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 ß-hCG), and fetal nuchal translucency (NT). METHOD: We analyzed 902 women, classified into four groups: healthy gravidas (n = 680, controls), gravidas with gestational hypertension (n = 61; GH), gravidas with preeclampsia (n = 90; PE), and gravidas with severe preeclampsia (n = 71, SPE). We then compared the multiple of median (MoM) of PAPP-A, free ß-hCG, and NT. A risk model was constructed and receiver operating characteristic curve (ROC) analysis was used to diagnose HDPs. RESULTS: Levels of PAPP-A and free ß-hCG levels in the GH, PE, and SPE groups were significantly lower than those in the control group (χ 2 = 7.522, P = 0.001; χ 2 = 17.775, P < 0.001). NT did not differ significantly when compared across all four groups (χ 2 = 1.592, P > 0.05). When the cut-off values for PAPP-A and free ß-hCG were 0.795 MoM and 1.185 MoM, the corresponding sensitivities and specificities were 0.514 and 0.635, and 0.734 and 0.450, respectively. The best risk calculation featured PAPP-A, free ß-hCG, and NT; this model exhibited the highest diagnostic value in the SPE group, followed by the GH group and then the PE group. CONCLUSION: The use of prenatal screening markers during early pregnancy can identify fetal aneuploidy and can also predict HDPs. The development of innovative screening strategies for gravidas and the targeted prevention of HDPs in high-risk gravidas are essential for perinatal care and early intervention, thus creating significant opportunities for predictive and preventive personalized medicine. In our study, we found that the combination of a series of prenatal screening markers in early pregnancy is better than a single marker; our data clearly demonstrate the diagnostic value of combining PAPP-A, free ß-hCG, and NT for patients with SPE.

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. METHODS: First, this papaer used the common Aprioro algorithm to find the association rules between weather factors and the occurrence of cotton pests. Then, in this paper, the problem of predicting the occurrence of pests and diseases is formulated as time series prediction, and an LSTM-based method was developed to solve the problem. RESULTS: The association analysis reveals that moderate temperature, humid air, low wind spreed and rain fall in autumn and winter are more likely to occur cotton pests and diseases. The discovery was then used to predict the occurrence of pests and diseases. Experimental results showed that LSTM performs well on the prediction of occurrence of pests and diseases in cotton fields, and yields the Area Under the Curve (AUC) of 0.97. CONCLUSION: Suitable temperature, humidity, low rainfall, low wind speed, suitable sunshine time and low evaporation are more likely to cause cotton pests and diseases. Based on these associations as well as historical weather and pest records, LSTM network is a good predictor for future pest and disease occurrences. Moreover, compared to the traditional machine learning models (i.e., SVM and Random Forest), the LSTM network performs the best.

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. This work presents a 2-class ensemble classifier model for predicting DILI, with 2D molecular descriptors and fingerprints on a dataset of 450 compounds. The purpose of our study is to investigate which are the key molecular fingerprints that may cause DILI risk, and then to obtain a reliable ensemble model to predict DILI risk with these key factors. Experimental results suggested that 8 molecular fingerprints are very critical for predicting DILI, and also obtained the best ratio of molecular fingerprints to molecular descriptors. The result of the 5-fold cross-validation of the ensemble vote classifier method obtain an accuracy of 77.25%, and the accuracy of the test set was 81.67%. This model could be used for drug-induced liver injury prediction.