Monitoring emergence of SARS-CoV-2 B.1.1.7 Variant through the Spanish National SARS-CoV-2 Wastewater Surveillance System (VATar COVID-19) from December 2020 to March 2021

BackgroundSince its first identification in the United Kingdom in late 2020, the highly transmissible B.1.1.7 variant of SARS-CoV-2, become dominant in several European countries raising great concern. AimThe aim of this study was to develop a duplex real-time RT-qPCR assay to detect, discriminate and quantitate SARS-CoV-2 variants containing one of its mutation signatures, the {Delta}HV69/70 deletion, to trace the community circulation of the B.1.1.7 variant in Spain through the Spanish National SARS-CoV-2 Wastewater Surveillance System (VATar COVID-19). ResultsB.1.1.7 variant was first detected in sewage from the Southern city of Malaga (Andalucia) in week 20_52, and multiple introductions during Christmas holidays were inferred in different parts of the country, earlier than clinical epidemiological reporting by the local authorities. Wastewater-based B.1.1.7 tracking showed a good correlation with clinical data and provided information at the local level. Data from WWTPs which reached B.1.1.7 prevalences higher than 90% for [≥] 2 consecutive weeks showed that 8.1{+/-}1.8 weeks were required for B.1.1.7 to become dominant. ConclusionThe study highlights the applicability of RT-qPCR-based strategies to track specific mutations of variants of concern (VOCs) as soon as they are identified by clinical sequencing, and its integration into existing wastewater surveillance programs, as a cost-effective approach to complement clinical testing during the COVID-19 pandemic.

Development of a severity of disease score and classification model by machine learning for hospitalized COVID-19 patients

BACKGROUNDEfficient and early triage of hospitalized Covid-19 patients to detect those with higher risk of severe disease is essential for appropriate case management. METHODSWe trained, validated, and externally tested a machine-learning model to early identify patients who will die or require mechanical ventilation during hospitalization from clinical and laboratory features obtained at admission. A development cohort with 918 Covid-19 patients was used for training and internal validation, and 352 patients from another hospital were used for external testing. Performance of the model was evaluated by calculating the area under the receiver-operating-characteristic curve (AUC), sensitivity and specificity. RESULTSA total of 363 of 918 (39.5%) and 128 of 352 (36.4%) Covid-19 patients from the development and external testing cohort, respectively, required mechanical ventilation or died during hospitalization. In the development cohort, the model obtained an AUC of 0.85 (95% confidence interval [CI], 0.82 to 0.87) for predicting severity of disease progression. Variables ranked according to their contribution to the model were the peripheral blood oxygen saturation (SpO2)/fraction of inspired oxygen (FiO2) ratio, age, estimated glomerular filtration rate, procalcitonin, C-reactive protein, updated Charlson comorbidity index and lymphocytes. In the external testing cohort, the model performed an AUC of 0.83 (95% CI, 0.81 to 0.85). This model is deployed in an open source calculator, in which Covid-19 patients at admission are individually stratified as being at high or non-high risk for severe disease progression. CONCLUSIONSThis machine-learning model, applied at hospital admission, predicts risk of severe disease progression in Covid-19 patients.

Erratum: Glycolysis, tumor metabolism, cancer growth and dissemination. A new pH-based etiopathogenic perspective and therapeutic approach to an old cancer question.

[This corrects the article on p. 777 in vol. 1, PMID: 25621294.].

Glycolysis, tumor metabolism, cancer growth and dissemination. A new pH-based etiopathogenic perspective and therapeutic approach to an old cancer question.

Cancer cells acquire an unusual glycolytic behavior relative, to a large extent, to their intracellular alkaline pH (pHi). This effect is part of the metabolic alterations found in most, if not all, cancer cells to deal with unfavorable conditions, mainly hypoxia and low nutrient supply, in order to preserve its evolutionary trajectory with the production of lactate after ten steps of glycolysis. Thus, cancer cells reprogram their cellular metabolism in a way that gives them their evolutionary and thermodynamic advantage. Tumors exist within a highly heterogeneous microenvironment and cancer cells survive within any of the different habitats that lie within tumors thanks to the overexpression of different membrane-bound proton transporters. This creates a highly abnormal and selective proton reversal in cancer cells and tissues that is involved in local cancer growth and in the metastatic process. Because of this environmental heterogeneity, cancer cells within one part of the tumor may have a different genotype and phenotype than within another part. This phenomenon has frustrated the potential of single-target therapy of this type of reductionist therapeutic approach over the last decades. Here, we present a detailed biochemical framework on every step of tumor glycolysis and then proposea new paradigm and therapeutic strategy based upon the dynamics of the hydrogen ion in cancer cells and tissues in order to overcome the old paradigm of one enzyme-one target approach to cancer treatment. Finally, a new and integral explanation of the Warburg effect is advanced.