Background: Coronary artery disease (CAD) is a chronic, inflammatory, and complex disease associated with vascular risk factors. Nowadays, the coronary artery calcium (CAC) is a specific marker of the presence and extent of atherosclerosis. Additionally, CAC is a predictor of future coronary events in asymptomatic individuals diagnosed with subclinical atherosclerosis (CAC > 0). In this study, our aim is to evaluate the participation of two polymorphisms of the PCSK9 gene as genetic markers for developing subclinical atherosclerosis and cardiometabolic risk factors in asymptomatic individuals. Methods: We analyzed two PCSK9 polymorphisms (rs2479409 and rs615563) in 394 individuals with subclinical atherosclerosis and 1102 healthy controls using real time- polymerase chain reaction (PCR). Results: Under various inheritance models adjusted for different confounding factors, the rs2479409 polymorphism was associated with an increased risk of developing subclinical atherosclerosis (OR = 1.53, P recessive = 0.041). Both polymorphisms were significantly associated with several cardiometabolic parameters. Conclusions: Our data suggest that rs2479409 polymorphism could be envisaged as a risk marker for subclinical atherosclerosis.

Introducción: El tratamiento antirretroviral (TAR) ha convertido al VIH en una condición crónica controlable. No obstante, su efectividad depende en gran medida del momento del diagnóstico. En México, hasta el 50% de los pacientes son diagnosticados en etapas tardías, lo cual compromete los resultados clínicos y aumenta la carga sobre el sistema de salud. Objetivos: Analizar la efectividad del tratamiento antirretroviral en pacientes con diagnóstico tardío y temprano de VIH atendidos en el Hospital General de Zona No. 46 del IMSS entre el 1 de enero de 2022 y el 31 de diciembre de 2024. Material y métodos Estudio de cohorte retrospectivo con 150 pacientes diagnosticados entre enero de 2022 y diciembre de 2024. Se clasificaron según tipo de diagnóstico y se evaluaron carga viral y conteo de CD4. Se aplicaron pruebas t de Student o U de Mann-Whitney y se calculó riesgo relativo (RR). El análisis estadístico incluyó estadística descriptiva, con intervalos de confianza al 95% y regresión logística en SPSS v22. Resultados: El 86% de los pacientes con diagnóstico temprano logró supresión viral (<50 copias/ml) frente al 58% en diagnóstico tardío (RR=1.48; IC95%: 1.25–1.75; p<0.01). El conteo de CD4 fue significativamente mayor en el grupo temprano. Conclusión: El diagnóstico temprano mejora la efectividad del TAR. Se requiere reforzar la detección oportuna para optimizar los resultados clínicos y reducir la mortalidad.

Background COVID-19 is a global public health problem. Aim The main objective of this research is to evaluate and compare the performance of the algorithms: Random Forest, Support Vector Machine, Logistic Regression, Decision Tree, and Neural Network, using metrics such as precision, recall, F1-score and accuracy. Methods A dataset (n=138) was used, with numerical and categorical variables. The algorithms Random Forest, Support Vector Machine, Logistic Regression, Decision Tree, and Neural Network were considered. These were trained using an 80-20 ratio. The following metrics were evaluated: precision, recall, F1-Score, and 5-fold stratified cross-validation. Results The Random Forest algorithm was superior, achieving a maximum score of 0.9727 in cross-validation. The correlation analysis identified ferritin (0.8277) and oxygen saturation (-0.6444). The heuristic model was compared with metaheuristics models. Models obtained through metaheuristic search could maintaining the metrics with 3 variables and stable weight distribution. A perplexity analysis it allows to differentiate between the best models. The features of creatinine and ALT are highlighted in the model with the best CV score and the lowest perplexity. Conclusion Comparative analysis of different classification models was carried out to predict the severity of COVID-19 cases with biological markers.