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The gestational weight gain (GWG) range of Chinese women with gestational diabetes mellitus (GDM) remains unclear. Our objective was to identify the ranges of GWG in Chinese women with GDM and to investigate the associations between prepregnancy body mass index (BMI), GWG and maternal-infant adverse outcomes. Cases of GDM women who delivered singletons from 2013 to 2018 in a public hospital were collected. Logistic regression analysis was used to assess the joint effects of prepregnancy BMI and GWG on maternal-infant adverse outcomes. Ultimately, 14,578 women were collected. The ranges of GWG in Chinese women with GDM were different from the National Academy of Medicine’s (NAM) recommendation. The ranges of GWG of Chinese women with GDM in the underweight, normal weight, overweight and obese groups were 5.95–21.95 kg, 4.23–21.83 kg, 0.88–21.12 kg and − 1.76 to 19.95 kg, respectively. The risks of large for gestational age (LGA), macrosomia and caesarean delivery were significantly increased with the increasing prepregnancy BMI. Furthermore, the risks of LGA, macrosomia and caesarean delivery were significantly higher in the normal weight group with a GWG higher than the NAM recommendation. Similarly, in the overweight group with a GWG higher than the NAM recommendation, the risks of LGA were significantly higher, while the risks of macrosomia were significantly lower. Overall, we determined the range of GWG in different prepregnancy BMI groups. And GDM women with high prepregnancy BMI and excessive GWG were associated with the higher risks of maternal-infants adverse outcomes in China.

Angiotensin II type-1 receptor–neprilysin inhibitor (ARNi) is consisted of Angiotensin II type-1 receptor (AT1) antagonist and neprilysin (NEP) inhibitor, which could simultaneously increase the vasodilators of the natriuretic peptides and antagonize vasoconstrictors of Ang II. ARNi has been proved a superior effect and lower risks of death on chronic heart failure (CHF) and hypertension. In this paper, ARNi from Traditional Chinese Medicines (TCM) was discovered based on target combination of AT1 and NEP by virtual screening, biological assay and molecular dynamics (MD) simulations. Two customized strategies of combinatorial virtual screening were implemented to discover AT1 antagonist and NEP inhibitor based on pharmacophore modeling and docking computation respectively. Gyrophoric acid (PubChem CID: 135728) from Parmelia saxatilis was selected as AT1 antagonist and assayed with IC50 of 29.76 μM by calcium influx assay. And 3,5,3′-triiodothyronine (PubChem CID: 861) from Bos taurus domesticus was screened as NEP inhibitor and has a dose dependent inhibitory activity by biochemistry fluorescence assay. Combined with MD simulations, these compounds can generate interaction with the target, key interactive residues of ARG167, TRP84, and VAL108 in AT1, and HIS711 in NEP were also identified respectively. This study designs the combinatorial strategy to discover novel frames of ARNi from TCM, and gyrophoric acid and 3,5,3′-triiodothyronine could provide the clues and revelations of drug design and therapeutic method of CHF and hypertension for TCM clinical applications.

Endophytes coexist with plants, in part, due to cellulase that allow saccharification of plant cell walls. The cellulase enzymes found in naturally occurring endophytes may exhibit stronger activity and more specificity than commercially available cellulase for enzyme-assisted extraction of compounds from medicinal plant materials. In order to identify endophytes with high cellulase activity, we screened endophytes taken from different parts of Angelica sinensis using the Congo red staining method. We identified three strains with higher cellulase activity. Of the 3 strains identified, No.Lut1201 increased the yield of extracted Z-ligustilide 2 fold compared to commercially available cellulase (Ningxia Sunson) using a cellulase-assisted extraction method and traditional extraction methods. Scanning electron microscopy clearly demonstrated that the cellulase extracted from endophytes enhance cell wall polysaccharide degradation as well as Z-ligustilide extraction from Radix Angelica sinensis (RAS). The current study provides a new method and ideas of using cellulase of endophytes for improving the extraction of compounds from medicinal plants.

Cancer is one of the main causes of threats to human life. Identification of anticancer peptides is important for developing effective anticancer drugs. In this paper, we developed an improved predictor to identify the anticancer peptides. The amino acid composition (AAC), the average chemical shifts (acACS) and the reduced amino acid composition (RAAC) were selected to predict the anticancer peptides by using the support vector machine (SVM). The overall prediction accuracy reaches to 93.61% in jackknife test. The results indicated that the combined parameter was helpful to the prediction for anticancer peptides.

Previous studies demonstrated that acute alcohol intoxication caused hepatic lipid accumulation. The present study showed that acute alcohol intoxication caused hepatic lipid accumulation in Tlr4-wild-type mice but not in Tlr4-mutant mice. Hepatic sterol-regulatory element binding protein (SREBP)-1, a transcription factor regulating fatty acid and triglyceride (TG) synthesis, was activated in alcohol-treated Tlr4-wild-type mice but not in Tlr4-mutant mice. Hepatic Fas, Acc, Scd-1 and Dgat-2, the key genes for fatty acid and TG synthesis, were up-regulated in alcohol-treated Tlr4-wild-type mice but not in Tlr4-mutant mice. Additional experiment showed that hepatic MyD88 was elevated in alcohol-treated Tlr4-wild-type mice but not in Tlr4-mutant mice. Hepatic NF-κB was activated in alcohol-treated Tlr4-wild-type mice but not in Tlr4-mutant mice. Moreover, hepatic GSH content was reduced and hepatic MDA level was elevated in alcohol-treated Tlr4-wild-type mice but not in Tlr4-mutant mice. Hepatic CYP2E1 was elevated in alcohol-treated Tlr4-wild-type mice but not in Tlr4-mutant mice. Hepatic p67phox and gp91phox, two NADPH oxidase subunits, were up-regulated in alcohol-treated Tlr4-wild-type mice but not in Tlr4-mutant mice. Alpha-phenyl-N-t-butylnitrone (PBN), a free radical spin-trapping agent, protected against alcohol-induced hepatic SREBP-1 activation and hepatic lipid accumulation. In conclusion, Tlr4-mutant mice are resistant to acute alcohol-induced hepatic SREBP-1 activation and hepatic lipid accumulation.

This study assessed the potential antinociceptive effects of liquiritigenin, a plant-derived compound with transient receptor potential melastatin 3 blocking activity in a rat model of persistent neuropathic pain. Chronic constriction injury (CCI) to the sciatic nerve was induced in male Sprague-Dawley rats to model human peripheral neuropathic pain. Liquiritigenin (1, 3, or 9 mg/kg) was administered intraperitoneally to examine the effects on mechanical, thermal, and cold hyperalgesia using the von Frey test, plantar test, and cold plate test, respectively. A rotarod test was also conducted to examine motor function. Liquiritigenin dose dependently alleviated mechanical, thermal and cold hyperalgesia. In addition, daily repeated treatment with liquiritigenin did not demonstrate significant antinociceptive tolerance in the measures of hyperalgesia. Within the doses studied, liquiritigenin did not significantly affect motor performance. These results suggest that liquiritigenin may be potentially useful novel treatments for neuropathic pain.

Stock price prediction is an important and challenging problem for studying financial markets. Existing studies are mainly based on the time series of stock price or the operation performance of listed company. In this paper, we propose to predict stock price based on investors' trading behavior. For each stock, we characterize the daily trading relationship among its investors using a trading network. We then classify the nodes of trading network into three roles according to their connectivity pattern. Strong Granger causality is found between stock price and trading relationship indices, i.e., the fraction of trading relationship among nodes with different roles. We further predict stock price by incorporating these trading relationship indices into a neural network based on time series of stock price. Experimental results on 51 stocks in two Chinese Stock Exchanges demonstrate the accuracy of stock price prediction is significantly improved by the inclusion of trading relationship indices.