Aporte a la estandarización de un extracto de cálices de Physalis peruviana

Physalis peruviana es una planta utilizada en la medicina tradicional por sus propiedades antioxidantes las cuales se le atribuyen a los compuestos fenólicos que se encuentran en la misma. En el presente trabajo se evaluó el efecto de algunas variables del proceso de extracción de cálices de P. peruviana sobre el contenido de fenoles totales, contenido de rutina y actividad antioxidante in vitro del extracto obtenido, así como la influencia de las condiciones de almacenamiento y factores de estrés sobre dichas variables respuesta. El primer paso fue la evaluación de la influencia de determinados factores del proceso de extracción sobre las variables respuesta mencionadas, en éste primer paso es de suma importancia realizar una estandarización del proceso de extracción que permita tener uniformidad en los resultados, para lo cual el material vegetal recolectado en tres regiones de Colombia fue sometido a extracción por percolación con variación en determinados factores de extracción. Posteriormente se llevó a cabo la evaluación de las variables respuesta por medio de ensayos in vitro como la captación de óxido nítrico y DPPH (1,1-difenil-2-picril-hidrazilo) en el caso de la actividad antioxidante, metodología Folin-Ciocalteu para el contenido de fenoles totales y la cuantificación de rutina se utilizó cromatografía líquida de alta eficiencia CLAE. Posteriormente se realizaron estudios de condiciones de almacenamiento para lo cual los extractos fueron almacenados bajo condiciones ambientales 14 ± 8ºC, 72 % HR y en cámara de almacenamiento 30 ± 2ºC, 65 ± 5% HR por un periodo de cuatro semanas en ambas condiciones. Finalmente se llevaron a cabo los ensayos de estrés donde las muestras fueron sometidas a hidrólisis ácida y alcalina así como también a oxidación. Cómo resultado se logró determinar que las condiciones óptimas para obtener los mejores resultados en las variables respuesta evaluadas fueron solvente de extracción (etanol al 70%) y tiempo de percolación de 72 horas; la región de origen del material vegetal también fue un factor importante para obtener los mejores resultados. Una vez establecidas las mejores condiciones se comprobó que el proceso de extracción fuera reproducible elaborando tres lotes y caracterizando las variables respuestas evaluadas. En cuanto a los estudios de condiciones de almacenamiento se logró determinar que la actividad antioxidante, contenido de rutina y los fenoles totales no tuvieron variaciones significativas bajo la condiciones de almacenamiento ambientales 14 ± 8ºC, 72 % HR mientras que en cámara de almacenamiento en condiciones controladas 30 ± 2ºC, 65 ± 5% HR sí se presentaron variaciones significativas en el contenido de rutina. En los ensayos de estrés, los resultados mostraron una importante disminución en las variables respuesta evaluadas principalmente con la hidrólisis alcalina.Abstract. Physalis peruviana is a plant used in traditional medicine for its antioxidant properties, which are attributed to their phenolic compounds. In this work, it was evaluated the effect of some variables of the extraction process of calyces of Physalis peruviana on the total phenolic content, rutin quantity and in vitro antioxidant activity of the extract obtained, Also it was evaluated the influence of and the storage conditions and some stress factors on the same response variables. At first was to evaluate the influence of certain factors of the extraction process on the response variables mentioned, in this step it´s very important perform an extraction process standardization that allows having uniformity in the results. The vegetable material was harvested in three Colombian regions was subjected to extraction by percolation with variation on certain factors extraction. Subsequently it was evaluated the antioxidant activity by using in vitro assays as (degradation of sodium nitropusiate and DPPH reduction) , phenolic compounds by (Folin-Ciocalteu) and rutin quantity by (HPLC). Later were performed a study of influence of storage conditions, for which, the extracts were stored under ambient conditions 14 ± 8 ° C, 72 % HR and in a chamber 30 ± 2°C, 65 ± 5% RH for four - weeks for both conditions. Finally was performed stability study under stress conditions, where samples were subjected to acid and alkaline hydrolysis as well as oxidation. It was determined the optimal conditions to get the best results on the response variables evaluated being the extraction solvent (ethanol 70%) and percolation time of 72 hours the factors that influenced in the results; the origin region of vegetable material was also an important factor to obtain the best results. Once established the best extraction conditions was verified that the extraction process was reproducible doing three batches and characterizing the response variables evaluated. Regarding storage conditions studies it was possible to determined that the antioxidant activity, the rutin quantity and total phenols had no significant changes under ambient storage conditions 14 ± 8°C, 72% HR while on camera storage under controlled conditions 30 ± 2°C, 65 ± 5% RH significant variations occurred in rutin quantity. In stability study under stress conditions, the results showed a significant decrease in the response variables evaluated primarily with alkaline hydrolysis.Maestrí

At-admission prediction of mortality and pulmonary embolism in an international cohort of hospitalised patients with COVID-19 using statistical and machine learning methods

By September 2022, more than 600 million cases of SARS-CoV-2 infection have been reported globally, resulting in over 6.5 million deaths. COVID-19 mortality risk estimators are often, however, developed with small unrepresentative samples and with methodological limitations. It is highly important to develop predictive tools for pulmonary embolism (PE) in COVID-19 patients as one of the most severe preventable complications of COVID-19. Early recognition can help provide life-saving targeted anti-coagulation therapy right at admission. Using a dataset of more than 800,000 COVID-19 patients from an international cohort, we propose a cost-sensitive gradient-boosted machine learning model that predicts occurrence of PE and death at admission. Logistic regression, Cox proportional hazards models, and Shapley values were used to identify key predictors for PE and death. Our prediction model had a test AUROC of 75.9% and 74.2%, and sensitivities of 67.5% and 72.7% for PE and all-cause mortality respectively on a highly diverse and held-out test set. The PE prediction model was also evaluated on patients in UK and Spain separately with test results of 74.5% AUROC, 63.5% sensitivity and 78.9% AUROC, 95.7% sensitivity. Age, sex, region of admission, comorbidities (chronic cardiac and pulmonary disease, dementia, diabetes, hypertension, cancer, obesity, smoking), and symptoms (any, confusion, chest pain, fatigue, headache, fever, muscle or joint pain, shortness of breath) were the most important clinical predictors at admission. Age, overall presence of symptoms, shortness of breath, and hypertension were found to be key predictors for PE using our extreme gradient boosted model. This analysis based on the, until now, largest global dataset for this set of problems can inform hospital prioritisation policy and guide long term clinical research and decision-making for COVID-19 patients globally. Our machine learning model developed from an international cohort can serve to better regulate hospital risk prioritisation of at-risk patients

ISARIC-COVID-19 dataset: A Prospective, Standardized, Global Dataset of Patients Hospitalized with COVID-19

The International Severe Acute Respiratory and Emerging Infection Consortium (ISARIC) COVID-19 dataset is one of the largest international databases of prospectively collected clinical data on people hospitalized with COVID-19. This dataset was compiled during the COVID-19 pandemic by a network of hospitals that collect data using the ISARIC-World Health Organization Clinical Characterization Protocol and data tools. The database includes data from more than 705,000 patients, collected in more than 60 countries and 1,500 centres worldwide. Patient data are available from acute hospital admissions with COVID-19 and outpatient follow-ups. The data include signs and symptoms, pre-existing comorbidities, vital signs, chronic and acute treatments, complications, dates of hospitalization and discharge, mortality, viral strains, vaccination status, and other data. Here, we present the dataset characteristics, explain its architecture and how to gain access, and provide tools to facilitate its use