Do Pigs Have Adequate Space in Animal Transportation Vehicles?—Planimetric Measurement of the Floor Area Covered by Finishing Pigs in Various Body Positions

In this study, the floor area covered by individual finishing pigs in various body positions was measured using a contrast-based planimetric method for computer-assisted analysis of two-dimensional images. Two hundred and thirty-two finishing pigs were weighed during the last fifth of the fattening period and measured in different body positions using contrast-based planimetry. Thirteen body positions were defined based on characteristic directions of the head, legs and body. The lowest average covered floor area was found for body position A (pig standing up straight, nose touching the ground) with 0.288 ± 0.026 m2. The highest average covered floor area for a standing pig amounted to 0.335 ± 0.030 m2 in body posture ES (pig standing curved sideways, head raised above the dorsal line) and, for a lying pig, 0.486 ± 0.040 m2 (posture LL, pig lying in fully lateral recumbent position). The covered floor surface significantly depended on the weight of the animal and the body posture. Allometric estimations previously described for calculating the floor area physically covered by a pig's body are not consistently precise in depicting the actual areas covered. The minimal floor area offered in animal transportation vehicles, according to European legislation, is insufficient in the case of all pigs lying in the fully recumbent position simultaneously, without the pigs being forced to partially overlap one another. Therefore, both allometric formulas and legislation should be modified on the basis of these results and further studies with pigs of modern genetic origin should be conducted

Optimization of the Treatment of Squamous Cell Carcinoma Cells by Combining Photodynamic Therapy with Cold Atmospheric Plasma

Actinic keratosis (AK) is characterized by a reddish or occasionally skin-toned rough patch on sun-damaged skin, and it is regarded as a precursor to squamous cell carcinoma (SCC). Photodynamic therapy (PDT), utilizing 5-aminolevulinic acid (ALA) along with red light, is a recognized treatment option for AK that is limited by the penetration depth of light and the distribution of the photosensitizer into the skin. Cold atmospheric plasma (CAP) is a partially ionized gas with permeability-enhancing and anti-cancer properties. This study analyzed, in vitro, whether a combined treatment of CAP and ALA-PDT may improve the efficacy of the treatment. In addition, the effect of the application sequence of ALA and CAP was investigated using in vitro assays and the molecular characterization of human oral SCC cell lines (SCC-9, SCC-15, SCC-111), human cutaneous SCC cell lines (SCL-1, SCL-2, A431), and normal human epidermal keratinocytes (HEKn). The anti-tumor effect was determined by migration, invasion, and apoptosis assays and supported the improved efficacy of ALA-PDT in combination with CAP. However, the application sequence ALA-CAP–red light seems to be more efficacious than CAP-ALA–red light, which is probably due to increased intracellular ROS levels when ALA is applied first, followed by CAP and red light treatment. Furthermore, the expression of apoptosis- and senescence-related molecules (caspase-3, -6, -9, p16 INK4a , p21 CIP1 ) was increased, and different genes of the junctional network (ZO-1, CX31, CLDN1, CTNNB1) were induced after the combined treatment of CAP plus ALA-PDT. HEKn, however, were much less affected than SCC cells. Overall, the results show that CAP may improve the anti-tumor effects of conventional ALA-PDT on SCC cells. Whether this combined application is successful in treating AK in vivo has to be carefully examined in follow-up studies.This research was funded by the Wilhelm Sander-Stiftung (project 2023.011.1).Wilhelm Sander-Stiftun

Type D personality is associated with increased metabolic syndrome prevalence and an unhealthy lifestyle in a cross-sectional Dutch community sample

<p>Abstract</p> <p>Background</p> <p>People with Type D-Distressed-personality have a general tendency towards increased negative affectivity (NA), while at the same time inhibiting these emotions in social situations (SI). Type D personality is associated with an increased risk of adverse outcomes in patients with cardiovascular disease. Whether Type D personality is a cardiovascular risk factor in healthy populations remains to be investigated. In the present study, the relations between Type D personality and classical cardiovascular risk factors, i.e. metabolic syndrome and lifestyle were investigated in a Dutch community sample.</p> <p>Methods</p> <p>In a cross-sectional study 1592 participants were included, aged 20-80 years. Metabolic syndrome was defined by self-report, following the International Diabetes Federation-IDF-guidelines including an increased waist circumference, dyslipidemia, hypertension, and diabetes. In addition lifestyle factors smoking, alcohol use, exercise and dietary habits were examined. Metabolic syndrome prevalence was stratified by Type D personality (a high score on both NA and SI), lifestyle and confounders age, gender, having a partner, higher education level, cardiac history, family history of cardiovascular disease.</p> <p>Results</p> <p>Metabolic syndrome was more prevalent in persons with a Type D personality (13% vs. 6%). Persons with Type D personality made poorer lifestyle choices, adhered less to the physical activity norm (OR = 1.5, 95%CI = 1.1-2.0, <it>p </it>= .02), had a less varied diet (OR = 0.50, 95%CI = 0.40-0.70, <it>p </it>< .0005), and were less likely to restrict their fat intake (OR = 0.70, 95%CI = 0.50-0.90, <it>p </it>= .01). Type D personality was related to a twofold increased risk of metabolic syndrome (OR = 2.2, 95%CI = 1.2-4.0, <it>p </it>= .011), independent of lifestyle factors and confounders.</p> <p>Conclusions</p> <p>Type D personality is related to an increased prevalence of metabolic syndrome and unhealthy lifestyle, which suggests both behavioral and biological vulnerability for development of cardiovascular disorders and diabetes.</p

A coordinate-based meta-analysis of white matter alterations in patients with alcohol use disorder

Introduction: Besides the commonly described grey matter (GM) deficits, there is growing evidence of significant white matter (WM) alterations in patients with alcohol use disorder (AUD). WM changes can be assessed using volumetric and diffusive magnetic resonance imaging methods, such as voxel-based morphometry (VBM) and diffusion tensor imaging (DTI). The aim of the present meta-analysis is to investigate the spatial convergence of the reported findings on WM alterations in AUD. Methods: Systematic literature search on PubMed and further databases revealed 18 studies eligible for inclusion, entailing a total of 462 AUD patients and 416 healthy controls (up to January 18, 2021). All studies that had used either VBM or DTI whole-brain analyzing methods and reported results as peak-coordinates in standard reference space were considered for inclusion. We excluded studies using approaches nonconcordant with recent guidelines for neuroimaging meta-analyses and studies investigating patient groups with Korsakoff syndrome or other comorbid substance use disorders (except tobacco). Results: Anatomical Likelihood Estimation (ALE) revealed four significant clusters of convergent macro- and microstructural WM alterations in AUD patients that were assigned to the genu and body of the corpus callosum, anterior and posterior cingulum, fornix, and the right posterior limb of the internal capsule. Discussion: The changes in WM could to some extent explain the deteriorations in motor, cognitive, affective, and perceptual functions seen in AUD. Future studies are needed to clarify how WM alterations vary over the course of the disorder and to what extent they are reversible with prolonged abstinence

Learn to Train: Improving Training Data for a Neural Network to Detect Pecking Injuries in Turkeys

This study aimed to develop a camera-based system using artificial intelligence for automated detection of pecking injuries in turkeys. Videos were recorded and split into individual images for further processing. Using specifically developed software, the injuries visible on these images were marked by humans, and a neural network was trained with these annotations. Due to unacceptable agreement between the annotations of humans and the network, several work steps were initiated to improve the training data. First, a costly work step was used to create high-quality annotations (HQA) for which multiple observers evaluated already annotated injuries. Therefore, each labeled detection had to be validated by three observers before it was saved as “finished”, and for each image, all detections had to be verified three times. Then, a network was trained with these HQA to assist observers in annotating more data. Finally, the benefit of the work step generating HQA was tested, and it was shown that the value of the agreement between the annotations of humans and the network could be doubled. Although the system is not yet capable of ensuring adequate detection of pecking injuries, the study demonstrated the importance of such validation steps in order to obtain good training data

Alter, Technik, Ethik. Ein Fragen- und Kriterienkatalog

Im Fragen- und Kriterienkatalog werden zunächst zentrale Fragen zu den Themen Alter, Technik, Ethik bearbeitet. Hieraus werden vier Analysefelder gespannt: Menschen und Technik, Technikentwicklung und Partizipation, Technikgestaltung sowieTechnik für das Gute Leben im Alter. Diese werden als Grundlage für die daran anschließenden Reflexionsfragen und -kriterien aufgearbeitet. Das Kapitel Fokus Demenz schließt den Kriterienkatalog a

Keypoint Detection for Injury Identification during Turkey Husbandry Using Neural Networks

Injurious pecking against conspecifics is a serious problem in turkey husbandry. Bloody injuries act as a trigger mechanism to induce further pecking, and timely detection and intervention can prevent massive animal welfare impairments and costly losses. Thus, the overarching aim is to develop a camera-based system to monitor the flock and detect injuries using neural networks. In a preliminary study, images of turkeys were annotated by labelling potential injuries. These were used to train a network for injury detection. Here, we applied a keypoint detection model to provide more information on animal position and indicate injury location. Therefore, seven turkey keypoints were defined, and 244 images (showing 7660 birds) were manually annotated. Two state-of-the-art approaches for pose estimation were adjusted, and their results were compared. Subsequently, a better keypoint detection model (HRNet-W48) was combined with the segmentation model for injury detection. For example, individual injuries were classified using “near tail” or “near head” labels. Summarizing, the keypoint detection showed good results and could clearly differentiate between individual animals even in crowded situations