Background:The effects of aerobic exercise on fat loss and cardiometabolic health are well-documented, but it is unknown whether a high-intensity interval training (HIIT) elicit a greater health benefit in obese children and adolescents.Methods:Relevant studies in Pubmed, Web of Science, Embase, the Cochrane Library, EBSCO, and CNKI will be searched for studies with language restriction in English and Chinese, which were published from inception to December 1, 2018. Only randomized controlled trials of HIIT on pediatric obesity will be included, and observational studies, prospective cohort studies, and systematic reviews will be excluded. Two reviewers will independently screen the studies; risk of bias assessment and data extraction, and the results are inconsistent when discussed or resolved by a third reviewer. Data analysis and synthesis will be completed by the Revman 5.3 software and Stata 12.0 software. This study will be conducted by following the guideline of the Preferred Reporting Items for Systematic Review and Meta-analysis Protocols.Conclusion:This study will be conducted by previously published data, thus ethics approval is not required. This finding will be published in a related peer-reviewed journal and present it at international conferences.PROSPERO registration number:CRD42018111308,
The roles of bone morphogenetic protein (BMP) signaling in palatogenesis were well documented in the developing hard palate; however, little is known about how BMP signaling regulates the development of soft palate. In this study, we overexpressed Noggin transgene via Osr2-creKI allele to suppress BMP signaling in the developing soft palate. We found that BMP-Smad signaling was detected in the palatal muscles and surrounding mesenchyme. When BMP-Smad signaling was suppressed by the overexpressed Noggin, the soft palatal shelves were reduced in size with the hypoplastic muscles and the extroversive hypophosphatasia (HPP). The downregulated cell proliferation and survival in the Osr2-creKI;pMes-Noggin soft palates were suggested to result from the repressed Shh transcription and Gli1 activity, implicating that the BMP-Shh-Gli1 network played a similar role in soft palate development as in the hard palate. The downregulated Sox9, Tenascin-C (TnC), and Col1 expression in Osr2-creKI;pMes-Noggin soft palate indicated the impaired differentiation of the aponeurosis and tendons, which was suggested to result in the hypoplasia of palatal muscles. Intriguingly, in the Myf5-creKI;pMes-Noggin and the Myf5-creKI;Rosa26R-DTA soft palates, the hypoplastic or abrogated muscles affected little the fusion of soft palate. Although the Scx, Tnc, and Co1 transcription was significantly repressed in the tenogenic mesenchyme of the Myf5-creKI;pMes-Noggin soft palate, the Sox9 expression, and the Tnc and Col1 transcription in aponeurosis mesenchyme were almost unaffected. It implicated that the fusion of soft palate was controlled by the mesenchymal clues at the tensor veli palatini (TVP) and levator veli palatini (LVP) levels, but by the myogenic components at the palatopharyngeus (PLP) level.
Gestational diabetes mellitus (GDM) is a kind of chronic inflammatory condition with carbohydrate metabolism disorder. Interleukin-1beta (IL-1β) plays an important role in inflammatory response, but its role in GDM development remains unknown. The aim of this study was to analyze the association between Interleukin 1beta (IL1B) rs1143623 and rs16944 polymorphisms and susceptibility to GDM. In total, 300 pregnant women with GDM and 261 healthy pregnant women were included in the study. In both groups, single nucleotide polymorphism (SNP) rs1143623 and rs16944 were analyzed by using snapshot technology. IL-1β serum values were determined by ELISA. Serum IL-1β levels involvement in GDM development. According to the results, we found the association between the IL1B rs1143623 polymorphism and susceptibility to GDM. In further analysis, IL1B rs1143623 GG genotype had a higher level of total cholesterol (TCHO) and lower level of high density lipoprotein (HDL) in GDM patients compared with the CC/GC genotypes. However, there were no statistically significant difference between the GDM and healthy control groups in terms of rs16944 polymorphism. Our results indicated that rs1143623 in IL1B gene may lead to GDM in the southwest of china. However, no significant difference was found between GDM and rs16944. The rs1143623 genotype may significantly impact the fat metabolism, especially the levels of TCHO and HDL. We believe that our findings will contribute to understanding of the etiology and possible novel prognostic markers for GDM.
Context.— Platelet (PLT) counting with impedance (PLT-I) is widely used but has low specificity. PLT counting with fluorescence (PLT-F), tested by the Sysmex XN series with high specificity, can be a complementary method to PLT-I. Objective.— To identify red blood cell (RBC)– and PLT-related parameters as potential influencing factors for PLT-I and establish PLT reflex test rules with PLT-F. Design.— We prospectively tested both PLT-I and PLT-F in all 3480 samples. In a development data set of 3000 samples, differences between the reflex and nonreflex groups were compared and influencing factors for PLT-I were identified by logistic regression. The area under the receiver operating characteristic (ROC) curve and cutoff values were obtained by ROC curve analysis. Validation was conducted in the remaining 480 samples (validation data set). Results.— PLT-F showed comparable results with immunoplatelet counting. In logistic regression, increased micro-RBC absolute count (micro-RBC#), fragmented RBCs absolute count (FRC#), PLT distribution width (PDW), mean PLT volume (MPV), PLT–large cell ratio (P-LCR), and immature PLT fraction absolute count (IPF#) were influencing factors for PLT-I. In ROC curve analysis, the cutoff values of micro-RBC#, FRC#, PDW, MPV, and P-LCR were 0.64 × 106/μL, 0.082 × 106/μL, 15.40 fL, 11.15 fL, and 33.95%, respectively. The areas under the ROC curve of micro-RBC# and FRC# were 0.77 and 0.79, respectively. Conclusions.— Micro-RBC#, FRC#, PDW, MPV, P-LCR, and IPF# were factors affecting PLT-I. Among them, micro-RBC# and FRC# were the most impactful factors. From our study results, micro-RBC#, FRC#, MPV, PDW, and P-LCR can be used to establish reflex test rules for PLT counting in clinical work.
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