Regresión lineal para sars-cov-2 en aguas residuales y la dinámica infecciosa de covid-19 en el baix llobregat, España

Detection of SARS-CoV-2 RNA in wastewater is helpful to identify the presence of COVID-19 in the community. This method provides additional information, cheap and indicative of the COVID-19 contagion.  The current research provides information about the Baix Llobregat case in Catalonia, Spain. Methods: This research used an open dataset from “Generalitat of Catalonia” for the Baix Llobregat. The time series of COVID-19 dynamics and COVID-19 genes in wastewater were analysed for 2020-2022.  Simpler and multiple linear regression was performed for Genes N1 and N2 in wastewater and dynamics COVID-19 variables. Hypothesis analyses use a p-value<0.05 for statistics tests. Results: Linear regression between N1 and N2 COVID-19 genes shows a high correlation for 2020 and 2021. The best corresponding variable for the N1 gene was the cumulative incidence and the best associative variable for the N2 gene was %PCR-RAT positive. In multiple linear regression, the model acceptable results when considering RNA SARS-CoV-2 and the highest epidemiologic indicators with significant values (p<0.05). Discussion: COVID-19 in water waste could be useful to determine COVID-19 dynamics in the community. In this study, Cumulative Incidence and PCR-RAT% positive showed high performance in linear regression. The graphical results admit similar trends with COVID-19 genes in water waste and epidemiologic rates for time series.La detección del ARN del SARS-CoV-2 en las aguas residuales es útil para identificar la presencia del COVID-19 en la comunidad. Este método proporciona información adicional, barata e indicativa del contagio de COVID-19.  La presente investigación estudia el caso del Baix Llobregat en Cataluña, España. Métodos: Esta investigación utilizó un conjunto de datos abiertos de la "Generalitat de Cataluña" para el Baix Llobregat. Se analizaron las series temporales de la dinámica de COVID-19 y de los genes de COVID-19 en las aguas residuales para 2020-2022.  Se realizó una regresión lineal simple y múltiple para las variables Genes N1 y N2 en aguas residuales y los indicadores epidemiológicos de COVID-19. Se utilizó un valor p<0,05 para los análisis estadísticos. Resultados: La regresión lineal entre los genes N1 y N2 de COVID-19 muestra una alta correlación para 2020 y 2021. La variable con mejor correlación para el gen N1 fue la incidencia acumulada y la mejor variable asociativa para el gen N2 fue el %PCR-RAT positivo. En la regresión lineal múltiple, el modelo resulta aceptable al considerar el ARN SARS-CoV-2 y los indicadores epidemiológicos más altos con valores significativos (p<0,05). Discusión: La presencia de COVID-19 en aguas residuales podría ser útil para determinar la dinámica de COVID-19 en la comunidad. En este estudio, la incidencia acumulada y el PCR-RAT% positivo mostraron un alto rendimiento en la regresión lineal. Los resultados gráficos revelan tendencias similares con los genes de COVID-19 en los residuos del agua y las tasas epidemiológicas para las series temporale

Predicción con modelo ARIMA en series temporales de Salmonella spp en Chile entre 2014-2022

Introduction. Foodborne Diseases (ETA) constitute a public health problem of worldwide relevance, being a reason for epidemiological surveillance. ETA are the result of the consumption of foods that contain toxins or pathogenic microorganisms. The objective of this study is to analyze the time series of Salmonella spp. for 2014-2022 in Chile and to perform a predictive model of Autoregressive Moving Average  (ARIMA).Methods. The decomposition of the series was performed to study trends and seasonality. The Dickey-Fuller test was used for seasonality and the Kruskall-Wallis test for group comparison. The ARIMA model was applied to make a prediction of cases in a year ahead. Results. The study series for Salmonella spp. had a seasonal performance without significant differences between groups (periods). The ARIMA model performed well in predicting cases in a continuous series. Conclusions. The analysis of time series in epidemiology is a valuable tool to anticipate future outbreaks or epidemics in the national territory. The ARIMA model has a satisfactory performance in the seasonal series analyzed for Salmonella spp in deposition samples.Introducción. Las Enfermedades Transmitidas por Alimentos ( ETA), constituyen un problema de salud pública de relevancia mundial, siendo motivo de vigilancia epidemiológica y son el resultado del consumo de alimentos que contienen toxinas o microorganismos patógenos vivos. El objetivo de este estudio es analizar las series temporales de Salmonella spp. para el periodo 2014-2022 en Chile y desarrollar un modelo predictivo de Media Móvil Autorregresiva  (ARIMA). Métodos. Se realizó una descomposición de la serie para estudiar tendencia y estacionalidad. Se utilizó la prueba de Dickey-Fuller para estacionalidad y Kruskall-Wallis para comparación de grupos. Se aplicó el modelo ARIMA para realizar una predicción de casos en un año adelante. Resultados. La serie de estudio para Salmonella spp. tuvo un comportamiento estacional sin diferencias significativas entre grupos (periodos). El modelo ARIMA tuvo un buen desempeño para predecir casos en una serie continua. Conclusiones. El análisis de series temporales en epidemiología es una herramienta valiosa para prever futuros brotes o epidemias en el territorio nacional. El modelo ARIMA tiene un buen desempeño en la serie estacional analizada para Salmonella spp en muestras de deposición

Hemoglobina glicosilada en población diabética en periodo de pandemia covid-19 en un centro de atención primaria

Introduction. Type 2 diabetes mellitus (DM2) is a metabolic disease that affects all aspects of the individual and family life of the person who suffers from it. The pandemic caused by the SARS-COV-2 virus has trigged a relevant problem at the level of the health system, causing a significant overload and a new multi-complexity in the patient treatment. The foregoing problem has led many people to lose the doctor visits and worsen their self-care in health. Methods. The Wilcoxon test was performed to compare groups of patients and the continuous variables. A simpler linear regression model was applied to study the association between glycemia and glycosylated haemoglobin. A p-value <0.05 was considered to accept the alternative hypothesis for the statistical tests. Results. Significant differences were founded between the population with glycosylated haemoglobin levels less than 9% and a level greater than or equal to 9% in variables such as glycemia, total cholesterol, HDL cholesterol, LDL cholesterol, and triglycerides. In the linear regression model, R2 (0.61) and   (0.22) were obtained between glycemia and glycosylated haemoglobin with statistical significance at all levels (p-value <0.05). Conclusions. A poor adjustment of HBA1C levels. in the population with DM2, could generate a series of comorbidities such as dyslipidaemia, hypertension, cardiovascular disease, or acute myocardial infarction because of glucotoxicity and lipotoxicity.Introducción. La diabetes mellitus tipo 2 (DM2) es una enfermedad metabólica que afecta todas las aristas de la vida individual y familiar de la persona que la padece. La pandemia causada por el virus de SARS-COV-2 ha generado un problema relevante a nivel del sistema de salud, provocando una sobrecarga importante y una complejizarían de los servicios para atender la infección. Lo anterior, ha llevado a que muchas personas pierdan sus controles crónicos y no puedan cuidarse de manera adecuada. Métodos. La prueba de Wilcoxon se utilizó para comparar grupos de pacientes y variables continuas. Se aplicó un modelo de regresión lineal para estudiar la asociación entre la glicemia y la hemoglobina glicosilada. Se consideró un valor p <0.05 para aceptar la hipótesis alternativa de las pruebas estadísticas. Resultados. Se encontraron diferencias significativas entre población con niveles inferiores a 9% de hemoglobina glicosilada y niveles superiores o iguales a 9% en variables como glicemia, colesterol total, colesterol HDL, colesterol LDL y triglicéridos. En el modelo de regresión lineal se reportó R2  (0.61) y  (0.22) entre la glicemia y la hemoglobina glicosilada con significancia estadística en todos los niveles (valor p<0.05). Conclusiones.  Un mal ajuste de los niveles de HBA1C en población con DM2 podría generar una serie de comorbilidades como dislipidemias, hipertensión, enfermedad cardiovascular o infarto agudo al miocardio producto de la glucotoxicidad y lipotoxicidad

Development and validation of a score to predict postoperative respiratory failure in a multicentre European cohort : A prospective, observational study

BACKGROUND Postoperative respiratory failure (PRF) is the most frequent respiratory complication following surgery. OBJECTIVE The objective of this study was to build a clinically useful predictive model for the development of PRF. DESIGN A prospective observational study of a multicentre cohort. SETTING Sixty-three hospitals across Europe. PATIENTS Patients undergoing any surgical procedure under general or regional anaesthesia during 7-day recruitment periods. MAIN OUTCOME MEASURES Development of PRF within 5 days of surgery. PRF was defined by a partial pressure of oxygen in arterial blood (PaO2) less than 8 kPa or new onset oxyhaemoglobin saturation measured by pulse oximetry (SpO(2)) less than 90% whilst breathing room air that required conventional oxygen therapy, noninvasive or invasive mechanical ventilation. RESULTS PRF developed in 224 patients (4.2% of the 5384 patients studied). In-hospital mortality [95% confidence interval (95% CI)] was higher in patients who developed PRF [10.3% (6.3 to 14.3) vs. 0.4% (0.2 to 0.6)]. Regression modelling identified a predictive PRF score that includes seven independent risk factors: low preoperative SpO(2); at least one preoperative respiratory symptom; preoperative chronic liver disease; history of congestive heart failure; open intrathoracic or upper abdominal surgery; surgical procedure lasting at least 2 h; and emergency surgery. The area under the receiver operating characteristic curve (c-statistic) was 0.82 (95% CI 0.79 to 0.85) and the Hosmer-Lemeshow goodness-of-fit statistic was 7.08 (P = 0.253). CONCLUSION A risk score based on seven objective, easily assessed factors was able to predict which patients would develop PRF. The score could potentially facilitate preoperative risk assessment and management and provide a basis for testing interventions to improve outcomes. The study was registered at ClinicalTrials.gov (identifier NCT01346709)

Development of a prediction model for postoperative pneumonia A multicentre prospective observational study

BACKGROUND Postoperative pneumonia is associated with increased morbidity, mortality and costs. Prediction models of pneumonia that are currently available are based on retrospectively collected data and administrative coding systems. OBJECTIVE To identify independent variables associated with the occurrence of postoperative pneumonia. DESIGN A prospective observational study of a multicentre cohort (Prospective Evaluation of a RIsk Score for postoperative pulmonary COmPlications in Europe database). SETTING Sixty-three hospitals in Europe. PATIENTS Patients undergoing surgery under general and/or regional anaesthesia during a 7-day recruitment period. MAIN OUTCOME MEASURE The primary outcome was postoperative pneumonia. Definition: the need for treatment with antibiotics for a respiratory infection and at least one of the following criteria: new or changed sputum; new or changed lung opacities on a clinically indicated chest radiograph; temperature more than 38.3 degrees C; leucocyte count more than 12 000 mu l(-1). RESULTS Postoperative pneumonia occurred in 120 out of 5094 patients (2.4%). Eighty-two of the 120 (68.3%) patients with pneumonia required ICU admission, compared with 399 of the 4974 (8.0%) without pneumonia (P < 0.001). We identified five variables independently associated with postoperative pneumonia: functional status [odds ratio (OR) 2.28, 95% confidence interval (CI) 1.58 to 3.12], pre-operative SpO(2) values while breathing room air (OR 0.83, 95% CI 0.78 to 0.84), intra-operative colloid administration (OR 2.97, 95% CI 1.94 to 3.99), intra-operative blood transfusion (OR 2.19, 95% CI 1.41 to 4.71) and surgical site (open upper abdominal surgery OR 3.98, 95% CI 2.19 to 7.59). The model had good discrimination (c-statistic 0.89) and calibration (Hosmer-Lemeshow P = 0.572). CONCLUSION We identified five variables independently associated with postoperative pneumonia. The model performed well and after external validation may be used for risk stratification and management of patients at risk of postoperative pneumonia

DUNE Offline Computing Conceptual Design Report

This document describes Offline Software and Computing for the Deep Underground Neutrino Experiment (DUNE) experiment, in particular, the conceptual design of the offline computing needed to accomplish its physics goals. Our emphasis in this document is the development of the computing infrastructure needed to acquire, catalog, reconstruct, simulate and analyze the data from the DUNE experiment and its prototypes. In this effort, we concentrate on developing the tools and systems thatfacilitate the development and deployment of advanced algorithms. Rather than prescribing particular algorithms, our goal is to provide resources that are flexible and accessible enough to support creative software solutions as HEP computing evolves and to provide computing that achieves the physics goals of the DUNE experiment

DUNE Offline Computing Conceptual Design Report

International audienceThis document describes Offline Software and Computing for the Deep Underground Neutrino Experiment (DUNE) experiment, in particular, the conceptual design of the offline computing needed to accomplish its physics goals. Our emphasis in this document is the development of the computing infrastructure needed to acquire, catalog, reconstruct, simulate and analyze the data from the DUNE experiment and its prototypes. In this effort, we concentrate on developing the tools and systems thatfacilitate the development and deployment of advanced algorithms. Rather than prescribing particular algorithms, our goal is to provide resources that are flexible and accessible enough to support creative software solutions as HEP computing evolves and to provide computing that achieves the physics goals of the DUNE experiment

Highly-parallelized simulation of a pixelated LArTPC on a GPU

The rapid development of general-purpose computing on graphics processing units (GPGPU) is allowing the implementation of highly-parallelized Monte Carlo simulation chains for particle physics experiments. This technique is particularly suitable for the simulation of a pixelated charge readout for time projection chambers, given the large number of channels that this technology employs. Here we present the first implementation of a full microphysical simulator of a liquid argon time projection chamber (LArTPC) equipped with light readout and pixelated charge readout, developed for the DUNE Near Detector. The software is implemented with an end-to-end set of GPU-optimized algorithms. The algorithms have been written in Python and translated into CUDA kernels using Numba, a just-in-time compiler for a subset of Python and NumPy instructions. The GPU implementation achieves a speed up of four orders of magnitude compared with the equivalent CPU version. The simulation of the current induced on $10^3$ pixels takes around 1 ms on the GPU, compared with approximately 10 s on the CPU. The results of the simulation are compared against data from a pixel-readout LArTPC prototype