The Gut Microbiota Impacts Gastrointestinal Cancers through Obesity, Diabetes, and Chronic Inflammation.

The gut microbiota's pivotal role in human health is increasingly evident, particularly in chronic conditions like obesity, diabetes, and inflammatory diseases. This intricate symbiotic relationship influences metabolic balance and immune responses. Notably, gut microbial dysbiosis is linked to obesity's metabolic disruption and chronic low-grade inflammation.

A phase III study to access the safety and efficacy of prolgolimab 250 mg fixed dose administered every 3 weeks versus prolgolimab 1 mg/kg every 2 weeks in patients with metastatic melanoma (FLAT).

Background: Prolgolimab is the first Russian PD-1 inhibitor approved for the first-line treatment of unresectable or metastatic melanoma and advanced non-small cell lung cancer. It was approved in two weight-based regimens of 1 mg/kg Q2W and 3 mg/kg Q3W, but because of re-evaluation of weight-based dosing paradigm, studying of a fixed-dose regimen was considered perspective.

Methods: We conducted a multicenter, single-arm, open-label efficacy, pharmacokinetics, and safety study to obtain data that would allow the approval of the new flat dosing regimen of prolgolimab in patients with previously untreated unresectable or metastatic melanoma (BCD-100-8/FLAT, NCT05783882).

Maternal Age at Menarche Genes Determines Fetal Growth Restriction Risk.

We aimed to explore the potential link of maternal age at menarche (mAAM) gene polymorphisms with risk of the fetal growth restriction (FGR). This case (FGR)-control (FGR free) study included 904 women (273 FGR and 631 control) in the third trimester of gestation examined/treated in the Departments of Obstetrics. For single nucleotide polymorphism (SNP) multiplex genotyping, 50 candidate loci of mAAM were chosen.

Neural machine translation of clinical text: an empirical investigation into multilingual pre-trained language models and transfer-learning.

Clinical text and documents contain very rich information and knowledge in healthcare, and their processing using state-of-the-art language technology becomes very important for building intelligent systems for supporting healthcare and social good. This processing includes creating language understanding models and translating resources into other natural languages to share domain-specific cross-lingual knowledge. In this work, we conduct investigations on clinical text machine translation by examining multilingual neural network models using deep learning such as Transformer based structures.