Intracellular trafficking and cellular uptake mechanism of PHBV nanoparticles for targeted delivery in epithelial cell lines

Indexación: Web of Science; Scopus; Scielo.Background: Nanotechnology is a science that involves imaging, measurement, modeling and a manipulation of matter at the nanometric scale. One application of this technology is drug delivery systems based on nanoparticles obtained from natural or synthetic sources. An example of these systems is synthetized from poly(3-hydroxybutyrate-co-3-hydroxyvalerate), which is a biodegradable, biocompatible and a low production cost polymer. The aim of this work was to investigate the uptake mechanism of PHBV nanoparticles in two different epithelial cell lines (HeLa and SKOV-3). Results: As a first step, we characterized size, shape and surface charge of nanoparticles using dynamic light scattering and transmission electron microscopy. Intracellular incorporation was evaluated through flow cytometry and fluorescence microscopy using intracellular markers. We concluded that cellular uptake mechanism is carried out in a time, concentration and energy dependent way. Our results showed that nanoparticle uptake displays a cell-specific pattern, since we have observed different colocalization in two different cell lines. In HeLa (Cervical cancer cells) this process may occur via classical endocytosis pathway and some internalization via caveolin-dependent was also observed, whereas in SKOV-3 (Ovarian cancer cells) these patterns were not observed. Rearrangement of actin filaments showed differential nanoparticle internalization patterns for HeLa and SKOV-3. Additionally, final fate of nanoparticles was also determined, showing that in both cell lines, nanoparticles ended up in lysosomes but at different times, where they are finally degraded, thereby releasing their contents. Conclusions: Our results, provide novel insight about PHBV nanoparticles internalization suggesting that for develop a proper drug delivery system is critical understand the uptake mechanism.https://jnanobiotechnology.biomedcentral.com/articles/10.1186/s12951-016-0241-

Electrical Properties and Electromagnetic Interference Shielding Effectiveness of Interlayered Systems Composed by Carbon Nanotube Filled Carbon Nanofiber Mats and Polymer Composites

The demand for multifunctional requirements in aerospace, military, automobile, sports, and energy applications has encouraged the investigation of new composite materials. This study focuses on the development of multiwall carbon nanotube (MWCNT) filled polypropylene composites and carbon nanofiber composite mats. The developed systems were then used to prepare interlayered composites that exhibited improved electrical conductivity and electromagnetic interference (EMI) shielding efficiency. MWCNT-carbon nanofiber composite mats were developed by centrifugally spinning mixtures of MWCNT suspended in aqueous poly(vinyl alcohol) solutions. The developed nanofibers were then dehydrated under sulfuric acid vapors and then heat treated. Interlayered samples were fabricated using a nanoreinforced polypropylene composite as a matrix and then filled with carbon fiber composite mats. The in-plane and through-plane electrical conductivity of an eight-layered flexible carbon composite (0.65 mm thick) were shown to be 6.1 and 3.0 × 10−2 S·cm−1, respectively. The EMI shielding effectiveness at 900 MHz increased from 17 dB for the one-layered composite to 52 dB for the eight-layered composite. It was found that the reflection of the electromagnetic waves was the dominating mechanism for EMI shielding in the developed materials. This study opens up new opportunities for the fabrication of novel lightweight materials that are to be used in communication systems

Prevalence of overweight and obesity in spanish working population along the Covid-19 pandemic. Adiposity indicators and related variables

Introduction: Obesity is a multifactorial and complex disease, being the Body Mass Index (BMI) the standardized method used to define and evaluate overweight or obesity in epidemiological studies, however and compared to adiposity indicators, this method presents low sensitivity and shows a high inter-individual variability. Methods: A descriptive cross-sectional study was performed in 815 workers, aged between 18 and 66 years with data collected along regular health surveillance examinations of participating companies from March 2020 to June 2021. The following variables were collected: socio-demographic: age, sex, cultural level and social class; occupational variables: type of work and role; anthropometric variables: weight, height and BMI; and adiposity indicators: visceral fat, body fat, waist circumference and waist/height, and waist/hip indices, establishing interrelationships between them. Results: Significant differences were found between obesity prevalence and gender, being higher in men and increasing with age. As well, the prevalence was higher in workers with elementary education as the highest degree obtained. In women, it was observed an inverse correlation between social class level and obesity prevalence. In men with non-manual jobs (white collar) and women with manual jobs (blue collar), the prevalence established was higher. It is worth highlighting the association between BMI, body fat and waist/height index. Conclusions: The average BMI results of the workers were found to be overweight, showing higher values in men (27.49) than in women (26.33) and a relation to age and occupations. The BMI shows concordance with all the indicators of adiposity, with body and visceral fat and the waist/height index standing out.Introducción: La obesidad es una enfermedad multifactorial y compleja, siendo el Índice de Masa Corporal (IMC) el método estandarizado utilizado para definir y evaluar el sobrepeso u obesidad en los estudios epidemiológicos, sin embargo y en comparación con los indicadores de adiposidad, este método presenta una baja sensibilidad y muestra una alta variabilidad interindividual. Métodos: Se realizó un estudio descriptivo transversal en 815 trabajadores, con edades comprendidas entre los 18 y los 66 años con datos recogidos a lo largo de los exámenes periódicos de vigilancia de la salud de las empresas participantes desde marzo de 2020 hasta junio de 2021. Se recogieron las siguientes variables: sociodemográficas: edad, sexo, nivel cultural y clase social; ocupacionales: tipo de trabajo y rol; antropométricas: peso, talla e IMC; e indicadores de adiposidad: grasa visceral, grasa corporal, perímetro de cintura y cintura/altura, e índices de cintura/cadera, estableciendo interrelaciones entre ellos. Resultados: Se encontraron diferencias significativas entre la prevalencia de obesidad y el género, siendo mayor en los hombres y aumentando con la edad. Asimismo, la prevalencia fue mayor en los trabajadores con estudios primarios como máxima titulación obtenida. En las mujeres se observó una correlación inversa entre el nivel de clase social y la prevalencia de obesidad. En los hombres con trabajos no manuales (cuello blanco) y en las mujeres con trabajos manuales (cuello azul), la prevalencia establecida fue mayor. Cabe destacar la asociación entre el IMC, la grasa corporal y el índice cintura/altura. Conclusiones: Los resultados del IMC promedio de los trabajadores se encontraron con sobrepeso, mostrando valores más altos en los hombres (27,49) que en las mujeres (26,33) y una relación con la edad y las ocupaciones. El IMC muestra concordancia con todos los indicadores de adiposidad, destacando la grasa corporal y visceral y el índice cintura/altura. Palabras clave: Obesidad, Adiposidad visceral, Antropometría, Índice de masa corporal

MAP entropy estimation: applications in robust image filtering

We introduce a new approach for image filtering in a Bayesian framework. In this case the probability density function (pdf) of thelikelihood function is approximated using the concept of non-parametric or kernel estimation. The method is based on the generalizedGaussian Markov random fields (GGMRF), a class of Markov random fields which are used as prior information into the Bayesian rule, whichprincipal objective is to eliminate those effects caused by the excessive smoothness on the reconstruction process of images which arerich in contours or edges. Accordingly to the hypothesis made for the present work, it is assumed a limited knowledge of the noise pdf,so the idea is to use a non-parametric estimator to estimate such a pdf and then apply the entropy to construct the cost function for thelikelihood term. The previous idea leads to the construction of Maximum a posteriori (MAP) robust estimators, since the real systems arealways exposed to continuous perturbations of unknown nature. Some promising results of three new MAP entropy estimators (MAPEE) forimage filtering are presented, together with some concluding remarks