Compositional Structure of the Genome: A Review.

As the genome carries the historical information of a species' biotic and environmental interactions, analyzing changes in genome structure over time by using powerful statistical physics methods (such as entropic segmentation algorithms, fluctuation analysis in DNA walks, or measures of compositional complexity) provides valuable insights into genome evolution. Nucleotide frequencies tend to vary along the DNA chain, resulting in a hierarchically patchy chromosome structure with heterogeneities at different length scales that range from a few nucleotides to tens of millions of them. Fluctuation analysis reveals that these compositional structures can be classified into three main categories: (1) short-range heterogeneities (below a few kilobase pairs (Kbp)) primarily attributed to the alternation of coding and noncoding regions, interspersed or tandem repeats densities, etc.; (2) isochores, spanning tens to hundreds of tens of Kbp; and (3) superstructures, reaching sizes of tens of megabase pairs (Mbp) or even larger. The obtained isochore and superstructure coordinates in the first complete T2T human sequence are now shared in a public database. In this way, interested researchers can use T2T isochore data, as well as the annotations for different genome elements, to check a specific hypothesis about genome structure. Similarly to other levels of biological organization, a hierarchical compositional structure is prevalent in the genome. Once the compositional structure of a genome is identified, various measures can be derived to quantify the heterogeneity of such structure. The distribution of segment G+C content has recently been proposed as a new genome signature that proves to be useful for comparing complete genomes. Another meaningful measure is the sequence compositional complexity (SCC), which has been used for genome structure comparisons. Lastly, we review the recent genome comparisons in species of the ancient phylum Cyanobacteria, conducted by phylogenetic regression of SCC against time, which have revealed positive trends towards higher genome complexity. These findings provide the first evidence for a driven progressive evolution of genome compositional structure.

Aplicación de la Telemática en el monitoreo remoto de Bancos de Muestras Biológicas. Estudio Preliminar

Las serotecas son espacios destinados para el resguardo de Muestras Biológicas (MB) de procesos diagnósticos y científicos. El Instituto de Investigaciones en Ciencias de la Salud (IICS) cuenta con ocho congeladores de Ultra Baja Temperatura (UBT) distribuidos en dos serotecas. El sistema de monitoreo diseñado se evaluó durante cuatro meses, mientras registraba y enviaba alertas de tres UBT instalados en una de las serotecas y de su temperatura ambiente. Se recabó información de los usuarios respecto al rango de las temperaturas de cada UBT, tipo de MB almacenadas y su criterio de conservación. Se emplearon controladores de temperatura con sensores PT 100 conectados a un convertidor RS485/Ethernet en cada congelador. El sistema monitoreó, registró y alertó vía correo electrónico a los usuarios y técnicos biomédicos sobre los incidentes por temperaturas fuera del rango y falla de comunicación. En total se registraron 25 incidentes, 17 referentes al tiempo de apertura de puerta, 5 por temperatura elevada del ambiente y 3 por problemas en la conexión de red. La aplicación de la telemática fue determinante para monitorear en tiempo real las temperaturas de los congeladores UBT y del ambiente para garantizar que la cadena de frío no se vea afectada. De esta forma se cuenta con una herramienta que notifica a los usuarios de serotecas y biobancos los incidentes eléctricos o eventos que afecten el rango de temperatura necesario para la preservación de los materiales biológicos, permitiéndoles realizar una intervención oportuna y así garantizar la correcta preservación de las MB.

The serum banks are spaces used for the protection of Biological Samples (BS) of diagnostic and scientific processes. The Instituto de Investigaciones en Ciencias de la Salud (IICS) has eight Ultra Low Temperature (ULT) freezers distributed in two serum banks. The designed monitoring system was evaluated for four months, while recording and sending alerts of three ULTs installed in one of the serum banks and their ambient temperature. Information was collected from users regarding the temperature range of each ULT, type of stored BS and their conservation criteria. Temperature controllers with PT 100 sensors connected to an RS485/Ethernet converter were used in each freezer. The system monitored, recorded and alerted users and biomedical technicians via email about incidents due to temperatures outside the range and communication failure. In total, 25 incidents were recorded, 17 related to door opening time, 5 due to high ambient temperature and 3 due to network connection problems. The application of telematics was decisive in monitoring the temperatures of the ULT freezers and the environment in real time to ensure that the cold chain was not affected. In this way, there is a tool that notifies users of serum banks and biobanks of electrical incidents or events that affect the temperature range necessary for the preservation of biological materials, allowing them to perform a timely intervention and thus guaranteeing the correct preservation of the BS.

Respuesta a la carta al editor: Crítica al estudio factibilidad de la utilización de la inteligencia artificial para el cribado de pacientes con COVID-19 en Paraguay

[EXTRACTO]. En respuesta a la carta al editor titulada: Critica al estudio de factibilidad de la utilización de la inteligencia artificial para el cribado de pacientes con COVID-19 en Paraguay, los autores del artículo de referencia elevan a consideración la réplica sobre el contenido de esta, con el objeto de esclarecer los cuestionamientos mencionados en la misma. Respecto al primer problema mencionado en la carta al editor; el estudio utilizó el programa de inteligencia artificial (IA) que fue desarrollado por un equipo de informáticos biomédicos, neumólogos y radiólogos (imagenólogos). El programa utilizado dispone de un método de aprendizaje profundo para realizar el diagnóstico rápido de COVID-19; es decir, cuenta con un algoritmo para detectar patologías neumológicas y un algoritmo de diagnóstico de neumopatías compatibles con COVID-19. En cuanto al segundo problema mencionado en la carta al editor; el presente estudio se realizó entre marzo del 2020 y junio del 2021 en 14 hospitales de las 18 regiones sanitarias del Ministerio de Salud Pública y Bienestar Social (MSPBS), que ya contaban con al menos un tomógrafo funcionando al momento del estudio. El informe de diagnóstico del médico radiólogo y el resultado del diagnóstico por IA fueron remitidos luego a un equipo de neumólogos para su valoración, análisis, correlación y validación; los neumólogos correlacionaron los valores porcentuales del diagnóstico por IA con el resultado de los médicos radió- logos, el resultado del análisis molecular (RT-PCR) y el cuadro clínico del paciente para determinar los grados de concordancia o discordancia entre los resultados, y llegar a un diagnóstico definitivo que permitiera informar al médico del hospital donde se trataba al paciente en cuestión. Esto permitió reducir las aglomeraciones en los centros especializados y optimizar el uso de los limitados recursos disponibles. Esta respuesta se refiere a la carta disponible en: https://doi.org/10. 26633/RPSP.2022.193

Decrease of heart rate variability during exercise: An index of cardiorespiratory fitness.

The present study proposes to measure and quantify the heart rate variability (HRV) changes during effort as a function of the heart rate and to test the capacity of the produced indices to predict cardiorespiratory fitness measures. Therefore, the beat-to-beat cardiac time interval series of 18 adolescent athletes (15.2 ± 2.0 years) measured during maximal graded effort test were detrended using a dynamical first-order differential equation model. HRV was then calculated as the standard deviation of the detrended RR intervals (SDRR) within successive windows of one minute. The variation of this measure of HRV during exercise is properly fitted by an exponential decrease of the heart rate: the SDRR is divided by 2 every increase of heart rate of 20 beats/min. The HR increase necessary to divide by 2 the HRV is linearly inversely correlated with the maximum oxygen consumption (r = -0.60, p = 0.006), the maximal aerobic power (r = -0.62, p = 0.006), and, to a lesser extent, to the power at the ventilatory thresholds (r = -0.53, p = 0.02 and r = -0.47, p = 0.05 for the first and second threshold). It indicates that the decrease of the HRV when the heart rate increases is faster among athletes with better fitness. This analysis, based only on cardiac measurements, provides a promising tool for the study of cardiac measurements generated by portable devices.

[Feasibility of using artificial intelligence for screening COVID-19 patients in ParaguayViabilidade do uso de inteligência artificial na triagem de pacientes com COVID-19 no Paraguai]. / Factibilidad de la utilización de la inteligencia artificial para el cribado de pacientes con COVID-19 en Paraguay.

Objective: Study the feasibility of using artificial intelligence as a sensitive and specific method for COVID-19 screening in patients with respiratory conditions, using chest CT scan images and a telemedicine platform. Methods: From March 2020 to June 2021, the authors conducted an observational descriptive multicenter feasibility study based on artificial intelligence (AI) for COVID-19 screening using chest images of patients with respiratory conditions who presented at public hospitals. The AI platform was used to diagnose chest CT scan images; this was then compared with molecular diagnosis (RT-PCR) to determine whether they matched and to analyze the feasibility of AI for screening patients with suspected COVID-19. A telemedicine platform was used to send images and diagnostic results. Results: Screening of 3 514 patients with a suspected COVID-19 diagnosis was performed in 14 hospitals around the country. Most patients were aged 27 to 59 years, followed by those over 60. The average age was 48.6 years; 52.8% were male. The most frequent findings were severe pneumonia, bilateral pneumonia with pleural effusion, bilateral pulmonary emphysema, and diffuse ground glass opacity, among others. There was an average of 93% matching and 7% mismatching between images analyzed by AI and RT-PCR. Sensitivity and specificity of the AI system, obtained by comparing AI and RT-PCR screening results, were 93% and 80% respectively. Conclusions: The use of sensitive and specific AI for stratified rapid detection of COVID-19 in patients with respiratory conditions by using chest CT scan images and a telemedicine platform in public hospitals in Paraguay is feasible.

Objetivo: Examinar a viabilidade do uso de inteligência artificial como um método sensível e específico de triagem de COVID-19 em pacientes com afecções respiratórias, empregando imagens obtidas por exame de tomografia do tórax e uma plataforma de telemedicina. Métodos: Entre março de 2020 e junho de 2021, foi realizado um estudo observacional descritivo multicêntrico sobre a viabilidade do uso de inteligência artificial (IA) para a triagem de COVID-19, empregando imagens do tórax de pacientes com afecções respiratórias atendidos em hospitais da rede pública. O diagnóstico das imagens obtidas em tomografia do tórax foi realizado por meio de uma plataforma de IA e, em seguida, cotejado com o diagnóstico molecular (RT-PCR) para determinar a concordância entre os métodos utilizados e analisar a viabilidade deste processo para a triagem de pacientes com suspeita de COVID-19. As imagens e os resultados do exame diagnóstico foram disponibilizados em uma plataforma de telemedicina. Resultados: Foi realizada a triagem de 3 514 pacientes com suspeita de COVID-19 atendidos em 14 hospitais de todo o país. Os pacientes, na sua maioria, tinham entre 27 e 59 anos de idade ou pertenciam à faixa etária acima de 60 anos, com média de idade de 48,6 anos, sendo que 52,8% eram do sexo masculino. Os achados mais comuns foram pneumonia grave, pneumonia bilateral com derrame pleural, enfisema pulmonar bilateral e opacidade difusa em vidro fosco, entre outros. Verificou-se, em média, 93% de concordância e 7% de discordância entre as imagens analisadas com uso de IA e os resultados do exame de RT-PCR, com uma sensibilidade de 93% e especificidade de 80% desse sistema de triagem. Conclusões: Demonstrou-se que o uso de um sistema de IA sensível e específico é viável nos hospitais públicos do Paraguai para a detecção rápida estratificada de COVID-19 em pacientes com afecções respiratórias, empregando imagens de exame de tomografia do tórax e uma plataforma de telemedicina.

Factibilidad de la utilización de la inteligencia artificial para el cribado de pacientes con COVID-19 en Paraguay

[RESUMEN]. Objetivo. Estudiar la factibilidad de utilización de la inteligencia artificial como método sensible y específico para el cribado de COVID-19 en pacientes con afecciones respiratorias empleando imágenes de tórax obtenidas con tomógrafo y una plataforma de telemedicina. Métodos. Entre marzo del 2020 y junio del 2021 se realizó un estudio observacional descriptivo multicéntrico de factibilidad basada en inteligencia artificial (IA) para el cribado de COVID-19 en imágenes de tórax de pacientes con afecciones respiratorias que acudieron a hospitales públicos. El diagnóstico de las imágenes tomográficas de tórax se realizó a través de la plataforma de IA; luego, se comparó con el diagnóstico molecular (RT-PCR) para determinar la concordancia entre ambos y analizar su factibilidad para el cribado de pacientes con sospecha de COVID-19. Las imágenes y los resultados diagnóstico se enviaron a través de una plataforma de telemedicina. Resultados. Se realizó el cribado de 3 514 pacientes con sospecha diagnóstica de COVID-19, en 14 hospitales a nivel nacional. La mayoría de los pacientes tenían entre 27 y 59 años, seguidos por los mayores de 60 años. La edad promedio fue de 48,6 años; el 52,8% eran de sexo masculino. Los hallazgos más frecuentes fueron neumonía grave, neumonía bilateral con derrame pleural, enfisema pulmonar bilateral y opacidad difusa en vidrio esmerilado, entre otros. Se determinó un promedio de 93% de concordancia y 7% de discordancia entre las imágenes analizadas mediante IA y la RT-PCR. La sensibilidad y especificidad del sistema de IA, obtenidas comparando el resultado del cribado obtenido por IA con la RT-PCR, fueron de 93% y 80% respectivamente. Conclusiones. Es viable la utilización de IA sensible y específica para la detección rápida estratificada de COVID-19 en pacientes con afecciones respiratorias utilizando imágenes obtenidas mediante tomografía de tórax y una plataforma de telemedicina en los hospitales públicos de Paraguay.

[ABSTRACT]. Objective. Study the feasibility of using artificial intelligence as a sensitive and specific method for COVID-19 screening in patients with respiratory conditions, using chest CT scan images and a telemedicine platform. Methods. From March 2020 to June 2021, the authors conducted an observational descriptive multicenter feasibility study based on artificial intelligence (AI) for COVID-19 screening using chest images of patients with respiratory conditions who presented at public hospitals. The AI platform was used to diagnose chest CT scan images; this was then compared with molecular diagnosis (RT-PCR) to determine whether they matched and to analyze the feasibility of AI for screening patients with suspected COVID-19. A telemedicine platform was used to send images and diagnostic results. Results. Screening of 3 514 patients with a suspected COVID-19 diagnosis was performed in 14 hospitals around the country. Most patients were aged 27 to 59 years, followed by those over 60. The average age was 48.6 years; 52.8% were male. The most frequent findings were severe pneumonia, bilateral pneumonia with pleural effusion, bilateral pulmonary emphysema, and diffuse ground glass opacity, among others. There was an average of 93% matching and 7% mismatching between images analyzed by AI and RT-PCR. Sensitivity and specificity of the AI system, obtained by comparing AI and RT-PCR screening results, were 93% and 80% respectively. Conclusions. The use of sensitive and specific AI for stratified rapid detection of COVID-19 in patients with respiratory conditions by using chest CT scan images and a telemedicine platform in public hospitals in Paraguay is feasible.

[RESUMO]. Objetivo. Examinar a viabilidade do uso de inteligência artificial como um método sensível e específico de triagem de COVID-19 em pacientes com afecções respiratórias, empregando imagens obtidas por exame de tomografia do tórax e uma plataforma de telemedicina. Métodos. Entre março de 2020 e junho de 2021, foi realizado um estudo observacional descritivo multicêntrico sobre a viabilidade do uso de inteligência artificial (IA) para a triagem de COVID-19, empregando imagens do tórax de pacientes com afecções respiratórias atendidos em hospitais da rede pública. O diagnóstico das imagens obtidas em tomografia do tórax foi realizado por meio de uma plataforma de IA e, em seguida, cotejado com o diagnóstico molecular (RT-PCR) para determinar a concordância entre os métodos utilizados e analisar a viabilidade deste processo para a triagem de pacientes com suspeita de COVID-19. As imagens e os resultados do exame diagnóstico foram disponibilizados em uma plataforma de telemedicina. Resultados. Foi realizada a triagem de 3 514 pacientes com suspeita de COVID-19 atendidos em 14 hospitais de todo o país. Os pacientes, na sua maioria, tinham entre 27 e 59 anos de idade ou pertenciam à faixa etária acima de 60 anos, com média de idade de 48,6 anos, sendo que 52,8% eram do sexo masculino. Os achados mais comuns foram pneumonia grave, pneumonia bilateral com derrame pleural, enfisema pulmonar bilateral e opacidade difusa em vidro fosco, entre outros. Verificou-se, em média, 93% de concordância e 7% de discordância entre as imagens analisadas com uso de IA e os resultados do exame de RT-PCR, com uma sensibilidade de 93% e especificidade de 80% desse sistema de triagem. Conclusões. Demonstrou-se que o uso de um sistema de IA sensível e específico é viável nos hospitais públicos do Paraguai para a detecção rápida estratificada de COVID-19 em pacientes com afecções respiratórias, empregando imagens de exame de tomografia do tórax e uma plataforma de telemedicina.

Factibilidad de la utilización de la inteligencia artificial para el cribado de pacientes con COVID-19 en Paraguay / Feasibility of using artificial intelligence for screening COVID-19 patients in Paraguay / Viabilidade do uso de inteligência artificial na triagem de pacientes com COVID-19 no Paraguai

RESUMEN Objetivo. Estudiar la factibilidad de utilización de la inteligencia artificial como método sensible y específico para el cribado de COVID-19 en pacientes con afecciones respiratorias empleando imágenes de tórax obtenidas con tomógrafo y una plataforma de telemedicina. Métodos. Entre marzo del 2020 y junio del 2021 se realizó un estudio observacional descriptivo multicéntrico de factibilidad basada en inteligencia artificial (IA) para el cribado de COVID-19 en imágenes de tórax de pacientes con afecciones respiratorias que acudieron a hospitales públicos. El diagnóstico de las imágenes tomográficas de tórax se realizó a través de la plataforma de IA; luego, se comparó con el diagnóstico molecular (RT-PCR) para determinar la concordancia entre ambos y analizar su factibilidad para el cribado de pacientes con sospecha de COVID-19. Las imágenes y los resultados diagnóstico se enviaron a través de una plataforma de telemedicina. Resultados. Se realizó el cribado de 3 514 pacientes con sospecha diagnóstica de COVID-19, en 14 hospitales a nivel nacional. La mayoría de los pacientes tenían entre 27 y 59 años, seguidos por los mayores de 60 años. La edad promedio fue de 48,6 años; el 52,8% eran de sexo masculino. Los hallazgos más frecuentes fueron neumonía grave, neumonía bilateral con derrame pleural, enfisema pulmonar bilateral y opacidad difusa en vidrio esmerilado, entre otros. Se determinó un promedio de 93% de concordancia y 7% de discordancia entre las imágenes analizadas mediante IA y la RT-PCR. La sensibilidad y especificidad del sistema de IA, obtenidas comparando el resultado del cribado obtenido por IA con la RT-PCR, fueron de 93% y 80% respectivamente. Conclusiones. Es viable la utilización de IA sensible y específica para la detección rápida estratificada de COVID-19 en pacientes con afecciones respiratorias utilizando imágenes obtenidas mediante tomografía de tórax y una plataforma de telemedicina en los hospitales públicos de Paraguay.

ABSTRACT Objective. Study the feasibility of using artificial intelligence as a sensitive and specific method for COVID-19 screening in patients with respiratory conditions, using chest CT scan images and a telemedicine platform. Methods. From March 2020 to June 2021, the authors conducted an observational descriptive multicenter feasibility study based on artificial intelligence (AI) for COVID-19 screening using chest images of patients with respiratory conditions who presented at public hospitals. The AI platform was used to diagnose chest CT scan images; this was then compared with molecular diagnosis (RT-PCR) to determine whether they matched and to analyze the feasibility of AI for screening patients with suspected COVID-19. A telemedicine platform was used to send images and diagnostic results. Results. Screening of 3 514 patients with a suspected COVID-19 diagnosis was performed in 14 hospitals around the country. Most patients were aged 27 to 59 years, followed by those over 60. The average age was 48.6 years; 52.8% were male. The most frequent findings were severe pneumonia, bilateral pneumonia with pleural effusion, bilateral pulmonary emphysema, and diffuse ground glass opacity, among others. There was an average of 93% matching and 7% mismatching between images analyzed by AI and RT-PCR. Sensitivity and specificity of the AI system, obtained by comparing AI and RT-PCR screening results, were 93% and 80% respectively. Conclusions. The use of sensitive and specific AI for stratified rapid detection of COVID-19 in patients with respiratory conditions by using chest CT scan images and a telemedicine platform in public hospitals in Paraguay is feasible.

RESUMO Objetivo. Examinar a viabilidade do uso de inteligência artificial como um método sensível e específico de triagem de COVID-19 em pacientes com afecções respiratórias, empregando imagens obtidas por exame de tomografia do tórax e uma plataforma de telemedicina. Métodos. Entre março de 2020 e junho de 2021, foi realizado um estudo observacional descritivo multicêntrico sobre a viabilidade do uso de inteligência artificial (IA) para a triagem de COVID-19, empregando imagens do tórax de pacientes com afecções respiratórias atendidos em hospitais da rede pública. O diagnóstico das imagens obtidas em tomografia do tórax foi realizado por meio de uma plataforma de IA e, em seguida, cotejado com o diagnóstico molecular (RT-PCR) para determinar a concordância entre os métodos utilizados e analisar a viabilidade deste processo para a triagem de pacientes com suspeita de COVID-19. As imagens e os resultados do exame diagnóstico foram disponibilizados em uma plataforma de telemedicina. Resultados. Foi realizada a triagem de 3 514 pacientes com suspeita de COVID-19 atendidos em 14 hospitais de todo o país. Os pacientes, na sua maioria, tinham entre 27 e 59 anos de idade ou pertenciam à faixa etária acima de 60 anos, com média de idade de 48,6 anos, sendo que 52,8% eram do sexo masculino. Os achados mais comuns foram pneumonia grave, pneumonia bilateral com derrame pleural, enfisema pulmonar bilateral e opacidade difusa em vidro fosco, entre outros. Verificou-se, em média, 93% de concordância e 7% de discordância entre as imagens analisadas com uso de IA e os resultados do exame de RT-PCR, com uma sensibilidade de 93% e especificidade de 80% desse sistema de triagem. Conclusões. Demonstrou-se que o uso de um sistema de IA sensível e específico é viável nos hospitais públicos do Paraguai para a detecção rápida estratificada de COVID-19 em pacientes com afecções respiratórias, empregando imagens de exame de tomografia do tórax e uma plataforma de telemedicina.

Carbamazepine and Diclofenac Removal Double Treatment: Oxidation and Adsorption.

In the present research, the effect of two hybrid treatments, ozone followed by powdered activated carbon (PAC) or PAC followed by ozone (O3), was studied for the removal of two drugs present in water: diclofenac and carbamazepine. In the study, two initial concentrations of each of the contaminants, 0.7 mg L-1 and 1.8 mg L-1, were used. Different doses of PAC between 4-20 mg L-1 were studied as variables, as well as different doses of O3 between 0.056-0.280 mg L-1. The evolution of the concentration of each contaminant over time was evaluated. From the results obtained, it was concluded that the combined treatment with ozone followed by PAC reduces between 50% and 75% the time required to achieve 90% removal of diclofenac when compared with the time required when only activated carbon was used. In the case of carbamazepine, the time required was 97% less. For carbamazepine, to achieve reduction percentages of up to 90%, O3 treatment followed by PAC acted faster than PAC followed by O3. In the case of diclofenac, PAC treatment followed by O3 was faster to reach concentrations of up to 90%. However, to reach yields below 80%, O3 treatment followed by PAC was more efficient.

[Mortality among workers in metallurgical industry: literature review.] / Mortalidad en trabajadores en la industria metalúrgica: revisión bibliográfica.

BACKGROUND: The analysis of mortality offers an important indicator for assessing the state of workers' occupational health. Workers involved in the extraction, refining, alloying and manufacturing of metals are frequently exposed to occupational risks that can lead to their death. The objective of this work was to synthesize the scientific evidence about factors associated with mortality among workers in the metallurgical industry. METHODS: A bibliographic review was conducted using the PubMed database. Seventeen studies were included, where topics addressed specific problems that influence the mortality of workers in the metallurgical industry sector. Complete texts of the articles were reviewed. RESULTS: Findings show the highest probabilities of death due to malignant neoplasms (48%), diseases of the circulatory system (28%), work accidents (15%), suicide and violence (9%). CONCLUSIONS: Despite the research carried out, there are gaps and limitations in the study of mortality in workers in the metallurgical industry, mainly related to the relationship of the cause of death with occupational risk factors.

OBJETIVO: El análisis de la mortalidad es un indicador que contribuye a evaluar el estado de la salud laboral de los trabajadores. Los trabajadores involucrados en la extracción, refinación, aleación y fabricación de metales están frecuentemente expuestos a riesgos laborales que pueden conducir a su muerte. El objetivo de este trabajo fue sintetizar la evidencia científica sobre mortalidad en trabajadores de la industria del metal. METODOS: Es una revisión bibliográfica de artículos científicos mediante la base de datos PubMed. Se incluyeron 17 estudios, donde los temas tratados abordaban problemas específicos que influyen en la mortalidad de los trabajadores del sector de la industria metalúrgica. Se revisaron los textos completos de los artículos. RESULTADOS: Los hallazgos del estudio mostraron mayores probabilidades de causa de muerte por neoplasias malignas (48%), enfermedades del sistema circulatorio (28%), accidentes laborales (15%), suicidio y violencia (9%). CONCLUSIONES: A pesar de las investigaciones realizadas, existen lagunas y limitaciones en el estudio de la mortalidad en los trabajadores de la industria metalúrgica, relacionadas fundamentalmente con la relación de la causa de muerte y los factores de riesgos laborales.

Efficacy of atrazine pesticide reduction in aqueous solution using activated carbon, ozone and a combination of both.

In this study, the reduction of the emerging organic contaminant atrazine in water, was investigated by adsorption, oxidation and a combination of both technologies. Adsorption tests were performed using method ASTM D3860-98 with two types of activated carbon: powdered activated carbon and granular activated carbon. For the oxidation tests, advanced ozone oxidation technology was used. Finally, in the combined tests, firstly adsorption treatment was applied followed by oxidation and then the order was reversed. We studied the contaminant removal percentage using different treatments at various reaction times. Results for the different treatments under study showed that, for an initial atrazine concentration of 0.7 mg L-1 and a dose of 16 mg L-1 of powdered activated carbon, with contact times of 60 min, 24 h and 48 h, percentage reductions of the contaminant of 81%, 92% and 94% respectively were obtained. For the same concentration of contaminant, but instead using granular activated carbon, the percentage reduction of atrazine at 60 min was 2%, this percentage rising to 34% and 35% after 24 and 48 h of contact time, respectively. For the same initial contaminant concentration, when ozone was applied at a dose of 19.7 mg L-1, and with a reaction time of 18 min, a reduction of atrazine of 93% was obtained, but oxidation by-products were also produced. For the combined treatments, with the same initial concentration of contaminant and the same doses of carbon and ozone as previously indicated, better contaminant reductions were obtained when the treatment started with activated carbon followed by ozone, achieving a 90% reduction of atrazine observing a 17 minute contact time with powdered activated carbon and a 3 day contact time using the granulated carbon. When the order was reversed by starting with ozone, the contact time was 52 min and 4 days, respectively.

Rapid screening for COVID-19 by applying artificial intelligence to chest computed tomography images: A feasibility study.

Aim: The aim of the study was to present the results and impact of the application of artificial intelligence (AI) in the rapid diagnosis of COVID-19 by telemedicine in public health in Paraguay. Methods: This is a descriptive, multi-centered, observational design feasibility study based on an AI tool for the rapid detection of COVID-19 in chest computed tomography (CT) images of patients with respiratory difficulties attending the country's public hospitals. The patients' digital CT images were transmitted to the AI diagnostic platform, and after a few minutes, radiologists and pneumologists specialized in COVID-19 downloaded the images for evaluation, confirmation of diagnosis, and comparison with the genetic diagnosis (reverse transcription polymerase chain reaction (RT-PCR)). It was also determined the percentage of agreement between two similar AI systems applied in parallel to study the viability of using it as an alternative method of screening patients with COVID-19 through telemedicine. Results: Between March and August 2020, 911 rapid diagnostic tests were carried out on patients with respiratory disorders to rule out COVID-19 in 14 hospitals nationwide. The average age of patients was 50.7 years, 62.6% were male and 37.4% female. Most of the diagnosed respiratory conditions corresponded to the age group of 27-59 years (252 studies), the second most frequent corresponded to the group over 60 years, and the third to the group of 19-26 years. The most frequent findings of the radiologists/pneumologists were severe pneumonia, bilateral pneumonia with pleural effusion, bilateral pulmonary emphysema, diffuse ground glass opacity, hemidiaphragmatic paresis, calcified granuloma in the lower right lobe, bilateral pleural effusion, sequelae of tuberculosis, bilateral emphysema, and fibrotic changes, among others. Overall, an average of 86% agreement and 14% diagnostic discordance was determined between the two AI systems. The sensitivity of the AI system was 93% and the specificity 80% compared with RT-PCR. Conclusion: Paraguay has an AI-based telemedicine screening system for the rapid stratified detection of COVID-19 from chest CT images of patients with respiratory conditions. This application strengthens the integrated network of health services, rationalizing the use of specialized human resources, equipment, and inputs for laboratory diagnosis.

On the Validity of Detrended Fluctuation Analysis at Short Scales.

Detrended Fluctuation Analysis (DFA) has become a standard method to quantify the correlations and scaling properties of real-world complex time series. For a given scale ℓ of observation, DFA provides the function F(ℓ), which quantifies the fluctuations of the time series around the local trend, which is substracted (detrended). If the time series exhibits scaling properties, then F(ℓ)∼ℓα asymptotically, and the scaling exponent α is typically estimated as the slope of a linear fitting in the logF(ℓ) vs. log(ℓ) plot. In this way, α measures the strength of the correlations and characterizes the underlying dynamical system. However, in many cases, and especially in a physiological time series, the scaling behavior is different at short and long scales, resulting in logF(ℓ) vs. log(ℓ) plots with two different slopes, α1 at short scales and α2 at large scales of observation. These two exponents are usually associated with the existence of different mechanisms that work at distinct time scales acting on the underlying dynamical system. Here, however, and since the power-law behavior of F(ℓ) is asymptotic, we question the use of α1 to characterize the correlations at short scales. To this end, we show first that, even for artificial time series with perfect scaling, i.e., with a single exponent α valid for all scales, DFA provides an α1 value that systematically overestimates the true exponent α. In addition, second, when artificial time series with two different scaling exponents at short and large scales are considered, the α1 value provided by DFA not only can severely underestimate or overestimate the true short-scale exponent, but also depends on the value of the large scale exponent. This behavior should prevent the use of α1 to describe the scaling properties at short scales: if DFA is used in two time series with the same scaling behavior at short scales but very different scaling properties at large scales, very different values of α1 will be obtained, although the short scale properties are identical. These artifacts may lead to wrong interpretations when analyzing real-world time series: on the one hand, for time series with truly perfect scaling, the spurious value of α1 could lead to wrongly thinking that there exists some specific mechanism acting only at short time scales in the dynamical system. On the other hand, for time series with true different scaling at short and large scales, the incorrect α1 value would not characterize properly the short scale behavior of the dynamical system.

Driven progressive evolution of genome sequence complexity in Cyanobacteria.

Progressive evolution, or the tendency towards increasing complexity, is a controversial issue in biology, which resolution entails a proper measurement of complexity. Genomes are the best entities to address this challenge, as they encode the historical information of a species' biotic and environmental interactions. As a case study, we have measured genome sequence complexity in the ancient phylum Cyanobacteria. To arrive at an appropriate measure of genome sequence complexity, we have chosen metrics that do not decipher biological functionality but that show strong phylogenetic signal. Using a ridge regression of those metrics against root-to-tip distance, we detected positive trends towards higher complexity in three of them. Lastly, we applied three standard tests to detect if progressive evolution is passive or driven-the minimum, ancestor-descendant, and sub-clade tests. These results provide evidence for driven progressive evolution at the genome-level in the phylum Cyanobacteria.

Transforming Gaussian correlations. Applications to generating long-range power-law correlated time series with arbitrary distribution.

The observable outputs of many complex dynamical systems consist of time series exhibiting autocorrelation functions of great diversity of behaviors, including long-range power-law autocorrelation functions, as a signature of interactions operating at many temporal or spatial scales. Often, numerical algorithms able to generate correlated noises reproducing the properties of real time series are used to study and characterize such systems. Typically, many of those algorithms produce a Gaussian time series. However, the real, experimentally observed time series are often non-Gaussian and may follow distributions with a diversity of behaviors concerning the support, the symmetry, or the tail properties. It is always possible to transform a correlated Gaussian time series into a time series with a different marginal distribution, but the question is how this transformation affects the behavior of the autocorrelation function. Here, we study analytically and numerically how the Pearson's correlation of two Gaussian variables changes when the variables are transformed to follow a different destination distribution. Specifically, we consider bounded and unbounded distributions, symmetric and non-symmetric distributions, and distributions with different tail properties from decays faster than exponential to heavy-tail cases including power laws, and we find how these properties affect the correlation of the final variables. We extend these results to a Gaussian time series, which are transformed to have a different marginal distribution, and show how the autocorrelation function of the final non-Gaussian time series depends on the Gaussian correlations and on the final marginal distribution. As an application of our results, we propose how to generalize standard algorithms producing a Gaussian power-law correlated time series in order to create a synthetic time series with an arbitrary distribution and controlled power-law correlations. Finally, we show a practical example of this algorithm by generating time series mimicking the marginal distribution and the power-law tail of the autocorrelation function of real time series: the absolute returns of stock prices.

Aplicación de tecnologías disruptivas en telemedicina para la cobertura universal de servicios de salud / Application of disruptive technologies in telemedicine for universal coverage of health services

La aplicación de tecnologías disruptivas en telemedicina facilita la accesibilidad a tecnologías diagnósticas de poblaciones remotas sin acceso a especialistas y mejora la cobertura universal de servicios de salud. Este estudio realizado por la Unidad de Telemedicina del Ministerio de Salud Pública y Bienestar Social (MSPBS) en colaboración con el Dpto. de Ingeniería Biomédica e Imágenes del Instituto de Investigaciones en Ciencias (IICS-UNA) sirvió para evaluar la utilidad de aplicaciones de tecnologías disruptivas en telemedicina para la cobertura universal de servicios de salud. Para el efecto se analizaron los resultados obtenidos por la red de telediagnóstico implementado en 67 hospitales del MSPBS. En dicho sentido se analizaron 540.397 diagnósticos remotos realizados entre enero del 2014 y septiembre de 2019. Del total, el 33,174 % (179.274) correspondieron a estudios de tomografía, 64,825 % (350.313) a electrocardiografía (ECG), 1,997 % (10.791) a electroencefalografía (EEG) y 0,004 % (19) a ecografía. La concordancia entre el diagnostico remoto y el diagnóstico "cara a cara" fue del 95 %. Con el diagnostico remoto se logró una reducción del coste que supone un beneficio importante para cada ciudadano del interior del país. Los resultados obtenidos evidencian que la aplicación de tecnologías disruptivas en telemedicina puede contribuir para la cobertura universal de servicios con tecnologías diagnósticas, maximizando el tiempo y productividad del profesional, aumentando el acceso y la equidad, y disminuyendo los costos. Sin embargo antes de su implementación generalizada se deberá contextualizar con el perfil epidemiológico regional. Palabras claves: tecnología disruptiva, aplicación, tecnología diagnóstica, telemedicina, cobertura universal, servicios de salud, innovación tecnológica.

Introduction: The application of disruptive technologies in telemedicine facilitates accessibility to diagnostic technologies of remote populations without access to specialists and improves universal coverage of health services. This study was carried out by the Telemedicine Unit of the Ministry of Public Health and Social Welfare (MSPBS) in collaboration with the Department of Biomedical Engineering and Imaging of the Institute of Research in Sciences (IICS-UNA). Objective: to evaluate the usefulness of disruptive technology applications in telemedicine for universal coverage of health services January 2014 to September 2019. Material and Method: observational and descriptive design study included 540,397 patients. For this purpose, the results obtained by the telediagnostic network implemented in 67 MSPBS hospitals were analyzed. In this regard, 540,397 remote diagnoses carried out between January 2014 and September 2019 were analysed. Results: of the total, 33.174% (179,274) were CT studies, 64.825% (350,313) electrocardiography (ECG), 1.997% (10,791) electroencephalography (EEG) and 0.004% (19) ultrasound. The concordance between remote diagnosis and "face-to-face" diagnosis was 95%. Conclusion: remote diagnosis achieved a cost reduction that is an important benefit for every citizen of the interior of the country. The results show that the application of disruptive technologies in telemedicine can contribute to the universal coverage of services with diagnostic technologies, maximizing the time and productivity of the professional, increasing access and equity, and lowering costs. However, prior to widespread implementation, the regional epidemiological profile should be contextualized. Keywords: Disruptive technology, application, diagnostic technology, telemedicine, universal coverage, health services, technological innovation.

Critical Dynamics and Coupling in Bursts of Cortical Rhythms Indicate Non-Homeostatic Mechanism for Sleep-Stage Transitions and Dual Role of VLPO Neurons in Both Sleep and Wake.

Origin and functions of intermittent transitions among sleep stages, including brief awakenings and arousals, constitute a challenge to the current homeostatic framework for sleep regulation, focusing on factors modulating sleep over large time scales. Here we propose that the complex micro-architecture characterizing sleep on scales of seconds and minutes results from intrinsic non-equilibrium critical dynamics. We investigate θ- and δ-wave dynamics in control rats and in rats where the sleep-promoting ventrolateral preoptic nucleus (VLPO) is lesioned (male Sprague-Dawley rats). We demonstrate that bursts in θ and δ cortical rhythms exhibit complex temporal organization, with long-range correlations and robust duality of power-law (θ-bursts, active phase) and exponential-like (δ-bursts, quiescent phase) duration distributions, features typical of non-equilibrium systems self-organizing at criticality. We show that such non-equilibrium behavior relates to anti-correlated coupling between θ- and δ-bursts, persists across a range of time scales, and is independent of the dominant physiologic state; indications of a basic principle in sleep regulation. Further, we find that VLPO lesions lead to a modulation of cortical dynamics resulting in altered dynamical parameters of θ- and δ-bursts and significant reduction in θ-δ coupling. Our empirical findings and model simulations demonstrate that θ-δ coupling is essential for the emerging non-equilibrium critical dynamics observed across the sleep-wake cycle, and indicate that VLPO neurons may have dual role for both sleep and arousal/brief wake activation. The uncovered critical behavior in sleep- and wake-related cortical rhythms indicates a mechanism essential for the micro-architecture of spontaneous sleep-stage and arousal transitions within a novel, non-homeostatic paradigm of sleep regulation.SIGNIFICANCE STATEMENT We show that the complex micro-architecture of sleep-stage/arousal transitions arises from intrinsic non-equilibrium critical dynamics, connecting the temporal organization of dominant cortical rhythms with empirical observations across scales. We link such behavior to sleep-promoting neuronal population, and demonstrate that VLPO lesion (model of insomnia) alters dynamical features of θ and δ rhythms, and leads to significant reduction in θ-δ coupling. This indicates that VLPO neurons may have dual role for both sleep and arousal/brief wake control. The reported empirical findings and modeling simulations constitute first evidences of a neurophysiological fingerprint of self-organization and criticality in sleep- and wake-related cortical rhythms; a mechanism essential for spontaneous sleep-stage and arousal transitions that lays the bases for a novel, non-homeostatic paradigm of sleep regulation.

Comparison of methods for the assessment of nonlinearity in short-term heart rate variability under different physiopathological states.

Despite the widespread diffusion of nonlinear methods for heart rate variability (HRV) analysis, the presence and the extent to which nonlinear dynamics contribute to short-term HRV are still controversial. This work aims at testing the hypothesis that different types of nonlinearity can be observed in HRV depending on the method adopted and on the physiopathological state. Two entropy-based measures of time series complexity (normalized complexity index, NCI) and regularity (information storage, IS), and a measure quantifying deviations from linear correlations in a time series (Gaussian linear contrast, GLC), are applied to short HRV recordings obtained in young (Y) and old (O) healthy subjects and in myocardial infarction (MI) patients monitored in the resting supine position and in the upright position reached through head-up tilt. The method of surrogate data is employed to detect the presence and quantify the contribution of nonlinear dynamics to HRV. We find that the three measures differ both in their variations across groups and conditions and in the percentage and strength of nonlinear HRV dynamics. NCI and IS displayed opposite variations, suggesting more complex dynamics in O and MI compared to Y and less complex dynamics during tilt. The strength of nonlinear dynamics is reduced by tilt using all measures in Y, while only GLC detects a significant strengthening of such dynamics in MI. A large percentage of detected nonlinear dynamics is revealed only by the IS measure in the Y group at rest, with a decrease in O and MI and during T, while NCI and GLC detect lower percentages in all groups and conditions. While these results suggest that distinct dynamic structures may lie beneath short-term HRV in different physiological states and pathological conditions, the strong dependence on the measure adopted and on their implementation suggests that physiological interpretations should be provided with caution.

Telemedicina en el Paraguay: Aportes del Instituto de Investigaciones en Ciencias de la Salud, Universidad Nacional de Asunción (IICS-UNA) / Telemedicine in Paraguay: Contributions of the Institute of Research in Health Sciences, National University of Asunción (IICS-UNA)

Entendemos a la Telemedicina como un sistema de prestación de servicios que tiene como objetivo principal apoyar a la medicina por medio de la tecnología. En el Paraguay existen áreas rurales o remotas de difícil acceso en donde no llegan los servicios especializados que muchas veces son necesarios en esas comunidades, la Telemedicina se convirtió en una herramienta muy eficaz para dar una solución confiable, eficaz y barata. Este artículo pretende evidenciar los aportes del grupo de investigación del Departamento de Ingeniería Biomédica e Imágenes (IBI) del Instituto de Investigaciones en Ciencias de la Salud de la Universidad Nacional de Asunción (IICS-UNA) desde 1999 y en colaboración estratégica con otras instituciones, en cuanto al desarrollo y aplicaciones de la Telemedicina en el Paraguay. Para eso se realizó una revisión histórica de dichos aportes en las diferentes aplicaciones que comprenden la Telemedicina; Telediagnóstico, Telemática y en Teleeducación. El producto de más impacto a nivel de la salud pública indudablemente es el Sistema Nacional de Telemedicina, que desde 2014 y hasta la fecha presenta más de 400.000 diagnósticos especializados -Tele ecografías, Tele electrocardiografía, Tele electroencefalografía, Tele tomografía - en las regiones más necesitadas del país(AU)