Evaluación fitotoxicológica del efluente de agua residual tratada en lagunas de oxidación utilizada para riego agrícola

El agua residual se considera como una mezcla compleja debido a los diversos compuestos que tiene. Por ello su tratamiento puede dificultarse debido a compuestos y/o mezclas recalcitrantes. Una forma de evaluar la eficiencia de los sistemas de tratamiento de agua residual es a través de bioensayos de toxicidad tales como los que utilizan plantas, ya que presentan las ventajas de ser de bajo costo y fáciles de realizar El objetivo del presente trabajo fue realizar un ensayo fitotoxicológico del agua residual tratada utilizada para riego agrícola en la Facultad de Agronomía de la Universidad Autónoma de Nuevo León (FAUANL) utilizando lechuga Lactuca sativa L y pepino Cucumis sativus L. Se observó que el agua en proceso de tratamiento, así como el efluente utilizado en riego agrícola, producen un efecto inhibitorio en el crecimiento de la radícula de las dos especies utilizadas. Esto sugiere que el tratamiento del agua residual en las lagunas de oxidación no es suficiente para disminuir la toxicidad del influente. Por ello se recomienda realizar evaluaciones ecotoxicológicas más extensas en el área de estudio, a fin de determinar el riesgo ecológico que representa el agua residual utilizada en riego agrícola en la FAUAN

Concentración de fructosa en frutos de toronja (Citrus paradisi Macf.) en desarrollo

El estudio se realizó en el ciclo 2014-2015, en una huerta de toronja (Citrus paradisi Macf.) variedad Rio Red, del Municipio de General Terán, N.L. México. El objetivo fue evaluar la concentración de fructosa en frutos de toronja en desarrollo procedentes de árboles de producción elevada («on») y producción escasa («off»), en un ciclo de producción en respuesta a la aplicación de ácido giberélico (Acigib 10% de GA3 , 25 ppm), urea foliar (1 kg 100 L-1) y anillado de ramas. La fructosa fue cuantificada en frutos de toronja a los 30, 60 y 90 dda (días después de antesis). El análisis de fructosa se realizó mediante Cromatografía Líquida de Alta Presión (HPLC). A los 30 dda, las aplicaciones de urea foliar y GA3 en árboles «on» resultaron en los valores más altos en la concentración de fructosa en frutos. A los 60 dda no se presentaron diferencias estadísticas entre los tratamientos. A los 90 dda los frutos de árboles «on» con urea foliar y con anillado se comportaron estadísticamente diferentes al resto de los tratamientos, con menor concentración de fructosa. El comportamiento de la concentración de fructosa en frutos de toronja entre muestreos fue de un incremento en todos los tratamientos de árboles «on» y «off». Abstract This research was conducted during the production year 2014- 15 on a 18-year in a grapefruit (Citrus paradasi Macf.) grove, Rio Red variety in the municipality of General Teran, N.L. Mexico. The objective was to evaluate the concentration of fructose in small fruits from trees with a high production year («on») and others with a low production year («off») throughout the whole productive cycle, as a result of the application of gibberellic acid (Acigib 10% of GA3 , 25 ppm), foliar urea (1 kg 100 L-1) and girdling. Fructose was quantified in fruits of grapefruit at 30, 60 and 90 days after anthesis (DDA). Fructose analysis was done by High Pressure Liquid Chromatography (HPLC). According with the results, application of foliar urea and GA3 on trees «on» at 30 DDA, resulted in the highest values in the concentration of fructose in fruit. Treatments applied at 60 DDA resulted in not statistically differences for fructose concentration in fruits. Fruit fructose concentration from samples at 90 DDA showed statistical differences in trees «on» treated with foliar urea and girdling obtained the lowest concentration of fructose. The behavior of the concentration of fructose between samples was increased in all treatments of trees «on» and «off». Keywords: Girdling, carbohydrates, citrus, gibberellic acid, foliar urea

Adaptation of the Wound Healing Questionnaire universal-reporter outcome measure for use in global surgery trials (TALON-1 study): mixed-methods study and Rasch analysis

BackgroundThe Bluebelle Wound Healing Questionnaire (WHQ) is a universal-reporter outcome measure developed in the UK for remote detection of surgical-site infection after abdominal surgery. This study aimed to explore cross-cultural equivalence, acceptability, and content validity of the WHQ for use across low- and middle-income countries, and to make recommendations for its adaptation.MethodsThis was a mixed-methods study within a trial (SWAT) embedded in an international randomized trial, conducted according to best practice guidelines, and co-produced with community and patient partners (TALON-1). Structured interviews and focus groups were used to gather data regarding cross-cultural, cross-contextual equivalence of the individual items and scale, and conduct a translatability assessment. Translation was completed into five languages in accordance with Mapi recommendations. Next, data from a prospective cohort (SWAT) were interpreted using Rasch analysis to explore scaling and measurement properties of the WHQ. Finally, qualitative and quantitative data were triangulated using a modified, exploratory, instrumental design model.ResultsIn the qualitative phase, 10 structured interviews and six focus groups took place with a total of 47 investigators across six countries. Themes related to comprehension, response mapping, retrieval, and judgement were identified with rich cross-cultural insights. In the quantitative phase, an exploratory Rasch model was fitted to data from 537 patients (369 excluding extremes). Owing to the number of extreme (floor) values, the overall level of power was low. The single WHQ scale satisfied tests of unidimensionality, indicating validity of the ordinal total WHQ score. There was significant overall model misfit of five items (5, 9, 14, 15, 16) and local dependency in 11 item pairs. The person separation index was estimated as 0.48 suggesting weak discrimination between classes, whereas Cronbach's α was high at 0.86. Triangulation of qualitative data with the Rasch analysis supported recommendations for cross-cultural adaptation of the WHQ items 1 (redness), 3 (clear fluid), 7 (deep wound opening), 10 (pain), 11 (fever), 15 (antibiotics), 16 (debridement), 18 (drainage), and 19 (reoperation). Changes to three item response categories (1, not at all; 2, a little; 3, a lot) were adopted for symptom items 1 to 10, and two categories (0, no; 1, yes) for item 11 (fever).ConclusionThis study made recommendations for cross-cultural adaptation of the WHQ for use in global surgical research and practice, using co-produced mixed-methods data from three continents. Translations are now available for implementation into remote wound assessment pathways

Liver injury in hospitalized patients with COVID-19: An International observational cohort study

Background: Using a large dataset, we evaluated prevalence and severity of alterations in liver enzymes in COVID-19 and association with patient-centred outcomes.MethodsWe included hospitalized patients with confirmed or suspected SARS-CoV-2 infection from the International Severe Acute Respiratory and emerging Infection Consortium (ISARIC) database. Key exposure was baseline liver enzymes (AST, ALT, bilirubin). Patients were assigned Liver Injury Classification score based on 3 components of enzymes at admission: Normal; Stage I) Liver injury: any component between 1-3x upper limit of normal (ULN); Stage II) Severe liver injury: any component & GE;3x ULN. Outcomes were hospital mortality, utilization of selected resources, complications, and durations of hospital and ICU stay. Analyses used logistic regression with associations expressed as adjusted odds ratios (OR) with 95% confidence intervals (CI).ResultsOf 17,531 included patients, 46.2% (8099) and 8.2% (1430) of patients had stage 1 and 2 liver injury respectively. Compared to normal, stages 1 and 2 were associated with higher odds of mortality (OR 1.53 [1.37-1.71]; OR 2.50 [2.10-2.96]), ICU admission (OR 1.63 [1.48-1.79]; OR 1.90 [1.62-2.23]), and invasive mechanical ventilation (OR 1.43 [1.27-1.70]; OR 1.95 (1.55-2.45). Stages 1 and 2 were also associated with higher odds of developing sepsis (OR 1.38 [1.27-1.50]; OR 1.46 [1.25-1.70]), acute kidney injury (OR 1.13 [1.00-1.27]; OR 1.59 [1.32-1.91]), and acute respiratory distress syndrome (OR 1.38 [1.22-1.55]; OR 1.80 [1.49-2.17]).ConclusionsLiver enzyme abnormalities are common among COVID-19 patients and associated with worse outcomes

The value of open-source clinical science in pandemic response: lessons from ISARIC

International audienc

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