Comparison of human uterine cervical electrical impedance measurements derived using two tetrapolar probes of different sizes

BACKGROUND We sought to compare uterine cervical electrical impedance spectroscopy measurements employing two probes of different sizes, and to employ a finite element model to predict and compare the fraction of electrical current derived from subepithelial stromal tissue. METHODS Cervical impedance was measured in 12 subjects during early pregnancy using 2 different sizes of the probes on each subject. RESULTS Mean cervical resistivity was significantly higher (5.4 vs. 2.8 Ωm; p < 0.001) with the smaller probe in the frequency rage of 4–819 kHz. There was no difference in the short-term intra-observer variability between the two probes. The cervical impedance measurements derived in vivo followed the pattern predicted by the finite element model. CONCLUSION Inter-electrode distance on the probes for measuring cervical impedance influences the tissue resistivity values obtained. Determining the appropriate probe size is necessary when conducting clinical studies of resistivity of the cervix and other human tissues

Prediction of time between CIS onset and clinical conversion to MS using Random Forests

CIS is diagnosed after a first neurological attack and can be considered an early stage of MS as ~80% of all CIS patients will have a second relapse within 20 years. The prediction of this second clinical relapse which marks the clinical conversion to MS (i.e., clinically-definite MS, CDMS) is very challenging, and many clinical and radiological predictors of CDMS have been identified. Machine learning techniques such as support vector machines (SVMs) have been widely applied to neuroimaging data in order to associate MRI features with binary clinical outcomes. A single-centre study has shown that it is possible to predict short-time conversion after 1 and 3 years with an accuracy of ~75 % using a priori defined features from baseline MRI measures and clinical characteristics, which were applied to support vector machines (SVMs). Random forests are another type of machine learning techniques that can easily be applied to regression problems, and consist of an ensemble of decision trees for regression where each tree is created from independent bootstraps from the input data. The present study shows the feasibility of using random forests with European multi-centre MRI data (obtained at CIS onset) to predict the actual date of conversion to CDMS rather than just a binary outcome at a fixed time point

External validation of a claims-based algorithm for classifying kidney-cancer surgeries

<p>Abstract</p> <p>Background</p> <p>Unlike other malignancies, there is no literature supporting the accuracy of medical claims data for identifying surgical treatments among patients with kidney cancer. We sought to validate externally a previously published Medicare-claims-based algorithm for classifying surgical treatments among patients with early-stage kidney cancer. To achieve this aim, we compared procedure assignments based on Medicare claims with the type of surgery specified in SEER registry data and clinical operative reports.</p> <p>Methods</p> <p>Using linked SEER-Medicare data, we calculated the agreement between Medicare claims and SEER data for identification of cancer-directed surgery among 6,515 patients diagnosed with early-stage kidney cancer. Next, for a subset of 120 cases, we determined the agreement between the claims algorithm and the medical record. Finally, using the medical record as the reference-standard, we calculated the sensitivity, specificity, and positive and negative predictive values of the claims algorithm.</p> <p>Results</p> <p>Among 6,515 cases, Medicare claims and SEER data identified 5,483 (84.1%) and 5,774 (88.6%) patients, respectively, who underwent cancer-directed surgery (observed agreement = 93%, κ = 0.69, 95% CI 0.66 – 0.71). The two data sources demonstrated 97% agreement for classification of partial versus radical nephrectomy (κ = 0.83, 95% CI 0.81 – 0.86). We observed 97% agreement between the claims algorithm and clinical operative reports; the positive predictive value of the claims algorithm exceeded 90% for identification of both partial nephrectomy and laparoscopic surgery.</p> <p>Conclusion</p> <p>Medicare claims represent an accurate data source for ascertainment of population-based patterns of surgical care among patients with early-stage kidney cancer.</p

A revised edition of the readiness to change questionnaire (treatment version)

The UK Alcohol Treatment Trial provided an opportunity to examine the factor structure of the Readiness to Change Questionnaire-Treatment Version (RCQ[TV]) in a large sample (N = 742) of individuals in treatment for alcohol problems who were given the RCQ[TV] at baseline, 3-months and 12-months follow-up. Confirmatory factor analysis of the previously reported factor structure (5 items for each of Precontemplation, Contemplation and Action scales) resulted in a relatively poor fit to the data. Removal of one item from each of the scales resulted in a 12-item instrument for which goodness-of-fit indices were improved, without loss of internal consistency of the three scales, on all three measurement occasions. Inspection of relationships between stage allocation by the new instrument and negative alcohol outcome expectancies provided evidence of improved construct validity for the revised edition of the RCQ[TV]. There was also a strong relationship between stage allocation at 3-months follow-up and outcome of treatment at 12 months. The revised edition of the RCQ[TV] offers researchers and clinicians a shorter and improved measurement of stage of change in the alcohol treatment population

Transcriptomic analysis of the response of Acropora millepora to hypo-osmotic stress provides insights into DMSP biosynthesis by corals

© 2017 The Author(s). Background: Dimethylsulfoniopropionate (DMSP) is a small sulphur compound which is produced in prodigious amounts in the oceans and plays a pivotal role in the marine sulfur cycle. Until recently, DMSP was believed to be synthesized exclusively by photosynthetic organisms; however we now know that corals and specific bacteria can also produce this compound. Corals are major sources of DMSP, but the molecular basis for its biosynthesis is unknown in these organisms. Results: Here we used salinity stress, which is known to trigger DMSP production in other organisms, in conjunction with transcriptomics to identify coral genes likely to be involved in DMSP biosynthesis. We focused specifically on both adults and juveniles of the coral Acropora millepora: after 24 h of exposure to hyposaline conditions, DMSP concentrations increased significantly by 2.6 fold in adult corals and 1.2 fold in juveniles. Concomitantly, candidate genes enabling each of the necessary steps leading to DMSP production were up-regulated. Conclusions: The data presented strongly suggest that corals use an algal-like pathway to generate DMSP from methionine, and are able to rapidly change expression of the corresponding genes in response to environmental stress. However, our data also indicate that DMSP is unlikely to function primarily as an osmolyte in corals, instead potentially serving as a scavenger of ROS and as a molecular sink for excess methionine produced as a consequence of proteolysis and osmolyte catabolism in corals under hypo-osmotic conditions

What is the evidence for the contribution of forests to poverty alleviation? A systematic map protocol

This is the final version of the article. Available from BioMed Central via the DOI in this record.Background: Forests provide an essential resource that support the livelihoods of an estimated 20% of the global population. Forests are thought to serve in three primary roles to support livelihoods: subsistence, safety nets, and pathways to prosperity. While we have a working understanding of how poor people depend on forests in individual sites and countries, much of this evidence is dispersed and not easily accessible. Thus, while the importance of forest ecosystems and resources to contribute to poverty alleviation has been increasingly emphasized in international policies, conservation and development initiatives and investments- the strength of evidence to support how forests can affect poverty outcomes is still unclear. This study takes a systematic mapping approach to scope, identify and describe studies that measure the effect of forest-based activities on poverty outcomes at local and regional scales. This effort builds upon an existing systematic map on linkages between conservation and human well-being in order to make this process more efficient. We will conduct a refined and updated search strategy pertinent to forests-poverty linkages to glean additional evidence from studies outside the scope of the original map. Results of this study can be used for informing conservation and development policy and practices in global forest ecosystems and highlight evidence gaps where future primary studies and systematic reviews can add value. Methods: We build upon the search strategy outlined in McKinnon et al. (Environ Evid 1-25, 2016) and expand our search to cover a total of 7 bibliographic databases, 15 organizational websites, 8 existing systematic reviews and maps, and evidence gap maps, and solicit key informants. All searches will be conducted in English and encompass all nations. Search results will be screened at title, abstract, and full text levels, recording both the number of excluded articles and reasons for exclusion. Full text assessment will be conducted on all included article and extracted data will be reported in a narrative review that will summarize trends in the evidence, report any knowledge gaps and gluts, and provide insight for policy, practice and future research. The data from this systematic map will be made available as well, through an open access, searchable data portal and visualization tool.We are grateful for funding support from the Program on Forests (PROFOR) (SA, SO, SC, RG) and the USDA National Institute of Food and Agriculture, Hatch Project #1009327 (DM)

Reductions in global biodiversity loss predicted from conservation spending

Halting global biodiversity loss is central to both the Convention on Biological Diversity (CBD) and United Nations Sustainable Development Goals (SDGs)1,2, but success to date has been very limited3–5. A critical determinant of overall strategic success (or failure) is the financing committed to biodiversity6–9; however, financing decisions are still hindered by considerable uncertainty over what any investment is likely to achieve6–9.. For greater effectiveness, we need an evidence-based model (EBM)10–12 showing how conservation spending quantitatively reduces the rate of loss. Here, we empirically quantify how i$14.4 billion of conservation investment reduced biodiversity loss across 109 signatory countries between 1996 and 2008, by an average 29% per country. We also show that biodiversity change in signatory countries can be predicted with high accuracy, using a dual model that combines the positive impact of conservation investment with the negative impact of economic, agricultural and population growth (i.e. human development pressures)13–18. Decision-makers can use this dual model to forecast the improvement that any proposed biodiversity budget would achieve under various scenarios of human development pressure, comparing those forecasts to any chosen policy target (including the CBD and SDGs). Importantly, we further find that spending impacts shrink as human development pressures grow, implying that funding may need to increase over time. The model therefore offers a flexible tool for balancing the SDGs of human development and biodiversity, by predicting the dynamic changes needed in conservation finance as human development proceeds

Antagomir-mediated suppression of microRNA-134 reduces kainic acid-induced seizures in immature mice

MicroRNAs are short non-coding RNAs that negatively regulate protein levels and perform important roles in establishing and maintaining neuronal network function. Previous studies in adult rodents have detected upregulation of microRNA-134 after prolonged seizures (status epilepticus) and demonstrated that silencing microRNA-134 using antisense oligonucleotides, termed antagomirs, has potent and long-lasting seizure-suppressive effects. Here we investigated whether targeting microRNA-134 can reduce or delay acute seizures in the immature brain. Status epilepticus was induced in 21 day-old (P21) male mice by systemic injection of 5 mg/kg kainic acid. This triggered prolonged electrographic seizures and select bilateral neuronal death within the CA3 subfield of the hippocampus. Expression of microRNA-134 and functional loading to Argonaute-2 was not significantly changed in the hippocampus after seizures in the model. Nevertheless, when levels of microRNA-134 were reduced by prior intracerebroventricular injection of an antagomir, kainic acid-induced seizures were delayed and less severe and mice displayed reduced neuronal death in the hippocampus. These studies demonstrate targeting microRNA-134 may have therapeutic applications for the treatment of seizures in children

Destabilized reporters for background-subtracted, chemically-gated, and multiplexed deep-tissue imaging

Tracking gene expression in deep tissues requires genetic reporters that can be unambiguously detected using tissue penetrant techniques. Magnetic resonance imaging (MRI) is uniquely suited for this purpose; however, there is a dearth of reporters that can be reliably linked to gene expression with minimal interference from background tissue signals. Here, we present a conceptually new method for generating background-subtracted, drug-gated, multiplex images of gene expression using MRI. Specifically, we engineered chemically erasable reporters consisting of a water channel, aquaporin-1, fused to destabilizing domains, which are stabilized by binding to cell-permeable small-molecule ligands. We showed that this approach allows for highly specific detection of gene expression through differential imaging. In addition, by engineering destabilized aquaporin-1 variants with orthogonal ligand requirements, it is possible to distinguish distinct subpopulations of cells in mixed cultures. Finally, we demonstrated this approach in a mouse tumor model through differential imaging of gene expression with minimal background