Composition, kit and application thereof, and application of microorganisms

Abstract

The invention discloses a composition which comprises a lysis buffer solution, an ATL buffer solution and protease K, a kit containing the composition, and application of vaginal microorganisms in construction of a diagnostic model of dinoprostone thrombosis sensitivity. Therefore, the prediction model of the dinoprostone labor induction sensitivity can be constructed based on vaginal microorganisms, so that decisions 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, applications of the composition and the kit, and applications of microorganisms. 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 still has a certain degree of unpredictability, but there is currently no reliable indicator to predict the sensitivity of dinoprostone for 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, cervicitis, pelvic inflammatory disease, miscarriage, and preterm birth. Increased richness and diversity of vaginal microbiota, as well as specific vaginal microbial compositions, are widely associated with an increased risk of spontaneous preterm birth. Although direct evidence currently demonstrates a relationship between vaginal microbiota and cervical ripening, several studies have suggested that vaginal microbiota may influence cervical remodeling by affecting the cervical epithelial barrier, secreting proteases, and inducing inflammatory responses. Furthermore, recent studies have found significant differences in vaginal microbial diversity between pregnant women with mature and immature cervixes at similar gestational ages. However, there are currently no predictors of sensitivity to dinoprostone for induction of labor. Therefore, identifying microbial populations with predictive value within the vaginal microbiome and developing predictive models are crucial for informing clinicians and reducing patient distress. 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 induced labor using vaginal microorganisms. This model can accurately determine a parturient's sensitivity to dinoprostone for induced labor.

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

[0008] Scheme 1. A composition comprising: a lysis buffer, an ATL buffer, and proteinase K.

[0009] Solution 2: The composition described in Solution 1 is used to prepare a kit for constructing a diagnostic model for sensitivity to dinoprostone suppositories for induced labor using vaginal microorganisms.

[0010] Scheme 3: The composition of Scheme 2, wherein the method for constructing a diagnostic model for sensitivity to dinoprostone suppositories for induced labor using vaginal microorganisms comprises the following steps: Step 1: Collect and preserve vaginal secretion samples; Step 2: Sample DNA extraction; Step 3, 16S rRNA gene amplification and sequencing; Step 4: Data processing and analysis; Step 5: Feature selection and model building.

[0011] Option 4: The composition of Option 2, wherein the vaginal microorganism is: Lactobacillus 、 Gardnerella、 Bifidobacterium 、 Streptococcus 、 Aerococcus 、 Levilactobacillus .

[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 sample was taken out of the -80°C freezer and thawed on ice. Sterile tweezers were used to place the swab head into a 1.5 mL centrifuge tube of the corresponding number and contacted with the composition, namely, 50 µL~1.0 mL of lysis buffer was added to the centrifuge tube, and 180 µL of ATL buffer and 20 µL of proteinase K were added, mixed and incubated, the lysed liquid was transferred to a DNA binding column, ethanol was added, mixed and transferred to a DNA binding column for centrifugation, the binding column was placed in a new collection tube, wash buffer was added and centrifuged, and then the binding column was placed in a new 1.5 mL centrifuge tube, 100 µL of elution buffer was added, and the tube was allowed to stand for 1 minute and then centrifuged. The DNA in the eluate was collected into a new centrifuge tube of the corresponding number for subsequent PCR amplification and sequencing.

[0014] Option 7. The composition described in Option 3 above, wherein, in step 3, 16S rRNA gene amplification is first performed, and then the PCR product is purified using a QIAquick PCR Purification Kit, and then the purified PCR product is used for PacBio library construction, and finally high-throughput sequencing is performed.

[0015] Option 8: The composition described in Option 3 above, wherein the step 4 comprises: The raw data files were obtained by basecall from PacBio data, and HiFi sequence files were obtained in fastq format using PacBio's SMRTLink v11.0 analysis software. Initial data processing included: analyzing the quality of sequencing data using FastQC, including base quality scores, GC content, and N-base distribution; Microbial community analysis included: OTU clustering using Uparse: extracting non-repetitive sequences from the optimized sequences and removing single sequences without duplication; performing OTU clustering on non-repetitive sequences at 97% similarity, removing chimeras during the clustering process, and obtaining representative OTU sequences; using the RDP classifier Bayesian algorithm to perform taxonomic analysis on representative OTU sequences at a 97% similarity level, and statistically analyzing the community species composition of each sample at each taxonomic level: Domain, Kingdom, Phylum, Class, Order, Family, Genus, and Species; performing Alpha diversity analysis to evaluate the species richness and Shannon index within the samples, and performing Beta diversity analysis to evaluate the differences in microbial communities between different samples.

[0016] Option 9: The composition described in Option 3 above, wherein the step five comprises: Based on the obtained community abundance order, the significance level of species abundance differences was evaluated using the stats package of R (version 3.3.1) and the scipy package of Python. The species with significant differences between the two groups were obtained, and "species" was selected as the classification level. Based on the above analysis method, the top 6 bacterial species with the smallest P-value were screened out, and the vaginal microbial species that were upregulated in the dinoprostone-insensitive group were selected. 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 according to any one of options 1 to 9 above.

[0018] Solution 11: The kit described in Solution 10, wherein a diagnostic model for sensitivity to dinoprostone suppositories for induced labor is constructed using vaginal microorganisms according to the following method: Step 1: Collect and preserve vaginal secretion samples; Step 2: Sample DNA extraction; Step 3, 16S rRNA gene amplification and sequencing; Step 4: Data processing and analysis; Step 5: Feature selection and model building.

[0019] Scheme 12: Use of vaginal microorganisms in constructing a diagnostic model for sensitivity to dinoprostone suppositories for induced labor, wherein the composition described in any one of Schemes 1 to 9 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 DNA extraction; Step 3, 16S rRNA gene amplification and sequencing; Step 4: Data processing and analysis; Step 5: Feature selection and model building.

[0021] Solution 14. The use according to solution 13, wherein the vaginal microorganism is: Lactobacillus 、 Gardnerella 、 Bifidobacterium 、 Streptococcus 、 Aerococcus 、 Levilactobacillus .

[0022] Technical effects of the present invention The above technical solution can construct a prediction model for dinoprostone-induced labor sensitivity based on vaginal microorganisms, thereby providing decision-making and assistance for clinical physicians 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 the sensitivity of dinoprostone to induction of labor for 6 vaginal microorganisms; Figure 2 Receiver operating characteristic (ROC) curves for the prediction of dinoprostone sensitivity to labor induction using six vaginal microorganisms 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 view of the problems existing in the prior art, the present invention provides a composition comprising a lysis buffer, an ATL buffer and proteinase K.

[0027] By using the above composition to process vaginal microbial 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 combination of the present invention, the content of the lysis buffer is 50 μL~1.0 mL, preferably 100 μL~500 μL, more preferably 100 μL~200 μL, particularly preferably 150 μL, 200 μL, etc.; the content of the ATL buffer is preferably 50 μL~500 μL, preferably 100 μL~300 μL, particularly preferably 150 μL, 180 μL, 200 μL, 220 μL, etc.; the content of the proteinase K is preferably 1 μL~100 μL, preferably 5 μL~80 μL, particularly preferably 10 μL, 20 μL, 2, 30 μL, 45 μL, 50 μL, 60 μL, etc.

[0029] In actual operation, the above composition is used to process vaginal microbial samples and obtain relevant data through gene amplification and sequencing. These data are then processed and interpreted, and characteristic species are then selected for model construction.

[0030] 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.

[0031] The composition can be used to prepare a kit for constructing a diagnostic model of dinoprostone suppository induced labor sensitivity through vaginal microorganisms.

[0032] In one embodiment, a kit is provided, comprising the above-described composition. In actual operation, the kit is contacted with a vaginal microbial sample, the vaginal microbial sample is processed, and relevant data is obtained by liquid chromatography-mass spectrometry. The data is then processed and interpreted, and characteristic species are selected for model construction.

[0033] The vaginal microorganisms are: Lactobacillus 、 Gardnerella 、 Bifidobacterium 、 Streptococcus 、 Aerococcus 、 Levilactobacillus .

[0034] The reason for using the above vaginal microorganisms as indicators of sensitivity to dinoprostone suppositories for induced labor is that, first, these six microorganisms are widely distributed in the vaginas of various pregnant women. In addition, there is a significant difference in the concentration distribution of these six microorganisms in the vaginas of pregnant women who are sensitive to dinoprostone suppositories for induced labor and those who are not sensitive to dinoprostone suppositories for induced labor.

[0035] The specific method for constructing a dinoprostone-induced labor sensitivity diagnostic model using the composition and the kit of the present invention through vaginal microorganisms comprises the following steps: Step 1: Collect and preserve vaginal secretion samples; Step 2: Sample DNA extraction; Step 3, 16S rRNA gene amplification and sequencing; Step 4: Data processing and analysis; Step 5: Feature selection and model building.

[0036] The model finally established by the present invention includes the following microorganisms: Lactobacillus 、 Gardnerella 、 Bifidobacterium 、 Streptococcus 、 Aerococcus 、 Levilactobacillus .

[0037] 1. Collection and storage of vaginal secretion samples Pregnant women undergoing labor induction with 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 administration of a single drug tablet; insensitive group: no labor or cervical ripening within 24 hours after administration of a single drug tablet).

[0038] 2. Sample DNA Extraction Remove the sample from a -80°C freezer and thaw on ice. Use sterile tweezers to place the swab tip into a 1.5 mL centrifuge tube containing lysis buffer. Add the above-mentioned composition of the present invention, specifically 180 µL of ATL buffer and 20 µL of proteinase K. Vortex for 10 seconds to mix thoroughly, and incubate at 37°C for 30 minutes to 1 hour. Alternatively, remove the sample from a -80°C freezer and thaw on ice. Use sterile tweezers to place the swab tip into the above-mentioned kit of the present invention, which contains 180 µL of ATL buffer and 20 µL of proteinase K. Vortex for 10 seconds to mix thoroughly, and incubate at 37°C for 30 minutes to 1 hour. Then, transfer the lysed liquid to a DNA binding column, add an equal volume of ethanol to the lysed liquid, mix thoroughly, transfer to the DNA binding column, and centrifuge at 8000 rpm for 1 minute. Place the column in a new collection tube, add 500 µL of AW1 wash buffer, centrifuge at 8000 rpm for 1 minute, and discard the flow-through. Add another 500 µL of AW2 wash buffer, centrifuge at 14,000 rpm for 3 minutes, and discard the flow-through. Then, place the binding column in a new 1.5 mL centrifuge tube and add 100 µL of elution buffer (such as AE buffer or sterile water). Let it sit for 1 minute, then centrifuge at 8,000 rpm for 1 minute. Collect the DNA in the eluate into a new, correspondingly numbered centrifuge tube for subsequent PCR amplification and sequencing. (Use a NanoDrop to determine DNA concentration and purity, recording the A260 / A280 and A260 / A230 ratios to ensure DNA quality.)

[0039] 3. 16S rRNA Gene Amplification and Sequencing 1. 16S rRNA gene amplification: Using Pro Taq, a 20 μl reaction system [10 μl 2× Pro Taq, 0.8 μl each of 5 μM forward primer (27F) and reverse primer (1492R), 10 ng / μl Template DNA, 0.5 μl high-fidelity Taq DNA polymerase, and sterile water to 20 μl] was used. Aseptically, the above reagents were added to a PCR tube in the appropriate proportions. Vortex for 10 seconds to mix thoroughly. The following cycling conditions were set (initial denaturation at 95°C for 3 minutes; 30 cycles of denaturation at 95°C for 30 seconds, annealing at 60°C for 30 seconds, and extension at 72°C for 45 seconds; final extension at 72°C for 10 minutes; hold at 4°C). The reaction tube was placed in a PCR instrument and run. Each sample was replicated three times. PCR products from the same sample were mixed and analyzed by 2% agarose gel electrophoresis. PCR products were excised using the AxyPrep DNA Gel Extraction Kit (AXYGEN), eluted with Tris-HCl buffer, and analyzed by 2% agarose gel electrophoresis. Based on preliminary electrophoresis results, PCR products were quantified using the QuantiFluor™-ST Blue Fluorescence Quantitation System (Promega). The samples were then mixed in appropriate proportions based on the sequencing load required for each sample.

[0040] 2. Purify the PCR product using the QIAquick PCR Purification Kit: Mix the PCR reaction with 5 volumes of Buffered Polymerase Chain Reaction (PB) and transfer it to a QIAquick column. Centrifuge the column to bind the DNA to the silica membrane (6000 x g for 1 minute). Add 750 µL of Buffered Polymerase Chain Reaction (PE) and centrifuge for 1 minute. Centrifuge the column again for 1 minute to remove any remaining wash buffer. Place the QIAquick column in a new 1.5 mL microcentrifuge tube and add 30-50 µL of Buffered Polymerase Chain Reaction (EB) or sterile water to the center of the column. Let stand for 1 minute, then centrifuge for 1 minute to collect the purified DNA.

[0041] 3. Sequencing Library Construction: Purified PCR products are used for PacBio library construction. 1) End repair: First, blunt-end the fragment ends. Circular adapter ligation: Attach a circular single-stranded sequence to each end, and then connect the ends of the single-stranded sequence to the double-stranded positive and negative strands, respectively, to form a dumbbell-like structure (called a "sleeveless ring"), known as a SMRT bell. 2) Remove any sequences not ligated to the adapter. 3) Anneal the single-stranded loops of the library with primers, which are then bound to a polymerase immobilized at the bottom of a ZMW (zero-mode waveguide).

[0042] 4. High-throughput sequencing: The constructed library is loaded into the PacBio sequencing system. 1) Four dNTPs are labeled with four different fluorescent molecules. 2) Numerous small holes corresponding to the wavelength of the incident light are created at the bottom of the sequencing chip. The specific hole diameter ensures that the incident light travels only a short distance in the holes, just enough to illuminate the fluorescent dNTP substrate that interacts with the enzyme. 3) The polymerase is anchored to the bottom of the sequencing chip. 4) The DNA chain is allowed to bind to the enzyme and sequenced. 5) During sequencing, the fluorescent dNTP forms a complex with the enzyme and DNA template, briefly binding. 6) The fluorescent dNTP is illuminated by a laser, emitting fluorescence that is detected. 7) The enzyme reaction causes the chain to extend while simultaneously removing the fluorescent group on the dNTP. 8) The polymerization reaction continues, and sequencing proceeds simultaneously.

[0043] 4. Data Processing and Analysis The raw data files of PacBio were obtained through basecall, and the HiFi sequence files were obtained through PacBio's SMRTLink v11.0 analysis software. The files were in fastq format.

[0044] 1. Preliminary Data Processing: FastQC was used to analyze the quality of the sequencing data, including base quality scores, GC content, and N-base distribution. MultiQC was used to aggregate multiple FastQC reports for overall quality assessment. Trimmomatic was used to remove low-quality reads and adapter sequences, and VSEARCH was used to remove redundant sequences.

[0045] 2. Microbial Community Analysis: OTU clustering was performed using Uparse. Non-repeating sequences were extracted from the optimized sequences, and singleton sequences without duplication were removed. Non-repeating sequences (excluding singleton sequences) were clustered into OTUs at 97% similarity, and chimeras were removed during the clustering process to obtain representative OTU sequences. All optimized sequences were mapped to the representative OTU sequences, and sequences with at least 97% similarity to the representative OTU sequences were selected to generate an OTU table. Taxonomic analysis was performed on the representative OTU sequences at the 97% similarity level using the RDP classifier Bayesian algorithm. The species composition of each sample community was calculated at each taxonomic level: Domain, Kingdom, Phylum, Class, Order, Family, Genus, and Species. Taxonomic annotation was then performed using the Greengenes, SILVA, or RDP databases. Alpha diversity analysis was performed to assess the microbial diversity within the samples (species richness and Shannon index). Beta diversity analysis was performed to assess the differences in microbial communities among different samples (principal coordinate analysis (PCoA) and nonmetric multidimensional scaling (NMDS)).

[0046] 5. Feature selection and model building: (1) Based on the obtained community abundance order, the stats package of R (version 3.3.1) and the scipy package of Python were used to evaluate the significance level of species abundance differences using the Wilcoxon rank sum test with a two-tailed test and a confidence interval of 0.95. The species with significant differences between the two groups were obtained, and “species” was selected as the classification level. (2) Based on the above analysis method, the top 10 bacterial species with the smallest P-value were screened out, and the vaginal microbial species that were upregulated in the dinoprostone-insensitive group were selected; (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.

[0047] 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

A composition comprising: a lysis buffer, an ATL buffer and proteinase K.

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 using vaginal microorganisms.

3. The composition according to claim 2, wherein The method for constructing a diagnostic model for sensitivity of dinoprostone suppositories to induced labor by vaginal microorganisms comprises the following steps: Step 1: Collect and preserve vaginal secretion samples; Step 2: Sample DNA extraction; Step 3, 16S rRNA gene amplification and sequencing; Step 4: Data processing and analysis; Step 5: Feature selection and model building.

4. The composition according to claim 2, wherein The vaginal microorganisms are: Lactobacillus 、 Gardnerella 、 Bifidobacterium 、 Streptococcus 、 Aerococcus 、 Levilactobacillus .

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: The sample was taken out of the -80°C freezer and thawed on ice. Sterile tweezers were used to place the swab head into a 1.5 mL centrifuge tube of the corresponding number and contacted with the composition, namely, 50 µL~1.0 mL of lysis buffer was added to the centrifuge tube, and then 180 µL of ATL buffer and 20 µL of proteinase K were added, mixed and incubated, the lysed liquid was transferred to a DNA binding column, ethanol was added, mixed and transferred to a DNA binding column for centrifugation, the binding column was placed on a new collection tube, wash buffer was added and centrifuged, and then the binding column was placed in a new 1.5 mL centrifuge tube, 100 µL of elution buffer was added, and the tube was allowed to stand for 1 minute and then centrifuged. The DNA in the eluate was collected into a new centrifuge tube of the corresponding number for subsequent PCR amplification and sequencing.

7. The composition according to claim 3, wherein In step three, 16S rRNA gene amplification is first performed, and then the PCR product is purified using a QIAquick PCR Purification Kit. The purified PCR product is then used for PacBio library construction, and finally high-throughput sequencing is performed.

8. The composition according to claim 3, wherein The fourth step includes: The raw data files were obtained by basecall from PacBio data, and HiFi sequence files were obtained in fastq format using PacBio's SMRTLink v11.0 analysis software. Initial data processing included: analyzing the quality of sequencing data using FastQC, including base quality scores, GC content, and N-base distribution; Microbial community analysis included: OTU clustering using Uparse: extracting non-repetitive sequences from the optimized sequences and removing single sequences without duplication; performing OTU clustering on non-repetitive sequences at 97% similarity, removing chimeras during the clustering process, and obtaining representative OTU sequences; using the RDP classifier Bayesian algorithm to perform taxonomic analysis on representative OTU sequences at a 97% similarity level, and statistically analyzing the community species composition of each sample at each taxonomic level: Domain, Kingdom, Phylum, Class, Order, Family, Genus, and Species; performing Alpha diversity analysis to evaluate the species richness and Shannon index within the samples, and performing Beta diversity analysis to evaluate the differences in microbial communities between different samples.

9. The composition according to claim 3, wherein The step five includes: Based on the obtained community abundance order, the significance level of species abundance differences was evaluated using the stats package in R (version 3.3.1) and the scipy package in Python. The species with significant differences between the two groups were obtained, and "species" was selected as the classification level. Based on the above analysis method, the top 6 bacterial species with the smallest P-value were screened out, and the vaginal microbial species that were upregulated in the dinoprostone-insensitive group were selected. 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 .

11. The kit according to claim 10, wherein the diagnostic model for sensitivity of dinoprostone suppositories to induced labor is constructed using vaginal microorganisms according to the following method: Step 1: Collect and preserve vaginal secretion samples; Step 2: Sample DNA extraction; Step 3, 16S rRNA gene amplification and sequencing; Step 4: Data processing and analysis; Step 5: Feature selection and model building.

12. Application of vaginal microorganisms in constructing a diagnostic model for sensitivity to dinoprostone suppositories for induced labor, including: The composition according to any one of claims 1 to 9, 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 DNA extraction; Step 3, 16S rRNA gene amplification and sequencing; Step 4: Data processing and analysis; Step 5: Feature selection and model building.

14. The use according to claim 13, wherein The vaginal microorganisms are: Lactobacillus 、 Gardnerella 、 Bifidobacterium 、 Streptococcus 、 Aerococcus 、 Levilactobacillus .

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