ABSTRACT Background: The coronavirus disease 2019 (COVID-19) pandemic posed challenges for health systems in rapidly diagnosing diseases. Methods: Community workshops and a self-administered questionnaire were implemented to determine the feasibility of self-testing for future epidemics and gain an understanding of community interest and the capacity to implement self-tests in two rural areas in Paraguay. Results: Comprehension of the self-test procedures and their application was demonstrated by 99% of the participants. The participants expressed interest in accessing these tests at home. Conclusions: Self-tests could be a valuable tool in urban and rural communities that enable health systems to focus on severe cases.

ABSTRACT Background: Low QRS complex voltage is an important predictor of death in Chagas disease. However, the parameters applied to the low-voltage classification were described by the Minnesota Code and not specifically for Chagas disease. This study aimed to analyze low QRS voltage by determining the ADOC index and averages in the frontal and horizontal electrocardiographic planes, establishing possible clinical implications. Methods: A cross-sectional study of patients with Chagas disease was performed using the Mann-Whitney U test and Spearman’s correlation. The amplitudes of each QRS were analyzed, and the sum of the DII and V5 derivations of the ADOC index and the arithmetic means of the QRS complexes in the frontal and horizontal planes were determined. Results: The ADOC index was correlated with the highest risk of stroke and death according to the Rassi score. The ADOC index (p=0.046) and mean mQRS were inversely proportional to the Rassi risk of death score (p=0.038). The ADOC index proved to be more sensitive (75.0%) and accurate (67.4%) in identifying patients at elevated death risk using the Rassi score. Finally, a positive correlation was observed between the QRSFm and QRSHm indicators and ADOC index (r=0.590 and r=0.857, respectively). Discussion: The ADOC index and mean of the QRS complexes are possible tools correlated with the Rassi score and risk of stroke in patients with Chagas disease.

ABSTRACT The reference methodology for evaluating antifungal susceptibility is based on determining the minimum inhibitory concentration (MIC), which is the lowest drug concentration capable of inhibiting fungal growth. However, such MIC data are insufficient to measure antifungal susceptibility if a strain is heteroresistant to the tested drug. In such cases, a minority subpopulation of fungal cells, originating from an initially susceptible lineage, can grow at antifungal drug concentrations above the MIC. In studies on fluconazole heteroresistance in Cryptococcus spp., chromosomal disomy has been shown to result in the overexpression of two genes located on chromosome 1 (Chr1) linked to antifungal resistance: ERG11 and AFR1. This review addresses the underlying mechanisms of antifungal resistance, the evolution of methods for determining antifungal susceptibility, and the clinical implications of Cryptococcus heteroresistance to fluconazole. The analysis of the findings indicated a correlation between heteroresistance and adverse clinical outcomes, although this observation still lacks definite confirmation in the literature. This highlights the need to implement more efficient therapeutic strategies and improve antifungal susceptibility and heteroresistance testing.

ABSTRACT Background: Emerging infections have posed persistent threats to humanity throughout history. Rapid and unprecedented anthropogenic, behavioral, and social transformations witnessed in the past century have expedited the emergence of novel pathogens, intensifying their impact on the global human population. Methods: This study aimed to comprehensively analyze and compare the genomic sequences of four distinct viruses: SARS-CoV-2, SARS, MERS, and Ebola. Advanced genomic sequencing techniques and a Gated Recurrent Unit-based deep learning model were used to examine the intricate genetic makeup of these viruses. The proposed study sheds light on their evolutionary dynamics, transmission patterns, and pathogenicity and contributes to the development of effective diagnostic and therapeutic interventions. Results: This model exhibited exceptional performance as evidenced by accuracy values of 99.01%, 98.91%, 98.35%, and 98.04% for SARS-CoV-2, SARS, MERS, and Ebola respectively. Precision values ranged from 98.1% to 98.72%, recall values consistently surpassed 92%, and F1 scores ranged from 95.47% to 96.37%. Conclusions: These results underscore the robustness of this model and its potential utility in genomic analysis, paving the way for enhanced understanding, preparedness, and response to emerging viral threats. In the future, this research will focus on creating better diagnostic instruments for the early identification of viral illnesses, developing vaccinations, and tailoring treatments based on the genetic composition and evolutionary patterns of different viruses. This model can be modified to examine a more extensive variety of diseases and recently discovered viruses to predict future outbreaks and their effects on global health.