Decolorization of kraft bleaching effluent by advanced oxidation processes using copper (II) as electron acceptor

Two advanced oxidation processes (AOPs), TiO2/UV/O2 and TiO2/UV/Cu (II), were used to remove color from a Kraft bleaching effluent. The optimal decoloration rate was determined by multivariate analysis, obtaining a mathematical model to evaluate the effect among variables. TiO2 and Cu (II) concentrations and the reaction times were optimized. The experimental design resulted in a quadratic matrix of 30 experiments. Additionally, the pH influence on the color removal was determined by multivariate analysis. Results indicate that color removal was 94% at acidic pH (3.0) in the presence of Cu (11) as an electron acceptor. Under this condition, the biodegradation of the effluent increased from 0.3 to 0.6. Moreover, 70% of COD (chemical oxygen demand) was removed, and the ecotoxicity, measured by Daphnia magna, was reduced. Photocatalytic oxidation to remove the color contained in the Kraft mill bleaching effluent was effective under the following conditions: short reaction time, acidic pH values, and without the addition of oxygen due to the presence of Cu (II) in the effluent. Moreover, residual Cu (II) was a minimum (0.05.mg L(-1)) and was not toxic to the next biological stage. The experimental design methodology indicated that a quadratic polynomial model may be used to representthe efficiencyfor degradation of the Kraft bleach pulp effluent by a photocatalytic process.     

Study of Oligonucleotides Access and Distribution in Human Peripheral Blood Mononuclear Cells

Therapeutic oligonucleotides have achieved great clinical interest since their approval as drug agents by regulatory agencies but their access and distribution in blood cells are not completely known. We evaluated by flow cytometry the ability of short fluorescent scramble oligonucleotides (ON*) to access human peripheral blood mononuclear cells (PBMC) after incubating with ON* during 1 h and 7 days of culture follow-up ‘in vitro’. Blood samples were treated with chemically modified oligonucleotides (phosphorothioate backbone and 2′ O-Me ends) to resist nuclease digestion under culture conditions. The ON* internalization was determined after discarding the membrane-associated fluorescence by trypan blue quenching. Whereas the oligonucleotide accessed neutrophils and monocytes rapidly, achieving their maximum in 1 h and 24 h, respectively, lymphocytes required 7 days to achieve the maximum (80% of cells) transfection. The ON*ability to access lymphocyte types (T, B, and NK) and T cell subtypes (CD4+, CD8+, and CD4-CD8-) were similar, with T cells being more accessible. Regulatory CD4+ and CD8+ T cells were classified in low and high Foxp3 expressers, whose expression proved not to alter the ON* internalization during the first hour, achieving 53% of CD4+Foxp3+ and 40% of CD8+Foxp3+ cells. Our results contribute to understanding and improving the management of therapeutic ONs.     

Ternary Complexes of Flurbiprofen with HP-ß-CD and Ethanolamines Characterization and Transdermal Delivery

ABSTRACT

Binary and multicomponent systems complexes prepared with HP-ß-CD and/or with monoethanolamine (MEA), diethanolamine (DEA) or triethanolamine (TEA) were obtained.

The results of solid-state studies indicated the presence of strong interactions between the components in the binary and the ternary systems. Drug solubility and dissolution rate in water were notably improved by employing the HP-ß-CD and the alkanolamines. The combined use of cosolvency and complexation with MEA in the presence of HP-ß-CD on the permeation of flurbiprofen through the human skin was evaluated. The combination of IPM, PG, and HP-ß-CD yield the highest permeation for the flurbiprofen–MEA complex.     

Detection of cardiac amyloidosis on electrocardiogram images using machine learning and deep learning techniques

Cardiac amyloidosis is an uncommon disease that has been known for a long time. Moreover, modern advancement in noninvasive imaging of heart via ultrasound, magnetic resonance imaging has enhanced the detection of secret cardiac amyloidosis in patients identified with the heart disease. This article focused on detecting the heart disease especially cardiac amyloidosis on electro cardio gram images using recent technology of both machine learning and deep learning approaches. In addition, apart from detecting the disease on images, we are categorizing the heart images as normal and cardiac amyloidosis if any deviations occur. For CA disease identification along with its classification, 300 cardiac images have taken and those images are analyzed using machine learning algorithms namely nearest centroid, gradient boosting and random forest. Several metrics such as precision, recall, f-score, sensitivity, accuracy, and confusion matrix based on binary classification which classifies the images into positive (CA) and negative (non-CA) are estimated. Among these approaches, gradient boosting method achieves 95% accuracy as better outcomes which measure the model performance in detecting cardiac amyloidosis disease as well as ECG images are categorized into either normal or abnormal via classification metrics. Furthermore, we applied deep learning based neural network “DeepNet” model is applied on augmented data along with CNN which attains 93% accuracy in CA disease identification.