Effect of Diode-Laser and AC Magnetic Field of Bovine Serum Albumin Nanospheres Loaded with Phthalocyanine and Magnetic Particles

This study reports on the development and characterization of bovine serum albumin (BSA) nanospheres containing Silicon(IV) phthalocyanine (NzPc) and/or maghemite nanoparticles (MNP), the latter introduced via ionic magnetic fluid (MF). The nanosized BSA-loaded samples were designed for synergic application while combining Photodynamic Therapy and Hyperthermia. Incorporation of MNP in the albumin-based template, allowing full control of the magnetic content, was accomplished by adding a highly-stable ionic magnetic fluid sample to the albumin suspension, following heat denaturing. The material`s evaluation was performed using Zeta potential measurements and scanning electron microscopy. The samples were characterized by steady-state techniques and time-resolved fluorescence. The in vitro assay, using human fibroblasts, revealed no cytotoxic effect in all samples investigated, demonstrating the potential of the tested system as a synergistic drug delivery system.

Characterization of optically driven fluid stress fields with optical tweezers

We present a controlled stress microviscometer with applications to complex fluids. It generates and measures microscopic fluid velocity fields, based on dual beam optical tweezers. This allows an investigation of bulk viscous properties and local inhomogeneities at the probe particle surface. The accuracy of the method is demonstrated in water. In a complex fluid model (hyaluronic acid), we observe a strong deviation of the flow field from classical behavior. Knowledge of the deviation together with an optical torque measurement is used to determine the bulk viscosity. Furthermore, we model the observed deviation and derive microscopic parameters.

Improving Alzheimer`s Disease Diagnosis with Machine Learning Techniques

There is not a specific test to diagnose Alzheimer`s disease (AD). Its diagnosis should be based upon clinical history, neuropsychological and laboratory tests, neuroimaging and electroencephalography (EEG). Therefore, new approaches are necessary to enable earlier and more accurate diagnosis and to follow treatment results. In this study we used a Machine Learning (ML) technique, named Support Vector Machine (SVM), to search patterns in EEG epochs to differentiate AD patients from controls. As a result, we developed a quantitative EEG (qEEG) processing method for automatic differentiation of patients with AD from normal individuals, as a complement to the diagnosis of probable dementia. We studied EEGs from 19 normal subjects (14 females/5 males, mean age 71.6 years) and 16 probable mild to moderate symptoms AD patients (14 females/2 males, mean age 73.4 years. The results obtained from analysis of EEG epochs were accuracy 79.9% and sensitivity 83.2%. The analysis considering the diagnosis of each individual patient reached 87.0% accuracy and 91.7% sensitivity.