Adrenergic/Cholinergic Immunomodulation in the Rat Model—In Vivo Veritas?

For several years, our group has been studying the in vivo role of adrenergic and cholinergic mechanisms in the immune-neuroendocrine dialogue in the rat model. The main results of these studies can be summarized as follows: (1) exogenous or endogenous catecholamines suppress PBL functions through alpha-2-receptor-mediated mechanisms, lymphocytes of the spleen are resistant to adrenergic in vivo stimulation, (2) direct or indirect cholinergic treatment leads to enhanced ex vivo functions of splenic and thymic lymphocytes leaving PBL unaffected, (3) cholinergic pathways play a critical role in the “talking back” of the immune system to the brain, (4) acetylcholine inhibits apoptosis of thymocytes possibly via direct effects on thymic epithelial cells, and may thereby influence T-cell maturation, (5) lymphocytes of the various immunological compartments were found to be equipped with the key enzymes for the synthesis of both acetylcholine and norepinephrine, and to secrete these neurotransmitters in culture supernatant

Integrating Decision-Making Support in Geocollaboration Tools

info:eu-repo/semantics/publishedVersio

Validation of artificial intelligence prediction models for skin cancer diagnosis using dermoscopy images: the 2019 International Skin Imaging Collaboration Grand Challenge

Previous studies of artificial intelligence (AI) applied to dermatology have shown AI to have higher diagnostic classification accuracy than expert dermatologists; however, these studies did not adequately assess clinically realistic scenarios, such as how AI systems behave when presented with images of disease categories that are not included in the training dataset or images drawn from statistical distributions with significant shifts from training distributions. We aimed to simulate these real-world scenarios and evaluate the effects of image source institution, diagnoses outside of the training set, and other image artifacts on classification accuracy, with the goal of informing clinicians and regulatory agencies about safety and real-world accuracy.We designed a large dermoscopic image classification challenge to quantify the performance of machine learning algorithms for the task of skin cancer classification from dermoscopic images, and how this performance is affected by shifts in statistical distributions of data, disease categories not represented in training datasets, and imaging or lesion artifacts. Factors that might be beneficial to performance, such as clinical metadata and external training data collected by challenge participants, were also evaluated. 25?331 training images collected from two datasets (in Vienna [HAM10000] and Barcelona [BCN20000]) between Jan 1, 2000, and Dec 31, 2018, across eight skin diseases, were provided to challenge participants to design appropriate algorithms. The trained algorithms were then tested for balanced accuracy against the HAM10000 and BCN20000 test datasets and data from countries not included in the training dataset (Turkey, New Zealand, Sweden, and Argentina). Test datasets contained images of all diagnostic categories available in training plus other diagnoses not included in training data (not trained category). We compared the performance of the algorithms against that of 18 dermatologists in a simulated setting that reflected intended clinical use.64 teams submitted 129 state-of-the-art algorithm predictions on a test set of 8238 images. The best performing algorithm achieved 58·8% balanced accuracy on the BCN20000 data, which was designed to better reflect realistic clinical scenarios, compared with 82·0% balanced accuracy on HAM10000, which was used in a previously published benchmark. Shifted statistical distributions and disease categories not included in training data contributed to decreases in accuracy. Image artifacts, including hair, pen markings, ulceration, and imaging source institution, decreased accuracy in a complex manner that varied based on the underlying diagnosis. When comparing algorithms to expert dermatologists (2460 ratings on 1269 images), algorithms performed better than experts in most categories, except for actinic keratoses (similar accuracy on average) and images from categories not included in training data (26% correct for experts vs 6% correct for algorithms, p<0·0001). For the top 25 submitted algorithms, 47·1% of the images from categories not included in training data were misclassified as malignant diagnoses, which would lead to a substantial number of unnecessary biopsies if current state-of-the-art AI technologies were clinically deployed.We have identified specific deficiencies and safety issues in AI diagnostic systems for skin cancer that should be addressed in future diagnostic evaluation protocols to improve safety and reliability in clinical practice.Melanoma Research Alliance and La Marató de TV3.Copyright © 2022 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY-NC-ND 4.0 license. Published by Elsevier Ltd.. All rights reserved

A critical review on the use of molecular imprinting for trace heavy metal and micropollutant detection

Molecular recognition has been described as the “ultimate” form of sensing and plays a fundamental role in biological processes. There is a move towards biomimetic recognition elements to overcome inherent problems of natural receptors such as limited stability, high-cost, and variation in response. In recent years, several alternatives have emerged which have found their first commercial applications. In this review, we focus on molecularly imprinted polymers (MIPs) since they present an attractive alternative due to recent breakthroughs in polymer science and nanotechnology. For example, innovative solid-phase synthesis methods can produce MIPs with sometimes greater affinities than natural receptors. Although industry and environmental agencies require sensors for continuous monitoring, the regulatory barrier for employing MIP-based sensors is still low for environmental applications. Despite this, there are currently no sensors in this area, which is likely due to low profitability and the need for new legislation to promote the development of MIP-based sensors for pollutant and heavy metal monitoring. The increased demand for point-of-use devices and home testing kits is driving an exponential growth in biosensor production, leading to an expected market value of over GPB 25 billion by 2023. A key requirement of point-of-use devices is portability, since the test must be conducted at “the time and place” to pinpoint sources of contamination in food and/or water samples. Therefore, this review will focus on MIP-based sensors for monitoring pollutants and heavy metals by critically evaluating relevant literature sources from 1993 to 2022

Asteroids' physical models from combined dense and sparse photometry and scaling of the YORP effect by the observed obliquity distribution

The larger number of models of asteroid shapes and their rotational states derived by the lightcurve inversion give us better insight into both the nature of individual objects and the whole asteroid population. With a larger statistical sample we can study the physical properties of asteroid populations, such as main-belt asteroids or individual asteroid families, in more detail. Shape models can also be used in combination with other types of observational data (IR, adaptive optics images, stellar occultations), e.g., to determine sizes and thermal properties. We use all available photometric data of asteroids to derive their physical models by the lightcurve inversion method and compare the observed pole latitude distributions of all asteroids with known convex shape models with the simulated pole latitude distributions. We used classical dense photometric lightcurves from several sources and sparse-in-time photometry from the U.S. Naval Observatory in Flagstaff, Catalina Sky Survey, and La Palma surveys (IAU codes 689, 703, 950) in the lightcurve inversion method to determine asteroid convex models and their rotational states. We also extended a simple dynamical model for the spin evolution of asteroids used in our previous paper. We present 119 new asteroid models derived from combined dense and sparse-in-time photometry. We discuss the reliability of asteroid shape models derived only from Catalina Sky Survey data (IAU code 703) and present 20 such models. By using different values for a scaling parameter cYORP (corresponds to the magnitude of the YORP momentum) in the dynamical model for the spin evolution and by comparing synthetics and observed pole-latitude distributions, we were able to constrain the typical values of the cYORP parameter as between 0.05 and 0.6.Comment: Accepted for publication in A&A, January 15, 201

Human melanoma brain metastases cell line MUG-Mel1, isolated clones and their detailed characterization

Melanoma is a leading cause of high mortality that frequently spreads to the brain and is associated with deterioration in quality and quantity of life. Treatment opportunities have been restricted until now and new therapy options are urgently required. Our focus was to reveal the potential heterogeneity of melanoma brain metastasis. We succeeded to establish a brain melanoma metastasis cell line, namely MUG-Mel1 and two resulting clones D5 and C8 by morphological variety, differences in lipidome, growth behavior, surface, and stem cell markers. Mutation analysis by next-generation sequencing, copy number profiling, and cytogenetics demonstrated the different genetic profile of MUG-Mel1 and clones. Tumorigenicity was unsuccessfully tested in various mouse systems and finally established in a zebra fish model. As innovative treatment option, with high potential to pass the blood-brain barrier a peptide isolated from lactoferricin was studied in potential toxicity. Brain metastases are a major clinical challenge, therefore the development of relevant in vitro and in vivo models derived from brain melanoma metastases provides valuable information about tumor biology and offers great potential to screen for new innovative therapies.Animal science

Illusionary Self-Motion Perception in Zebrafish

Zebrafish mutant belladonna (bel) carries a mutation in the lhx2 gene (encoding a Lim domain homeobox transcription factor) that results in a defect in retinotectal axon pathfinding, which can lead to uncrossed optic nerves failing to form an optic chiasm. Here, we report on a novel swimming behavior of the bel mutants, best described as looping. Together with two previously reported oculomotor instabilities that have been related to achiasmatic bel mutants, reversed optokinetic response (OKR) and congenital nystagmus (CN, involuntary conjugate oscillations of both eyes), looping opens a door to study the influence of visual input and eye movements on postural balance. Our result shows that looping correlates perfectly with reversed OKR and CN and is vision-dependent and contrast sensitive. CN precedes looping and the direction of the CN slow phase is predictive of the looping direction, but is absent during looping. Therefore, looping may be triggered by CN in bel. Moreover, looping in wild-type fish can also be evoked by whole-field motion, suggesting that looping in a bel mutant larvae is a result of self-motion perception. In contrary to previous hypotheses, our findings indicate that postural control in vertebrates relies on both direct visual input (afference signal) and eye-movement-related signals (efference copy or reafference signal)