Thalamic inflammation after brain trauma is associated with thalamo-cortical white matter damage

Background Traumatic brain injury can trigger chronic neuroinflammation, which may predispose to neurodegeneration. Animal models and human pathological studies demonstrate persistent inflammation in the thalamus associated with axonal injury, but this relationship has never been shown in vivo. Findings Using [11C]-PK11195 positron emission tomography, a marker of microglial activation, we previously demonstrated thalamic inflammation up to 17 years after traumatic brain injury. Here, we use diffusion MRI to estimate axonal injury and show that thalamic inflammation is correlated with thalamo-cortical tract damage. Conclusions These findings support a link between axonal damage and persistent inflammation after brain injury

Shipborne eddy covariance observations of methane fluxes constrain Arctic sea emissions

We demonstrate direct eddy covariance (EC) observations of methane (CH4) fluxes between the sea and atmosphere from an icebreaker in the eastern Arctic Ocean. EC-derived CH4 emissions averaged 4.58, 1.74, and 0.14 mg m−2 day−1 in the Laptev, East Siberian, and Chukchi seas, respectively, corresponding to annual sea-wide fluxes of 0.83, 0.62, and 0.03 Tg year−1. These EC results answer concerns that previous diffusive emission estimates, which excluded bubbling, may underestimate total emissions. We assert that bubbling dominates sea-air CH4 fluxes in only small constrained areas: A ~100-m2 area of the East Siberian Sea showed sea-air CH4 fluxes exceeding 600 mg m−2 day−1; in a similarly sized area of the Laptev Sea, peak CH4 fluxes were ~170 mg m−2 day−1. Calculating additional emissions below the noise level of our EC system suggests total ESAS CH4 emissions of 3.02 Tg year−1, closely matching an earlier diffusive emission estimate of 2.9 Tg year−1

Direct determination of the air-sea CO₂ gas transfer velocity in Arctic sea-ice regions

The Arctic Ocean is an important sink for atmospheric CO₂. The impact of decreasing sea-ice extent and expanding marginal ice zones on Arctic air-sea CO₂ exchange depends on the rate of gas transfer in the presence of sea ice. Sea ice acts to limit air-sea gas exchange by reducing contact between air and water, but is also hypothesised to enhance gas transfer rates across surrounding open water surfaces through physical processes such as increased surface-ocean turbulence from ice-water shear and ice-edge form drag. Here we present the first direct determination of the CO₂ air-sea gas transfer velocity in a wide range of Arctic sea-ice conditions. We show that the gas transfer velocity increases near-linearly with decreasing sea-ice concentration. We also show that previous modeling approaches overestimate gas transfer rates in sea-ice regions

The Virtual Physiological Human: Ten Years After

Biomedical research and clinical practice are struggling to cope with the growing complexity that the progress of health care involves. The most challenging diseases, those with the largest socioeconomic impact (cardiovascular conditions; musculoskeletal conditions; cancer; metabolic, immunity, and neurodegenerative conditions), are all characterized by a complex genotype–phenotype interaction and by a “systemic” nature that poses a challenge to the traditional reductionist approach. In 2005 a small group of researchers discussed how the vision of computational physiology promoted by the Physiome Project could be translated into clinical practice and formally proposed the term Virtual Physiological Human. Our knowledge about these diseases is fragmentary, as it is associated with molecular and cellular processes on the one hand and with tissue and organ phenotype changes (related to clinical symptoms of disease conditions) on the other. The problem could be solved if we could capture all these fragments of knowledge into predictive models and then compose them into hypermodels that help us tame the complexity that such systemic behavior involves. In 2005 this was simply not possible—the necessary methods and technologies were not available. Now, 10 years later, it seems the right time to reflect on the original vision, the results achieved so far, and what remains to be done

Evidence for the Mitochondrial Lactate Oxidation Complex in Rat Neurons: Demonstration of an Essential Component of Brain Lactate Shuttles

To evaluate the presence of components of a putative Intracellular Lactate Shuttle (ILS) in neurons, we attempted to determine if monocarboxylate (e.g. lactate) transporter isoforms (MCT1 and -2) and lactate dehydrogenase (LDH) are coexpressed in neuronal mitochondria of rat brains. Immunohistochemical analyses of rat brain cross-sections showed MCT1, MCT2, and LDH to colocalize with the mitochondrial inner membrane marker cytochrome oxidase (COX) in cortical, hippocampal, and thalamic neurons. Immunoblotting after immunoprecipitation (IP) of mitochondria from brain homogenates supported the histochemical observations by demonstrating that COX coprecipitated MCT1, MCT2, and LDH. Additionally, using primary cultures from rat cortex and hippocampus as well as immunohistochemistry and immunocoprecipitation techniques, we demonstrated that MCT2 and LDH are coexpressed in mitochondria of cultured neurons. These findings can be interpreted to mean that, as in skeletal muscle, neurons contain a mitochondrial lactate oxidation complex (mLOC) that has the potential to facilitate both intracellular and cell-cell lactate shuttles in brain

Effects of facilitated family case conferencing for advanced dementia: A cluster randomised clinical trial

© 2017 Agar et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Background: Palliative care planning for nursing home residents with advanced dementia is often suboptimal. This study compared effects of facilitated case conferencing (FCC) with usual care (UC) on end-of-life care. Methods: A two arm parallel cluster randomised controlled trial was conducted. The sample included people with advanced dementia from 20 Australian nursing homes and their families and professional caregivers. In each intervention nursing home (n = 10), Palliative Care Planning Coordinators (PCPCs) facilitated family case conferences and trained staff in person-centred palliative care for 16 hours per week over 18 months. The primary outcome was family-rated quality of end-of-life care (End-of-Life Dementia [EOLD] Scales). Secondary outcomes included nurse-rated EOLD scales, resident quality of life (Quality of Life in Late-stage Dementia [QUALID]) and quality of care over the last month of life (pharmacological/ non-pharmacological palliative strategies, hospitalization or inappropriate interventions). Results: Two-hundred-eighty-six people with advanced dementia took part but only 131 died (64 in UC and 67 in FCC which was fewer than anticipated), rendering the primary analysis underpowered with no group effect seen in EOLD scales. Significant differences in pharmacological (P < 0.01) and non-pharmacological (P < 0.05) palliative management in last month of life were seen. Intercurrent illness was associated with lower family-rated EOLD Satisfaction with Care (coefficient 2.97, P < 0.05) and lower staff-rated EOLD Comfort Assessment with Dying (coefficient 4.37, P < 0.01). Per protocol analyses showed positive relationships between EOLD and staff hours to bed ratios, proportion of residents with dementia and staff attitudes. Conclusion: FCC facilitates a palliative approach to care. Future trials of case conferencing should consider outcomes and processes regarding decision making and planning for anticipated events and acute illness. Trial registration: Australian New Zealand Clinical Trial Registry ACTRN12612001164886

A review of RCTs in four medical journals to assess the use of imputation to overcome missing data in quality of life outcomes

Background: Randomised controlled trials (RCTs) are perceived as the gold-standard method for evaluating healthcare interventions, and increasingly include quality of life (QoL) measures. The observed results are susceptible to bias if a substantial proportion of outcome data are missing. The review aimed to determine whether imputation was used to deal with missing QoL outcomes. Methods: A random selection of 285 RCTs published during 2005/6 in the British Medical Journal, Lancet, New England Journal of Medicine and Journal of American Medical Association were identified. Results: QoL outcomes were reported in 61 (21%) trials. Six (10%) reported having no missing data, 20 (33%) reported ≤ 10% missing, eleven (18%) 11%–20% missing, and eleven (18%) reported >20% missing. Missingness was unclear in 13 (21%). Missing data were imputed in 19 (31%) of the 61 trials. Imputation was part of the primary analysis in 13 trials, but a sensitivity analysis in six. Last value carried forward was used in 12 trials and multiple imputation in two. Following imputation, the most common analysis method was analysis of covariance (10 trials). Conclusion: The majority of studies did not impute missing data and carried out a complete-case analysis. For those studies that did impute missing data, researchers tended to prefer simpler methods of imputation, despite more sophisticated methods being available.The Health Services Research Unit is funded by the Chief Scientist Office of the Scottish Government Health Directorate. Shona Fielding is also currently funded by the Chief Scientist Office on a Research Training Fellowship (CZF/1/31)

A review of RCTs in four medical journals to assess the use of imputation to overcome missing data in quality of life outcomes

Peer reviewedPublisher PD

Latent cluster analysis of ALS phenotypes identifies prognostically differing groups

BACKGROUND Amyotrophic lateral sclerosis (ALS) is a degenerative disease predominantly affecting motor neurons and manifesting as several different phenotypes. Whether these phenotypes correspond to different underlying disease processes is unknown. We used latent cluster analysis to identify groupings of clinical variables in an objective and unbiased way to improve phenotyping for clinical and research purposes. METHODS Latent class cluster analysis was applied to a large database consisting of 1467 records of people with ALS, using discrete variables which can be readily determined at the first clinic appointment. The model was tested for clinical relevance by survival analysis of the phenotypic groupings using the Kaplan-Meier method. RESULTS The best model generated five distinct phenotypic classes that strongly predicted survival (p<0.0001). Eight variables were used for the latent class analysis, but a good estimate of the classification could be obtained using just two variables: site of first symptoms (bulbar or limb) and time from symptom onset to diagnosis (p<0.00001). CONCLUSION The five phenotypic classes identified using latent cluster analysis can predict prognosis. They could be used to stratify patients recruited into clinical trials and generating more homogeneous disease groups for genetic, proteomic and risk factor research