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Gastroptosis is a condition in which the stomach is displaced downward and is a condition affects the spontaneous muscle mobility in the stomach. The reason for its current prevalence remains unclear as the medical literature is scarce on the condition in children. In this study, we describe the case of a 17-year-old girl suffering from chronic, position-dependent epigastric pain. The symptoms were observed during post-meal activity, with a significant increase in pain intensity while in an upright position. An inferior stomach displacement was noted in an upper gastrointestinal X-ray study using barium.

Cell communication via exosomes is capable of influencing cell fate in stress situations such as exposure to ionizing radiation. In vitro and in vivo studies have shown that exosomes might play a role in out-of-target radiation effects by carrying molecular signaling mediators of radiation damage, as well as opposite protective functions resulting in resistance to radiotherapy. However, a global understanding of exosomes and their radiation-induced regulation, especially within the context of an intact mammalian organism, has been lacking. In this in vivo study, we demonstrate that, compared to sham-irradiated (SI) mice, a distinct pattern of proteins and miRNAs is found packaged into circulating plasma exosomes after whole-body and partial-body irradiation (WBI and PBI) with 2 Gy X-rays. A high number of deregulated proteins (59% of WBI and 67% of PBI) was found in the exosomes of irradiated mice. In total, 57 and 13 miRNAs were deregulated in WBI and PBI groups, respectively, suggesting that the miRNA cargo is influenced by the tissue volume exposed to radiation. In addition, five miRNAs (miR-99b-3p, miR-200a-3p, miR-200a, miR-182-5p, miR-182) were commonly overexpressed in the exosomes from the WBI and PBI groups. In this study, particular emphasis was also given to the determination of the in vivo effect of exosome transfer by intracranial injection in the highly radiosensitive neonatal cerebellum at postnatal day 3. In accordance with a major overall anti-apoptotic function of the commonly deregulated miRNAs, here, we report that exosomes from the plasma of irradiated mice, especially in the case of WBI, prevent radiation-induced apoptosis, thus holding promise for exosome-based future therapeutic applications against radiation injury.

The brain undergoes ionizing radiation exposure in many clinical situations, particularly during radiotherapy for brain tumors. The critical role of the hippocampus in the pathogenesis of radiation-induced neurocognitive dysfunction is well recognized. The goal of this study is to test the potential contribution of non-targeted effects in the detrimental response of the hippocampus to irradiation and to elucidate the mechanisms involved. C57Bl/6 mice were whole body (WBI) or partial body (PBI) irradiated with 0.1 or 2.0 Gy of X-rays or sham irradiated. PBI consisted of the exposure of the lower third of the mouse body, whilst the upper two thirds were shielded. Hippocampi were collected 15 days or 6 months post-irradiation and a multi-omics approach was adopted to assess the molecular changes in non-coding RNAs, proteins and metabolic levels, as well as histological changes in the rate of hippocampal neurogenesis. Notably, at 2.0 Gy the pattern of early molecular and histopathological changes induced in the hippocampus at 15 days following PBI were similar in quality and quantity to the effects induced by WBI, thus providing a proof of principle of the existence of out-of-target radiation response in the hippocampus of conventional mice. We detected major alterations in DAG/IP3 and TGF-β signaling pathways as well as in the expression of proteins involved in the regulation of long-term neuronal synaptic plasticity and synapse organization, coupled with defects in neural stem cells self-renewal in the hippocampal dentate gyrus. However, compared to the persistence of the WBI effects, most of the PBI effects were only transient and tended to decrease at 6 months post-irradiation, indicating important mechanistic difference. On the contrary, at low dose we identified a progressive accumulation of molecular defects that tended to manifest at later post-irradiation times. These data, indicating that both targeted and non-targeted radiation effects might contribute to the pathogenesis of hippocampal radiation-damage, have general implications for human health.

Molecular communication between irradiated and unirradiated neighbouring cells initiates radiation-induced bystander effects (RIBE) and out-of-field (abscopal) effects which are both an example of the non-targeted effects (NTE) of ionising radiation (IR). Exosomes are small membrane vesicles of endosomal origin and newly identified mediators of NTE. Although exosome-mediated changes are well documented in radiation therapy and oncology, there is a lack of knowledge regarding the role of exosomes derived from inside and outside the radiation field in the early and delayed induction of NTE following IR. Therefore, here we investigated the changes in exosome profile and the role of exosomes as possible molecular signalling mediators of radiation damage. Exosomes derived from organs of whole body irradiated (WBI) or partial body irradiated (PBI) mice after 24 h and 15 days post-irradiation were transferred to recipient mouse embryonic fibroblast (MEF) cells and changes in cellular viability, DNA damage and calcium, reactive oxygen species and nitric oxide signalling were evaluated compared to that of MEF cells treated with exosomes derived from unirradiated mice. Taken together, our results show that whole and partial-body irradiation increases the number of exosomes, instigating changes in exosome-treated MEF cells, depending on the source organ and time after exposure.

Chronic exposure to low-dose ionizing radiation is associated with an increased risk of cardiovascular disease. Alteration in energy metabolism has been suggested to contribute to radiation-induced heart pathology, mitochondrial dysfunction being a hallmark of this disease. The goal of this study was to investigate the regulatory role of acetylation in heart mitochondria in the long-term response to chronic radiation. ApoE-deficient C57Bl/6J mice were exposed to low-dose-rate (20 mGy/day) gamma radiation for 300 days, resulting in a cumulative total body dose of 6.0 Gy. Heart mitochondria were isolated and analyzed using quantitative proteomics. Radiation-induced proteome and acetylome alterations were further validated using immunoblotting, enzyme activity assays, and ELISA. In total, 71 proteins showed peptides with a changed acetylation status following irradiation. The great majority (94%) of the hyperacetylated proteins were involved in the TCA cycle, fatty acid oxidation, oxidative stress response and sirtuin pathway. The elevated acetylation patterns coincided with reduced activity of mitochondrial sirtuins, increased the level of Acetyl-CoA, and were accompanied by inactivation of major cardiac metabolic regulators PGC-1 alpha and PPAR alpha. These observations suggest that the changes in mitochondrial acetylation after irradiation is associated with impairment of heart metabolism. We propose a novel mechanism involved in the development of late cardiac damage following chronic irradiation.

Forestry work, which is considered one of the most demanding and dangerous professions in the world, is claiming more and more lives. In a country as small as Austria, more than 50 forestry workers are killed in accidents every year, and the number is increasing rapidly. This serves as a catalyst for us to implement more stringent measures for workplace safety in order to achieve the sustainability objective of SDG 3, which focuses on health and well-being. This study contributes to the analysis of occupational accidents and focuses on two large real-world datasets from both the Austrian Federal Forests (ÖBf) and the Austrian Workers’ Compensation Board (AUVA). Decision trees, random forests, and fully connected neural networks are used for the analysis. By exploring different interpretation methods, this study sheds light on the decision-making processes ranging from basic association to causal inference and emphasizes the importance of causal inference in providing actionable insights for accident prevention. This paper contributes to the topic of explainable AI, specifically in its application to occupational safety in forestry. As a result, it introduces novel aspects to decision support systems in this application domain.

In many domains of our daily life (e.g., agriculture, forestry, health, etc.), both laymen and experts need to classify entities into two binary classes (yes/no, good/bad, sufficient/insufficient, benign/malign, etc.). For many entities, this decision is difficult and we need another class called “maybe”, which contains a corresponding quantifiable tendency toward one of these two opposites. Human domain experts are often able to mark any entity, place it in a different class and adjust the position of the slope in the class. Moreover, they can often explain the classification space linguistically—depending on their individual domain experience and previous knowledge. We consider this human-in-the-loop extremely important and call our approach actionable explainable AI. Consequently, the parameters of the functions are adapted to these requirements and the solution is explained to the domain experts accordingly. Specifically, this paper contains three novelties going beyond the state-of-the-art: (1) A novel method for detecting the appropriate parameter range for the averaging function to treat the slope in the “maybe” class, along with a proposal for a better generalisation than the existing solution. (2) the insight that for a given problem, the family of t-norms and t-conorms covering the whole range of nilpotency is suitable because we need a clear “no” or “yes” not only for the borderline cases. Consequently, we adopted the Schweizer–Sklar family of t-norms or t-conorms in ordinal sums. (3) A new fuzzy quasi-dissimilarity function for classification into three classes: Main difference, irrelevant difference and partial difference. We conducted all of our experiments with real-world datasets.

The main impetus for the global efforts toward the current digital transformation in almost all areas of our daily lives is due to the great successes of artificial intelligence (AI), and in particular, the workhorse of AI, statistical machine learning (ML). The intelligent analysis, modeling, and management of agricultural and forest ecosystems, and of the use and protection of soils, already play important roles in securing our planet for future generations and will become irreplaceable in the future. Technical solutions must encompass the entire agricultural and forestry value chain. The process of digital transformation is supported by cyber-physical systems enabled by advances in ML, the availability of big data and increasing computing power. For certain tasks, algorithms today achieve performances that exceed human levels. The challenge is to use multimodal information fusion, i.e., to integrate data from different sources (sensor data, images, *omics), and explain to an expert why a certain result was achieved. However, ML models often react to even small changes, and disturbances can have dramatic effects on their results. Therefore, the use of AI in areas that matter to human life (agriculture, forestry, climate, health, etc.) has led to an increased need for trustworthy AI with two main components: explainability and robustness. One step toward making AI more robust is to leverage expert knowledge. For example, a farmer/forester in the loop can often bring in experience and conceptual understanding to the AI pipeline—no AI can do this. Consequently, human-centered AI (HCAI) is a combination of “artificial intelligence” and “natural intelligence” to empower, amplify, and augment human performance, rather than replace people. To achieve practical success of HCAI in agriculture and forestry, this article identifies three important frontier research areas: (1) intelligent information fusion; (2) robotics and embodied intelligence; and (3) augmentation, explanation, and verification for trusted decision support. This goal will also require an agile, human-centered design approach for three generations (G). G1: Enabling easily realizable applications through immediate deployment of existing technology. G2: Medium-term modification of existing technology. G3: Advanced adaptation and evolution beyond state-of-the-art.