This is a comparative study tackling named entity recognition and relation extraction from PubMed abstracts with focus on the gut-brain interplay. The proposed systems for named entity recognition cover a range of models and techniques from traditional gazetteer-based approaches, transformer-based approaches, transformer domain adaptation, large models pre-training as well as LLM prompting. The best performing model among these achieves 82.53% F1-score. The relation extraction task is addressed with ATLOP and LLMs and their best results reach F1 up to 63.80% on binary relation extraction, 89.40% on ternary tag-based relation extraction and 40.32% on ternary mention-based relation extraction.