Safety of herbal medicines used in early gestations among the Chinese population: A population-based cohort study.

Background: Herbal medicines have a long history of use for pregnant women around the world. However, their use in the early pregnancy is often questioned in terms of safety on offspring.

Purpose: To investigate whether herbal medicines used at early pregnancy are associated with an increased risk of birth defects.

The N6-methyladenosine Epitranscriptomic Landscape of Lung Adenocarcinoma.

Comprehensive N6-methyladenosine (m6A) epitranscriptomic profiling of primary tumors remains largely uncharted. Here, we profiled the m6A epitranscriptome of 10 nonneoplastic lung tissues and 51 lung adenocarcinoma (LUAD) tumors, integrating the corresponding transcriptomic, proteomic, and extensive clinical annotations. We identified distinct clusters and genes that were exclusively linked to disease progression through m6A modifications.

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.

A real-world data analysis of acetylsalicylic acid in FDA Adverse Event Reporting System (FAERS) database.

Background: Acetylsalicylic acid (Aspirin), one of the oldest medicines, is widely used in various clinical fields. However, numerous adverse events (AEs) have been reported. In this study, we aimed to investigate adverse drug reactions (ADRs) of aspirin using real-worlddata from the US Food and Drug Administration Adverse Event Reporting System (FAERS) database.

A real-world data analysis of topotecan in the FDA Adverse Event Reporting System (FAERS) database.

Objectives: The objective of this study was to monitor and identify adverse events (AEs) associated with topotecan, a medication used for the treatment of solid tumors, in order to improve patient safety and guide medication usage.

Methods: To assess the disproportionality of topotecan-related AEs in real-world data, four algorithms (ROR, PRR, BCPNN, and EBGM) were employed as measures to detect signals of topotecan-associated AEs.

Results: A statistical analysis was conducted using data from the FAERS database, encompassing 9,511,161 case reports from 2004Q1 to 2021Q4.

Modeling of Genome-Wide Polyadenylation Signals in .

Alternative polyadenylation (APA) is an important post-transcriptional modification event to process messenger RNA (mRNA) for transcriptional termination, transport, and translation. In the present study, we characterized poly(A) signals in using 70,918 highly confident poly(A) sites derived from 16,511 protein-coding genes to understand their roles in the regulation of embryo development and gender difference. We examined potential factors, including the gene length, the number of introns in a gene, and the intron length, that may affect the prevalence of APA.

A survey on identification and quantification of alternative polyadenylation sites from RNA-seq data.

Alternative polyadenylation (APA) has been implicated to play an important role in post-transcriptional regulation by regulating mRNA abundance, stability, localization and translation, which contributes considerably to transcriptome diversity and gene expression regulation. RNA-seq has become a routine approach for transcriptome profiling, generating unprecedented data that could be used to identify and quantify APA site usage. A number of computational approaches for identifying APA sites and/or dynamic APA events from RNA-seq data have emerged in the literature, which provide valuable yet preliminary results that should be refined to yield credible guidelines for the scientific community.

AStrap: identification of alternative splicing from transcript sequences without a reference genome.

Summary: Alternative splicing (AS) is a well-established mechanism for increasing transcriptome and proteome diversity, however, detecting AS events and distinguishing among AS types in organisms without available reference genomes remains challenging. We developed a de novo approach called AStrap for AS analysis without using a reference genome. AStrap identifies AS events by extensive pair-wise alignments of transcript sequences and predicts AS types by a machine-learning model integrating more than 500 assembled features.

TSAPA: identification of tissue-specific alternative polyadenylation sites in plants.

Summary: Alternative polyadenylation (APA) is now emerging as a widespread mechanism modulated tissue-specifically, which highlights the need to define tissue-specific poly(A) sites for profiling APA dynamics across tissues. We have developed an R package called TSAPA based on the machine learning model for identifying tissue-specific poly(A) sites in plants. A feature space including more than 200 features was assembled to specifically characterize poly(A) sites in plants.

AEGS: identifying aberrantly expressed gene sets for differential variability analysis.

Motivation: In gene expression studies, differential expression (DE) analysis has been widely used to identify genes with shifted expression mean between groups. Recently, differential variability (DV) analysis has been increasingly applied as analyzing changed expression variability (e.g.

Integrating multiple fitting regression and Bayes decision for cancer diagnosis with transcriptomic data from tumor-educated blood platelets.

The application of machine learning in cancer diagnostics has shown great promise and is of importance in clinic settings. Here we consider applying machine learning methods to transcriptomic data derived from tumor-educated platelets (TEPs) from individuals with different types of cancer. We aim to define a reliability measure for diagnostic purposes to increase the potential for facilitating personalized treatments.