Is group cognitive behaviour therapy for postnatal depression evidence-based practice? A systematic review

Background: There is evidence that psychological therapies including cognitive behaviour therapy (CBT) may be effective in reducing postnatal depression (PND) when offered to individuals. In clinical practice, this is also implemented in a group therapy format, which, although not recommended in guidelines, is seen as a cost-effective alternative. To consider the extent to which group methods can be seen as evidence-based, we systematically review and synthesise the evidence for the efficacy of group CBT compared to currently used packages of care for women with PND, and we discuss further factors which may contribute to clinician confidence in implementing an intervention. Methods: Seventeen electronic databases were searched. All full papers were read by two reviewers and a third reviewer was consulted in the event of a disagreement on inclusion. Selected studies were quality assessed, using the Cochrane Risk of Bias Tool, were data extracted by two reviewers using a standardised data extraction form and statistically synthesised where appropriate using the fixed-effect inverse-variance method. Results: Seven studies met the inclusion criteria. Meta-analyses showed group CBT to be effective in reducing depression compared to routine primary care, usual care or waiting list groups. A pooled effect size of d = 0.57 (95% CI 0.34 to 0.80, p < 0.001) was observed at 10–13 weeks post-randomisation, reducing to d = 0.28 (95% CI 0.03 to 0.53, p = 0.025) at 6 months. The non-randomised comparisons against waiting list controls at 10–13 weeks was associated with a larger effect size of d = 0.94 (95% CI 0.42 to 1.47, p < 0.001). However due to the limitations of the available data, such as ill-specified definitions of the CBT component of the group programmes, these results should be interpreted with caution. Conclusions: Although the evidence available is limited, group CBT was shown to be effective. We argue, therefore, that there is sufficient evidence to implement group CBT, conditional upon routinely collected outcomes being benchmarked against those obtained in trials of individual CBT, and with other important factors such as patient preference, clinical experience, and information from the local context taken into account when making the treatment decision

Deletion of Glucose Transporter GLUT8 in Mice Increases Locomotor Activity

Transport of glucose into neuronal cells is predominantly mediated by the glucose transporters GLUT1 and GLUT3. In addition, GLUT8 is expressed in some regions of the brain. By in situ hybridization we detected GLUT8-mRNA in hippocampus, thalamus, and cortex. However, its cellular and physiological function is still unknown. Thus, GLUT8 knockout (Slc2a8−/−) mice were used for a screening approach in the modified hole board (mHB) behavioral test to analyze the role of GLUT8 in the central nervous system. Slc2a8−/− mice showed increased mean velocity, total distance traveled and performed more turns in the mHB test. This hyperactivity of Slc2a8−/− mice was confirmed by monitoring locomotor activity in the home cage and voluntary activity in a running wheel. In addition, Slc2a8−/− mice showed increased arousal as indicated by elevated defecation, reduced latency to the first defecation and a tendency to altered grooming. Furthermore, the mHB test gave evidence that Slc2a8−/− mice exhibit a reduced risk assessment because they performed less rearings in an unprotected area and showed significantly reduced latency to stretched body posture. Our data suggest that behavioral alterations of Slc2a8−/− mice are due to dysfunctions in neuronal processes presumably as a consequence of defects in the glucose metabolism

The Great American Biotic Interchange: Dispersals, Tectonics, Climate, Sea Level and Holding Pens

The biotic and geologic dynamics of the Great American Biotic Interchange are reviewed and revised. Information on the Marine Isotope Stage chronology, sea level changes as well as Pliocene and Pleistocene vegetation changes in Central and northern South America add to a discussion of the role of climate in facilitating trans-isthmian exchanges. Trans-isthmian land mammal exchanges during the Pleistocene glacial intervals appear to have been promoted by the development of diverse non-tropical ecologies

A systematic review of patient reported factors associated with uptake and completion of cardiovascular lifestyle behaviour change

Background: Healthy lifestyles are an important facet of cardiovascular risk management. Unfortunately many individuals fail to engage with lifestyle change programmes. There are many factors that patients report as influencing their decisions about initiating lifestyle change. This is challenging for health care professionals who may lack the skills and time to address a broad range of barriers to lifestyle behaviour. Guidance on which factors to focus on during lifestyle consultations may assist healthcare professionals to hone their skills and knowledge leading to more productive patient interactions with ultimately better uptake of lifestyle behaviour change support. The aim of our study was to clarify which influences reported by patients predict uptake and completion of formal lifestyle change programmes. Methods: A systematic narrative review of quantitative observational studies reporting factors (influences) associated with uptake and completion of lifestyle behaviour change programmes. Quantitative observational studies involving patients at high risk of cardiovascular events were identified through electronic searching and screened against pre-defined selection criteria. Factors were extracted and organised into an existing qualitative framework. Results: 374 factors were extracted from 32 studies. Factors most consistently associated with uptake of lifestyle change related to support from family and friends, transport and other costs, and beliefs about the causes of illness and lifestyle change. Depression and anxiety also appear to influence uptake as well as completion. Many factors show inconsistent patterns with respect to uptake and completion of lifestyle change programmes. Conclusion: There are a small number of factors that consistently appear to influence uptake and completion of cardiovascular lifestyle behaviour change. These factors could be considered during patient consultations to promote a tailored approach to decision making about the most suitable type and level lifestyle behaviour change support

Functional MRI and Diffusion Tensor Imaging of Brain Reorganization After Experimental Stroke

The potential of the adult brain to reorganize after ischemic injury is critical for functional recovery and provides a significant target for therapeutic strategies to promote brain repair. Despite the accumulating evidence of brain plasticity, the interaction and significance of morphological and physiological modifications in post-stroke brain tissue remain mostly unclear. Neuroimaging techniques such as functional MRI (fMRI) and diffusion tensor imaging (DTI) enable in vivo assessment of the spatial and temporal pattern of functional and structural changes inside and outside ischemic lesion areas. This can contribute to the elucidation of critical aspects in post-stroke brain remodeling. Task/stimulus-related fMRI, resting-state fMRI, or pharmacological MRI enables direct or indirect measurement of neuronal activation, functional connectivity, or neurotransmitter system responses, respectively. DTI allows estimation of the structural integrity and connectivity of white matter tracts. Together, these MRI methods provide an unprecedented means to (a) measure longitudinal changes in tissue structure and function close by and remote from ischemic lesion areas, (b) evaluate the organizational profile of neural networks after stroke, and (c) identify degenerative and restorative processes that affect post-stroke functional outcome. Besides, the availability of MRI in clinical institutions as well as research laboratories provides an optimal basis for translational research on stroke recovery. This review gives an overview of the current status and perspectives of fMRI and DTI applications to study brain reorganization in experimental stroke models

Bayesian Orthogonal Least Squares (BOLS) algorithm for reverse engineering of gene regulatory networks

<p>Abstract</p> <p>Background</p> <p>A reverse engineering of gene regulatory network with large number of genes and limited number of experimental data points is a computationally challenging task. In particular, reverse engineering using linear systems is an underdetermined and ill conditioned problem, i.e. the amount of microarray data is limited and the solution is very sensitive to noise in the data. Therefore, the reverse engineering of gene regulatory networks with large number of genes and limited number of data points requires rigorous optimization algorithm.</p> <p>Results</p> <p>This study presents a novel algorithm for reverse engineering with linear systems. The proposed algorithm is a combination of the orthogonal least squares, second order derivative for network pruning, and Bayesian model comparison. In this study, the entire network is decomposed into a set of small networks that are defined as unit networks. The algorithm provides each unit network with P(D|H_i), which is used as confidence level. The unit network with higher P(D|H_i) has a higher confidence such that the unit network is correctly elucidated. Thus, the proposed algorithm is able to locate true positive interactions using P(D|H_i), which is a unique property of the proposed algorithm.</p> <p>The algorithm is evaluated with synthetic and <it>Saccharomyces cerevisiae </it>expression data using the dynamic Bayesian network. With synthetic data, it is shown that the performance of the algorithm depends on the number of genes, noise level, and the number of data points. With Yeast expression data, it is shown that there is remarkable number of known physical or genetic events among all interactions elucidated by the proposed algorithm.</p> <p>The performance of the algorithm is compared with Sparse Bayesian Learning algorithm using both synthetic and <it>Saccharomyces cerevisiae </it>expression data sets. The comparison experiments show that the algorithm produces sparser solutions with less false positives than Sparse Bayesian Learning algorithm.</p> <p>Conclusion</p> <p>From our evaluation experiments, we draw the conclusion as follows: 1) Simulation results show that the algorithm can be used to elucidate gene regulatory networks using limited number of experimental data points. 2) Simulation results also show that the algorithm is able to handle the problem with noisy data. 3) The experiment with Yeast expression data shows that the proposed algorithm reliably elucidates known physical or genetic events. 4) The comparison experiments show that the algorithm more efficiently performs than Sparse Bayesian Learning algorithm with noisy and limited number of data.</p