Anti-biofouling assembly strategies for protein & cell repellent surfaces: a mini-review.

The protein/cell interactions with the surface at the blood-biomaterial interface generally control the efficiency of biomedical devices. A wide range of active processes and slow kinetics occur simultaneously with many biomaterials in healthcare applications, leading to multiple biological reactions and reduced clinical functions. In this work, we present a brief review of studies as the interface between proteins and biomaterials.

Application of Electrospray in Preparing Solid Lipid Nanoparticles and Optimization of Nanoparticles Using Artificial Neural Networks.

Background: Electrospray (Electrohydrodynamic atomization) has been introduced as a novel approach to prepare nanoparticles. This work aimed to prepare SLNs through electrospray and evaluate factors affecting particle size of prepared Solid Lipid Nanoparticles (SLNs).

Methods: SLNs were prepared by electrospray method.

Enhancing analgesic and anti-inflammatory effects of capsaicin when loaded into olive oil nanoemulsion: An in vivo study.

Topical preparations of capsaicin, the major pungent ingredient of hot pepper, are being used for management of pain and inflammatory disorders. Purpose of this study was to use nanoemulsion as an effective topical drug carrier for in vivo delivery of capsaicin. An oil-in-water nanoemulsion containing capsaicin was prepared by spontaneous emulsification method.

Budesonide-loaded solid lipid nanoparticles for pulmonary delivery: preparation, optimization, and aerodynamic behavior.

Advantages of lipid nanoparticles for pulmonary applications are possibility of deep lung deposition with prolonged release and low toxicity. This study aimed to evaluate the effects of formulation and processing parameters on particle size of prepared SLNs. Budesonide-loaded solid lipid nanoparticles (BUD-SLNs) were prepared with different values of drug content, ultrasonication amplitude, and homogenization time and the data were modeled using artificial neural networks (ANNs).

Size Control in the Nanoprecipitation Process of Stable Iodine (¹²⁷I) Using Microchannel Reactor-Optimization by Artificial Neural Networks.

In this study, nanosuspension of stable iodine ((127)I) was prepared by nanoprecipitation process in microfluidic devices. Then, size of particles was optimized using artificial neural networks (ANNs) modeling. The size of prepared particles was evaluated by dynamic light scattering.

Optimization of paclitaxel-loaded poly (d,l-lactide-co-glycolide-N-p-maleimido benzoic hydrazide) nanoparticles size using artificial neural networks.

The aim of this study was to find a model using artificial neural networks (ANNs) to predict PLGA-PMBH nanoparticles (NPs) size in preparation by modified nanoprecipitation. The input variables were polymer content, drug content, power of sonication and ratio of organic/aqueous phase (i.e.

Annual effective dose from environmental gamma radiation in Bushehr city.

Background: Present study was an attempt to measure outdoor and indoor gamma dose rates in Bushehr city to determine corresponding annual effective dose and, to assess effect of active nuclear power plant located in Bushehr city on background radiation level of this city.

Methods: All measurements were performed by G.M (Geiger Muller) detector (X5C plus) calibrated in Iran Atomic Energy Agency.

An update on mobile phones interference with medical devices.

Mobile phones' electromagnetic interference with medical devices is an important issue for the medical safety of patients who are using life-supporting medical devices. This review mainly focuses on mobile phones' interference with implanted medical devices and with medical equipment located in critical areas of hospitals. A close look at the findings reveals that mobile phones may adversely affect the functioning of medical devices, and the specific effect and the degree of interference depend on the applied technology and the separation distance.

The use of artificial neural networks for optimizing polydispersity index (PDI) in nanoprecipitation process of acetaminophen in microfluidic devices.

Artificial neural networks (ANNs) were used in this study to determine factors that control the polydispersity index (PDI) in an acetaminophen nanosuspension which was prepared using nanoprecipitation in microfluidic devices. The PDI of prepared formulations was measured by dynamic light scattering. Afterwards, the ANNs were applied to model the data.

Preparation and optimization of acetaminophen nanosuspension through nanoprecipitation using microfluidic devices: an artificial neural networks study.

The purpose of this study was to find an artificial neural networks model for determining major factors impacting the stability of an acetaminophen nanosuspansion that was prepared using nanoprecipitation in microfluidic reactors. Four variables, namely concentration of surfactant, solvent and antisolvent flow rate and solvent temperature were used as input variables and time of sedimentation of nanoparticles was considered as output variable. The particle size of optimized formulation was measured by transmission electron microscope and dynamic light scattering.