Thai emergency nurses’ management of patients with severe traumatic brain injury: comparison of knowledge and clinical management with best available evidence

Background:
In Thailand, the rate of TBI-related hospitalisation is increasing, however, little is known about the evidence-based management of severe TBI in the developing world. The aim of this study was to explore Thai emergency nurses’ management of patients with severe TBI.

Methods:
An exploratory descriptive mixed method design was used to conduct this two stage study: survey methods were used to examine emergency nurses’ knowledge regarding management of patients with severe TBI (Stage 1) and observational methods were used to examine emergency nurses’ clinical management of patients with severe TBI (Stage 2). The study setting was the emergency department (ED) at a regional hospital in Southern Thailand.

Results:
34 nurses participated in Stage 1 (response rate 91.9%) and the number of correct responses ranged from 33.3% to 95.2%. In Stage 2, a total of 160 points of measurement were observed in 20 patients with severe TBI over 40 h. In this study there were five major areas identified for the improvement of care of patients with severe TBI: (i) end-tidal carbon dioxide (ETCO2) monitoring and targets; (ii) use of analgesia and sedation; (iii) patient positioning; (iv) frequency of nursing assessment; and (v) dose of Mannitol diuretic.

Conclusions:
There is variation in Thai nurses’ knowledge and care practices for patients with severe TBI. To increase consistency of evidence-based TBI care in the Thai context, a knowledge translation intervention that is ecologically valid, appropriate to the Thai healthcare context and acceptable to the multidisciplinary care team is needed.

A low power micro deep brain stimulation device for murine preclinical research

Deep brain stimulation has emerged as an effective medical procedure that has therapeutic efficacy in a number of neuropsychiatric disorders. Preclinical research involving laboratory animals is being conducted to study the principles, mechanisms, and therapeutic effects of deep brain stimulation. A bottleneck is, however, the lack of deep brain stimulation devices that enable long term brain stimulation in freely moving laboratory animals. Most of the existing devices employ complex circuitry, and are thus bulky. These devices are usually connected to the electrode that is implanted into the animal brain using long fixed wires. In long term behavioral trials, however, laboratory animals often need to continuously receive brain stimulation for days without interruption, which is difficult with existing technology. This paper presents a low power and lightweight portable microdeep brain stimulation device for laboratory animals. Three different configurations of the device are presented as follows: 1) single piece head mountable; 2) single piece back mountable; and 3) two piece back mountable. The device can be easily carried by the animal during the course of a clinical trial, and that it can produce non-stop stimulation current pulses of desired characteristics for over 12 days on a single battery. It employs passive charge balancing to minimize undesirable effects on the target tissue. The results of bench, in-vitro, and in-vivo tests to evaluate the performance of the device are presented.

Brain metastases in breast cancer

Despite therapeutic advances, the development of breast cancer brain metastases (BCBM) is still the harbinger of a dismal prognosis. Patient outcomes vary depending on factors, including tumor phenotype, extent of disease within and outside the brain, as well as patient performance status. Treatment includes surgery, radiation therapy and systemic therapy determined by patient and tumor characteristics. Despite these approaches, novel treatments are needed and there is growing interest in systemic therapies. However, the efficacy of pharmacologic agents is hampered by poor penetration of drugs across the blood–brain barrier. Therefore, there is a pressing need for a greater understanding of the natural history of BCBM to guide the development of further therapies. This review analyzes prognosis and treatment of BCBM by tumor phenotype and discusses ongoing research into new therapies.

Informing brain health behaviour choices: the efficacy of a high-school brain awareness pilot study

Aims:
Lifestyle choices such as diet and exercise significantly impact mental wellbeing and this is particularly so during the period of adolescence. The aim of the current study was to determine whether neuroscience concepts could be introduced to the classroom in a manner that improved high school student awareness of how health behaviour choices impact brain health. 

Study Design:
This study was a quantitative study that measured 47 assertions relating to brain health and neuroscience pre and post an interactive seminar.

Place and Duration of Study:
A Victorian high school in Geelong, Australia. Participation in the seminar took approximately 100 minutes, including time to complete the questionnaires.

Methodology:
The current study trialed a ‘Brain Basics’ educational program in a Victorian high-school. The neuro-educative interactive seminar was presented to 48female year 11 students. The level of student understanding, interest and enjoyment was assessed prior to and following an interactive seminar.

Results:
Student understanding of brain health significantly improved in 31 out of 47 questionnaire items and interest and enjoyment were highly rated.

Conclusion:
This supports the notion that basic neuroscience concepts can be introduced into Victorian schools to increase brain health awareness of our youth during this criticaltime of brain development. - See more at: http://www.sciencedomain.org/abstract.php?iid=431&id=21&aid=3887#.UykK5oXAwZm

An empirical comparison of classification algorithms for diagnosis of depression from brain sMRI scans

To be diagnostically effective, structural magnetic resonance imaging (sMRI) must reliably distinguish a depressed individual from a healthy individual at individual scans level. One of the tasks in the automated diagnosis of depression from brain sMRI is the classification. It determines the class to which a sample belongs (i.e., depressed/not depressed, remitted/not-remitted depression) based on the values of its features. Thus far, very limited works have been reported for identification of a suitable classification algorithm for depression detection. In this paper, different types of classification algorithms are compared for effective diagnosis of depression. Ten independent classification schemas are applied and a comparative study is carried out. The algorithms are: Naïve Bayes, Support Vector Machines (SVM) with Radial Basis Function (RBF), SVM Sigmoid, J48, Random Forest, Random Tree, Voting Feature Intervals (VFI), LogitBoost, Simple KMeans Classification Via Clustering (KMeans) and Classification Via Clustering Expectation Minimization (EM) respectively. The performances of the algorithms are determined through a set of experiments on sMRI brain scans. An experimental procedure is developed to measure the performance of the tested algorithms. A classification accuracy evaluation method was employed for evaluation and comparison of the performance of the examined classifiers.