The Exon Junction Complex component EIF4A3 plays a splicing-linked oncogenic role in pancreatic ductal adenocarcinoma.

Pancreatic ductal adenocarcinoma (PDAC) is one of the most lethal cancers, underscoring the urgent need for in-depth biological research. The phenomenon of alternative RNA splicing dysregulation is a common hallmark in cancer, including PDAC, presenting new avenues for understanding and developing diagnostic and therapeutic tools. Our research focuses on EIF4A3, a core component of the Exon Junction Complex intimately linked to RNA splicing, and its role in PDAC.

Comparing a Large Language Model with Previous Deep Learning Models on Named Entity Recognition of Adverse Drug Events.

The ability to fine-tune pre-trained deep learning models to learn how to process a downstream task using a large training set allow to significantly improve performances of named entity recognition. Large language models are recent models based on the Transformers architecture that may be conditioned on a new task with in-context learning, by providing a series of instructions or prompt. These models only require few examples and such approach is defined as few shot learning.

Monitoring Adverse Drug Events in Web Forums: Evaluation of a Pipeline and Use Case Study.

Background: To mitigate safety concerns, regulatory agencies must make informed decisions regarding drug usage and adverse drug events (ADEs). The primary pharmacovigilance data stem from spontaneous reports by health care professionals. However, underreporting poses a notable challenge within the current system.