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.     

Chemical Composition and Evaluation of Insecticidal Activity of Calendula incana subsp. maritima and Laserpitium siler subsp. siculum Essential Oils against Stored Products Pests

The problems of the environment and human health related to the use of synthetic and broad-spectrum insecticides have increasingly motivated scientific research on different alternatives and among these, the use of green systems, such as essential oils, have been explored. Several species of the Apiaceae and Asteraceae families, aromatic herbs rich in secondary bioactive metabolites, are used in the industrial field for pharmaceutical, cosmetic, and food purposes. Different essential oils extracted from some species of these families have shown acute toxicity and attractive and/or repellent effects towards different insects. In our work, we investigated the toxic potential of Calendula incana subsp. maritima and Laserpitium siler subsp. siculum essential oils against four insect species, Sitophilus oryzae, Lasioderma serricorne, Necrobia rufipes, and Rhyzoperta dominica, which are common pests of stored products. The composition of both oils, extracted by hydrodistillation from the aerial parts of the two plants, was evaluated by GC×GC-MS. Calendula incana subsp. maritima essential oil was rich in oxygenated sesquiterpenoids, such as cubebol (35.39%), 4-epi-cubebol (22.99%), and cubenol (12.77%), while the Laserpitium siler subsp. siculum essential oil was composed mainly of monoterpene hydrocarbons, such as β-phellandrene (42.16%), limonene (23.87%), and β-terpinene (11.80%). The toxicity Petri dish bioassays indicated that C. maritima oil killed a mean of 65.50% of S. oryzae and 44.00% of R. dominica adults, indicating a higher biocidal activity in comparison with L. siculum oil, while toward the other species, no significant differences in mortality were recorded. Calendula maritima oil could be, then, considered a promising candidate for further tests as an alternative biocide toward S. oryzae and R. dominica. The possibility that the relatively high content of oxygenated sesquiterpenoids in C. maritima essential oil determines its higher biocidal activity is discussed.     

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.     

The Potential of 2-Substituted Quinolines as Antileishmanial Drug Candidates

There is a need for new, cost-effective drugs to treat leishmaniasis. A strategy based on traditional medicine practiced in Bolivia led to the discovery of the 2-substituted quinoline series as a source of molecules with antileishmanial activity and low toxicity. This review documents the development of the series from the first isolated natural compounds through several hundred synthetized molecules to an optimized compound exhibiting an in vitro IC₅₀ value of 0.2 µM against Leishmania donovani, and a selectivity index value of 187, together with in vivo activity on the L. donovani/hamster model. Attempts to establish structure–activity relationships are described, as well as studies that have attempted to determine the mechanism of action. For the latter, it appears that molecules of this series act on multiple targets, possibly including the immune system, which could explain the observed lack of drug resistance after in vitro drug pressure. We also show how nanotechnology strategies could valorize these drugs through adapted formulations and how a mechanistic targeting approach could generate new compounds with increased activity.