A Ferrofluid with Surface Modified Nanoparticles for Magnetic Hyperthermia and High ROS Production

A ferrofluid with 1,2-Benzenediol-coated iron oxide nanoparticles was synthesized and physicochemically analyzed. This colloidal system was prepared following the typical co-precipitation method, and superparamagnetic nanoparticles of 13.5 nm average diameter, 34 emu/g of magnetic saturation, and 285 K of blocking temperature were obtained. Additionally, the zeta potential showed a suitable colloidal stability for cancer therapy assays and the magneto-calorimetric trails determined a high power absorption density. In addition, the oxidative capability of the ferrofluid was corroborated by performing the Fenton reaction with methylene blue (MB) dissolved in water, where the ferrofluid was suitable for producing reactive oxygen species (ROS), and surprisingly a strong degradation of MB was also observed when it was combined with H₂O₂. The intracellular ROS production was qualitatively corroborated using the HT-29 human cell line, by detecting the fluorescent rise induced in 2,7-dichlorofluorescein diacetate. In other experiments, cell metabolic activity was measured, and no toxicity was observed, even with concentrations of up to 4 mg/mL of magnetic nanoparticles (MNPs). When the cells were treated with magnetic hyperthermia, 80% of cells were dead at 43 °C using 3 mg/mL of MNPs and applying a magnetic field of 530 kHz with 20 kA/m amplitude.     

Ftir study of hydrogen bonding and phase behaviour in poly(vinylpyridines)/poly(ethylene-CO-vinyl alcohol) blends

The specific interactions between polyvinylpyridines and ethylene-vinyl alcohol copolymers of different compositions have been studied by Fourier transform infrared spectroscopy (FTIR). Hydrogen bonding between both polymer components leads to significant spectral modification in pyridine rings spectral bands. Minor modifications in other spectral modes have been also detected. The spectral results indicate at least three competing equilibria processes in the blends: hydroxyl-hydroxyl copolymer self-association and hydroxyl-pyridine interassociation. Painter-Coleman association model was successfully applied and the phase diagram for these systems has been predicted.     

Self-assembling and phase coexistence of SW trimers as complex amphiphile analogues. I. Simulations

We analyse by discrete molecular dynamics the self-assembly of SW trimer particles that contain a different number of attractive and repulsive spheres. They also have different geometries: linear, obtuse, rectangular and equilateral. We identify that some of these molecules exhibit liquid–vapour equilibria while others do not. For all of them, we show the morphological phase diagram built up from the different supra-molecular structures formed by each type of trimer. We simulated 14 different systems with a total of 321 states. The main features of the supra-molecular structures depend only on the composition and geometry of the trimer: triple SW trimers do not form supra-structures, double SW trimers and single SW trimers form monolayers, bilayers and worm-like micelles. The liquid–vapour coexistence properties are also exhibited. These trimers can be used to model complex amphiphiles beyond the standard ones, such as the Gemini and the Bola surfactants as well as colloidal particles.     

A Comparative Analysis of Machine Learning Techniques for Muon Count in UHECR Extensive Air-Showers

The main goal of this work is to adapt a Physics problem to the Machine Learning (ML) domain and to compare several techniques to solve it. The problem consists of how to perform muon count from the signal registered by particle detectors which record a mix of electromagnetic and muonic signals. Finding a good solution could be a building block on future experiments. After proposing an approach to solve the problem, the experiments show a performance comparison of some popular ML models using two different hadronic models for the test data. The results show that the problem is suitable to be solved using ML as well as how critical the feature selection stage is regarding precision and model complexity.     

Machine Learning Algorithm to Predict Acidemia Using Electronic Fetal Monitoring Recording Parameters

Background: Electronic fetal monitoring (EFM) is the universal method for the surveillance of fetal well-being in intrapartum. Our objective was to predict acidemia from fetal heart signal features using machine learning algorithms. Methods: A case–control 1:2 study was carried out compromising 378 infants, born in the Miguel Servet University Hospital, Spain. Neonatal acidemia was defined as pH < 7.10. Using EFM recording logistic regression, random forest and neural networks models were built to predict acidemia. Validation of models was performed by means of discrimination, calibration, and clinical utility. Results: Best performance was attained using a random forest model built with 100 trees. The discrimination ability was good, with an area under the Receiver Operating Characteristic curve (AUC) of 0.865. The calibration showed a slight overestimation of acidemia occurrence for probabilities above 0.4. The clinical utility showed that for 33% cutoff point, missing 5% of acidotic cases, 46% of unnecessary cesarean sections could be prevented. Logistic regression and neural networks showed similar discrimination ability but with worse calibration and clinical utility. Conclusions: The combination of the variables extracted from EFM recording provided a predictive model of acidemia that showed good accuracy and provides a practical tool to prevent unnecessary cesarean sections.     

Seasonal Variability of a Caryophyllane Chemotype Essential Oil of Eugenia patrisii Vahl Occurring in the Brazilian Amazon

Eugenia patrisii Vahl is a native and non-endemic myrtaceous species of the Brazilian Amazon. Due to few botanical and phytochemical reports of this species, the objective of the present work was to evaluate the seasonal variability of their leaf essential oils, performed by GC and GC-MS and chemometric analysis. The results indicated that the variation in oil yields (0.7 ± 0.1%) could be correlated with climatic conditions and rainy (R) and dry seasons (D). (E)-caryophyllene (R = 17.1% ± 16.0, D = 20.2% ± 17.7) and caryophyllene oxide (R = 30.1% ± 18.4, D = 14.1% ± 19.3) are the major constituents and did not display significant differences between the two seasons. However, statistically, a potential correlation between the main constituents of E. patrisii essential oil and the climatic parameters is possible. It was observed that the higher temperature and insolation rates and the lower humidity rate, which are characteristics of the dry season, lead to an increase in the (E)-caryophyllene contents, while lower temperature and insolation and higher humidity, which occur in the rainy season, lead to an increase in the caryophyllene oxide content. The knowledge of variations in the E. patrisii essential oil composition could help choose the best plant chemical profile for medicinal purposes.     

Multi‐responses Methodology Applied in the Electroanalytical Determination of Hair Dye by Using Printed Carbon Electrode Modified with Graphene

This paper describes a rapid and sensitive method for determination of the hair dye Basic Blue 41 in wastewater samples using screen‐printed carbon electrodes modified with graphene (SPCE/Gr). The method is based on the reversible reduction of azo groups of the dye at potential of ?0.23 V/?0.26 V, where both the anodic and cathodic currents increased 1,300 % when compared to screen‐printed carbon (SPCE) and glassy carbon electrodes (GCE). The optimization of a square wave voltammetric method was performed by means of 2³ factorial design, Doehlert matrix and multi‐response assays, and the best parameters were: frequency (54.8 Hz), step potential (6 mV), pulse amplitude (43.7 mV) and pH 4.5. The analytical curve was constructed from 3.00×10^?8 to 2.01×10^?6 mol L^?1, with detection and quantification limits of 5.00×10^?9 and 1.70×10^?8 mol L^?1, respectively. The repeatability of the method evaluated for 10 consecutive measurements at concentrations of 1.70×10^?7 mol L^?1 and 1.70×10^?6 mol L^?1, showed relative standard deviation of 3.56 and 0.57 %, respectively. The sensor based in SPCE/Gr was successfully applied in wastewater samples collected from a drinking water treatment plant and validated by comparison with HPLC‐DAD method with good accuracy.