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To identify laboratory markers among platelet indices, coagulation parameters, blood lipid parameters, and liver/kidney function variables that can be used to predict preeclampsia.We studied records of 568 women with preeclampsia, gestational hypertension (GH), or normal term pregnancies hospitalized in the Obstetrics Department of the Fujian Maternal and Child Health Hospital from September 2014 to September 2018. We divided the patients' records into three groups (216 with preeclampsia, 136 with gestational hypertension, and 216 with normal pregnancies). We conducted retrospective analyses to compare variable measurements between the groups and find correlations. We looked into maternal pre-onset platelet indices, coagulation parameters (thrombin time [TT], fibrinogen [FIB]), biochemical parameters (total cholesterol [TC], triglycerides [TG], high-density lipoproteins [HDL], alanine transaminase [ALT], serum creatinine [CRE], blood urea nitrogen [BUN], uric acid [UA]), maternal complications, and perinatal outcomes. In addition to our statistical analysis, we trained a back-propagation (BP) neural network to identify the strongest predictors of preeclampsia.We found significant differences among the groups in terms of values for PLT, MPV, PDW, PLCR, TT, FIB, TG, LDH, BUN, and others. After adjusting for confounding factors in a multivariate ordered logistic regression model, we found that mean values for MPV, BUN, TG, and LDH can independently predict the risk of preeclampsia (the OR values were 1.858, 1.583, 1.104, and 1.020, respectively), the C-index (concordance statistic) was 0.73. Also, our BP neural network derived ALB, MPV, BUN, LDH and TG as the strongest predictors of preeclampsia.MPV, TG, LDH, and BUN can help establish the risk for the development of preeclampsia to apply active measures and improve maternal and perinatal outcomes. The BP neural network can be used to study predictive models of preeclampsia.

The effect of long-term dietary supplementation with rutin on the lactation performance, ruminal fermentation and metabolism of dairy cows were investigated in this study. Twenty multiparous Chinese Holstein cows were randomly divided into four groups, and each was offered a basal diet supplemented with 0, 1.5, 3.0 or 4.5 mg rutin/kg of diet. The milk yield of the cows receiving 3.0 and 4.5 mg rutin/kg was higher than that of the control group, and the milk yield was increased by 10.06% and 3.37% (p < 0.05). On the basis of that finding, the cows supplemented with 0 or 3.0 mg rutin/kg of diet were used to investigate the effect of rutin supplementation on blood metabolites and hormone levels. Compared with the control group, the serum blood urea nitrogen (BUN) concentration of the 3.0 mg rutin/kg group is significantly decreased (p < 0.05). In another trial, four adult cows with permanent rumen fistula and duodenal cannulae were attributed in a self-control design to investigate the peak occurrence of rutin and quercetin in different parts of the gastrointestinal tract, ruminal fermentation and microbial population in dairy cows. The cows supplemented with 3.0 mg rutin/kg in the diet differed from the control period. Samples of rumen fluid, duodenal fluid and blood were collected at 1, 2, 3, 4, 5, 6, 7 and 8 h after morning feeding. Compared to the control group, the pH, ammonia nitrogen concentration, number and protein content of rumen protozoa and blood urea nitrogen were lower, but the concentration of total volatile fatty acid (TVFA), microbial crude protein (MCP) and serum lysozyme content were higher for the cows fed the rutin diets. The addition of 3.0 mg rutin/kg to diets for a long term tended to increase the milk yield and improve the metabolism and digestibility of the dairy cows.