A population-based cohort of drug exposures and adverse pregnancy outcomes in China (DEEP): rationale, design, and baseline characteristics.

The DEEP cohort is the first population-based cohort of pregnant population in China that longitudinally documented drug uses throughout the pregnancy life course and adverse pregnancy outcomes. The main goal of the study aims to monitor and evaluate the safety of drug use through the pregnancy life course in the Chinese setting. The DEEP cohort is developed primarily based on the population-based data platforms in Xiamen, a municipal city of 5 million population in southeast China. Based on these data platforms, we developed a pregnancy database that documented health care services and outcomes in the maternal and other departments. For identifying drug uses, we developed a drug prescription database using electronic healthcare records documented in the platforms across the primary, secondary and tertiary hospitals. By linking these two databases, we developed the DEEP cohort. All the pregnant women and their offspring in Xiamen are provided with health care and followed up according to standard protocols, and the primary adverse outcomes - congenital malformations - are collected using a standardized Case Report Form. From January 2013 to December 2021, the DEEP cohort included 564,740 pregnancies among 470,137 mothers, and documented 526,276 live births, 14,090 miscarriages and 6,058 fetal deaths/stillbirths and 25,723 continuing pregnancies. In total, 13,284,982 prescriptions were documented, in which 2,096 chemicals drugs, 163 biological products, 847 Chinese patent medicines and 655 herbal medicines were prescribed. The overall incidence rate of congenital malformations was 2.0% (10,444/526,276), while there were 25,526 (4.9%) preterm births and 25,605 (4.9%) live births with low birth weight.

The use of Chinese herbal medicines throughout the pregnancy life course and their safety profiles: a population-based cohort study.

BACKGROUND: Chinese herbal medicines have been long used among pregnant populations in China. However, despite the high susceptibility of this population to drug exposure, it continued to remain unclear about how often they were used, to what extent they were used at different pregnancy stages, and whether their use was based on sound safety profiles, particularly when used in combination with pharmaceutical drugs. OBJECTIVE: This descriptive cohort study aimed to systematically investigate the use of Chinese herbal medicines throughout pregnancy and their safety profiles. STUDY DESIGN: A large medication use cohort was developed by linking a population-based pregnancy registry and a population-based pharmacy database, which documented all prescriptions at both outpatients and inpatients from conception to 7 days after delivery, including pharmaceutical drugs and processed Chinese herbal medicine formulas that were approved by the regulatory authority and prepared under the guidance of national quality standards. The prevalence of the use of Chinese herbal medicine formulas, prescription pattern, and combination use of pharmaceutical drugs throughout pregnancy were investigated. Multivariable log-binomial regression was performed to assess temporal trends and further explore the potential characteristics associated with the use of Chinese herbal medicines. Of note, 2 authors independently conducted a qualitative systematic review of patient package inserts of the top 100 Chinese herbal medicine formulas used to identify their safety profiles. RESULTS: This study included 199,710 pregnancies; of those pregnancies, 131,235 (65.71%) used Chinese herbal medicine formulas, including 26.13% during pregnancy (corresponding to 14.00%, 8.91%, and 8.26% in the first, second, and third trimesters of pregnancy) and 55.63% after delivery. The peak uses of Chinese herbal medicines occurred between 5 and 10 weeks of gestation. The use of Chinese herbal medicines significantly increased over the years (from 63.28% in 2014 to 69.59% in 2018; adjusted relative risk, 1.11; 95% confidence interval, 1.10-1.13), which was particularly great during pregnancy (from 18.47% in 2014 to 32.46% in 2018; adjusted relative risk, 1.84; 95% confidence interval, 1.77-1.90). Our study observed 291,836 prescriptions involving 469 Chinese herbal medicine formulas, and the top 100 most used Chinese herbal medicines accounted for 98.28% of the total prescriptions. Of those, a third (33.39%) were dispensed at outpatient visits; 6.79% were external use, and 0.29% were administered intravenously. However, Chinese herbal medicines were very often prescribed in combination with pharmaceutical drugs (94.96% overall), involving 1175 pharmaceutical drugs with 1,667,459 prescriptions. The median of pharmaceutical drugs prescribed in combination with Chinese herbal medicines per pregnancy was 10 (interquartile range, 5-18). The systematic review of drug patient package inserts found that the 100 most frequently prescribed Chinese herbal medicines contained a total of 240 herb constituents (median, 4.5); 7.00% were explicitly indicated for pregnancy or postpartum conditions; 43.00% were reported with efficacy or safety data from randomized controlled trials. Information was lacking about whether the medications had any reproductive toxicity, were excreted in human milk, or crossed the placenta. CONCLUSION: The use of Chinese herbal medicines was prevalent throughout pregnancy and increased over the years. The use of Chinese herbal medicines peaked in the first trimester of pregnancy and was very often used in combination with pharmaceutical drugs. However, their safety profiles were mostly unclear or incomplete, suggesting a strong need for postapproval surveillance for the use of Chinese herbal medicines during pregnancy.

scAPAmod: Profiling Alternative Polyadenylation Modalities in Single Cells from Single-Cell RNA-Seq Data.

Alternative polyadenylation (APA) is a key layer of gene expression regulation, and APA choice is finely modulated in cells. Advances in single-cell RNA-seq (scRNA-seq) have provided unprecedented opportunities to study APA in cell populations. However, existing studies that investigated APA in single cells were either confined to a few cells or focused on profiling APA dynamics between cell types or identifying APA sites. The diversity and pattern of APA usages on a genomic scale in single cells remains unappreciated. Here, we proposed an analysis framework based on a Gaussian mixture model, scAPAmod, to identify patterns of APA usage from homogeneous or heterogeneous cell populations at the single-cell level. We systematically evaluated the performance of scAPAmod using simulated data and scRNA-seq data. The results show that scAPAmod can accurately identify different patterns of APA usages at the single-cell level. We analyzed the dynamic changes in the pattern of APA usage using scAPAmod in different cell differentiation and developmental stages during mouse spermatogenesis and found that even the same gene has different patterns of APA usages in different differentiation stages. The preference of patterns of usages of APA sites in different genomic regions was also analyzed. We found that patterns of APA usages of the same gene in 3' UTRs (3' untranslated region) and non-3' UTRs are different. Moreover, we analyzed cell-type-specific APA usage patterns and changes in patterns of APA usages across cell types. Different from the conventional analysis of single-cell heterogeneity based on gene expression profiling, this study profiled the heterogeneous pattern of APA isoforms, which contributes to revealing the heterogeneity of single-cell gene expression with higher resolution.

Dose-response association between maternal pre-pregnancy bodyweight and gestational diabetes mellitus following ART treatment: a population-based cohort study.

BACKGROUND: The impact of maternal pre-pregnancy bodyweight on gestational diabetes mellitus (GDM) following assisted reproductive technology (ART) treatment has been insufficiently investigated. The aim of this study was to investigate the association between maternal pre-pregnancy bodyweight and GDM following ART. METHODS: From January 2014 to March 2019, this population-based retrospective cohort study included pregnancies achieved by ART treatment in a pregnancy registration database in China. Multivariate regression analysis and restricted cubic splines were used to explore the association between bodyweight and GDM. RESULTS: A total of 6,598 pregnancies were included. The incidence of GDM was 26.0% (1715/6598). A total of 868 (13.2%) pregnant women were underweight, 665 (10.8%) were overweight, and 145 (2.20%) were obesity. We found a linear dose-response relation between maternal body mass index and GDM by restricted cubic splines, where one unit body mass index increase was associated with the 15% elevated risk of GDM (adjusted odds ratio [OR] 1.15, 95% CI 1.08-1.22). Compared to the normal weight group, maternal underweight was associated with lower risk of GDM (adjusted OR 0.68, 95% CI 0.57-0.82), while increased risk was found for overweight (adjusted OR 1.54 95% CI 1.29-1.84) and obesity (adjusted OR 1.74, 95% CI 1.23-2.47). CONCLUSIONS: Our study found a linear dose-effect relationship between pre-pregnancy bodyweight and GDM following ART treatment. The findings in this study support the clinical recommendation of advising women with overweight or obesity to lose weight prior to ART treatment.

scLINE: A multi-network integration framework based on network embedding for representation of single-cell RNA-seq data.

Single-cell RNA sequencing (scRNA-seq) is fast becoming a powerful technology that revolutionizes biomedical studies related to development, immunology and cancer by providing genome-scale transcriptional profiles at unprecedented throughput and resolution. However, due to the low capture rate and frequent drop-out events in the sequencing process, scRNA-seq data suffer from extremely high sparsity and variability, challenging the data analysis. Here we proposed a novel method called scLINE for learning low dimensional representations of scRNA-seq data. scLINE is based on the network embedding model that jointly considers multiple gene-gene interaction networks, facilitating the incorporation of prior biological knowledge for signal extraction. We comprehensively evaluated scLINE on eight single-cell datasets. Results show that scLINE achieved comparable or higher performance than competing methods, including PCA, t-SNE and Isomap, in terms of internal validation metrics and clustering accuracy. The low dimensional representations learned by scLINE are effective for downstream single-cell analysis, such as visualization, clustering and cell typing. We have implemented scLINE as an easy-to-use R package, which can be incorporated in other existing scRNA-seq analysis pipelines or tools for data preprocessing.

Using real-world data to estimate the changing trends in the prevalence and incidence of type 2 diabetes mellitus in Xiamen of China from 2014 to 2019.

BACKGROUND: The prevalence of diabetes is increasing worldwide. Our study aimed to estimate the changing trends in the prevalence and incidence of diagnosed type 2 diabetes mellitus (T2DM) among Xiamen residents and the floating population using real-world data. METHOD: We used real-world data from the System of Xiamen Citizens Health Information from 2014 to 2019 to estimate the changing trends in the prevalence and incidence of diagnosed T2DM. The System included the diagnosis of diabetes and the prescription of hypoglycemic drugs. Prevalent cases of T2DM were individuals who were diagnosed with T2DM and/or using hypoglycemic drugs. Incident cases were individuals with diagnosed T2DM and/or using hypoglycemic drugs in 2014 or 2019 who had not been diagnosed and/or did not use hypoglycemic drugs in the past. RESULTS: In 2014 and 2019, the prevalence of T2DM in Xiamen was 4.04 and 4.84%, respectively. In 2014 and 2019, the incidence rate of T2DM in Xiamen was 14.1 per 1000 person-year and 15.0 per 1000 person-year, respectively. There was a significant increase in both the prevalence (Prevalence difference: 0.80, 95%CI 0.76-0.83%, P < 0.001) and the incidence of T2DM (Incidence difference: 0.9, 95%CI 0.7-1.1, P < 0.001). in Xiamen. The prevalence and incidence of T2DM in people aged 18-39 increased significantly (P < 0.001), while the prevalence and incidence of T2DM in people aged 40-69 reduced significantly (P < 0.001). CONCLUSIONS: There was a significant increase in the prevalence and incidence of T2DM in Xiamen from 2014 to 2019 especially among those with younger age.

Gene Module Analysis Reveals Cell-Type Specificity and Potential Target Genes in Autism's Pathogenesis.

Multiple genetic factors contribute to the pathogenesis of autism spectrum disorder (ASD), a kind of neurodevelopmental disorder. Genes were usually studied separately for their associations with ASD. However, genes associated with ASD do not act alone but interact with each other in a network module. The identification of these modules is the basis for the systematic understanding of the pathogenesis of ASD. Moreover, ASD is characterized by highly pathogenic heterogeneity, and gene modules associated with ASD are cell-type-specific. In this study, based on the single-nucleus RNA sequencing data of 41 post-mortem tissue samples from the prefrontal cortex and anterior cingulate cortex of 19 ASD patients and 16 control individuals, we applied sparse module activity factorization, a matrix decomposition method consistent with the multi-factor and heterogeneous characteristics of ASD pathogenesis, to identify cell-type-specific gene modules. Then, statistical procedures were performed to detect highly reproducible cell-type-specific ASD-associated gene modules. Through the enrichment analysis of cell markers, 31 cell-type-specific gene modules related to ASD were further screened out. These 31 gene modules are all enriched with curated ASD risk genes. Finally, we utilized the expression patterns of these cell-type-specific ASD-associated gene modules to build predictive models for ASD. The excellent predictive performance also proved the associations between these gene modules and ASD. Our study confirmed the multifactorial and cell-type-specific characteristics of ASD pathogeneses. The results showed that excitatory neurons such as L2/3, L4, and L5/6-CC play essential roles in ASD's pathogenic processes. We identified the potential ASD target genes that act together in cell-type-specific modules, such as NRG3, KCNIP4, BAI3, PTPRD, LRRTM4, and LINGO2 in the L2/3 gene modules. Our study offers new potential genomic targets for ASD and provides a novel method to study gene modules involved in the pathogenesis of ASD.

Impact of maternal infection with hepatitis B virus on pregnancy complications and neonatal outcomes for women undergoing assisted reproductive technology treatment: A population-based study.

The aim of this study was to investigate the impact of maternal hepatitis B virus (HBV) status on pregnancy complications and neonatal outcomes for women undergoing assisted reproductive technology (ART). A total of 7,011 pregnancies achieved by ART were included from a population-based database involving 523,111 pregnancies. Exposures of hepatitis B surface antigen (HBsAg) and hepatitis B e antigen (HBeAg) among pregnant women were routinely tested at the first antenatal visit for all pregnancies. We collected pregnancy complications (e.g., gestational diabetes mellitus [GDM], intrahepatic cholestasis of pregnancy [ICP]), neonatal outcomes and confounding variables from the same database. Univariate and multivariate analyses by adjusting confounders were conducted to evaluate the impact of maternal HBV infection. Prevalence of HBsAg seropositivity (HBsAg+) was 11.34% (95% CI 10.6-12.1) and that of HBsAg and HBeAg co-seropositivity (HBsAg+HBeAg+) was 2.55% (2.1-3.0) among included population. Compared with HBsAg-group, ICP risk in the HBsAg+group was higher (4.03% vs. 1.79%; adjusted odds ratio [aOR] 2.49, 1.65-3.77). Similarly, ICP prevalence in the HBsAg+HBeAg+ group was higher than that in the HBsAg-HBeAg- group (6.47% vs. 1.61%; aOR 4.78, 2.28-9.98). No associations were found between maternal HBV infection (i.e., HBsAg+, HBsAg+HBeAg+, or HBsAg+HBeAg-) and other adverse outcomes for women undergoing ART (i.e., GDM, pre-eclampsia, placental previa, premature separation of placenta, premature rupture of membranes, preterm birth and low birthweight) in this study. In conclusion, maternal HBV infection (HBsAg+or HBsAg+HBeAg+) probably increase ICP risk, but may not associate with other pregnancy complications or neonatal outcomes for pregnant women who underwent ART.

Length-of-Stay Prediction for Pediatric Patients With Respiratory Diseases Using Decision Tree Methods.

Accurate prediction of a patient's length-of-stay (LOS) in the hospital enables an efficient and effective management of hospital beds. This paper studies LOS prediction for pediatric patients with respiratory diseases using three decision tree methods: Bagging, Adaboost, and Random forest. A data set of 11,206 records retrieved from the hospital information system is used for analysis after preprocessing and transformation through a computation and an expansion method. Two tests, namely bisection test and periodic test, are designed to assess the performance of the prediction methods. Bagging shows the best result on the bisection test (0.296 RMSE, 0.831 R2, and 0.723 Acc ± 1) for the testing set of the whole data test. The performances of the three methods are similar on the periodic test, whereas Adaboost performs slightly better than the other two methods. Results indicate that the three methods are all effective for the LOS prediction. This study also investigates the importance of different data fields to the LOS prediction, and finds that hospital treatment-related data fields contribute more to the LOS prediction than other categories of fields.

scHinter: imputing dropout events for single-cell RNA-seq data with limited sample size.

MOTIVATION: Single-cell RNA-sequencing (scRNA-seq) is fast and becoming a powerful technique for studying dynamic gene regulation at unprecedented resolution. However, scRNA-seq data suffer from problems of extremely high dropout rate and cell-to-cell variability, demanding new methods to recover gene expression loss. Despite the availability of various dropout imputation approaches for scRNA-seq, most studies focus on data with a medium or large number of cells, while few studies have explicitly investigated the differential performance across different sample sizes or the applicability of the approach on small or imbalanced data. It is imperative to develop new imputation approaches with higher generalizability for data with various sample sizes. RESULTS: We proposed a method called scHinter for imputing dropout events for scRNA-seq with special emphasis on data with limited sample size. scHinter incorporates a voting-based ensemble distance and leverages the synthetic minority oversampling technique for random interpolation. A hierarchical framework is also embedded in scHinter to increase the reliability of the imputation for small samples. We demonstrated the ability of scHinter to recover gene expression measurements across a wide spectrum of scRNA-seq datasets with varied sample sizes. We comprehensively examined the impact of sample size and cluster number on imputation. Comprehensive evaluation of scHinter across diverse scRNA-seq datasets with imbalanced or limited sample size showed that scHinter achieved higher and more robust performance than competing approaches, including MAGIC, scImpute, SAVER and netSmooth. AVAILABILITY AND IMPLEMENTATION: Freely available for download at https://github.com/BMILAB/scHinter. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

PlantAPAdb: A Comprehensive Database for Alternative Polyadenylation Sites in Plants.

Alternative cleavage and polyadenylation (APA) is increasingly recognized as an important regulatory mechanism in eukaryotic gene expression and is dynamically modulated in a developmental, tissue-specific, or environmentally responsive manner. Given the functional importance of APA and the rapid accumulation of APA sites in plants, a comprehensive and easily accessible APA site database is necessary for improved understanding of APA-mediated gene expression regulation. We present a database called PlantAPAdb that catalogs the most comprehensive APA site data derived from sequences from diverse 3' sequencing protocols and biological samples in plants. Currently, PlantAPAdb contains APA sites in six species, Oryza sativa (japonica and indica), Arabidopsis (Arabidopsis thaliana), Medicago truncatula, Trifolium pratense, Phyllostachys edulis, and Chlamydomonas reinhardtii APA sites in PlantAPAdb are available for bulk download and can be queried in a Google-like manner. PlantAPAdb provides rich information of the whole-genome APA sites, including genomic locations, heterogeneous cleavage sites, expression levels, and sample information. It also provides comprehensive poly(A) signals for APA sites in different genomic regions according to distinct profiles of cis-elements in plants. In addition, PlantAPAdb contains events of 3' untranslated region shortening/lengthening resulting from APA, which helps to understand the mechanisms underlying systematic changes in 3' untranslated region lengths. Additional information about conservation of APA sites in plants is also available, providing insights into the evolutionary polyadenylation configuration across species. As a user-friendly database, PlantAPAdb is a large and extendable resource for elucidating APA mechanisms, APA conservation, and gene expression regulation.

[Building a SAN and NAS integrated storage system in hospitals].

We have recentely built a cluster and backup system based on a SAN + NAS integrated storage system at a low price. The integration of SAN + NAS provides a storage system of fine quality, high reliability and high stability for hospitals. The article mainly introduces the choice of the project, the design of SAN + NAS integration and its implementation.