A risk model of prenatal screening markers in first trimester for predicting hypertensive disorders of pregnancy.

Background: We aimed to construct a risk model to assess the diagnostic value of predicting hypertensive disorders of pregnancy (HDPs) by screening a range of prenatal markers, including pregnancy-associated plasma protein A (PAPP-A), free beta-human chorionic gonadotropin (free β-hCG), and fetal nuchal translucency (NT).

Method: We analyzed 902 women, classified into four groups: healthy gravidas ( = 680, controls), gravidas with gestational hypertension ( = 61; GH), gravidas with preeclampsia ( = 90; PE), and gravidas with severe preeclampsia ( = 71, SPE). We then compared the multiple of median (MoM) of PAPP-A, free β-hCG, and NT.

Occurrence prediction of pests and diseases in cotton on the basis of weather factors by long short term memory network.

Background: The occurrence of cotton pests and diseases has always been an important factor affecting the total cotton production. Cotton has a great dependence on environmental factors during its growth, especially climate change. In recent years, machine learning and especially deep learning methods have been widely used in many fields and have achieved good results.

In Silico Prediction of Drug-Induced Liver Injury Based on Ensemble Classifier Method.

Drug-induced liver injury (DILI) is a major factor in the development of drugs and the safety of drugs. If the DILI cannot be effectively predicted during the development of the drug, it will cause the drug to be withdrawn from markets. Therefore, DILI is crucial at the early stages of drug research.