PLATERO: A calibration protocol for plate reader green fluorescence measurements.

One of the most common sources of information in Synthetic Biology is the data coming from plate reader fluorescence measurements. These experiments provide a measure of the light emitted by a certain fluorescent molecule, such as the Green Fluorescent Protein (GFP). However, these measurements are generally expressed in arbitrary units and are affected by the measurement device gain. This limits the range of measurements in a single experiment and hampers the comparison of results among experiments. In this work, we describe PLATERO, a calibration protocol to express fluorescence measures in concentration units of a reference fluorophore. The protocol removes the gain effect of the measurement device on the acquired data. In addition, the fluorescence intensity values are transformed into units of concentration using a Fluorescein calibration model. Both steps are expressed in a single mathematical expression that returns normalized, gain-independent, and comparable data, even if the acquisition was done at different device gain levels. Most important, the PLATERO embeds a Linearity and Bias Analysis that provides an assessment of the uncertainty of the model estimations, and a Reproducibility and Repeatability analysis that evaluates the sources of variability originating from the measurements and the equipment. All the functions used to build the model, exploit it with new data, and perform the uncertainty and variability assessment are available in an open access repository.

Machine-learning-derived predictive score for early estimation of COVID-19 mortality risk in hospitalized patients.

The clinical course of COVID-19 is highly variable. It is therefore essential to predict as early and accurately as possible the severity level of the disease in a COVID-19 patient who is admitted to the hospital. This means identifying the contributing factors of mortality and developing an easy-to-use score that could enable a fast assessment of the mortality risk using only information recorded at the hospitalization. A large database of adult patients with a confirmed diagnosis of COVID-19 (n = 15,628; with 2,846 deceased) admitted to Spanish hospitals between December 2019 and July 2020 was analyzed. By means of multiple machine learning algorithms, we developed models that could accurately predict their mortality. We used the information about classifiers' performance metrics and about importance and coherence among the predictors to define a mortality score that can be easily calculated using a minimal number of mortality predictors and yielded accurate estimates of the patient severity status. The optimal predictive model encompassed five predictors (age, oxygen saturation, platelets, lactate dehydrogenase, and creatinine) and yielded a satisfactory classification of survived and deceased patients (area under the curve: 0.8454 with validation set). These five predictors were additionally used to define a mortality score for COVID-19 patients at their hospitalization. This score is not only easy to calculate but also to interpret since it ranges from zero to eight, along with a linear increase in the mortality risk from 0% to 80%. A simple risk score based on five commonly available clinical variables of adult COVID-19 patients admitted to hospital is able to accurately discriminate their mortality probability, and its interpretation is straightforward and useful.

TiO2 Nanostructures for Photoelectrocatalytic Degradation of Acetaminophen.

Advanced oxidation processes driven by renewable energy sources are gaining attention in degrading organic pollutants in waste waters in an efficient and sustainable way. The present work is focused on a study of TiO2 nanotubes as photocatalysts for photoelectrocatalytic (PEC) degradation of acetaminophen (AMP) at different pH (3, 7, and 9). In particular, different TiO2 photocatalysts were synthetized by stirring the electrode at different Reynolds numbers (Res) during electrochemical anodization. The morphology of the photocatalysts and their crystalline structure were evaluated by field emission scanning electron microscopy (FESEM) and Raman confocal laser microscopy (RCLM). These analyses revealed that anatase TiO2 nanotubes were obtained after anodization. In addition, photocurrent densities versus potential curves were performed in order to characterize the electrochemical properties of the photocatalysts. These results showed that increasing the Re during anodization led to an enhancement in the obtained photocurrents, since under hydrodynamic conditions part of the initiation layer formed over the tubes was removed. PEC degradation of acetaminophen was followed by ultraviolet-visible absorbance measurements and chemical oxygen demand tests. As drug mineralization was the most important issue, total organic carbon measurements were also carried out. The statistical significance analysis established that acetaminophen PEC degradation improved as hydrodynamic conditions linearly increased in the studied range (Re from 0 to 600). Additionally, acetaminophen conversion had a quadratic behavior with respect to the reaction pH, where the maximum conversion value was reached at pH 3. However, in this case, the diversity of the byproducts increased due to a different PEC degradation mechanism.