Capillary temperature (°C) 320, Spray voltage (+) (V) 3400, Spray voltage (-) (V) -3000, S-Lens RF level 70, Normalized collision energy (eV) 20, 40, 60, Resolution (Full MS) 60,000, Resolution (MS 2) 15,000.

[0041] Quality control samples (QC) are prepared by mixing equal volumes of extracts from all samples. The volume of each QC is the same as that of the sample and is processed and tested using the same methods as the analytical samples. During the instrument analysis process, a QC sample is inserted into every 5-15 analytical samples to examine the stability of the entire detection process.

[0042] 4. Data Processing and Interpretation Raw data were imported into the metabolomics processing software ProgenesisQI (Waters Corporation, Milford, USA) for baseline filtering, peak identification, integration, retention time correction, and peak alignment. This ultimately yielded a data matrix containing information such as retention time, mass-to-charge ratio, and peak intensity. This software was then used to perform a library search for characteristic peaks and match MS and MS / MS spectra against a metabolic database. The MS mass error was set to less than 10 ppm, and metabolites were identified based on secondary mass spectrometry match scores. The primary databases used were mainstream public databases such as http: / / www.hmdb.ca / and https: / / metlin.scripps.edu / , as well as self-developed databases.

[0043] 5. Feature Selection and Model Building (1) Use Meiji's own software to process mass spectrometry data, screen out the characteristic ions of each substance through the triple quadrupole, obtain the signal intensity (CPS) of the characteristic ions in the detector, open the mass spectrum file of the sample, perform chromatographic peak integration and correction, the peak area (Area) of each chromatographic peak represents the relative content of the corresponding substance, and finally export all chromatographic peak area integration data for storage; (2) Screening the top seven vaginal metabolites with variable importance in projection (VIP) greater than 1 and p-value less than 0.05 obtained based on the OPLS-DA model (biological replicates ≥ 3) and that were upregulated in dinoprostone-induced labor insensitivity; (3) Method for constructing ROC curve and calculating cutoff value: handle missing values ​​and outliers, and divide the data into training set and test set, usually in a ratio of 7:3; use the pROC package in the software R to draw the ROC curve with 1-specificity as the horizontal axis and sensitivity as the vertical axis; the larger the AUC value, the better the model performance; calculate the Youden index under each possible threshold, the formula is Youden index = sensitivity + specificity - 1, and the threshold corresponding to the maximum Youden index is the optimal cutoff value.

[0044] The above description is only a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any modifications, equivalent substitutions and improvements made by any technician familiar with this technical field within the technical scope disclosed by the present invention and within the spirit and principles of the present invention should be covered by the scope of protection of the present invention.

Claims

1. A composition comprising 400 μL of an extract and L-2-chlorophenylalanine as an internal standard, wherein the extract is methanol:water=4:1 (V:V).

2. The composition according to claim 1, which is used for preparing a kit for constructing a diagnostic model for sensitivity to dinoprostone suppositories for induced labor through vaginal metabolites.

3. The composition according to claim 2, wherein The method for constructing a diagnostic model for sensitivity of dinoprostone suppositories to induced labor through vaginal metabolites comprises the following steps: Step 1: Collect and preserve vaginal secretion samples; Step 2: sample processing; Step 3: Liquid chromatography-mass spectrometry detection; Step 4: data processing and interpretation; Step 5: Feature selection and model building.

4. The composition according to claim 2, wherein The vaginal metabolites are: Floionolic acid, Mycolactone, Glucose aspartate, Calcitroic acid, Erythronic acid, Tanacetol B, Leucyl-Threonine.

5. The composition according to claim 3, wherein The step 1 includes: using a disposable sterile cotton swab to rotate at the posterior vaginal fornix to obtain a sample, provided that the pregnant woman has vaginal bleeding and has not undergone vaginal operation within 24 hours; breaking the swab after collection and placing it in a sterile freezing box; labeling the box and quickly freezing it in liquid nitrogen; and then transporting the box to a laboratory and freezing it in a -80°C refrigerator for long-term storage.

6. The composition according to claim 3, wherein The second step includes: Transfer the entire sample to a 2 mL centrifuge tube and add a 6 mm diameter grinding bead; add the above composition, i.e., 400 µL of the extract (methanol: water = 4:1 (v:v)) containing L-2-chlorophenylalanine as an internal standard; Grind in a frozen tissue grinder at -10°C, 50 Hz for 6 min; Low-temperature ultrasonic extraction was performed at 5°C and 40 KHz for 30 min; The sample was placed at -20°C for 30 min; Centrifuge at 13000G for 15 min at 4°C, transfer the supernatant into an injection vial with an inner cannula and analyze on an analyzer; 20 μL of supernatant was taken from each sample and mixed to serve as quality control samples.

7. The composition according to claim 3, wherein In the step three, Chromatographic conditions were as follows: ACQUITY UPLC HSS T3 column, 100 mm × 2.1 mm id, 1.8 μm; Waters, Milford, USA; mobile phase A was 95% water + 5% acetonitrile containing 0.1% formic acid; mobile phase B was 47.5% acetonitrile + 47.5% isopropanol + 5% water containing 0.1% formic acid; injection volume was 3 μL; column temperature was 40°C; Mass spectrometry conditions: The samples were ionized by electrospray ionization, and the mass spectrometry signals were collected in positive and negative ion scanning modes, respectively.

8. The composition according to claim 3, wherein The fourth step includes: importing the raw data into the metabolomics processing software ProgenesisQI for baseline filtering, peak identification, integration, retention time correction, and peak alignment, and finally obtaining a data matrix including retention time, mass-to-charge ratio, and peak intensity information. The software is then used to search the library for characteristic peaks, match the MS and MS / MS mass spectrometry information with the metabolic database, set the MS mass error to less than 10 ppm, and identify metabolites based on the secondary mass spectrometry matching score.

9. The composition according to claim 3, wherein The step five includes: The characteristic ions of each substance are screened out by the triple quadrupole, and the signal intensity (CPS) of the characteristic ions is obtained in the detector. The mass spectrum file of the sample is opened and the chromatographic peaks are integrated and corrected. The peak area (Area) of each chromatographic peak represents the relative content of the corresponding substance. Finally, all the chromatographic peak area integration data are exported and saved; The top seven vaginal metabolites with variable importance projection greater than 1 and p-value less than 0.05 obtained based on the OPLS-DA model and upregulated in dinoprostone-induced labor insensitivity were screened; Method for constructing ROC curves and calculating cutoff values: handle missing values ​​and outliers, and divide the data into training and test sets, usually in a ratio of 7:3; use the pROC package in the R software to draw an ROC curve with 1-specificity as the horizontal axis and sensitivity as the vertical axis; the larger the AUC value, the better the model performance; calculate the Youden index at each possible threshold, the formula is Youden index = sensitivity + specificity - 1, and the threshold corresponding to the maximum Youden index is the optimal cutoff value. 10 . A kit comprising the test composition according to claim 1 and grinding beads.

11. The kit according to claim 10, wherein the diagnostic model for sensitivity of dinoprostone suppositories to induced labor is constructed by vaginal metabolites according to the following method: The method comprises: Step 1: Collect and preserve vaginal secretion samples; Step 2: sample processing; Step 3: Liquid chromatography-mass spectrometry detection; Step 4: data processing and interpretation; Step 5: Feature selection and model building.

12. Application of vaginal metabolites in the construction of a diagnostic model for sensitivity to dinoprostone suppositories for induced labor, including: The composition according to claim 1 or 2, or the kit according to claim 10 or 11 is used.

13. The use according to claim 12, wherein the method for constructing a diagnostic model for sensitivity to dinoprostone suppositories for induced labor comprises the following steps: Step 1: Collect and preserve vaginal secretion samples; Step 2: sample processing; Step 3: Liquid chromatography-mass spectrometry detection; Step 4: data processing and interpretation; Step 5: Feature selection and model building.

14. The use according to claim 13, wherein The vaginal metabolites are: Floionolic acid, Mycolactone, Glucose aspartate, Calcitroic acid, Erythronic acid, Tanacetol B, Leucyl-Threonine.

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