Programa del congreso

This study analyzed how 2.45 GHz radiofrequency (RF) radiation affects the immediate immune response in the RAW-264.7 macrophage cell line previously stimulated with the SARS-CoV-2 spike protein (CSP), bacterial LPS endotoxin or with both LPS and CSP. The RAW 264.7 cell line was continuously exposed for 24 hours in a GTEM chamber to a 2.45 GHz RF field at non-thermal SAR levels. Biological parameters of cellular metabolism such as cell viability and oxidative stress were studied by measuring nitrite levels, and progression of cell death was examined by measuring apoptosis and necrosis levels. The sustained effect of exposure to 2.45 GHz RF for 24 hours enhanced nitric oxide production and reduced cell death in macrophages stimulated with bacterial LPS. However, in the macrophages stimulated with LPS and CSP, exposure to RF did not modulate nitric oxide production or modify the progression of cell death.

Se presenta el diseño de un prototipo de sistema de exposición a radiofrecuencias de un modelo de laboratorio con gran potencial en el ámbito de la toxicología, poco a poco introducido para el estudio de los bioefectos de los campos electromagnéticos: el embrión del pez cebra. A pesar de que son varios los trabajos publicados con este modelo, la diversidad de configuraciones experimentales y de protocolos para la exposición RF del embrión en desarrollo dificulta la extracción de conclusiones fiables. Además, son escasos los estudios que evalúan la exposición mediante un análisis computacional y, en ningún caso, disponen de parámetros eléctricos específicos del embrión en desarrollo. Este trabajo presenta una propuesta integral, analizando el comportamiento de un sistema de exposición basado en una cámara reverberante y abordando, por primera vez, la evaluación dosimétrica del embrión del pez caracterizado eléctricamente. Se empleará para ello diversas técnicas y programas computacionales que permitirán comparar y validar los resultados, orientando la implementación futura del sistema de exposición a RF.

Hepatocellular carcinoma (HCC) accounts for 75% of primary liver tumors. Controlling risk factors and implementing screenings in risk populations does not seem sufficient to improve the prognosis at diagnosis. The development of a predictive prognostic model for mortality at the diagnosis of HCC is proposed. In this retrospective multicenter study, the analysis of data from 191 HCC patients was conducted using machine learning (ML) techniques to analyze the prognostic factors of mortality that are significant at diagnosis. Clinical and analytical data were gathered. Meeting Milan criteria, Barcelona Clinic Liver Cancer (BCLC) classification and albumin levels were the variables with the greatest impact on the prognosis of HCC patients. The ML algorithm that achieved the best results was random forest (RF). RF is useful and reliable in the analysis of prognostic factors in the diagnosis of HCC. The search for new prognostic factors is still necessary in patients with HCC

La pandemia de COVID-19 aceleró los avances en biología molecular y virología, facilitando la identificación de biomarcadores que diferencian los casos graves de los leves. Este estudio empleó inteligencia artificial y aprendizaje automático para analizar datos clínicos, epidemiológicos, analíticos y radiológicos de pacientes admitidos de manera consecutiva. Utilizando el algoritmo Random Forest, se procesaron más de 89 variables, logrando una precisión equilibrada del 92,61%. Los biomarcadores más predictivos de mortalidad fueron la procalcitonina, la lactato deshidrogenasa y la proteína C-reactiva, con especial énfasis en los infiltrados intersticiales observados en las radiografías de tórax y niveles elevados de dímero D. Estos hallazgos subrayan la eficacia del aprendizaje automático en entornos de emergencia para la identificación rápida de biomarcadores, mejorando en última instancia la precisión del pronóstico e informando estrategias de tratamiento personalizadas en pandemias emergentes.

Breast cancer is one of the most common types worldwide, and in many cases, it is treated with chemotherapy. To improve treatment follow-up, electromagnetic field-based technologies offer a non-invasive alternative for real-time monitoring. This study focuses on in vivo measurements of the dielectric permittivity of healthy, chemotherapy-treated, and tumor-affected breast tissues, aiming to assess their usefulness in monitoring treatment progress.

A coaxial probe (DAK 3.5) operating in the 1–8 GHz frequency range was used, following a standardized experimental protocol. Measurements were taken from 30 female volunteers. Results indicate an approximate 30% decrease in real and imaginary permittivity in breast tissue treated with chemotherapy compared to healthy tissue. Additionally, clear differences were observed between tumor and non-tumor tissues, suggesting the potential of this technique for therapeutic monitoring.