Human respiratory syncytial virus (RSV) is the predominant etiological agent responsible for lower respiratory tract infections in young children. Recurrent infections throughout an individual’s lifespan can lead to significant morbidity, particularly in the elderly and in adults, influencing the trends of hospitalization rates. Consequently, it is imperative to develop technologies that can sanitize environments from this pathogen while being compatible with human presence. Structure Resonant Energy Transfer (SRET) is the scientific principle underlying a sanitization technology that has demonstrated efficacy against several enveloped viruses, including SARS-CoV-2 and Influenza A viruses. SRET employs specific frequencies of electromagnetic waves to effectively disrupt the structural integrity of viral envelopes through dipole coupling. This disruption leads to the inactivation of the virus, rendering it non-infectious. The objective of this study is to analyse the effect of a specific SRET sanitization method on RSV. The sanitization test was conducted in aerosol form within a BSL-3 laboratory, exploring the frequency band from 8 to 16 GHz. An optimal sub-band was identified, giving an inactivation efficiency up to 99.5%. In conclusion, it has been demonstrated that the microwave non-thermal sanitization method is effective against RSV. These results confirm its potential as a viable approach for environmental decontamination.

The absence of a recognizable P wave in an electrocardiogram (ECG) is a critical indicator for the diagnosis of atrial fibrillation (AF). An algorithm capable of distinguishing between physiological and pathological states in a short period of time could serve as a valuable tool for timely and effective diagnosis, even in a home setting. To achieve this goal, a deterministic algorithm is proposed. The Fantasia Database and the AF Termination Challenge Database were used for training the model. Subsequently, for the test session, a one-minute recording was extracted from the Autonomic Aging Dataset and the Long-Term AF Database. After band-pass filtering, characteristic points such as R-peaks and P waves were extracted. The R-peak detection algorithm was compared with the gold standard Pan-Tompkins, obtaining a p-value > 0.05 on the Fantasia Database, which means that there is no statistical difference between them. Subsequently derived features such as duration, amplitude, subtended area, and P wave slope have been used to discriminate healthy subjects from AF patients. The P-wave slope emerged as the most effective feature, achieving a classification accuracy of 100% and 96% for the training and test sets, respectively. This algorithm thus represents a significant advancement as it achieves a performance comparable to other deterministic methods based on P wave analysis using only one-minute recordings, thereby enabling accurate diagnosis in a shorter time frame.