On December 31, 2021, Yamauchi, Takafumi; Ochi, Daisuke; Matsukawa, Naomi; Saigusa, Daisuke; Ishikuro, Mami; Obara, Taku; Tsunemoto, Yoshiki; Kumatani, Satsuki; Yamashita, Riu; Tanabe, Osamu; Minegishi, Naoko; Koshiba, Seizo; Metoki, Hirohito; Kuriyama, Shinichi; Yaegashi, Nobuo; Yamamoto, Masayuki; Nagasaki, Masao; Hiyama, Satoshi; Sugawara, Junichi published an article.Safety of 3-Hydroxyphenylacetic acid The title of the article was Machine learning approaches to predict gestational age in normal and complicated pregnancies via urinary metabolomics analysis. And the article contained the following:
The elucidation of dynamic metabolomic changes during gestation is particularly important for the development of methods to evaluate pregnancy status or achieve earlier detection of pregnancy-related complications. Some studies have constructed models to evaluate pregnancy status and predict gestational age using omics data from blood biospecimens; however, less invasive methods are desired. Here we propose a model to predict gestational age, using urinary metabolite information. In our prospective cohort study, we collected 2741 urine samples from 187 healthy pregnant women, 23 patients with hypertensive disorders of pregnancy, and 14 patients with spontaneous preterm birth. Using gas chromatog.-tandem mass spectrometry, we identified 184 urinary metabolites that showed dynamic systematic changes in healthy pregnant women according to gestational age. A model to predict gestational age during normal pregnancy progression was constructed; the correlation coefficient between actual and predicted weeks of gestation was 0.86. The predicted gestational ages of cases with hypertensive disorders of pregnancy exhibited significant progression, compared with actual gestational ages. This is the first study to predict gestational age in normal and complicated pregnancies by using urinary metabolite information. Minimally invasive urinary metabolomics might facilitate changes in the prediction of gestational age in various clin. settings. The experimental process involved the reaction of 3-Hydroxyphenylacetic acid(cas: 621-37-4).Safety of 3-Hydroxyphenylacetic acid
The Article related to pregnancy gestational age urinary metabolomic analysis machine learning approach, Biochemical Methods: Other (Not Covered At Other Subsections) and other aspects.Safety of 3-Hydroxyphenylacetic acid
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