Plasma Lnc-UCA1/miR-138 axis as a potential biomarker for gestational diabetes mellitus and neonatal prognosis

Objectives: This study aimed to explore the correlations of Lnc-UCA1/miR-138 axis with gestational diabetes mellitus (GDM) risk and neonatal prognosis. Material and methods: First, the blood samples from sixty GDM patients and 60 healthy pregnant women were collected to detect the change of Lnc-UCA1/miR-138 axis by using real-time polymerase chain reaction (RT-qPCR). The clinical characteristics of GDM patients, healthy controls, and neonates were recorded. Then, the correlation analysis of Lnc-UCA1, miR-138, and Lnc-UCA1/miR-138 axis levels with clinicopathological characteristics was performed to explore the clinical value of Lnc-UCA1/miR-138 axis in GDM. Finally, the specificity and sensitivity of Lnc-UCA1, miR-138, and Lnc-UCA1/miR-138 axis for GDM diagnosis was evaluated using receiver operating characteristic (ROC) curves. Results: Our present study found that, when compared with healthy pregnancies, the expression levels of Lnc-UCA1 and miR-138 were increased and decreased, respectively, and Lnc-UCA1/miR-138 axis profile was elevated. Second, Lnc-UCA1 and Lnc-UCA1/miR-138 axis were positively correlated with fasting glucose, one-hour glucose, and two-hour glucose, while miR-138 showed the opposite trend. Furthermore, the area under the ROC curve (AUC) were 0.8196, 0.8021, and 0.8901 for diagnostic efficiencies of Lnc-UCA1, miR-138, and Lnc-UCA1/miR-138, respectively. In addition, higher profiles of Lnc-UCA1 were correlated with birth asphyxia of neonate. Conclusions: Circulating Lnc-UCA1/miR-138 axis might be involved in the pathogenesis of GDM and could function as a novel and effective biomarker for GDM risk and neonatal prognosis

Effects of obesity and a history of gestational diabetes on the risk of postpartum diabetes and hyperglycemia in Chinese Women: Obesity, GDM and diabetes risk

Objective: To evaluate the independent or combined effects of gestational diabetes (GDM) and pre-pregnancy and postpartum BMI on the odds of postpartum diabetes and hyperglycemia. Methods: The study samples included 1263 women with prior GDM and 705 women without GDM. Postpartum 1-7 years diabetes was diagnosed by the standard oral glucose tolerance test. Results: The multivariable-adjusted odds ratios among women with prior GDM, compared with those without it, were 7.52 for diabetes and 2.27 for hyperglycemia. The multivariable-adjusted odds ratios at different postpartum BMI levels (= 28 kg/m(2)) were 1.00, 2.80, and 8.08 for diabetes (P-trend = 31.9%) or abdominal obesity (>= 85 cm) had a 2.7-6.9-fold higher odds ratio for diabetes or hyperglycemia. Women with both obesity and prior GDM had the highest risk of diabetes or hyperglycemia compared with non-obese women without GDM. Non-obese women with prior GDM had the same risk of diabetes and hyperglycemia as non-GDM women with obesity. When using Cox regression models, the results were very close to those using logistic regression models. Conclusions: Maternal prior GDM and pre-pregnancy or postpartum obesity contribute equally to postpartum diabetes and hyperglycemia risk. (C) 2019 Elsevier B.V. All rights reserved.Peer reviewe

High risk of metabolic syndrome after delivery in pregnancies complicated by gestational diabetes

Aims: To investigate the risk of postpartum metabolic syndrome in women with GDM compared with those without GDM in a Chinese population. Methods: Tianjin GDM observational study included 1263 women with a history of GDM and 705 women without GDM. Multivariate logistic regression was used to assess risks of postpartum metabolic syndrome between women with and without GDM. Postpartum metabolic syndrome was diagnosed by two commonly used criteria. Results: During a mean 3.53 years of follow up, 256 cases of metabolic syndrome were identified by using the NCEPATPIII criteria and 244 cases by using the IDF criteria. Multivariable-adjusted odds ratios of metabolic syndrome in women with GDM compared with those without GDM were 3.66 (95% confidence interval [CI] 2.02-6.63) for NCEP ATPIII criteria and 3.90 (95% CI 2.13-7.14) for IDF criteria. Women with GDM had higher multivariable-adjusted odds ratios of central obesity, hypertriglyceridemia, and high blood pressure than women without GDM. The multivariable-adjusted odds ratios of low HDL cholesterol and hyperglycemia were not significant between women with and without GDM, however, the multivariable-adjusted odds ratio of hyperglycemia became significant when we used the modified criteria. Conclusions: The present study indicated that women with prior GDM had significantly higher risks for postpartum metabolic syndrome, as well as its individual components. (C) 2019 Elsevier B.V. All rights reserved.Peer reviewe

Global analysis of the rat and human platelet proteome - the molecular blueprint for illustrating multi-functional platelets and cross-species function evolution

Emerging evidences indicate that blood platelets function in multiple biological processes including immune response, bone metastasis and liver regeneration in addition to their known roles in hemostasis and thrombosis. Global elucidation of platelet proteome will provide the molecular base of these platelet functions. Here, we set up a high throughput platform for maximum exploration of the rat/human platelet proteome using integrated proteomics technologies, and then applied to identify the largest number of the proteins expressed in both rat and human platelets. After stringent statistical filtration, a total of 837 unique proteins matched with at least two unique peptides were precisely identified, making it the first comprehensive protein database so far for rat platelets. Meanwhile, quantitative analyses of the thrombin-stimulated platelets offered great insights into the biological functions of platelet proteins and therefore confirmed our global profiling data. A comparative proteomic analysis between rat and human platelets was also conducted, which revealed not only a significant similarity, but also an across-species evolutionary link that the orthologous proteins representing ‘core proteome’, and the ‘evolutionary proteome’ is actually a relatively static proteome

Colon Carcinogenesis: The interplay between diet and gut microbiota

Colorectal cancer (CRC) incidence increases yearly, and is three to four times higher in developed countries compared to developing countries. The well-known risk factors have been attributed to low physical activity, overweight, obesity, dietary consumption including excessive consumption of red processed meats, alcohol, and low dietary fiber content. There is growing evidence of the interplay between diet and gut microbiota in CRC carcinogenesis. Although there appears to be a direct causal role for gut microbes in the development of CRC in some animal models, the link between diet, gut microbes, and colonic carcinogenesis has been established largely as an association rather than as a cause-and-effect relationship. This is especially true for human studies. As essential dietary factors influence CRC risk, the role of proteins, carbohydrates, fat, and their end products are considered as part of the interplay between diet and gut microbiota. The underlying molecular mechanisms of colon carcinogenesis mediated by gut microbiota are also discussed. Human biological responses such as inflammation, oxidative stress, deoxyribonucleic acid (DNA) damage can all influence dysbiosis and consequently CRC carcinogenesis. Dysbiosis could add to CRC risk by shifting the effect of dietary components toward promoting a colonic neoplasm together with interacting with gut microbiota. It follows that dietary intervention and gut microbiota modulation may play a vital role in reducing CRC risk

Assessing students’ information literacy skills in two secondary schools in Singapore

Appropriately measuring information literacy skills is essential to understand their educational impact as well as exploring pedagogies to improve teaching of these skills. This paper reports findings of a baseline study to understand the information literacy level of secondary school students in Singapore. A comprehensive instrument encompassing a new dimension of ethical usage of information as well as major IL principles and guidelines was developed for data collection. A total of 298 responses were gathered through the online survey. The findings indicate that overall there was a scope to improve students’ IL skills. That stated, higher order IL skills (such as information use, synthesis, and evaluation) demand more attention, as comparatively lower levels of proficiency were observed in this study.

Predicting 30-Day Readmissions: Performance of the LACE Index Compared with a Regression Model among General Medicine Patients in Singapore

The LACE index (length of stay, acuity of admission, Charlson comorbidity index, CCI, and number of emergency department visits in preceding 6 months) derived in Canada is simple and may have clinical utility in Singapore to predict readmission risk. We compared the performance of the LACE index with a derived model in identifying 30-day readmissions from a population of general medicine patients in Singapore. Additional variables include patient demographics, comorbidities, clinical and laboratory variables during the index admission, and prior healthcare utilization in the preceding year. 5,862 patients were analysed and 572 patients (9.8%) were readmitted in the 30 days following discharge. Age, CCI, count of surgical procedures during index admission, white cell count, serum albumin, and number of emergency department visits in previous 6 months were significantly associated with 30-day readmission risk. The final logistic regression model had fair discriminative ability c-statistic of 0.650 while the LACE index achieved c-statistic of 0.628 in predicting 30-day readmissions. Our derived model has the advantage of being available early in the admission to identify patients at high risk of readmission for interventions. Additional factors predicting readmission risk and machine learning techniques should be considered to improve model performance

Applying hybrid clustering in pulsar candidate sifting with multi-modality for FAST survey

Pulsar search is always the basis of pulsar navigation, gravitational wave detection and other research topics. Currently, the volume of pulsar candidates collected by Five-hundred-meter Aperture Spherical radio Telescope (FAST) shows an explosive growth rate that has brought challenges for its pulsar candidate filtering System. Particularly, the multi-view heterogeneous data and class imbalance between true pulsars and non-pulsar candidates have negative effects on traditional single-modal supervised classification methods. In this study, a multi-modal and semi-supervised learning based pulsar candidate sifting algorithm is presented, which adopts a hybrid ensemble clustering scheme of density-based and partition-based methods combined with a feature-level fusion strategy for input data and a data partition strategy for parallelization. Experiments on both HTRU (The High Time Resolution Universe Survey) 2 and FAST actual observation data demonstrate that the proposed algorithm could excellently identify the pulsars: On HTRU2, the precision and recall rates of its parallel mode reach 0.981 and 0.988. On FAST data, those of its parallel mode reach 0.891 and 0.961, meanwhile, the running time also significantly decrease with the increment of parallel nodes within limits. So, we can get the conclusion that our algorithm could be a feasible idea for large scale pulsar candidate sifting of FAST drift scan observation