Interferometric Biosensor for High Sensitive Label-Free Recording of HiPS Cardiomyocytes Contraction .

Heart disease remains a leading cause of global mortality, underscoring the need for advanced technologies to study cardiovascular diseases and develop effective treatments. We introduce an innovative interferometric biosensor for high-sensitivity and label-free recording of human induced pluripotent stem cell (hiPSC) cardiomyocyte contraction . Using an optical cavity, our device captures interference patterns caused by the contraction-induced displacement of a thin flexible membrane.

EEG Interchannel Causality to Identify Source/Sink Phase Connectivity Patterns in Developmental Dyslexia.

While the brain connectivity network can inform the understanding and diagnosis of developmental dyslexia, its cause-effect relationships have not yet enough been examined. Employing electroencephalography signals and band-limited white noise stimulus at 4.8 Hz (prosodic-syllabic frequency), we measure the phase Granger causalities among channels to identify differences between dyslexic learners and controls, thereby proposing a method to calculate directional connectivity.

SARS-CoV-2 antibody response after BNT162b2 mRNA vaccine in healthcare workers: Nine-month of follow-up.

We collected sequential serum samples (0, 4, 12 weeks, 9 months) for the determination of S-RDB IgG levels from 103 vaccinated healthy subjects (age 45 ± 13 years; 60 women), in order to evaluate neutralizing antibody response against SARS-CoV-2 in healthy healthcare workers (HCWs) after the administration of two doses of BNT162b2 SARS-CoV-2 mRNA vaccine. Every subject received two doses of mRNA vaccine BNT162b2 (Pfizer-BioNTech), 21 days apart (January-February 2021). Furthermore, antibody titer of 14 subjects who were hospitalized for symptomatic COVID-19 was evaluated.

Detecting Phase-Synchrony Connectivity Anomalies in EEG Signals. Application to Dyslexia Diagnosis.

Objective Dyslexia diagnosis is a challenging task, since traditional diagnosis methods are not based on biological markers but on behavioural tests. Although dyslexia diagnosis has been addressed by these tests in clinical practice, it is difficult to extract information about the brain processes involved in the different tasks and, then, to go deeper into its biological basis. Thus, the use of biomarkers can contribute not only to the diagnosis but also to a better understanding of specific learning disorders such as dyslexia.

Optimization of Deep Architectures for EEG Signal Classification: An AutoML Approach Using Evolutionary Algorithms.

Electroencephalography (EEG) signal classification is a challenging task due to the low signal-to-noise ratio and the usual presence of artifacts from different sources. Different classification techniques, which are usually based on a predefined set of features extracted from the EEG band power distribution profile, have been previously proposed. However, the classification of EEG still remains a challenge, depending on the experimental conditions and the responses to be captured.