A Bayesian Approach to Carrying Capacity Estimate: The Case of Greek Coastal Cage Aquaculture

The estimation of the carrying capacity (CC) is a fundamental process in integrated environmental management, policy making, and decision making. Aquaculture carrying capacity has been studied since the 1960s to allow estimation of the production limits of aquaculture projects and, hence, their maximum economic performance within sustainable limits for the local environment. One major drawback of these approaches is that they can provide CC estimates after a fish farm is installed and operates in a certain location (ex post approaches). This paper approaches the estimation of CC using a Bayesian/CHAID model of profiling information on the environmental quality, geomorphology, and human activities on the adjacent coastal area (land side) using as an indicator the trophic state of the marine area in terms of chlorophyll-a concentration (upper mesotrophic). This way, having the above information for a certain site, it is possible to calculate the maximum annual production of a cage fish farm so that the trophic state of the area will not exceed the environmental goal of the upper mesotrophic level. We examined the effects of 27 different physical, chemical, social and geomorphological parameters on CC (in fish biomass terms). CC was found to be correlated by particulate nitrogen (PN), silicates (Si-SiO4), salinity, and suspended particulate matter (SPM). The overall relationship found is: Biomassat CC level = +473.762[Chl-a] − 6856.64[PN] + 9.302[Salinity] − 473.5[Si-SiO4] + 341.864[SPM] − 207.046. The analysis performed allowed us to estimate the maximum levels for each factor to maintain a eutrophication status up to the upper mesotrophic level: particulate nitrogen < 0.018 mg/L, silicates < 0.137 mg/L, salinity > 38 PSU and SPM > 0.815 mg/L. Finally, the current fish farm licensing legislation in Greece concerning the CC estimation algorithm is discussed. © 2022 by the authors

Medicines for Obesity: Appraisal of Clinical Studies with Grading of Recommendations, Assessment, Development, and Evaluation Tool

We evaluated the quality of evidence from phase III/IV clinical trials of drugs against obesity using the principles of Grading of Recommendations, Assessment, Development, and Evaluation (GRADE) tool. Our systematic review evaluates the quality of clinical evidence from existing clinical trials and not the pharmacological efficacy of anti-obesity therapies. A literature search using select keywords in separate was performed in PubMed and ClinicalTrials.gov databases for phase III/IV clinical trials during the last ten years. Our findings indicate that the quality of existing clinical evidence from anti-obesity trials generally ranges from low to moderate. Most trials suffered from publication bias. Less frequently, trials suffered from the risk of bias mainly due to lack of blindness in the treatment. Our work indicates that additional higher-quality clinical trials are needed to gain more confidence in the estimate of the effect of currently used anti-obesity medicines, to allow more informed clinical decisions, thus reducing the risk of implementing potentially ineffective or even harmful therapeutic strategies

Computerized cognitive rehabilitation for treatment of cognitive impairment in multiple sclerosis: An explorative study

In this explorative study, forty-seven patients with relapsing-remitting multiple sclerosis were randomized to a custom 6-week cognitive rehabilitation intervention (n = 23) using the BrainHQTM web-based platform and to a control group condition (n = 24). Cognitive rehabilitation intervention consisted of two 40-minute sessions per week. All patients were tested with the Brief International Cognitive Assessment for Multiple Sclerosis battery, the Stroop Color-Word Test, and the trail making test, while the Beck Depression Inventory-Fast Screen questionnaire was used as a measure of mood and the cognitive reserve index as a measure of cognitive reserve. We used the reliable change index, to calculate clinically meaningful changes of performance, and to discriminate between responders and non-responders of this intervention. Statistically significant improvement of the group receiving treatment was observed mainly on measures of verbal and nonverbal episodic memory and, to a lesser extent, on reading speed, selective attention/response inhibition, and visual attention. Verbal memory and visual attention improvements remained significant after considering the corrected for multiple comparisons level of significance. According to reliable change index scores, 12/23 (52.2%) of patients in the intervention group presented meaningful improvement in at least one measure (Greek Verbal Learning Test: 26%, Brief Visuospatial Memory Test-Revised: 17.4%, Stroop-Words test: 13%). This explorative study provides evidence that, at least in the short term, cognitive rehabilitation may improve the cognitive performance of multiple sclerosis patients. © 2020 IMR Press Limited. All rights reserved

Tools for shared annotation of correlative 2D light microscopy and 3D X-ray histology

Histology is the microscopic study of biological tissues, typically performed by optical microscopy of thin sections. This informs biomedical research and clinical decision-making despite its limitation to two dimensions (2D). X-ray micro-computed tomography (CT) can non-destructively image tissues in 3D but lacks the biological specificity of classical histology. Combining optical microscopy with CT for correlative imaging of a single specimen joins the specificity of 2D histology with access to 3D microstructural information offered by CT, allowing exploration of tissue anatomy at a traditionally inaccessible level.However, integrating these different, but complementary image techniques is challenging due to differences in spatial resolution, orientation, and image contrast mechanisms of these images. 2D histology images have much higher spatial resolution than the CT image slices. Non-linear deformations are also introduced between CT and histology due to mechanical sectioning. Image registration can correct these deformations, but automated methods based on the pixel intensities is challenging due to the dissimilarities in image contrast mechanisms from the different imaging techniques.Conventional X-ray absorption-based CT has only recently been optimised for biological specimens1, so there is no established precedent on how biological structures may appear in CT compared to standard histology. Therefore, researchers often annotate histology images of matching regions of the specimen to identify biological structures of interest in CT datasets. Here, we present a workflow for transferring annotations made on 2D histology images to the corresponding 3D CT volume, and how these correlative images together provide a comprehensive view into tissue microstructure. This workflow covers semi-automated registration to match orientations of the images, transferring the histology annotations to the registered images, and generating visualisations for intuitive interpretation of the results (Figure 1). An example application of the workflow to identify growth patterns of lung adenocarcinoma tumours will be shown.<br/