Biology subjects

Hernandez, F. G.

Publications and source records attributed to Hernandez, F. G..

2 recordsLinked to original sources

bioRxiv2026

A role for aquaporin (Aqp1) in the control of Cryptococcus neoformans cell morphology

Aquaporins are small, integral membrane channels that facilitate the transport of water across cellular membranes and, in the case of aquaglyceroporins, can also conduct specific neutral solutes, such as glycerol. These proteins are conserved across biological kingdoms, yet their roles in fungal virulence remain relatively understudied. In Cryptococcus neoformans, an opportunistic fungal pathogen, we examined the organisms single aquaporin, Aqp1, and uncovered unanticipated influences on cellular morphology. Loss of Aqp1 resulted in smaller cells, whereas its presence promoted the formation of enlarged titan-like cells. This shift in size was closely linked to intracellular redox physiology. Consequently, the overexpression of the cryptococcal aquaporin increased sensitivity to oxidative stress and led to the largest titan-like cells; antioxidant supplementation suppressed this enlargement, consistent with a ROS-dependent regulatory mechanism. Additionally, Aqp1 overexpression produced vacuolar abnormalities in titan-like cells, suggesting that excessive water influx strained intracellular organization during rapid cell expansion. These findings position Aqp1 at a functional crossroads connecting membrane transport, oxidative balance, and size control, and they support a model in which an aquaporin contributes to the morphological plasticity that allows C. neoformans to adapt to environmental pressures.

microbiology↗

bioRxiv2024

Named Entity Recognition of Pharmacokinetic parameters in the scientific literature

The development of accurate predictions for a new drugs absorption, distribution, metabolism, and excretion profiles in the early stages of drug development is crucial due to high candidate failure rates. The absence of comprehensive, standardised, and updated pharmacokinetic (PK) repositories limits pre-clinical predictions and often requires searching through the scientific literature for PK parameter estimates from similar compounds. While text mining offers promising advancements in automatic PK parameter extraction, accurate Named Entity Recognition (NER) of PK terms remains a bottleneck due to limited resources. This work addresses this gap by introducing novel corpora and language models specifically designed for effective NER of PK parameters. Leveraging active learning approaches, we developed an annotated corpus containing over 4,000 entity mentions found across the PK literature on PubMed. To identify the most effective model for PK NER, we fine-tuned and evaluated different NER architectures on our corpus. Fine-tuning BioBERT exhibited the best results, achieving a strict F1 score of 90.37% in recognising PK parameter mentions, significantly outperforming heuristic approaches and models trained on existing corpora. To accelerate the development of end-to-end PK information extraction pipelines and improve pre-clinical PK predictions, the PK NER models and the labelled corpus were released open source at https://github.com/PKPDAI/PKNER.

pharmacology and toxicology↗