Quantifying relational change in community organisations participating in a Veteran suicide prevention learning collaborative: a social network analysis.

BACKGROUND: Veteran suicide remains a significant issue, as 17.5 Veterans die by suicide each day. The US Department of Veteran Affairs (VA) has implemented a robust suicide prevention program within its integrated behavioural health system. Further, the VA has increasingly contributed to suicide prevention in community settings, where a large proportion of Veterans receive health care and social services. One component integral to preventing suicide among Veterans receiving community services is ensuring that organisations are equipped with the latest evidence-based Veteran-specific suicide prevention strategies. METHODS: The Patient Safety Center of Inquiry-Suicide Prevention Collaborative piloted a Veteran suicide prevention learning collaborative in the Denver/Colorado Springs, CO region, spanning 16 months as a multimodal initiative to integrate community organisations and assist them in implementing Veteran suicide prevention strategies used within VA. Agencies completed social network analysis surveys at baseline (T1), year 1 (T2) and 16 months (T3) to examine social networks, partnerships and collaborations among community organisations and the VA over time. RESULTS: The quantity of learning collaborative relationships increased from 30 at T1 to 41 at T3 while the quality of relationships deepened over time from awareness and cooperative to more coordinated and integrated. CONCLUSION: Improvement in relationship quantity and quality facilitates community organisation engagement in collaborating to strengthen their Veteran suicide prevention programming. Learning collaboratives work with the individual organisation for intraorganisational facilitation of implementing suicide prevention strategies and engage and enhance interorganisational partnerships. This multimodal intervention can engage community organisations and provide a stronger safety net for Veterans at risk for suicide.

Evaluation of Ethnic Variations in Visceral, Subcutaneous, Intra-Pancreatic, and Intra-Hepatic Fat Depositions by Magnetic Resonance Imaging among New Zealanders.

Anthropometric indices, such as body mass index (BMI), waist circumference (WC), and waist to height ratio (WHtR), have limitations in accurately predicting the pathophysiology of diabetes mellitus, cardiovascular diseases, and metabolic syndrome due to ethnic differences in fat distribution. Recent studies showed that the visceral adipose tissue (VAT) deposition and fat content of internal organs, most notably intra-hepatic and intra-pancreatic fat, has emerged as a more important parameter. In this study, we aimed to assess the coordination between the traditional anthropometric indices and the various fat depositions within different ethnicities in New Zealand. We recruited 104 participants with different ethnic backgrounds, including New Zealand Europeans, Maori (the indigenous people of New Zealand), Pacific Islanders (PI), and Asians. Their weight, height, and WC were measured, and subcutaneous, visceral, intra-hepatic, and intra-pancreatic fat depositions were obtained by magnetic resonance imaging (MRI). The result showed VAT, but not subcutaneous adipose tissue (SAT) depositions at all levels were significantly varied among the three groups. BMI was associated best with L23SAT in NZ Europeans (30%) and L45VAT in Maori/PI (24.3%). WC and WHtR were correlated well with L45SAT in the total population (18.8% and 12.2%, respectively). Intra-pancreatic fat deposition had a positive Pearson relationship with NZ European BMI and Maori/PI WC, but no regression correlation with anthropometric indices. Conventional anthropometric indices did not correspond to the same fat depositions across different ethnic groups.

Analyzing large data sets acquired through telemetry from rats exposed to organophosphorous compounds: an EEG study.

The organophosphorous compound soman is an acetylcholinesterase inhibitor that causes damage to the brain. Exposure to soman causes neuropathology as a result of prolonged and recurrent seizures. In the present study, long-term recordings of cortical EEG were used to develop an unbiased means to quantify measures of seizure activity in a large data set while excluding other signal types. Rats were implanted with telemetry transmitters and exposed to soman followed by treatment with therapeutics similar to those administered in the field after nerve agent exposure. EEG, activity and temperature were recorded continuously for a minimum of 2 days pre-exposure and 15 days post-exposure. A set of automatic MATLAB algorithms have been developed to remove artifacts and measure the characteristics of long-term EEG recordings. The algorithms use short-time Fourier transforms to compute the power spectrum of the signal for 2-s intervals. The spectrum is then divided into the delta, theta, alpha, and beta frequency bands. A linear fit to the power spectrum is used to distinguish normal EEG activity from artifacts and high amplitude spike wave activity. Changes in time spent in seizure over a prolonged period are a powerful indicator of the effects of novel therapeutics against seizures. A graphical user interface has been created that simultaneously plots the raw EEG in the time domain, the power spectrum, and the wavelet transform. Motor activity and temperature are associated with EEG changes. The accuracy of this algorithm is also verified against visual inspection of video recordings up to 3 days after exposure.