Synthesis and characterization of ZnO nanostructures using palm olein as biotemplate

Background: A green approach to synthesize nanomaterials using biotemplates has been subjected to intense research due to several advantages. Palm olein as a biotemplate offers the benefits of eco-friendliness, low-cost and scale-up for large scale production. Therefore, the effect of palm olein on morphology and surface properties of ZnO nanostructures were investigated. Results: The results indicate that palm olein as a biotemplate can be used to modify the shape and size of ZnO particles synthesized by hydrothermal method. Different morphology including flake-, flower- and three dimensional star-like structures were obtained. FTIR study indicated the reaction between carboxyl group of palm olein and zinc species had taken place. Specific surface area enhanced while no considerable change were observed in optical properties. Conclusion: Phase-pure ZnO particles were successfully synthesized using palm olein as soft biotemplating agent by hydrothermal method. The physico-chemical properties of the resulting ZnO particles can be tuned using the ratio of palm olein to Zn cation

Comparison of Therapeutic Effects between Pulsed and Continuous Wave 810-nm Wavelength Laser Irradiation for Traumatic Brain Injury in Mice

Background and Objective Transcranial low-level laser therapy (LLLT) using near-infrared light can efficiently penetrate through the scalp and skull and could allow non-invasive treatment for traumatic brain injury (TBI). In the present study, we compared the therapeutic effect using 810-nm wavelength laser light in continuous and pulsed wave modes in a mouse model of TBI. Study Design/Materials and Methods TBI was induced by a controlled cortical-impact device and 4-hours post-TBI 1-group received a sham treatment and 3-groups received a single exposure to transcranial LLLT, either continuous wave or pulsed at 10-Hz or 100-Hz with a 50% duty cycle. An 810-nm Ga-Al-As diode laser delivered a spot with diameter of 1-cm onto the injured head with a power density of 50-mW/cm2 for 12-minutes giving a fluence of 36-J/cm2. Neurological severity score (NSS) and body weight were measured up to 4 weeks. Mice were sacrificed at 2, 15 and 28 days post-TBI and the lesion size was histologically analyzed. The quantity of ATP production in the brain tissue was determined immediately after laser irradiation. We examined the role of LLLT on the psychological state of the mice at 1 day and 4 weeks after TBI using tail suspension test and forced swim test. Results The 810-nm laser pulsed at 10-Hz was the most effective judged by improvement in NSS and body weight although the other laser regimens were also effective. The brain lesion volume of mice treated with 10-Hz pulsed-laser irradiation was significantly lower than control group at 15-days and 4-weeks post-TBI. Moreover, we found an antidepressant effect of LLLT at 4-weeks as shown by forced swim and tail suspension tests. Conclusion The therapeutic effect of LLLT for TBI with an 810-nm laser was more effective at 10-Hz pulse frequency than at CW and 100-Hz. This finding may provide a new insight into biological mechanisms of LLLT.National Institutes of Health (U.S.) (NIH grant R01AI050875)Center for Integration of Medicine and Innovative Technology (DAMD17-02-2-0006)United States. Dept. of Defense. Congressionally Directed Medical Research Programs (W81XWH-09-1-0514)United States. Air Force Office of Scientific Research (Military Photomedicine Program (FA9950-04-1-0079))Japan. Ministry of Education, Culture, Sports, Science and TechnologyJapan Society for the Promotion of Scienc

Big data in mHealth

The proliferation of mobile technologies has paved the way for the widespread use of mobile health (mHealth) devices. This in turn generates a large amount of data, which is essentially big data, that can be used for various purposes. In order to obtain the maximum benefit from mHealth data, emerging big data technologies can be employed. In this chapter, the relationship between mHealth and big data is investigated from a sociotechnical perspective. Following an overview of the state-of-the-art, stakeholders and their interests are identified, and the impact of big data on such interests is presented. The opportunities of using big data technologies in the mHealth domain are considered from several viewpoints. Social and economic implications of using big data technologies toward these ends are highlighted. Various challenges exist in the implementation and adoption of mHealth data processing. While there are social challenges including privacy, safety, and a false sense of confidence, there are also technical challenges such as security, standardization, correctness, timely analysis, and domain expertise. Some of these coincide with the challenges of the big data domain, and the others are related to human nature and human capabilities. The use of existing big data platforms requires significant expertise and know-how in data science domain which may hinder the adoption of big data technologies in mHealth. Hence, a solution in the form of a framework that provides higher abstraction level programming models is suggested to facilitate widespread user adoption. Accordingly, user aspects associated with big data in the mHealth domain are discussed