Quantification of network perfusion in ASL cerebral blood flow data with seed based and ICA approaches

Independent component analysis (ICA) or seed based approaches (SBA) in functional magnetic resonance imaging blood oxygenation level dependent (BOLD) data became widely applied tools to identify functionally connected, large scale brain networks. Differences between task conditions as well as specific alterations of the networks in patients as compared to healthy controls were reported. However, BOLD lacks the possibility of quantifying absolute network metabolic activity, which is of particular interest in the case of pathological alterations. In contrast, arterial spin labeling (ASL) techniques allow quantifying absolute cerebral blood flow (CBF) in rest and in task-related conditions. In this study, we explored the ability of identifying networks in ASL data using ICA and to quantify network activity in terms of absolute CBF values. Moreover, we compared the results to SBA and performed a test-retest analysis. Twelve healthy young subjects performed a fingertapping block-design experiment. During the task pseudo-continuous ASL was measured. After CBF quantification the individual datasets were concatenated and subjected to the ICA algorithm. ICA proved capable to identify the somato-motor and the default mode network. Moreover, absolute network CBF within the separate networks during either condition could be quantified. We could demonstrate that using ICA and SBA functional connectivity analysis is feasible and robust in ASL-CBF data. CBF functional connectivity is a novel approach that opens a new strategy to evaluate differences of network activity in terms of absolute network CBF and thus allows quantifying inter-individual differences in the resting state and task-related activations and deactivations.

Can falls risk prediction tools correctly identify fall-prone elderly rehabilitation inpatients? A systematic review and meta-analysis

Background Falls of elderly people may cause permanent disability or death. Particularly susceptible are elderly patients in rehabilitation hospitals. We systematically reviewed the literature to identify falls prediction tools available for assessing elderly inpatients in rehabilitation hospitals. Methods and Findings We searched six electronic databases using comprehensive search strategies developed for each database. Estimates of sensitivity and specificity were plotted in ROC space graphs and pooled across studies. Our search identified three studies which assessed the prediction properties of falls prediction tools in a total of 754 elderly inpatients in rehabilitation hospitals. Only the STRATIFY tool was assessed in all three studies; the other identified tools (PJC-FRAT and DOWNTON) were assessed by a single study. For a STRATIFY cut-score of two, pooled sensitivity was 73% (95%CI 63 to 81%) and pooled specificity was 42% (95%CI 34 to 51%). An indirect comparison of the tools across studies indicated that the DOWNTON tool has the highest sensitivity (92%), while the PJC-FRAT offers the best balance between sensitivity and specificity (73% and 75%, respectively). All studies presented major methodological limitations. Conclusions We did not identify any tool which had an optimal balance between sensitivity and specificity, or which were clearly better than a simple clinical judgment of risk of falling. The limited number of identified studies with major methodological limitations impairs sound conclusions on the usefulness of falls risk prediction tools in geriatric rehabilitation hospitals.

Bioinformatics tools and databases for the study of human growth hormone

Advances in novel molecular biological diagnostic methods are changing the way of diagnosis and study of metabolic disorders like growth hormone deficiency. Faster sequencing and genotyping methods require strong bioinformatics tools to make sense of the vast amount of data generated by modern laboratories. Advances in genome sequencing and computational power to analyze the whole genome sequences will guide the diagnostics of future. In this chapter, an overview of some basic bioinformatics resources that are needed to study metabolic disorders are reviewed and some examples of bioinformatics analysis of human growth hormone gene, protein and structure are provided.

Depicting community perspectives: repeat photography and participatory research as tools for assessing environmental services in Sagarmatha National Park, Nepal

Efforts have been made to provide a scientific basis for using environmental services as a conceptual tool to enhance conservation and improve livelihoods in protected mountain areas (MtPAS). Little attention has been paid to participatory research or locals’ concerns as environmental service (ES) users and providers. Such perspectives can illuminate the complex interplay between mountain ecosystems, environmental services and the determinants of human well-being. Repeat photography, long used in geographical fieldwork, is new as a qualitative research tool. This study uses a novel application of repeat photography as a diachronic photo-diary to examine local perceptions of change in ES in Sagarmatha National Park. Results show a consensus among locals on adverse changes to ES, particularly protection against natural hazards, such as landslides and floods, in the UNESCO World Heritage Site. We argue that our methodology could complement biophysical ecosystem assessments in MtPAS, especially since assessing ES, and acting on that, requires integrating diverse stakeholders’ knowledge, recognizing power imbalances and grappling with complex social-ecological systems.

Transfection of drug-specific T-cell receptors into hybridoma cells: tools to monitor drug interaction with T-cell receptors and evaluate cross-reactivity to related compounds

In the context of drug hypersensitivity, our group has recently proposed a new model based on the structural features of drugs (pharmacological interaction with immune receptors; p-i concept) to explain their recognition by T cells. According to this concept, even chemically inert drugs can stimulate T cells because certain drugs interact in a direct way with T-cell receptors (TCR) and possibly major histocompatibility complex molecules without the need for metabolism and covalent binding to a carrier. In this study, we investigated whether mouse T-cell hybridomas transfected with drug-specific human TCR can be used as an alternative to drug-specific T-cell clones (TCC). Indeed, they behaved like TCC and, in accordance with the p-i concept, the TCR recognize their specific drugs in a direct, processing-independent, and dose-dependent way. The presence of antigen-presenting cells was a prerequisite for interleukin-2 production by the TCR-transfected cells. The analysis of cross-reactivity confirmed the fine specificity of the TCR and also showed that TCR transfectants might provide a tool to evaluate the potential of new drugs to cause hypersensitivity due to cross-reactivity. Recombining the alpha- and beta-chains of sulfanilamide- and quinolone-specific TCR abrogated drug reactivity, suggesting that both original alpha- and beta-chains were involved in drug binding. The TCR-transfected hybridoma system showed that the recognition of two important classes of drugs (sulfanilamides and quinolones) by TCR occurred according to the p-i concept and provides an interesting tool to study drug-TCR interactions and their biological consequences and to evaluate the cross-reactivity potential of new drugs of the same class.