Context Accurate methods for early gestational diabetes mellitus (GDM) (during the first trimester of pregnancy) prediction in Chinese and other populations are lacking. Objectives Establishing effective models to predict early GDM. Setting Pregnancy data for 73 variables during the first trimester were extracted from the electronic medical record system. Main measures Based on a machine learning (ML) driven feature selection method, 17 variables were selected for early GDM prediction. In order to facilitate clinical application, 7 variables were selected from the 17-variable panel. Advanced ML approaches were then employed using the 7-variable dataset and the 73-variable dataset to build models predicting early GDM for different situations respectively. Results 16,819 and 14,992 cases were included in the training and testing sets, respectively. Using 73 variables, the deep neural network model achieved high discriminative power, with area under the curve (AUC) values of 0.80. The 7-variable logistic regression (LR) model also achieved effective discriminate power (AUC = 0.77). Low BMI (≤ 17) was related to an increased risk of GDM, compared to a BMI in the range of 17 to 18 (minimum risk interval) (11.8% vs 8.7%, P = 0.0935). TT3 and TT4 were superior to FT3 and FT4 in predicting GDM. Lipoprotein (a) was demonstrated a promising predictive value (AUC = 0.66). Conclusions We employed ML models that achieved high accuracy in predicting GDM in early pregnancy. A clinically cost-effective 7-variable LR model was simultaneously developed. The relationship of GDM with thyroxine and BMI was investigated in the Chinese population.

With the developments of DNA sequencing technology, large amounts of sequencing data have become available in recent years and provide unprecedented opportunities for advanced association studies between somatic point mutations and cancer types/subtypes, which may contribute to more accurate somatic point mutation based cancer classification (SMCC). However in existing SMCC methods, issues like high data sparsity, small volume of sample size, and the application of simple linear classifiers, are major obstacles in improving the classification performance. To address the obstacles in existing SMCC studies, we propose DeepGene, an advanced deep neural network (DNN) based classifier, that consists of three steps: firstly, the clustered gene filtering (CGF) concentrates the gene data by mutation occurrence frequency, filtering out the majority of irrelevant genes; secondly, the indexed sparsity reduction (ISR) converts the gene data into indexes of its non-zero elements, thereby significantly suppressing the impact of data sparsity; finally, the data after CGF and ISR is fed into a DNN classifier, which extracts high-level features for accurate classification. Experimental results on our curated TCGA-DeepGene dataset, which is a reformulated subset of the TCGA dataset containing 12 selected types of cancer, show that CGF, ISR and DNN all contribute in improving the overall classification performance. We further compare DeepGene with three widely adopted classifiers and demonstrate that DeepGene has at least 24% performance improvement in terms of testing accuracy. Based on deep learning and somatic point mutation data, we devise DeepGene, an advanced cancer type classifier, which addresses the obstacles in existing SMCC studies. Experiments indicate that DeepGene outperforms three widely adopted existing classifiers, which is mainly attributed to its deep learning module that is able to extract the high level features between combinatorial somatic point mutations and cancer types.

The differentiation and maturation trajectories of fetal liver stem/progenitor cells (LSPCs) are not fully understood at single-cell resolution, and a priori knowledge of limited biomarkers could restrict trajectory tracking. We employed marker-free single-cell RNA-Seq to characterize comprehensive transcriptional profiles of 507 cells randomly selected from seven stages between embryonic day 11.5 and postnatal day 2.5 during mouse liver development, and also 52 Epcam-positive cholangiocytes from postnatal day 3.25 mouse livers. LSPCs in developing mouse livers were identified via marker-free transcriptomic profiling. Single-cell resolution dynamic developmental trajectories of LSPCs exhibited contiguous but discrete genetic control through transcription factors and signaling pathways. The gene expression profiles of cholangiocytes were more close to that of embryonic day 11.5 rather than other later staged LSPCs, cuing the fate decision stage of LSPCs. Our marker-free approach also allows systematic assessment and prediction of isolation biomarkers for LSPCs. Our data provide not only a valuable resource but also novel insights into the fate decision and transcriptional control of self-renewal, differentiation and maturation of LSPCs.

The aim of this study was to investigate the association between cardiorespiratory fitness and gut microbiota composition in premenopausal women. The participants consisted of 71 premenopausal Finnish women (aged 19–49 years). Gut microbiota were analyzed using flow cytometry, 16S rRNA gene hybridization and DNA-staining. Maximum oxygen uptake (VO_2max) was assessed by respiratory gas analyzer and body composition by Bioimpdance. We found that participants with low VO_2max had lower Bacteroides, but higher Eubacterium rectale-Clostridium coccoides than the high VO_2max group (p < 0.05 for all). VO_2max was inversely associated with EreC (r = −0.309, p = 0.01) but not with other bacteria. VO_2max also negatively correlated with fat% (r = −0.755, p < 0.001), triglycerides (r = −0.274, p = 0.021) and leptin (r = −0.574, p < 0.001). By contrast, EreC was positively associated with fat% (r = 0.382, p = 0.002), dietary fat intake (r = 0.258, p = 0.034), triglycerides (r = 0.390, p = 0.002) and leptin (r = 0.424, p = 0.001), but negatively with carbohydrate intake (r = −0.252, p = 0.034) and HDL (r = −0.26, p = 0.028). After adjusting for age and dietary intake, all the significant associations remained. However, after adjusting for fat%, the associations between VO_2max and EreC disappeared. Our results suggest that cardiorespiratory fitness is associated with gut microbiota composition, independent of age and carbohydrate or fat intake. The association between VO_2max and EreC, however, appears to be mediated by body fatness.

Background: Ferroptosis, a form of regulated cell death, is an important topic in the field of cancer research. However, the signalling pathways and factors that sensitise tumour cells to ferroptosis remain elusive.Methods: We determined the level of ferroptosis in cells by measuring cell death and lipid reactive oxygen species (ROS) production. The expression of RB1‐inducible coiled‐coil 1 (RB1CC1) and related proteins was analyzed by immunoblotting and immunohistochemistry. Immunofluorescence was used to determine the subcellular localization of RB1CC1. We investigated the mechanism of RB1CC1 nuclear translocation by constructing a series of RB1CC1 variants. To examine the ferroptosis‐ and RB1CC1‐dependent transcriptional program in tumour cells, chromatin immunoprecipitation sequencing was performed. To assess the effect of c‐Jun N‐terminal kinase (JNK) agonists on strenthening imidazole ketone erastin (IKE) therapy, we constructed cell‐derived xenograft mouse models. Mouse models of hepatocellular carcinoma to elucidate the importance of Rb1cc1 in IKE‐based therapy of liver tumourigenesis.Results: RB1CC1 is upregulated by lipid ROS and that nuclear translocation of phosphorylation of RB1CC1 at Ser537 was essential for sensitising ferroptosis in tumour cells. Upon ferroptosis induction, nuclear RB1CC1 sharing forkhead box (FOX)‐binding motifs recruits elongator acetyltransferase complex subunit 3 (ELP3) to strengthen H4K12Ac histone modifications within enhancers linked to ferroptosis. This also stimulated transcription of ferroptosis‐associated genes, such as coiled‐coil–helix–coiled‐coil–helix domain containing 3 (CHCHD3), which enhanced mitochondrial function to elevate mitochondrial ROS early following induction of ferroptosis. FDA‐approved JNK activators reinforced RB1CC1 nuclear translocation and sensitised cells to ferroptosis, which strongly suggested that JNK is upstream of RB1CC1. Nuclear localisation of RB1CC1 correlated with lipid peroxidation in clinical lung cancer specimens. Rb1cc1 was essential for ferroptosis agonists to suppress liver tumourigenesis in mice.Conclusions: Our findings indicate that RB1CC1‐associated signalling sensitises tumour cells to ferroptosis and that targeting RB1CC1 may be beneficial for tumour treatment.

With the developments of DNA sequencing technology, large amounts of sequencing data have been produced that provides unprecedented opportunities for advanced association studies between somatic mutations and cancer types/subtypes which further contributes to more accurate somatic mutation based cancer typing (SMCT). In existing SMCT methods however, the absence of high-level feature extraction is a major obstacle in improving the classification performance. We propose DeepCNA, an advanced convolutional neural network (CNN) based classifier, which utilizes copy number aberrations (CNAs) and HiC data, to address this issue. DeepCNA first pre-process the CNA data by clipping, zero padding and reshaping. Then, the processed data is fed into a CNN classifier, which extracts high-level features for accurate classification. Experimental results on the COSMIC CNA dataset indicate that 2D CNN with both cell lines of HiC data lead to the best performance. We further compare DeepCNA with three widely adopted classifiers, and demonstrate that DeepCNA has at least 78% improvement of performance. This paper demonstrates the advantages and potential of the proposed DeepCNA model for processing of somatic point mutation based gene data, and proposes that its usage may be extended to other complex genotype-phenotype association studies.