Endogenous glucagon-like peptide 1 controls endocrine pancreatic secretion and antro-pyloro-duodenal motility in humans

Background: Exogenous use of the intestinal hormone glucagon-like peptide 1 (GLP-1) lowers glycaemia by stimulation of insulin, inhibition of glucagon, and delay of gastric emptying.Aims: To assess the effects of endogenous GLP-1 on endocrine pancreatic secretion and antro-pyloro-duodenal motility by utilising the GLP-1 receptor antagonist exendin(9-39)amide (ex(9-39)NH2).Methods: Nine healthy volunteers underwent four experiments each. In two experiments with and without intravenous infusion of ex(9-39)NH2 300 pmol/kg/min, a fasting period was followed by intraduodenal glucose perfusion at 1 and 2.5 kcal/min, with the higher dose stimulating GLP-1 release. Antro-pyloro-duodenal motility was measured by perfusion manometry. To calculate the incretin effect (that is, the proportion of plasma insulin stimulated by intestinal hormones) the glycaemia observed during the luminal glucose experiments was mimicked using intravenous glucose in two further experiments.Results: Ex(9-39)NH2 significantly increased glycaemia during fasting and duodenal glucose. It diminished plasma insulin during duodenal glucose and significantly reduced the incretin effect by approximately 50%. Ex(9-39)NH2 raised plasma glucagon during fasting and abolished the decrease in glucagon at the high duodenal glucose load. Ex(9-39)NH2 markedly stimulated antroduodenal contractility. At low duodenal glucose it reduced the stimulation of tonic and phasic pyloric motility. At the high duodenal glucose load it abolished pyloric stimulation.Conclusions: Endogenous GLP-1 stimulates postprandial insulin release. The pancreatic \textgreeka cell is under the tonic inhibitory control of GLP-1 thereby suppressing postprandial glucagon. GLP-1 tonically inhibits antroduodenal motility and mediates the postprandial inhibition of antral and stimulation of pyloric motility. We therefore suggest GLP-1 as a true incretin hormone and enterogastrone in humans

Saliency-Enhanced Content-Based Image Retrieval for Diagnosis Support in Dermatology Consultation: Reader Study.

BACKGROUND Previous research studies have demonstrated that medical content image retrieval can play an important role by assisting dermatologists in skin lesion diagnosis. However, current state-of-the-art approaches have not been adopted in routine consultation, partly due to the lack of interpretability limiting trust by clinical users. OBJECTIVE This study developed a new image retrieval architecture for polarized or dermoscopic imaging guided by interpretable saliency maps. This approach provides better feature extraction, leading to better quantitative retrieval performance as well as providing interpretability for an eventual real-world implementation. METHODS Content-based image retrieval (CBIR) algorithms rely on the comparison of image features embedded by convolutional neural network (CNN) against a labeled data set. Saliency maps are computer vision-interpretable methods that highlight the most relevant regions for the prediction made by a neural network. By introducing a fine-tuning stage that includes saliency maps to guide feature extraction, the accuracy of image retrieval is optimized. We refer to this approach as saliency-enhanced CBIR (SE-CBIR). A reader study was designed at the University Hospital Zurich Dermatology Clinic to evaluate SE-CBIR's retrieval accuracy as well as the impact of the participant's confidence on the diagnosis. RESULTS SE-CBIR improved the retrieval accuracy by 7% (77% vs 84%) when doing single-lesion retrieval against traditional CBIR. The reader study showed an overall increase in classification accuracy of 22% (62% vs 84%) when the participant is provided with SE-CBIR retrieved images. In addition, the overall confidence in the lesion's diagnosis increased by 24%. Finally, the use of SE-CBIR as a support tool helped the participants reduce the number of nonmelanoma lesions previously diagnosed as melanoma (overdiagnosis) by 53%. CONCLUSIONS SE-CBIR presents better retrieval accuracy compared to traditional CBIR CNN-based approaches. Furthermore, we have shown how these support tools can help dermatologists and residents improve diagnosis accuracy and confidence. Additionally, by introducing interpretable methods, we should expect increased acceptance and use of these tools in routine consultation

Saliency-Enhanced Content-Based Image Retrieval for Diagnosis Support in Dermatology Consultation: Reader Study

BACKGROUND Previous research studies have demonstrated that medical content image retrieval can play an important role by assisting dermatologists in skin lesion diagnosis. However, current state-of-the-art approaches have not been adopted in routine consultation, partly due to the lack of interpretability limiting trust by clinical users. OBJECTIVE This study developed a new image retrieval architecture for polarized or dermoscopic imaging guided by interpretable saliency maps. This approach provides better feature extraction, leading to better quantitative retrieval performance as well as providing interpretability for an eventual real-world implementation. METHODS Content-based image retrieval (CBIR) algorithms rely on the comparison of image features embedded by convolutional neural network (CNN) against a labeled data set. Saliency maps are computer vision-interpretable methods that highlight the most relevant regions for the prediction made by a neural network. By introducing a fine-tuning stage that includes saliency maps to guide feature extraction, the accuracy of image retrieval is optimized. We refer to this approach as saliency-enhanced CBIR (SE-CBIR). A reader study was designed at the University Hospital Zurich Dermatology Clinic to evaluate SE-CBIR's retrieval accuracy as well as the impact of the participant's confidence on the diagnosis. RESULTS SE-CBIR improved the retrieval accuracy by 7% (77% vs 84%) when doing single-lesion retrieval against traditional CBIR. The reader study showed an overall increase in classification accuracy of 22% (62% vs 84%) when the participant is provided with SE-CBIR retrieved images. In addition, the overall confidence in the lesion's diagnosis increased by 24%. Finally, the use of SE-CBIR as a support tool helped the participants reduce the number of nonmelanoma lesions previously diagnosed as melanoma (overdiagnosis) by 53%. CONCLUSIONS SE-CBIR presents better retrieval accuracy compared to traditional CBIR CNN-based approaches. Furthermore, we have shown how these support tools can help dermatologists and residents improve diagnosis accuracy and confidence. Additionally, by introducing interpretable methods, we should expect increased acceptance and use of these tools in routine consultation

Lung disease caused by ABCA3 mutations

Background Knowledge about the clinical spectrum of lung disease caused by variations in the ATP binding cassette subfamily A member 3 (ABCA3) gene is limited. Here we describe genotype-phenotype correlations in a European cohort. Methods We retrospectively analysed baseline and outcome characteristics of 40 patients with two disease-causing ABCA3 mutations collected between 2001 and 2015. Results Of 22 homozygous (15 male) and 18 compound heterozygous patients (3 male), 37 presented with neonatal respiratory distress syndrome as term babies. At follow-up, two major phenotypes are documented: patients with (1) early lethal mutations subdivided into (1a) dying within the first 6 months or (1b) before the age of 5 years, and (2) patients with prolonged survival into childhood, adolescence or adulthood. Patients with null/null mutations predicting complete ABCA3 deficiency died within the 1st weeks to months of life, while those with null/other or other/other mutations had a more variable presentation and outcome. Treatment with exogenous surfactant, systemic steroids, hydroxychloroquine and whole lung lavages had apparent but many times transient effects in individual subjects. Conclusions Overall long-term (>5 years) survival of subjects with two disease-causing ABCA3 mutations was <20%. Response to therapies needs to be ascertained in randomised controlled trials

The Oxygen Paradox, the French Paradox, and age-related diseases

open46openDavies, Joanna M. S.; Cillard, Josiane; Friguet, Bertrand; Cadenas, Enrique; Cadet, Jean; Cayce, Rachael; Fishmann, Andrew; Liao, David; Bulteau, Anne-Laure; Derbré, Frédéric; Rébillard, Amélie; Burstein, Steven; Hirsch, Etienne; Kloner, Robert A.; Jakowec, Michael; Petzinger, Giselle; Sauce, Delphine; Sennlaub, Florian; Limon, Isabelle; Ursini, Fulvio; Maiorino, Matilde; Economides, Christina; Pike, Christian J.; Cohen, Pinchas; Salvayre, Anne Negre; Halliday, Matthew R.; Lundquist, Adam J.; Jakowec, Nicolaus A.; Mechta-Grigoriou, Fatima; Mericskay, Mathias; Mariani, Jean; Li, Zhenlin; Huang, David; Grant, Ellsworth; Forman, Henry J.; Finch, Caleb E.; Sun, Patrick Y.; Pomatto, Laura C. D.; Agbulut, Onnik; Warburton, David; Neri, Christian; Rouis, Mustapha; Cillard, Pierre; Capeau, Jacqueline; Rosenbaum, Jean; Davies, Kelvin J. A.Davies, Joanna M. S.; Cillard, Josiane; Friguet, Bertrand; Cadenas, Enrique; Cadet, Jean; Cayce, Rachael; Fishmann, Andrew; Liao, David; Bulteau, Anne-Laure; Derbré, Frédéric; Rébillard, Amélie; Burstein, Steven; Hirsch, Etienne; Kloner, Robert A.; Jakowec, Michael; Petzinger, Giselle; Sauce, Delphine; Sennlaub, Florian; Limon, Isabelle; Ursini, Fulvio; Maiorino, Matilde; Economides, Christina; Pike, Christian J.; Cohen, Pinchas; Salvayre, Anne Negre; Halliday, Matthew R.; Lundquist, Adam J.; Jakowec, Nicolaus A.; Mechta-Grigoriou, Fatima; Mericskay, Mathias; Mariani, Jean; Li, Zhenlin; Huang, David; Grant, Ellsworth; Forman, HENRY J.; Finch, Caleb E.; Sun, Patrick Y.; Pomatto, Laura C. D.; Agbulut, Onnik; Warburton, David; Neri, Christian; Rouis, Mustapha; Cillard, Pierre; Capeau, Jacqueline; Rosenbaum, Jean; Davies, Kelvin J. A

Snow height on sea ice and sea ice drift from autonomous measurements from buoy 2017S48, deployed during POLARSTERN cruise PS103 (ANT-XXXII/2)

Snow height was measured by the Snow Depth Buoy 2017S48, an autonomous platform, drifting on Antarctic sea ice, during POLARSTERN cruise PS103 (ANT-XXXII/2). The resulting time series describes the evolution of snow depth as a function of place and time between 05 Jan 2017 and 14 Mar 2017 in sample intervals of 1 hour. The Snow Depth Buoy consists of four independent sonar measurements representing the area (approx. 10 m**2) around the buoy. The buoy was installed on attached fast ice in the Atka Bay. In addition to snow depth, geographic position (GPS), barometric pressure, air temperature, and an internal ice temperature were measured. Negative values of snow depth occur if surface ablation continues into the sea ice. Thus, these measurements describe the position of the sea ice surface relative to the original snow-ice interface. Differences between single sensors indicate small-scale variability of the snow pack around the buoy. The data set has been processed, including the removal of obvious inconsistencies (missing values). Records without any snow depth may still be used for sea ice drift analyses

Suspicious Skin Lesion Detection in Wide-Field Body Images using Deep Learning Outlier Detection

During consultation dermatologists have to address hundreds of lesions in a limited amount of time. They will not only evaluate the single lesion of interest but more importantly the context of it. Visually comparing the similarity of the majority of lesions within the same patient provides a strong indication for lesions with significantly differing aspects. Deep learning algorithms are capable to identify such outliers, i.e. images that differ considerably from the expected appearance on a larger cohort, and highlight the main differences in those cases. In the present study we evaluate the use of autoencoders as unsupervised tools to detect suspicious skin lesions based on evaluation of real world data acquired during consultation at the USZ Dermatology Clinic. Clinical Relevance— Deep learning algorithms are showing many promising results in dermatology lesion classification. However the context of the lesion is normally not considered in the analysis which prevents these tools to transition into routine practice. An outlier detector based on real world data would allow a dermatologist or general practitioner to detect the suspicious lesions for further examination. The algorithm would additionally provide useful insights by highlighting the feature differences between the original outlier (malignant lesion) and the lesion reconstructed by the autoencode