Pneumocystis jirovecii is a fungus responsible for human Pneumocystis pneumonia, one of the most severe infections encountered in immunodepressed individuals. The diagnosis of Pneumocystis pneumonia continues to be challenging due to the absence of specific symptoms in infected patients. Moreover, the standard diagnostic method employed for its diagnosis involves mainly PCR-based techniques, which besides being highly specific and sensitive, require specialized personnel and equipment and are time-consuming. Our aim is to demonstrate an optical biosensor methodology based on surface plasmon resonance to perform such diagnostics in an efficient and decentralized scheme. The biosensor methodology employs poly-purine reverse-Hoogsteen hairpin probes for the detection of the mitochondrial large subunit ribosomal RNA (mtLSU rRNA) gene, related to P. jirovecii detection. The biosensor device performs a real-time and label-free identification of the mtLSU rRNA gene with excellent selectivity and reproducibility, achieving limits of detection of around 2.11 nM. A preliminary evaluation of clinical samples showed rapid, label-free and specific identification of P. jirovecii in human lung fluids such as bronchoalveolar lavages or nasopharyngeal aspirates. These results offer a door for the future deployment of a sensitive diagnostic tool for fast, direct and selective detection of Pneumocystis pneumonia disease.

Dust deposition, river runoff and glacial melt are the main sources of trace metals to the surface ocean. In the Canary Islands, deposition is dominated by dry dust deposition from the Saharan desert. However, during 85 days, from 19 September to 13 December 2021, the main source of trace metals on the island of La Palma changed drastically. The eruption of the Tajogaite volcano released tonnes of volcanic ash. Concurrently, several lava flows reached the coastal waters. Volcanic activity on land became the main source of iron (Fe) into the coastal waters. Fe concentrations in seawater reached over 1900 nmol L^-1. ∼99 % of the iron was found in the particulate phase (particles >0.2 μm wide; 1920 ± 50 nmol L^-1). Colloids, smaller size particles (0.02 μm < colloids < 0.2 μm) represented ∼0.7 % of the iron pool (14.5 ± 0.5 nmol L^-1). While the truly soluble phase represented ∼0.03 %. However, the soluble phase presented concentrations reaching 0.66 ± 0.05 nmol L^-1, which is ∼10 times higher than generally found in open Atlantic Ocean waters. The results show how the Fe size fractionation evolved during the eruption, from a dominance of large particle phases to smaller-sized compounds.

Volcanic eruptions represent a variable source of trace metals to the ocean. This study focuses on the 2021 Tajogaite volcano eruption in La Palma (Canary Islands, Spain) and its significant impact on coastal iron (Fe) dynamics. Over 85 days of activity, the eruption contributed vast amounts of volcanic ash and lava to the ocean, resulting in elevated Fe concentrations. Measurements revealed that Fe levels in seawater reached over 1900 nmol L⁻¹, with 99% of the Fe in particulate form. Soluble Fe concentrations were approximately ten times higher than typical values in the open Atlantic Ocean, demonstrating the eruption’s role in enhancing bioavailable Fe.This poster explores Fe size fractionation during the eruption, observing a transition from large particulate dominance to increased colloidal and soluble Fe over time. Lava-seawater interactions produced hydrothermal plumes, characterized by increased temperatures, low pH, and elevated turbidity, significantly altering the local marine environment. Spatial and temporal variability in Fe concentrations highlight the dynamic nature of these coastal systems during volcanic events.Our findings underscore the potential of volcanic activity as a natural iron fertilization mechanism in nutrient-limited regions, with implications for primary productivity and carbon cycling. The episodic nature of these interactions necessitates refined models to incorporate volcanic contributions to ocean biogeochemistry models.