Machine learning approaches to predict gestational age in normal and complicated pregnancies via urinary metabolomics analysis was written by 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. And the article was included in Scientific Reports in 2021.Product Details of 31566-31-1 The following contents are mentioned in the article:
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. This study involved multiple reactions and reactants, such as Glyceryl monostearate (cas: 31566-31-1Product Details of 31566-31-1).
Glyceryl monostearate (cas: 31566-31-1) belongs to esters. Esters typically have a pleasant smell; those of low molecular weight are commonly used as fragrances and are found in essential oils and pheromones. Esters contain a carbonyl center, which gives rise to 120° C–C–O and O–C–O angles. Unlike amides, esters are structurally flexible functional groups because rotation about the C–O–C bonds has a low barrier. Their flexibility and low polarity is manifested in their physical properties; they tend to be less rigid (lower melting point) and more volatile (lower boiling point) than the corresponding amides. Product Details of 31566-31-1
Referemce:
Ester – Wikipedia,
Ester – an overview | ScienceDirect Topics