Objective To study the associations between heterogeneity of gestational diabetes mellitus (GDM) subtype/prepregnancy body mass index (pre‐BMI) and large‐for‐gestational‐age (LGA) infants of Chinese women. Methods We performed a retrospective case‐control study of 299 women with GDM and 204 women with normal glucose tolerance (NGT), using oral glucose tolerance test‐based indices performed at 24‐25 weeks of gestation. Women with GDM were classified into the following three physiologic subtypes: GDM with a predominant insulin‐secretion defect (GDM‐dysfunction), GDM with a predominant insulin‐sensitivity defect (GDM‐resistance), or GDM with both defects (GDM‐mixed). We then used a binary logistic regression model to evaluate the potential associations of GDM subtypes and pre‐BMI with newborn macrosomia or LGA. Results Women with GDM‐resistance had a higher pre‐BMI (P < 0.001), whereas women in the GDM‐dysfunction and GDM‐mixed groups had pre‐BMIs comparable to the NGT group. In the logistic regression model, women in the GDM‐mixed group exhibited an increased risk of bearing newborns with macrosomia and LGA, and women in the GDM‐dysfunction group tended to have newborns with LGA after adjusting for pre‐BMI and other potential confounders. Women who were overweight or obese prepregnancy manifested an increased risk of having newborns with macrosomia and LGA relative to normal‐weight women, regardless of whether values were unadjusted or adjusted for all potential confounders. There was no significant interaction between GDM subtype and pre‐BMI for any of the studied outcomes. Conclusions Heterogeneity of GDM (GDM‐dysfunction and GDM‐mixed) and prepregnancy overweight/obesity were independently associated with LGA in Chinese women. There was no significant interaction between GDM subtypes and pre‐BMI for LGA.

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The extraction of regions-of-interest (ROIs) in hyperspectral images of breast cancer specimens is currently carried out manually or by visual inspection. In order to address the labor-intensive and time-consuming process of the manual extraction of ROIs in hyperspectral images, an algorithm is developed in this paper to automate the extraction process. This is achieved by using a contrast module and a homogeneity module to duplicate the same manual or visual steps that an expert goes through in order to extract ROIs. The success of the automated process is determined by comparing the classification rates of the automated approach with the manual approach in terms of the ability to separate cancer cases from normal cases.

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