Mechanical Characterization of the Erythrocyte Membrane Using a Capacitor-Based Technique.

Pathological processes often change the mechanical properties of cells. Increased rigidity could be a marker of cellular malfunction. Erythrocytes are a type of cell that deforms to squeeze through tiny capillaries; changes in their rigidity can dramatically affect their functionality. Furthermore, differences in the homeostatic elasticity of the cell can be used as a tool for diagnosis and even for choosing the adequate treatment for some illnesses. More accurate types of equipment needed to study biomechanical phenomena at the single-cell level are very costly and thus out of reach for many laboratories around the world. This study presents a simple and low-cost technique to study the rigidity of red blood cells (RBCs) through the application of electric fields in a hand-made microfluidic chamber that uses a capacitor principle. As RBCs are deformed with the application of voltage, cells are observed under a light microscope. From mechanical force vs. deformation data, the elastic constant of the cells is determined. The results obtained with the capacitor-based method were compared with those obtained using optical tweezers, finding good agreement. In addition, P. falciparum-infected erythrocytes were tested with the electric field applicator. Our technique provides a simple means of testing the mechanical properties of individual cells.

Microwaves can kill malaria parasites non-thermally.

Malaria, which infected more than 240 million people and killed around six hundred thousand only in 2021, has reclaimed territory after the SARS-CoV-2 pandemic. Together with parasite resistance and a not-yet-optimal vaccine, the need for new approaches has become critical. While earlier, limited, studies have suggested that malaria parasites are affected by electromagnetic energy, the outcomes of this affectation vary and there has not been a study that looks into the mechanism of action behind these responses. In this study, through development and implementation of custom applicators for in vitro experimentation, conditions were generated in which microwave energy (MW) killed more than 90% of the parasites, not by a thermal effect but via a MW energy-induced programmed cell death that does not seem to affect mammalian cell lines. Transmission electron microscopy points to the involvement of the haemozoin-containing food vacuole, which becomes destroyed; while several other experimental approaches demonstrate the involvement of calcium signaling pathways in the resulting effects of exposure to MW. Furthermore, parasites were protected from the effects of MW by calcium channel blockers calmodulin and phosphoinositol. The findings presented here offer a molecular insight into the elusive interactions of oscillating electromagnetic fields with P. falciparum, prove that they are not related to temperature, and present an alternative technology to combat this devastating disease.

Analyzing Spanish-Language Public Sentiment in the Context of a Pandemic and Social Unrest: The Panama Case.

Over the past decade, an increase in global connectivity and social media users has changed the way in which opinions and sentiments are shared. Platforms such as Twitter can act as public forums for expressing opinions on non-personal matters, but often also as an outlet for individuals to share their feelings and personal thoughts. This becomes especially evident during times of crisis, such as a massive civil disorder or a pandemic. This study proposes the estimation and analysis of sentiments expressed by Twitter users of the Republic of Panama during the years 2019 and 2020. The proposed workflow is comprised of the extraction, quantification, processing and analysis of Spanish-language Twitter data based on Sentiment Analysis. This case of study highlights the importance of developing natural language processing resources explicitly devised for supporting opinion mining applications in Latin American countries, where language regionalisms can drastically change the lexicon on each country. A comparative analysis performed between popular machine learning algorithms demonstrated that a version of a distributed gradient boosting algorithm could infer sentiment polarity contained in Spanish text in an accurate and time-effective manner. This algorithm is the tool used to analyze over 20 million tweets produced between the years of 2019 and 2020 by residents of the Republic of Panama, accurately displaying strong sentiment responses to events occurred in the country over the two years that the analysis performed spanned. The obtained results highlight the potential that methodologies such as the one proposed in this study could have for transparent government monitoring of responses to public policies on a population scale.