Toward high electro-optic performance in III-V semiconductors
Peer-Reviewed Publication
Updates every hour. Last Updated: 12-Aug-2025 22:11 ET (13-Aug-2025 02:11 GMT/UTC)
A new mathematical model developed at the University of Waterloo can determine a baby’s overall drug exposure when their mother is taking medication. This is the first study to include drug transfer from the umbilical cord and through breastfeeding in determining the baby's total drug levels.
The research team from the School of Pharmacy at Waterloo looked specifically at Levetiracetam. It is a drug commonly prescribed long term for women with epilepsy, yet there was minimal data on the risk of adverse effects on breastfed infants.
In a paper published in National Science Review, a team of Chinese scientists developed an attention-based deep learning model, CGMformer, pretrained on a well-controlled and diverse corpus of continuous glucose monitoring (CGM) data to represent individual’s intrinsic metabolic state and enable clinical applications. It can accurately characterize individual dynamic glycemic behaviors such as maintenance of fasting blood glucose homeostasis and adaptation to postprandial hyperglycemia., It can assist in the diagnosis, disease duration assessment, and complication prediction of type 2 diabetes, subtype classification of non-diabetic populations, predict postprandial glucose responses accurately and provide personalized dietary recommendations for diabetes patients, thereby enabling lifestyle intervention recommendations.
Background: Over recent decades, findings on the potential correlation between type II diabetes mellitus (T2DM) and the risk of esophageal cancer (EC) have displayed considerable heterogeneity. Furthermore, metformin has emerged as a potentially protective agent against certain site-specific malignancies. This study aims to explore the causal relationship between T2DM, medication treatments (metformin, insulin, gliclazide), and EC risk while addressing the notable variability in previous research findings.
Methods: To elucidate the causal associations between T2DM, medication treatments, and EC, we employed a synergistic methodology that integrates the two-sample Mendelian randomization (MR) approach with meta-analysis. The genome-wide association studies (GWAS) pertaining to each exposure and EC were acquired from a publicly accessible database.
Results: For MR analyses, three out of seven GWAS datasets within the T2DM cohort exhibited statistical significance. Conversely, all MR analyses yielded non-significant results in the medication cohort. Meta-analyses suggested that a genetic predisposition to T2DM correlated with a reduced risk of EC [odds ratio (OR), 0.999612; 95% confidence interval (CI): 0.999468–0.999756; P=0.01; I2=0%]. Moreover, metformin intake was causally linked to a decreased prevalence of EC (OR, 0.988954; 95% CI: 0.979044–0.998963; P=0.03; I2=0%), whereas neither insulin nor gliclazide manifests statistical significance.
Conclusions: Our findings indicate T2DM and metformin are causally associated with diminished risk of EC, while no causal associations exist between insulin, gliclazide, and EC.