Examinando por Autor "Gomez, Jose"

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Lichen Biodiversity and Near-Infrared Metabolomic Fingerprint as Diagnostic and Prognostic Complementary Tools for Biomonitoring: A Case Study in the Eastern Iberian Peninsula
(MDPI, 2023-10-31) Moya, Patricia; Chiva, Salvador; Catalá, Myriam; Garmendia, Alfonso; Casale, Monica; Gomez, Jose; Pazos, Tamara; Giordani, Paolo; Calatayud, Vicent; Barreno, Eva
In the 1990s, a sampling network for the biomonitoring of forests using epiphytic lichen diversity was established in the eastern Iberian Peninsula. This area registered air pollution impacts by winds from the Andorra thermal power plant, as well as from photo-oxidants and nitrogen depositions from local and long-distance transport. In 1997, an assessment of the state of lichen communities was carried out by calculating the Index of Atmospheric Purity. In addition, visible symptoms of morphological injury were recorded in nine macrolichens pre-selected by the speed of symptom evolution and their wide distribution in the territory. The thermal power plant has been closed and inactive since 2020. During 2022, almost 25 years later, seven stations of this previously established biomonitoring were revaluated. To compare the results obtained in 1997 and 2022, the same methodology was used, and data from air quality stations were included. We tested if, by integrating innovative methodologies (NIRS) into biomonitoring tools, it is possible to render an integrated response. The results displayed a general decrease in biodiversity in several of the sampling plots and a generalised increase in damage symptoms in the target lichen species studied in 1997, which seem to be the consequence of a multifactorial response.
Near infrared spectroscopy (NIRS) and machine learning as a promising tandem for fast viral detection in serum microsamples: A preclinical proof of concept
(Elsevier, 2024) Gomez, Jose; Barquero-Pérez, Oscar; Gonzalo, Jennifer; Salgüero, Sergio; Riado, Daniel; Casas, Maria Luisa; Gutíerrez, Maria Luisa; Jaime, Elena; Pérez-Martínez, Enrique; García-Carretero, Rafael; Ramos, Javier; Fernández-Rodriguez, Conrado; Catalá, Myriam
Fast detection of viral infections is a key factor in the strategy for the prevention of epidemics expansion and follow-up. Hepatitis C is paradigmatic within viral infectious diseases and major challenges to elimination still remain. Near infrared spectroscopy (NIRS) is an inexpensive, clean, safe method for quickly detecting viral infection in transmission vectors, aiding epidemic prevention. Our objective is to evaluate the combined potential of machine learning and NIRS global molecular fingerprint (GMF) from biobank sera as an efficient method for HCV activity discrimination in serum. GMF of 151 serum biobank microsamples from hepatitis C patients were obtained with a FT-NIR spectrophotometer in reflectance mode. Multiple scatter correction, smoothing and Saviztsky-Golay second derivative were applied. Spectral analysis included Principal Component Analysis (PCA), Bootstrap and L1-penalized classification. Microsamples of 70 μl were sufficient for GMF acquisition. Bootstrap evidenced significant difference between HCV PCR positive and negative sera. PCA renders a neat discrimination between HCV PCR-positive and negative samples. PCA loadings together with L1-penalized classification allow the identification of discriminative bands. Active virus positive sera are associated to free molecular water, whereas water in solvation shells is associated to HCV negative samples. Divergences in the water matrix structure and the lipidome between HCV negative and positive sera, as well as the relevance of prooxidants and glucose metabolism are reported as potential biomarkers of viral activity. Our proof of concept demonstrates that NIRS GMF of hepatitis C patients’ sera aided by machine learning allows for efficient discrimination of viral presence and simultaneous potential biomarker identification.
Shikonin inhibits microglia activation and reduces CFA-induced mechanical hyperalgesia in an animal model of pain
(Elsevier, 2022-04-19) Biscaia, Miguel; Llorente, Ricardo; Gomez, Jose; Grassi, Daniela; Vega-Avelaira, David
Shikonin is an ointment produced from Lithospermun erythrorhizon which has been used in traditional medicine both in Europe and Asia for wound healing and is associated with anti-inflammatory properties. The goal of this work is to assess the analgesic properties of Shikonin in the CFA-induced inflammation model of pain. Rats were subjected to inflammation of the hind paw by CFA injection with a preventive injection of Shikonin and compared to either a control group or to a CFA-inflamed group with the vehicle drug solution. Inflammation of the hind paw by CFA was assessed by measurement of the dorsal to plantar diameter. Mechanical thresholds were established by means of the Von Frey filaments which are calibrated filaments that exert a defined force. Finally, the spinal cord of the studied animals was extracted to analyse the microglia population through immunohistochemistry using the specific marker Iba-1. Our results show that Shikonin reduces the paw oedema caused by CFA inflammation. Subsequently, there is a concomitant restoration of the mechanical thresholds reduced by CFA hind paw injection. Additionally, spinal microglia is activated after CFA-induced inflammation. Our results show that microglia is inhibited by Shikonin and has concomitant restoration of the mechanical thresholds. Our findings demonstrate for the first time that Shikonin inhibits microglia morphological changes and thereby ameliorates pain-like behaviour elicited by mechanical stimulation.