To determine the existing relationship between ethanol levels in biological fluids, such as blood and urine, and their correlation with causes of death in corpses admitted to the forensic medicine autopsy service in Honduras. The gas chromatography method was employed to determine the concentration of ethyl alcohol. After a statistical analysis using measures of central tendency, it was found that the urine sample presented a median of 227.30mg/dL, while in the blood, it was 276.86mg/dL. After some distribution tests and correlation, it was determined that higher alcohol concentrations influence the "ACCIDENTAL" cause of death, with values of median alcohol concentration of 228.56mg/dL in blood and 277.44 mg/dL in urine. Still, the most frequent cause of death was "HOMICIDE", which differs in the age of the subjects and their ethanol concentration, with values of median alcohol concentration of 227.20mg/dL in blood and 276.86mg/dL in urine; similarities of median indicates that both samples are related or share a standard feature. Subsequent statistical tests showed that blood concentration values are more representative than urine values since the latter represents the final metabolic stage of alcohol in the body and exhibits more excellent dispersion. The average age of the individuals analyzed was 33 years old. However, it should be noted that individuals involved in "ACCIDENTAL" causes of death were in the lower age range corresponding to the so-called young adults.
Keywords: Forensic sciences; blood alcohol concentration; autopsy; alcohol in urine
Emergency services worldwide have been exceeded in their capacities due to the SARS-CoV-2 pandemic, a generalized situation in countries with robust health systems and aggravated in lagging countries. As a result, focused computer solutions have been developed for self-diagnosis, triage, and follow-up of suspected and confirmed patients of SARS-CoV-2. But as it is a new disease, the symptoms evolve in a short time and the diagnostic protocols must be updated. The applications that integrate algorithms in their code to help sanitary processes need to be modified, recompiled, and published integrating these changes. This article presents a solution through the implementation of a neural network that only requires updating an external file without the need to modify whole applications.
Keywords: SARS-CoV-2; Neural Network; Triage; Telemedicine; Cloud; Public Health
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