Progress in the biological and chemical treatment technologies for emerging contaminant removal from wastewater: A critical review

© 2016 Elsevier B.V. This review focuses on the removal of emerging contaminants (ECs) by biological, chemical and hybrid technologies in effluents from wastewater treatment plants (WWTPs). Results showed that endocrine disruption chemicals (EDCs) were better removed by membrane bioreactor (MBR), activated sludge and aeration processes among different biological processes. Surfactants, EDCs and personal care products (PCPs) can be well removed by activated sludge process. Pesticides and pharmaceuticals showed good removal efficiencies by biological activated carbon. Microalgae treatment processes can remove almost all types of ECs to some extent. Other biological processes were found less effective in ECs removal from wastewater. Chemical oxidation processes such as ozonation/H2O2, UV photolysis/H2O2 and photo-Fenton processes can successfully remove up to 100% of pesticides, beta blockers and pharmaceuticals, while EDCs can be better removed by ozonation and UV photocatalysis. Fenton process was found less effective in the removal of any types of ECs. A hybrid system based on ozonation followed by biological activated carbon was found highly efficient in the removal of pesticides, beta blockers and pharmaceuticals. A hybrid ozonation-ultrasound system can remove up to 100% of many pharmaceuticals. Future research directions to enhance the removal of ECs have been elaborated

Alzheimer's disease: using gene/protein network machine learning for molecule discovery in olive oil

Alzheimer's disease (AD) poses a profound human, social, and economic burden. Previous studies suggest that extra virgin olive oil (EVOO) may be helpful in preventing cognitive decline. Here, we present a network machine learning method for identifying bioactive phytochemicals in EVOO with the highest potential to impact the protein network linked to the development and progression of the AD. A balanced classification accuracy of 70.3 ± 2.6% was achieved in fivefold cross-validation settings for predicting late-stage experimental drugs targeting AD from other clinically approved drugs. The calibrated machine learning algorithm was then used to predict the likelihood of existing drugs and known EVOO phytochemicals to be similar in action to the drugs impacting AD protein networks. These analyses identified the following ten EVOO phytochemicals with the highest likelihood of being active against AD: quercetin, genistein, luteolin, palmitoleate, stearic acid, apigenin, epicatechin, kaempferol, squalene, and daidzein (in the order from the highest to the lowest likelihood). This in silico study presents a framework that brings together artificial intelligence, analytical chemistry, and omics studies to identify unique therapeutic agents. It provides new insights into how EVOO constituents may help treat or prevent AD and potentially provide a basis for consideration in future clinical studies

An AI-powered patient triage platform for future viral outbreaks using COVID-19 as a disease model

Over the last century, outbreaks and pandemics have occurred with disturbing regularity, necessitating advance preparation and large-scale, coordinated response. Here, we developed a machine learning predictive model of disease severity and length of hospitalization for COVID-19, which can be utilized as a platform for future unknown viral outbreaks. We combined untargeted metabolomics on plasma data obtained from COVID-19 patients (n = 111) during hospitalization and healthy controls (n = 342), clinical and comorbidity data (n = 508) to build this patient triage platform, which consists of three parts: (i) the clinical decision tree, which amongst other biomarkers showed that patients with increased eosinophils have worse disease prognosis and can serve as a new potential biomarker with high accuracy (AUC = 0.974), (ii) the estimation of patient hospitalization length with ± 5 days error (R2 = 0.9765) and (iii) the prediction of the disease severity and the need of patient transfer to the intensive care unit. We report a significant decrease in serotonin levels in patients who needed positive airway pressure oxygen and/or were intubated. Furthermore, 5-hydroxy tryptophan, allantoin, and glucuronic acid metabolites were increased in COVID-19 patients and collectively they can serve as biomarkers to predict disease progression. The ability to quickly identify which patients will develop life-threatening illness would allow the efficient allocation of medical resources and implementation of the most effective medical interventions. We would advocate that the same approach could be utilized in future viral outbreaks to help hospitals triage patients more effectively and improve patient outcomes while optimizing healthcare resources

Determination of chromium by electrothermal atomic absorption spectrometry with various chemical modifiers

The determination of Cr in the presence of various isomorphous metals has been studied. The atomic absorption signal for Cr was increased and stabilized by the presence of 20 mu g of Mg(NO3)(2), 1 mu g Rh and 1 mu g Pt. Magnesium, Rh and Pt gave comparable characteristic masses of 3.2, 3.0 and 2.8 pg, respectively, when integrated absorbance was measured. The limits of detection were .0.18, 0.14 and 0.091 mu g l(-1), respectively, The efficiency of these modifiers was tested with the direct determination of Cr in rain-water and serum samples, Quantification was performed with aqueous standards in the-case of the rain-water samples, and with matrix-matched standards in the case of the serum samples. Recovery tests and a serum reference material were used to check the accuracy of the proposed methods, Accurate results and good agreement with the certified serum values were found in the presence of platinum as a modifier, Chemical modifiers were not necessary for the determination of Cr in rain water