Thymic neuroendocrine tumors in patients with multiple endocrine neoplasia type 1

Objective MEN1 is associated with an increased risk of developing tumors in different endocrine organs. Neuroendocrine tumors of the thymus (TNETs) are very rare but often have an aggressive nature. We evaluated patients with MEN1 and TNET in three university hospitals in Finland. Design/Methods We evaluated patient records of 183 MEN1-patients from three university hospitals between the years 1985-2019 with TNETs. Thymus tumor specimens were classified according to the new WHO 2021 classification of TNET. We collected data on treatments and outcomes of these patients. Results There were six patients (3.3%) with MEN1 and TNET. Five of them had the same common gene mutation occurring in Finland. They originated from common ancestors encompassing two pairs of brothers from sequential generations. The mean age at presentation of TNET was 44.7 +/- 11.9 years. TNET was classified as atypical carcinoid (AC) in five out of six patients. One patient had a largely necrotic main tumor with very few mitoses and another nodule with 25 mitoses per 2 mm(2), qualifying for the 2021 WHO diagnosis of large cell neuroendocrine carcinoma (LCNEC). In our patients, the 5-year survival of the TNET patients was 62.5% and 10-year survival 31.3%. Conclusion In this study, TNETs were observed in one large MEN1 founder pedigree, where an anticipation-like earlier disease onset was observed in the most recent generation. TNET in MEN1 patients is an aggressive disease. The prognosis can be better by systematic screening. We also show that LCNEC can be associated with TNET in MEN1 patients.Peer reviewe

Detecting, connecting and characterising drainage ditches from airborne LiDAR data

Artificial drainage networks sculpt landscapes as they result from the constant efforts of farmers adjusting landscapes to the needs and constraints imposed by agriculture. As structural landscape elements, they play a role in flood control and biogeochemical cycles. Furthermore, they are ecological hotspots and enhance connectivity within the landscape. Spatially distributed process models that estimate rainfall-runoff, predict flood levels or assess water resources increasingly rely on spatially explicit drainage network data. To increase confidence in model outputs, more complete and detailed input data on the drainage network is required. These detailed input data encompass the location and characteristics of the entire drainage network, including smaller watercourses such as ditches. Hydrographic reference data particularly lacks data on agricultural ditches. The emergence of airborne laser data with high spatial resolution and accuracy, offers the potential for developing innovative methods to detect and characterise drainage ditches. The general objective of this dissertation was to develop and evaluate automated methods for detecting ditches and extracting their morphologic characteristics. The most recent (2014/2015) acquired airborne LiDAR point cloud data and derived digital elevation models (DEM) were used in combination with multispectral aerial data. The developed methods allow to detect and characterise functional agricultural ditches and remnants of such ditches in former agricultural areas. Two approaches were followed to detect ditches. The first one is based on the derived rasterised DEMs by calculating a relative elevation attribute. The second one is based on the original LiDAR point clouds using trained random forest classifiers. The detection methods resulted in completenesses of 95% and 97% respectively, and this with a positional accuracy of less than 0.4m. The DEM based method turned out to be simpler and less time-consuming to implement and does not require any training data. A disadvantage common to both methods is that the hydrological connectivity is not accounted for. Ditch drainage networks consist of connected linear features, such as culverts but which may also be blocked due to poor maintenance. As no geographic ancillary data on culverts were available, the probabilities of candidate network segments to be true connecting segments were calculated with a logistic regression model. The independent variables were derived from the characteristics of the ditch centre lines and from topographic features based on the DEM. The logistic regression model allowed to connect 69% of the originally erroneous false disconnections in the network. The number of network fragments was reduced with 71%, representing the reference ditch network fairly well. Cross sectional profiles of ditches could be modelled in an automated way using the accurately detected and connected ditch network centre lines. The cross sectional profiles were modelled by extracting the LiDAR points making up the profile and using splines. The presence of water and vegetation affect the extraction of cross-sectional profiles. Nevertheless, the extracted ditch widths were highly correlated with the reference observations (R² = 0.87, ME = –0.15m). The extraction method proved to be robust with water levels being successfully determined. The potential, and limitations, of the developed methods for detecting and characterising drainage ditches for modelling water runoff and the leaching of nitrates from agricultural land was analysed for a 285 km² large subbasin in West-Flanders using the Nutrient Emission Model for agriculture (NEMO). The developed methods allowed to update hydrographic reference data sets on the occurrence of drainage ditches and on their actual drainage depth. The total density of the input watercourse network increased from 1.5 km/km² to 4 km/km². The originally fixed drainage depth of 0.9m was replaced by spatially variable drainage depths. The output of the NEMO model showed that for the downstream catchments, both the relative contribution of the groundwater runoff to the total runoff and the nitrate concentrations in the river surface water were most sensitive to the drainage extent. For the headwater catchment, the relative groundwater runoff and total nitrate concentrations were most sensitive to the spatially distributed drainage depths from the river cells, both for the original and updated drainage extent input. Where the hydrographic reference data was incomplete - or contained errors - it could be updated with the proposed methods using LiDAR data. Identifying areas with excessive runoff of water or nutrients with spatially distributed hydrologic models can also be improved with the proposed methods using airborne LiDAR data.status: publishe

All-Solid-State Textile Batteries Made from Nano-Emulsion Conducting Polymer Inks for Wearable Electronics

A rollable and all-solid-state textile lithium battery based on fabric matrix and polymer electrolyte that allows flexibility and fast-charging capability is reported. When immerged into poly(3,4-ethylenedioxythiophene) (PEDOT) nano-emulsion inks, an insulating fabric is converted into a conductive battery electrode for a fully solid state lithium battery with the highest specific energy capacity of 68 mAh/g. This is superior to most of the solid-state conducting polymer primary and/or secondary batteries reported. The bending radius of such a textile battery is less than 1.5 mm while lightening up an LED. This new material combination and inherent flexibility is well suited to provide an energy source for future wearable and woven electronics

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status: publishe

All-Solid-State Textile Batteries Made from Nano-Emulsion Conducting Polymer Inks for Wearable Electronics

A rollable and all-solid-state textile lithium battery based on fabric matrix and polymer electrolyte that allows flexibility and fast-charging capability is reported. When immerged into poly(3,4-ethylenedioxythiophene) (PEDOT) nano-emulsion inks, an insulating fabric is converted into a conductive battery electrode for a fully solid state lithium battery with the highest specific energy capacity of 68 mAh/g. This is superior to most of the solid-state conducting polymer primary and/or secondary batteries reported. The bending radius of such a textile battery is less than 1.5 mm while lightening up an LED. This new material combination and inherent flexibility is well suited to provide an energy source for future wearable and woven electronics