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In both the non-matched and matched cohort, the incidence of inadequate analgesia in the primiparas group was lower than that in the multiparas group (16.7% vs. 24.0%, P less then 0.001 and 16.1% vs. 23.5%, P = 0.002; respectively). The multiparas group was identified as being at risk of inadequate analgesia after cesarean delivery in both age groups (age ≥ 35 years, odds ratio 2.18, 95% confidence interval 1.20-3.95; age less then 35 years, odds ratio 1.43, 95% confidence interval 1.08-1.89). [https://www.selleckchem.com/products/dubs-in-1.html Dubs-IN-1] Conclusion Multiparas that undergo a repeat cesarean delivery had a significantly higher risk of inadequate postoperative pain treatment than primiparas. The maternal category should be considered when formulating the postoperative analgesia strategy after cesarean delivery. © 2020 Yang et al.The complexity of orphan diseases, which are those that do not have an effective treatment, together with the high dimensionality of the genetic data used for their analysis and the high degree of uncertainty in the understanding of the mechanisms and genetic pathways which are involved in their development, motivate the use of advanced techniques of artificial intelligence and in-depth knowledge of molecular biology, which is crucial in order to find plausible solutions in drug design, including drug repositioning. Particularly, we show that the use of robust deep sampling methodologies of the altered genetics serves to obtain meaningful results and dramatically decreases the cost of research and development in drug design, influencing very positively the use of precision medicine and the outcomes in patients. The target-centric approach and the use of strong prior hypotheses that are not matched against reality (disease genetic data) are undoubtedly the cause of the high number of drug design failures and attrition rates. Sampling and prediction under uncertain conditions cannot be avoided in the development of precision medicine. © 2020 Álvarez-Machancoses et al.Asthma is a chronic respiratory disease that affects 339 million people worldwide and has a considerable impact on the pediatric population. Asthma symptoms can be controlled by pharmacological treatment. However, some patients do not respond to therapy and continue suffering from symptoms, which impair the quality of life of patients and limit their daily activity. Genetic variation has been shown to have a role in treatment response. The aim of this review is to update the main findings described in pharmacogenetic studies of pediatric asthma published from January 1, 2018 to December 31, 2019. During this period, the response to short-acting beta-agonists and inhaled corticosteroids in childhood asthma has been evaluated by eleven candidate-gene studies, one meta-analysis of a candidate gene, and six pharmacogenomic studies. The findings have allowed validating the association of genes previously related to asthma treatment response (ADRB2, GSDMB, FCER2, VEGFA, SPAT2SL, ASB3, and COL2A1), and identifying novel associations (PRKG1, DNAH5, IL1RL1, CRISPLD2, MMP9, APOBEC3B-APOBEC3C, EDDM3B, and BBS9). However, some results are not consistent across studies, highlighting the need to conduct larger studies in diverse populations with more homogeneous definitions of treatment response. Once stronger evidence was established, genetic variants will have the potential to be applied in clinical practice as biomarkers of treatment response enhancing asthma management and improving the quality of life of asthma patients. © 2020 Perez-Garcia et al.Background Several studies have reported the relationship of diabetes mellitus (DM) and obesity with bone mineral density (BMD), but the conclusions remain unclear. This study aimed to provide more information for the relationship of plasma glucose and abdominal visceral fat (AVF) with BMD and bone mineral content (BMC) in women with different glucose metabolism status. Methods Patients were screened by oral glucose tolerance test (OGTT) and were divided into three groups normal glucose tolerance (NGT, n=132), pre-diabetes mellitus (pre-DM, n=28) and newly diagnosed type 2 DM (T2DM, n=27) groups. Plasma glucose concentrations, anthropometric measurements, body composition, and BMD were measured. Analysis of variance (ANOVA), pearson correlation, and multiple linear regression models were used to evaluate the relationship between BMD, plasma glucose, AVF, and other variables. Results The percentage of subjects with osteoporosis or low BMD was 29.9%, and 66.7% subjects in T2DM group were significantly higher than that in the pre-DM (28.6%) and NGT (22.7%) groups (p=0.005 and p less then 0.001, respectively). Both BMD at femoral neck (FN) and lumbar spine (LS) of T2DM group were lower than those in NGT group (p=0.009 and p=0.003, respectively), and BMC of T2DM group was lower than those of NGT and pre-DM groups (p less then 0.001). The results of statistical analysis revealed that both two-hour plasma glucose (2-h PG) and age showed negative correlation with BMC, FN BMD, and LS BMD. AVF showed positive correlation with BMC and LS BMD. Furthermore, the lean mass (LM) showed independent positive effects on BMC. Conclusion Our findings suggest that 1) Age is a strong negative predictor of bone mass. 2) A direct negative effect of increasing 2-h PG might be more prominent at bone mass in women. 3) A moderate increase in AVF is beneficial to bone mass, while excessive increase might be harmful. 4) LM is a positive predictor of BMC. © 2020 Jia et al.Purpose The purpose of this study was to explore the difference and association between intestinal microbiota and plasma metabolomics between type 2 diabetes mellitus (T2DM) and normal group and to identify potential microbiota biomarkers that contribute the most to the difference in metabolites. Methods Six male ZDF model (fa/fa) rats were fed by a Purina #5008 Lab Diet (crude protein 23.5%, crude fat 6.5%) for 3 weeks and their age-matched 6 ZDF control (fa/+) rats were fed by normal rodent diet. Their stool and blood samples were collected at 12&nbsp;weeks. To analyze the microbial populations in these samples, we used a 16S rRNA gene sequencing approach. Liquid chromatography-mass spectrometry (LC-MS) followed by multivariate statistical analysis was applied to the plasma metabolites profiling. Correlation analysis of them was calculated by Pearson statistical method. Results Twelve potential biomarkers of intestinal microbial flora and 357 differential metabolites were found in ZDF fa/fa rats, among which there are three flora that contributed the most to the perturbation of metabolites, including genus Phocea, Pseudoflavonifractor and species Lactobacillus intestinalis.