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Type 2 diabetes is characterized by impaired glucose homeostasis due to defects in insulin secretion, insulin resistance and the incretin response. GPR40 (FFAR1 or FFA1) is a G-protein-coupled receptor (GPCR), primarily expressed in insulin-producing pancreatic β-cells and incretin-producing enteroendocrine cells of the small intestine. Several GPR40 agonists, including AMG 837 and TAK-875, have been disclosed, but no GPR40 synthetic agonists have been reported that engage both the insulinogenic and incretinogenic axes. In this report we provide a molecular explanation and describe the discovery of a unique and potent class of GPR40 full agonists that engages the enteroinsular axis to promote dramatic improvement in glucose control in rodents. GPR40 full agonists AM-1638 and AM-6226 stimulate GLP-1 and GIP secretion from intestinal enteroendocrine cells and increase GSIS from pancreatic islets, leading to enhanced glucose control in the high fat fed, streptozotocin treated and NONcNZO10/LtJ mouse models of type 2 diabetes. The improvement in hyperglycemia by AM-1638 was reduced in the presence of the GLP-1 receptor antagonist Ex(9-39)NH(2).

Agonists of GPR40 (FFA1) have been proposed as a means to treat type 2 diabetes. Through lead optimization of a high throughput screening hit, we have identified a novel GPR40 agonist called AMG 837. The objective of these studies was to understand the preclinical pharmacological properties of AMG 837. The activity of AMG 837 on GPR40 was characterized through GTPγS binding, inositol phosphate accumulation and Ca(2+) flux assays. Activity of AMG 837 on insulin release was assessed on isolated primary mouse islets. To determine the anti-diabetic activity of AMG 837 in vivo, we tested AMG 837 using a glucose tolerance test in normal Sprague-Dawley rats and obese Zucker fatty rats. AMG 837 was a potent partial agonist in the calcium flux assay on the GPR40 receptor and potentiated glucose stimulated insulin secretion in vitro and in vivo. Acute administration of AMG 837 lowered glucose excursions and increased glucose stimulated insulin secretion during glucose tolerance tests in both normal and Zucker fatty rats. The improvement in glucose excursions persisted following daily dosing of AMG 837 for 21-days in Zucker fatty rats. Preclinical studies demonstrated that AMG 837 was a potent GPR40 partial agonist which lowered post-prandial glucose levels. These studies support the potential utility of AMG 837 for the treatment of type 2 diabetes.

Laser transmission spectroscopy (LTS) is a quantitative and rapid in vitro technique for measuring the size, shape, and number of nanoparticles in suspension. Here we report on the application of LTS as a novel detection method for species-specific DNA where the presence of one invasive species was differentiated from a closely related invasive sister species. The method employs carboxylated polystyrene nanoparticles functionalized with short DNA fragments that are complimentary to a specific target DNA sequence. In solution, the DNA strands containing targets bind to the tags resulting in a sizable increase in the nanoparticle diameter, which is rapidly and quantitatively measured using LTS. DNA strands that do not contain the target sequence do not bind and produce no size change of the carboxylated beads. The results show that LTS has the potential to become a quantitative and rapid DNA detection method suitable for many real-world applications.

ObjectiveOur goal was to review the available literature on prognostic risk prediction for incident hypertension, synthesize performance, and provide suggestions for future work on the topic.MethodsA systematic search on PUBMED and Web of Science databases was conducted for studies on prognostic risk prediction models for incident hypertension in generally healthy individuals. Study-quality was assessed using the Prediction model Risk of Bias Assessment Tool (PROBAST) checklist. Three-level meta-analyses were used to obtain pooled AUC/C-statistic estimates. Heterogeneity was explored using study and cohort characteristics in meta-regressions.ResultsFrom 5090 hits, we found 53 eligible studies, and included 47 in meta-analyses. Only four studies were assessed to have results with low risk of bias. Few models had been externally validated, with only the Framingham risk model validated more than thrice. The pooled AUC/C-statistics were 0.82 (0.77-0.86) for machine learning models and 0.78 (0.76-0.80) for traditional models, with high heterogeneity in both groups (I2 > 99%). Intra-class correlations within studies were 60% and 90%, respectively. Follow-up time (P = 0.0405) was significant for ML models and age (P = 0.0271) for traditional models in explaining heterogeneity. Validations of the Framingham risk model had high heterogeneity (I2 > 99%).ConclusionOverall, the quality of included studies was assessed as poor. AUC/C-statistic were mostly acceptable or good, and higher for ML models than traditional models. High heterogeneity implies large variability in the performance of new risk models. Further, large heterogeneity in validations of the Framingham risk model indicate variability in model performance on new populations. To enable researchers to assess hypertension risk models, we encourage adherence to existing guidelines for reporting and developing risk models, specifically reporting appropriate performance measures. Further, we recommend a stronger focus on validation of models by considering reasonable baseline models and performing external validations of existing models. Hence, developed risk models must be made available for external researchers.

PurposeThis study aims to explore training strategies to improve convolutional neural network-based image-to-image deformable registration for abdominal imaging.MethodsDifferent training strategies, loss functions, and transfer learning schemes were considered. Furthermore, an augmentation layer which generates artificial training image pairs on-the-fly was proposed, in addition to a loss layer that enables dynamic loss weighting.ResultsGuiding registration using segmentations in the training step proved beneficial for deep-learning-based image registration. Finetuning the pretrained model from the brain MRI dataset to the abdominal CT dataset further improved performance on the latter application, removing the need for a large dataset to yield satisfactory performance. Dynamic loss weighting also marginally improved performance, all without impacting inference runtime.ConclusionUsing simple concepts, we improved the performance of a commonly used deep image registration architecture, VoxelMorph. In future work, our framework, DDMR, should be validated on different datasets to further assess its value.

PurposeCancer is among the leading causes of death in the developed world, and lung cancer is the most lethal type. Early detection is crucial for better prognosis, but can be resource intensive to achieve. Automating tasks such as lung tumor localization and segmentation in radiological images can free valuable time for radiologists and other clinical personnel. Convolutional neural networks may be suited for such tasks, but require substantial amounts of labeled data to train. Obtaining labeled data is a challenge, especially in the medical domain.MethodsThis paper investigates the use of a teacher-student design to utilize datasets with different types of supervision to train an automatic model performing pulmonary tumor segmentation on computed tomography images. The framework consists of two models: the student that performs end-to-end automatic tumor segmentation and the teacher that supplies the student additional pseudo-annotated data during training.ResultsUsing only a small proportion of semantically labeled data and a large number of bounding box annotated data, we achieved competitive performance using a teacher-student design. Models trained on larger amounts of semantic annotations did not perform better than those trained on teacher-annotated data. Our model trained on a small number of semantically labeled data achieved a mean dice similarity coefficient of 71.0 on the MSD Lung dataset.ConclusionsOur results demonstrate the potential of utilizing teacher-student designs to reduce the annotation load, as less supervised annotation schemes may be performed, without any real degradation in segmentation accuracy.

Agonists of GPR40 (FFA1) have been proposed as a means to treat type 2 diabetes. Through lead optimization of a high throughput screening hit, we have identified a novel GPR40 agonist called AMG 837. The objective of these studies was to understand the preclinical pharmacological properties of AMG 837. The activity of AMG 837 on GPR40 was characterized through GTPγS binding, inositol phosphate accumulation and Ca2+ flux assays. Activity of AMG 837 on insulin release was assessed on isolated primary mouse islets. To determine the anti-diabetic activity of AMG 837 in vivo, we tested AMG 837 using a glucose tolerance test in normal Sprague-Dawley rats and obese Zucker fatty rats. AMG 837 was a potent partial agonist in the calcium flux assay on the GPR40 receptor and potentiated glucose stimulated insulin secretion in vitro and in vivo. Acute administration of AMG 837 lowered glucose excursions and increased glucose stimulated insulin secretion during glucose tolerance tests in both normal and Zucker fatty rats. The improvement in glucose excursions persisted following daily dosing of AMG 837 for 21-days in Zucker fatty rats. Preclinical studies demonstrated that AMG 837 was a potent GPR40 partial agonist which lowered post-prandial glucose levels. These studies support the potential utility of AMG 837 for the treatment of type 2 diabetes. [ABSTRACT FROM AUTHOR]

Visible light (400-700 nm) lies outside of the spectral range of what photobiologists define as deleterious radiation and as a result few studies have studied the effects of visible light range of wavelengths on skin. This oversight is important considering that during outdoors activities skin is exposed to the full solar spectrum, including visible light, and to multiple exposures at different times and doses. Although the contribution of the UV component of sunlight to skin damage has been established, few studies have examined the effects of non-UV solar radiation on skin physiology in terms of inflammation, and limited information is available regarding the role of visible light on pigmentation. The purpose of this study was to determine the effect of visible light on the pro-pigmentation pathways and melanin formation in skin. Exposure to visible light in ex-vivo and clinical studies demonstrated an induction of pigmentation in skin by visible light. Results showed that a single exposure to visible light induced very little pigmentation whereas multiple exposures with visible light resulted in darker and sustained pigmentation. These findings have potential implications on the management of photo-aggravated pigmentary disorders, the proper use of sunscreens, and the treatment of depigmented lesions.

Our motivation is increased bronchoscopic diagnostic yield and optimized preparation, for navigated bronchoscopy. In navigated bronchoscopy, virtual 3D airway visualization is often used to guide a bronchoscopic tool to peripheral lesions, synchronized with the real time video bronchoscopy. Visualization during navigated bronchoscopy, the segmentation time and methods, differs. Time consumption and logistics are two essential aspects that need to be optimized when integrating such technologies in the interventional room. We compared three different approaches to obtain airway centerlines and surface.CT lung dataset of 17 patients were processed in Mimics (Materialize, Leuven, Belgium), which provides a Basic module and a Pulmonology module (beta version) (MPM), OsiriX (Pixmeo, Geneva, Switzerland) and our Tube Segmentation Framework (TSF) method. Both MPM and TSF were evaluated with reference segmentation. Automatic and manual settings allowed us to segment the airways and obtain 3D models as well as the centrelines in all datasets. We compared the different procedures by user interactions such as number of clicks needed to process the data and quantitative measures concerning the quality of the segmentation and centrelines such as total length of the branches, number of branches, number of generations, and volume of the 3D model.The TSF method was the most automatic, while the Mimics Pulmonology Module (MPM) and the Mimics Basic Module (MBM) resulted in the highest number of branches. MPM is the software which demands the least number of clicks to process the data. We found that the freely available OsiriX was less accurate compared to the other methods regarding segmentation results. However, the TSF method provided results fastest regarding number of clicks. The MPM was able to find the highest number of branches and generations. On the other hand, the TSF is fully automatic and it provides the user with both segmentation of the airways and the centerlines. Reference segmentation comparison averages and standard deviations for MPM and TSF correspond to literature.The TSF is able to segment the airways and extract the centerlines in one single step. The number of branches found is lower for the TSF method than in Mimics. OsiriX demands the highest number of clicks to process the data, the segmentation is often sparse and extracting the centerline requires the use of another software system. Two of the software systems performed satisfactory with respect to be used in preprocessing CT images for navigated bronchoscopy, i.e. the TSF method and the MPM. According to reference segmentation both TSF and MPM are comparable with other segmentation methods. The level of automaticity and the resulting high number of branches plus the fact that both centerline and the surface of the airways were extracted, are requirements we considered particularly important. The in house method has the advantage of being an integrated part of a navigation platform for bronchoscopy, whilst the other methods can be considered preprocessing tools to a navigation system.

Most spectral data for the amphibian integument are limited to the visible spectrum of light and have been collected using point measurements with low spatial resolution. In the present study a dual camera setup consisting of two push broom hyperspectral imaging systems was employed, which produces reflectance images between 400 and 2500 nm with high spectral and spatial resolution and a high dynamic range.We briefly introduce the system and document the high efficiency of this technique analyzing exemplarily the spectral reflectivity of the integument of three arboreal anuran species (Litoria caerulea, Agalychnis callidryas and Hyla arborea), all of which appear green to the human eye. The imaging setup generates a high number of spectral bands within seconds and allows non-invasive characterization of spectral characteristics with relatively high working distance. Despite the comparatively uniform coloration, spectral reflectivity between 700 and 1100 nm differed markedly among the species. In contrast to H. arborea, L. caerulea and A. callidryas showed reflection in this range. For all three species, reflectivity above 1100 nm is primarily defined by water absorption. Furthermore, the high resolution allowed examining even small structures such as fingers and toes, which in A. callidryas showed an increased reflectivity in the near infrared part of the spectrum.Hyperspectral imaging was found to be a very useful alternative technique combining the spectral resolution of spectrometric measurements with a higher spatial resolution. In addition, we used Digital Infrared/Red-Edge Photography as new simple method to roughly determine the near infrared reflectivity of frog specimens in field, where hyperspectral imaging is typically difficult.

The validated EndoPredict assay is a novel tool to predict the risk of metastases of patients with estrogen receptor positive, HER2 negative breast cancer treated with endocrine therapy alone. It has been designed to integrate genomic and clinical information and includes clinico-pathological factors such as tumor size and nodal status. The test is feasible in a decentral setting in molecular pathology laboratories. In this project, we investigated the performance of this test in clinical practice, and performed a retrospective evaluation of its impact on treatment decisions in breast cancer. During one year, EndoPredict assays from 167 patients could be successfully performed. For retrospective evaluation of treatment decisions, a questionnaire was sent to the clinical partner. Regarding the molecular EP class, samples from 56 patients (33.5%) had a low-risk, whereas 111 patients (66.5%) showed a high-risk gene profile. After integration of the clinicopathological factors the combined clinical and molecular score (EPclin) resulted in a low-risk group of 77 patients (46.4%), while 89 (53.6%) had a high risk EPclin score. The EPclin-based estimated median 10-year-risk for metastases with endocrine therapy alone was 11% for the whole cohort. The median handling time averaged three days (range: 0 to 11 days), 59.3% of the tests could be performed in three or less than three days. Comparison of pre- and post-test therapy decisions showed a change of therapy in 37.7% of patients. 16 patients (12.3%) had a change to an additional chemotherapy while 25.4% of patients (n = 33) changed to an endocrine therapy alone. In 73 patients (56.2%) no change of therapy resulted. In 6.1% of patients (n = 8), the patients did not agree to the recommendation of the tumor board. Our results show that the EndoPredict assay could be routinely performed in decentral molecular pathology laboratories and the results markedly change treatment decisions.

BackgroundComputational discovery is playing an ever-greater role in supporting the processes of knowledge synthesis. A significant proportion of the more than 18 million manuscripts indexed in the PubMed database describe infectious disease syndromes and various infectious agents. This study is the first attempt to integrate online repositories of text-based publications and microbial genome databases in order to explore the dynamics of relationships between pathogens and infectious diseases.Methodology/principal findingsHerein we demonstrate how the knowledge space of infectious diseases can be computationally represented and quantified, and tracked over time. The knowledge space is explored by mapping of the infectious disease literature, looking at dynamics of literature deposition, zooming in from pathogen to genome level and searching for new associations. Syndromic signatures for different pathogens can be created to enable a new and clinically focussed reclassification of the microbial world. Examples of syndrome and pathogen networks illustrate how multilevel network representations of the relationships between infectious syndromes, pathogens and pathogen genomes can illuminate unexpected biological similarities in disease pathogenesis and epidemiology.Conclusions/significanceThis new approach based on text and data mining can support the discovery of previously hidden associations between diseases and microbial pathogens, clinically relevant reclassification of pathogenic microorganisms and accelerate the translational research enterprise.