Methods, systems and kits for predicting premature birth condition
A condition and sequencing technology, applied in biological testing, biochemical equipment and methods, measuring devices, etc., can solve problems such as unpredictable premature birth and inability to carry out
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Embodiment 1
[0147] Example 1 - Prediction of preterm birth status
[0148] In an example, a patient who is 6 months pregnant presents the following risk factors: low socioeconomic status, past history of bleeding during her pregnancy, and history of preterm delivery during previous pregnancies. Physicians need to identify the likelihood of preterm birth in a patient and suggest using the methods and systems provided herein to predict the likelihood of preterm birth. Obtain a sample of the patient's vaginal fluid for analysis of the vaginal microbiome. The vaginal sample is processed to generate data indicative of the distribution of different types of microbial populations in the vaginal sample. Algorithms are trained to identify different types of microorganisms, such as Lactobacillus, Escherichia, Prevotella, Enterococcus, Candida genus, staphylococcus and herpes virus. The trained algorithm predicted that the subjects had an approximately 88 percent risk of preterm birth. The train...
Embodiment 2
[0149] Example 2 - Prediction of Preterm Birth Risk
[0150] In this example, four pregnant women (ie, subjects #1-4) who showed signs of threatening preterm birth at different time points in pregnancy were assessed for preterm birth risk by the method of the present invention. Specifically, vaginal fluid samples were obtained from each subject and processed as described in Example 1. As shown in Example 1, the risk of a subject's preterm birth condition was predicted using the trained algorithm with an accuracy of 98.15%. Table 3 shows the predicted probability of preterm birth condition (PBC) and predicted birth outcomes based on the analysis of the microbial population in the vaginal samples, as well as data on the actual birth outcomes for each subject.
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