Association of maternal lipid levels with birth weight and cord blood insulin: a Bayesian network analysis

Objective: To assess the independent association of maternal lipid levels with birth weight and cord blood insulin (CBI) level. Setting: The Born in Guangzhou Cohort Study, Guangzhou, China. Participants: Women who delivered between January 2015 and June 2016 and with umbilical cord blood retained were eligible for this study. Those with prepregnancy health conditions, without an available fasting blood sample in the second trimester, or without demographic and glycaemic information were excluded. After random selection, data from 1522 mother–child pairs were used in this study. Exposures and outcome measures: Additive Bayesian network analysis was used to investigate the interdependency of lipid profiles with other metabolic risk factors (prepregnancy body mass index (BMI), fasting glucose and early gestational weight gain) in association with birth weight and CBI, along with multivariable linear regression models. Results: In multivariable linear regressions, maternal triglyceride was associated with increased birth weight (adjusted β=67.46, 95% CI 41.85 to 93.06 g per mmol/L) and CBI (adjusted β=0.89, 95% CI 0.06 to 1.72 μU/mL per mmol/L increase), while high-density lipoprotein cholesterol was associated with decreased birth weight (adjusted β=−45.29, 95% CI −85.49 to −5.09 g per mmol/L). After considering the interdependency of maternal metabolic risk factors in the Network analysis, none of the maternal lipid profiles was independently associated with birth weight and CBI. Instead, prepregnancy BMI was the global strongest factor for birth weight and CBI directly and indirectly. Conclusions: Gestational dyslipidaemia appears to be secondary to metabolic dysfunction with no clear association with metabolic adverse outcomes in neonates. Maternal prepregnancy overweight/obesity appears the most influential upstream metabolic risk factor for both maternal and neonatal metabolic health; these data imply weight management may need to be addressed from the preconception period and during early pregnancy

Classification and function of γδT cells and its research progress in anti-glioblastoma

Abstract Human peripheral blood T lymphocytes are classified into alpha–beta T (αβΤ) cells and gamma–delta T (γδΤ) cells based on the difference in T cell receptors (TCRs). αβT cells are crucial for the acquired immune response, while γδΤ cells, though only a small subset, can recognize antigenic substances. These antigens do not need to be processed and presented and are not restricted by MHC. This distinguishes γδΤ cells from αβT cells and highlights their distinct role in innate immunity. Despite their small number, γδΤ cells hold significant significance in anti-tumor, anti-infection and immune regulation. Glioblastoma (GBM) represents one of the most prevalent malignant tumors within the central nervous system (CNS). Surgical resection alone proves to be an ineffective method for curing this type of cancer. Even with the combination of surgical resection, radiotherapy, and chemotherapy, the prognosis of some individuals with glioblastoma is still poor, and the recurrence rate is high. In this research, the classification, biological, and immunological functions of γδT cells and their research progress in anti-glioblastoma were reviewed

A new barbeled goby from south China (Teleostei: Gobiidae)

Cui, Rongfeng, Pan, Yashu, Yang, Xinming, Wang, Yingyong (2013): A new barbeled goby from south China (Teleostei: Gobiidae). Zootaxa 3670 (2): 177-192, DOI: 10.11646/zootaxa.3670.2.

An Ontology-Based Approach to Data Cleaning

This paper describes an ontology-based approach to data cleaning. Data cleaning is the process of detecting and correcting errors in databases. An ontology is a formal explicit specification of a shared conceptualization of a domain. Our approach to data cleaning requires a set of ontologies describing the domains represented by the classes and their attributes. Using the ontology-based approach, we are able to clean data of not only syntactic errors but also some classes of semantic errors

A Review of Studies Involving the Effects of Climate Change on the Energy Consumption for Building Heating and Cooling

The world is faced with significant climate change, rapid urbanization, massive energy consumption, and tremendous pressure to reduce greenhouse gases. Building heating and cooling is one primary source of energy consumption and anthropogenic carbon dioxide emissions. First, this review presents previous studies that estimate the specific amount of climate change impact on building heating and cooling energy consumption, using the statistical method, physical model method, comprehensive assessment model method, and the combination method of statistical and physical model methods. Then, because the heating and cooling degree days indices can simply and reliably indicate the effects of climate on building heating and cooling energy consumption, previous studies were reviewed from the aspects of heating and cooling degree days indices, regional spatial-temporal variations in degree days and related indices, influencing factors of the spatial distributions of degree days, and the impacts of urbanization on degree days. Finally, several potential key issues or research directions were presented according to the research gaps or fields that need to be studied further in the future, such as developing methods to simply and accurately estimate the specified amounts of climate change impact on building cooling and heating energy consumption; using more effective methods to analyze the daytime, nighttime, and all-day spatial-temporal changes in different seasons in the past and future under various environment contexts by considering not only the air temperature but also the relative humidity, solar radiation, population, etc., and further exploring the corresponding more kinds of driving forces, including the various remotely sensed indices, albedo, nighttime light intensity, etc.; estimating the daytime, nighttime, and all-day impacts of urbanization on heating degree days (HDDs), cooling degree days (CDDs), and their sum (HDDs + CDDs) for vast cities in different environmental contexts at the station site, city, regional and global scales; producing and sharing of the related datasets; and analyzing the subsequent effects induced by climate change on the energy consumption for building heating and cooling, etc