The furin cleavage site, with an arginine doublet (RR), is one of the clues of the SARS-CoV-2 origin. This furin-RR is encoded by the CGG-CGG sequence. Because arginine can be encoded by six codons, in a previous work we found that in SARS-CoV-2, CGG was the minority arginine codon (3%). Also, analyzing the RR doublet from a large sample of furin cleavage sites of several kinds of viruses, we found that none of them were encoded by CGG-CGG. Here, we come back to the core of the matter, but from the perspective that in the human genome, in contrast, CGG is the majoroty arginine codon (21%). Here, we highlighted that the 6 arginine codons provide genetic markers to a traceability on the RR origin in the furin site, as well as, to weigh the probability of the theories about the origin of the virus.

Self-perceived health is an important indicator of occupational health. This research explores the relationship between poor self-perceived health and exposure to psychosocial risk factors, taking into account potential socio-demographic, occupational and employment determinants. Using data from the First Survey of Occupational Safety and Health Conditions covering 1049 salaried workers in Guayaquil, Ecuador descriptive and stratified binary logistic regression analyses (Odds Ratios with corresponding 95% confidence intervals) were carried out. No significant relationship was found between exposure to psychosocial risk factors and the probability of presenting poor self-perceived health by socio-demographic, occupational and employment characteristics. Occupational exposure factors to psychosocial risks are predictors of self-perceived ill health and are related to the variables analyzed; the most frequently expressed factors among the respondents were Cognitive Demands (DGOG) and Job Insecurity (IL). The results have implications in terms of designing effective workplace interventions pursuant of ensuring the health and wellbeing of employee.

Despite widespread vaccination, early treatments, and improved understanding of the disease, the effects of SARS-CoV-2 infection remain significant worldwide. Many patients still suffer from severe COVID-19, necessitating admission to intensive care units. Remdesivir is a primary treatment option among viral RNA polymerase inhibitors for hospitalized SARS-CoV-2 patients. However, there is a lack of studies examining factors influencing its effectiveness in this context. We conducted a retrospective study throughout 2022, analyzing clinical, laboratory, and sociodemographic data from 252 hospitalized COVID-19 patients treated with remdesivir. Six machine learning algorithms were compared and validated to predict factors associated with the loss of clinical benefit from remdesivir in terms of mortality and hospital stay. Data were extracted from electronic health records. The eXtreme Gradient Boost (XGB) method achieved the highest balanced accuracy for both mortality (95.45%) and hospital stay (94.24%). Factors associated with worse outcomes of remdesivir use in terms of mortality included limitation of life support treatment, need for ventilatory support (especially invasive mechanical ventilation) on day 14 after the first dose of remdesivir, lymphopenia, low levels of albumin and hemoglobin, presence of flu and/or coinfection, and cough. Factors associated with worse outcomes of remdesivir use in terms of hospital stay included the number of doses of the COVID-19 vaccine, patchy lung density, bilateral pulmonary radiological status, number of comorbidities, oxygen therapy, troponin and lactate dehydrogenase levels, and asthenia. These findings highlight the effectiveness of XGB as a strong candidate for accurately categorizing COVID-19 patients undergoing remdesivir treatment.