Age estimation in children by measurement of open apices in teeth: a European formula

The aim of the present paper was to improve and expand research with a larger number of children from various European countries and to provide a common formula useful for all these countries. Orthopantomographs taken from 2,652 European Caucasian children (1,382 boys, 1,270 girls) aged between 4 and 16 years were analyzed. The children came from Croatia, Germany, Kosovo, Italy, Slovenia, Spain, and the UK. Following the pilot study, subjects’ age was modeled as a function of gender (g), morphological variables (predictors)×5 (second premolar), s (sum of normalized open apices) N0, and the first-order interaction between s and N0. The results showed that all these variables contributed significantly to the fit, so that all were included in the regression model, yielding the following linear regression formula: Age=8.387+ 0.282 g−1.692×5+0.835 N0−0.116 s−0.139 s×N0, where g is a variable, 1 for males and 0 for females. The equation explained 86.1% (R2=0.861) of total deviance. The median of the residuals (=observed age minus predicted age) was −0.114 years, with (RefB.2) interquartile range=1.22 years

The Power of Place in Citizen Science

These authors answer the question: What are the links between motivations for citizen science, connec­tions to place, and conservation decision outcomes

Papillary carcinoma of the thyroid: methylation is not involved in the regulation of MET expression

Hypomethylation has been reported to be responsible for the activation of several oncogenes. The possibility that hypomethylation is involved in the regulation of MET transcription was investigated through the analysis of the methylation status of one CpG island containing 43 CpGs in six cases of papillary carcinoma, in the corresponding normal thyroid tissue, and in two cases of hyperplastic goitre. Evidence of methylation was not found in any of the analysed CpG. © 2004 Cancer Research UK

Full-length TrkB variant in NSCLC is associated with brain metastasis

Despite remarkable therapeutic advances have been made in the last few decades, non-small cell lung cancer (NSCLC) is still one of the leading causes of death worldwide. Brain metastases are a common complication of a wide range of human malignancies and in particular NSCLC. Brain-derived neurotrophic factor (BDNF), binding its high-affinity tyrosine kinase B receptor, has been shown to promote cancer progression and metastasis. We hereby investigated the expression of the BDNF and its TrkB receptor in its full-length and truncated isoform T1, in samples from primary adenocarcinomas (ADKs) of the lung and in their metastasis to evaluate if their expression was related to preferential tumor entry into the central nervous system (CNS). By immunohistochemistry, 80% of the ADKs that metastasize to central nervous system expressed TrkB receptor compared to 33% expressing of ADKs without CNS metastasis. Moreover, ADKs with CNS metastasis showed an elevated expression of the full-length TrkB receptor. The TrkB receptor FL/T1 ratio was statistically higher in primary ADKs with brain metastasis compared to ADKs without brain metastasis. Our data indicate that TrkB full-length isoform expression in primary ADK cells may be associated with higher risk to develop brain metastasis. Therefore, TrkB receptor may possess prognostic and therapeutic implications in lung ADK

Implementing textural features on GPUs for improved real-time pavement distress detection

The condition of municipal roads has deteriorated considerably in recent years, leading to large scale pavement distress such as cracks or potholes. In order to enable road maintenance, pavement distress should be timely detected. However, manual investigation, which is still the most widely applied approach toward pavement assessment, puts maintenance personnel at risk and is time-consuming. During the last decade, several efforts have been made to automatically assess the condition of the municipal roads without any human intervention. Vehicles are equipped with sensors and cameras in order to collect data related to pavement distress and record videos of the pavement surface. Yet, this data are usually not processed while driving, but instead it is recorded and later analyzed off-line. As a result, a vast amount of memory is required to store the data and the available memory may not be sufficient. To reduce the amount of saved data, the authors have previously proposed a graphics processing units (GPU)-enabled pavement distress detection approach based on the wavelet transform of pavement images. The GPU implementation enables pavement distress detection in real time. Although the method used in the approach provides very good results, the method can still be improved by incorporating pavement surface texture characteristics. This paper presents an implementation of textural features on GPUs for pavement distress detection. Textural features are based on gray-tone spatial dependencies in an image and characterize the image texture. To evaluate the computational efficiency of the GPU implementation, performance tests are carried out. The results show that the speedup achieved by implementing the textural features on the GPU is sufficient to enable real-time detection of pavement distress. In addition, classification results obtained by applying the approach on 16,601 pavement images are compared to the results without integrating textural features. There results demonstrate that an improvement of 27% is achieved by incorporating pavement surface texture characteristics

Using combined diagnostic test results to hindcast trends of infection from cross-sectional data

Infectious disease surveillance is key to limiting the consequences from infectious pathogens and maintaining animal and public health. Following the detection of a disease outbreak, a response in proportion to the severity of the outbreak is required. It is thus critical to obtain accurate information concerning the origin of the outbreak and its forward trajectory. However, there is often a lack of situational awareness that may lead to over- or under-reaction. There is a widening range of tests available for detecting pathogens, with typically different temporal characteristics, e.g. in terms of when peak test response occurs relative to time of exposure. We have developed a statistical framework that combines response level data from multiple diagnostic tests and is able to ‘hindcast’ (infer the historical trend of) an infectious disease epidemic. Assuming diagnostic test data from a cross-sectional sample of individuals infected with a pathogen during an outbreak, we use a Bayesian Markov Chain Monte Carlo (MCMC) approach to estimate time of exposure, and the overall epidemic trend in the population prior to the time of sampling. We evaluate the performance of this statistical framework on simulated data from epidemic trend curves and show that we can recover the parameter values of those trends. We also apply the framework to epidemic trend curves taken from two historical outbreaks: a bluetongue outbreak in cattle, and a whooping cough outbreak in humans. Together, these results show that hindcasting can estimate the time since infection for individuals and provide accurate estimates of epidemic trends, and can be used to distinguish whether an outbreak is increasing or past its peak. We conclude that if temporal characteristics of diagnostics are known, it is possible to recover epidemic trends of both human and animal pathogens from cross-sectional data collected at a single point in time

A multi-targeted approach to suppress tumor-promoting inflammation

Cancers harbor significant genetic heterogeneity and patterns of relapse following many therapies are due to evolved resistance to treatment. While efforts have been made to combine targeted therapies, significant levels of toxicity have stymied efforts to effectively treat cancer with multi-drug combinations using currently approved therapeutics. We discuss the relationship between tumor-promoting inflammation and cancer as part of a larger effort to develop a broad-spectrum therapeutic approach aimed at a wide range of targets to address this heterogeneity. Specifically, macrophage migration inhibitory factor, cyclooxygenase-2, transcription factor nuclear factor-κB, tumor necrosis factor alpha, inducible nitric oxide synthase, protein kinase B, and CXC chemokines are reviewed as important antiinflammatory targets while curcumin, resveratrol, epigallocatechin gallate, genistein, lycopene, and anthocyanins are reviewed as low-cost, low toxicity means by which these targets might all be reached simultaneously. Future translational work will need to assess the resulting synergies of rationally designed antiinflammatory mixtures (employing low-toxicity constituents), and then combine this with similar approaches targeting the most important pathways across the range of cancer hallmark phenotypes

Simultaneous detection of lung fusions using a multiplex RT-PCR next generation sequencing-based approach:A multi-institutional research study

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