Even though some methods for the detection of colorectal cancer have been used clinically, most of the techniques used do not consider the in situ detection of colorectal cancer (CRC) biomarkers, which would favor in vivo real-time monitoring of the carcinogenesis process and consequent studies of the disease. In order to give a scientific and computational framework ideal for the evaluation of diagnosis techniques based on the early detection of biomarker molecules modeled as spherical particles from the computational point of view, a computational representation of the rectum, stool and biomarker particles was developed. As consequence of the transport of stool, there was a displacement of CRC biomarker particles that entered the system as a result of the cellular apoptosis processes in polyps with a length lower than 1 cm, reaching a maximum velocity of $3.47\times {10}^{-3}$ m/s. The biomarkers studied showed trajectories distant to regions of the polyp of origin in 1 min of simulation. The research results show that the biomarker particles for CRC respond to the variations in the movements of the stool with trajectories and speeds that depend on the location of the injury, which will allow locating the regions with the highest possibilities of catching particles through in situ measurement instruments in the future.

This article presents a general framework to model the population growth of Colombia through machine learning, which allows incorporating variables such as CO₂ emissions. The data are obtained through the DANE Geoportal and the World Bank portal database. In recent years, several cities in Colombia have experienced an accelerated population and urban growth as a result of an overwhelming increase in the immigrant population, a situation that has been affecting the urban order of the city, mainly due to population confinement, increased environmental pollution, unhealthiness, poverty among other factors. The population growth prediction fully shows the effects of the fertility rate, mortality, and migration of people from one territory to another, essential factors in determining population and health system conditions. Accurate information can be obtained from population census measurements, a strategy with a high cost; However, the capabilities of machine learning are growing, being also implemented to determine predictions of systems and behaviors in demographic, environmental, and socioeconomic issues, which contributes to the economic and energy-saving that implies the gathering and processing of demographic information for the country. In this work, to predict population growth, the concept of machine learning was applied, carrying out a data analysis using an Artificial Neural Network (ANN). The results obtained provide a predictive model with the potential to provide regional and governmental entities with suggestions, strategies, and alternatives for studying the population and making decisions for urban redevelopment.

Uno de los grandes problemas que se vienen presentando en el mundo, especialmente Colombia, es debido a un acelerado crecimiento poblacional generado por el aumento desbordado de la circulación migrante o movilidad de personas por otras causas o situaciones eventuales de la naturaleza. Estas situaciones sociales vienen afectando sensiblemente el orden urbanístico de las ciudades, particularmente por el confinamiento de la población y el aumento de la contaminación ambiental que aporta este incremento, además de la insalubridad, pobreza, entre otros factores. Para estudiar el crecimiento poblacional en función de variables demográficas y factores ambientales, se construyó una Red Neuronal Artificial para llevar a cabo un análisis de datos que incorporó variables relacionadas tales como CO₂ y otros agentes contaminantes. El propósito fue identificar la influencia de estos agentes tóxicos con el crecimiento de una población. Una vez verificada predicción dinámica mediante la red neuronal, se construyó un modelo matemático para estudiar un caso específico del comportamiento demográfico para una región colombiana por medio de un Diagrama de Forrester bajo condiciones migratorias, natalidad, morbilidad, mortalidad, flujo migratorio (o movilidad de personas debido a otros eventos de la naturaleza) y agentes contaminantes que afectan la salud humana principalmente en enfermedades respiratorias. Los resultados entregaron predicciones y del Diagrama de Forrester constatando relación entre los agentes tóxicos y en los aspectos demográficos.

Colorectal cancer is currently treated by surgical procedures, chemotherapy and radiotherapy; however, these latest treatments are highly aggressive, with side effects that affect the patient’s quality of life. The scientific union has been investigating other more favorable alternatives, such as targeted therapy, which seeks greater selectivity in the type of target cells. This type of treatment can significantly reduce side effects in the patient. The goal of this research is to computationally visualize the behavior of nanocarriers in the colon tumor microenvironment, as well as their capacity for deepening, selective coupling and differentiating between healthy and cancerous tissue. A group of histological samples of cancerous tissue was selected, based on morphological criteria and the stage of the disease. This was used to elaborate 2D and 3D models to study different cases using artificial vision and computer simulation techniques. The results indicated velocities of the nanocarriers that reached values between 1.40 and $8.69\times {10}^{-7}\frac{\mathrm{m}}{\mathrm{s}}$ for a time of 3.88 h, with a vectorized deposition efficiency of 1.0 to 4.46%. In addition, selective mating events were achieved at a maximum depth of 4.68 × 10⁻⁴ m. This scientific knowledge can contribute to the estimation of the efficacy of the treatment, as well as the assessment for different dosage levels and frequency of drug administration from the studies carried out on the lesion.