Revised criteria system for a national assessment of threatened habitats in Germany

The Red List of threatened habitat types in Germany was first published in 1994 and it is updated approximately every ten years. In 2017 the third version was published by the German Federal Agency for Nature Conservation. In the course of the revision, the criteria system was also extended. In doing so, an attempt was made to find a compromise between the consideration of international developments that had taken place and existing national requirements. In particular, short-term developments should become visible through the German Red List status. In addition to ‘National long-term Threat’, the valuation now also includes ‘Current Trend’ and ‘Rarity’. Following the IUCN’s approach, the collapse risk is now represented on the basis of several criteria. However, in contrast to the IUCN procedure, where the worst evaluated criterion is determinative for Red List status, in our procedure all criteria are included in the evaluation. To counteract misleading signal-effects for management decisions, all significant criteria have an influence on the resulting German Red List status (RLG). They are combined in an assessment scheme. In order to map the overall risk of loss, both the long-term threat as a historical reference value and furthermore the current trend must have an influence on RLG. As a result, 65% of habitat types have differing risk of loss

Bornavirus encephalitis shows a characteristic magnetic resonance phenotype in humans

Objective: The number of diagnosed fatal encephalitis cases in humans caused by the classical Borna disease virus (BoDV-1) has been increasing, ever since it was proved that BoDV-1 can cause human infections. However, awareness of this entity is low, and a specific imaging pattern has not yet been identified. We therefore provide the first comprehensive description of the morphology of human BoDV-1 encephalitis, with histopathological verification of imaging abnormalities. Methods: In an institutional review board-approved multicenter study, we carried out a retrospective analysis of 55 magnetic resonance imaging (MRI) examinations of 19 patients with confirmed BoDV-1 encephalitis. Fifty brain regions were analyzed systematically (T1w, T2w, T2*w, T1w + Gd, and DWI), in order to discern a specific pattern of inflammation. Histopathological analysis of 25 locations in one patient served as correlation for MRI abnormalities. Results: Baseline imaging, acquired at a mean of 11 ± 10 days after symptom onset, in addition to follow-up scans of 16 patients, revealed characteristic T2 hyperintensities with a predilection for the head of the caudate nucleus, insula, and cortical spread to the limbic system, whereas the occipital lobes and cerebellar hemispheres were unaffected. This gradient was confirmed by histology. Nine patients (47.4%) developed T1 hyperintensities of the basal ganglia, corresponding to accumulated lipid phagocytes on histology and typical for late-stage necrosis. Interpretation: BoDV-1 encephalitis shows a distinct pattern of inflammation in both the early and late stages of the disease. Its appearance can mimic sporadic Creutzfeldt–Jakob disease on MRI and should be considered a differential diagnosis in the case of atypical clinical presentation

Hepatic pyruvate and alanine metabolism are critical and complementary for maintenance of antioxidant capacity and resistance to oxidative insult

Objective: Mitochondrial pyruvate is a critical intermediary metabolite in gluconeogenesis, lipogenesis, and NADH production. As a result, the mitochondrial pyruvate carrier (MPC) complex has emerged as a promising therapeutic target in metabolic diseases. Clinical trials are currently underway. However, recent in vitro data indicate that MPC inhibition diverts glutamine/glutamate away from glutathione synthesis and toward glutaminolysis to compensate for loss of pyruvate oxidation, possibly sensitizing cells to oxidative insult. Here, we explored this in vivo using the clinically relevant acetaminophen (APAP) overdose model of acute liver injury, which is driven by oxidative stress. Methods: We used pharmacological and genetic approaches to inhibit MPC2 and alanine aminotransferase 2 (ALT2), individually and concomitantly, in mice and cell culture models and determined the effects on APAP hepatotoxicity. Results: We found that MPC inhibition sensitizes the liver to APAP-induced injury in vivo only with concomitant loss of alanine aminotransferase 2 (ALT2). Pharmacological and genetic manipulation of neither MPC2 nor ALT2 alone affected APAP toxicity, but liver-specific double knockout (DKO) significantly worsened APAP-induced liver damage. Further investigation indicated that DKO impaired glutathione synthesis and increased urea cycle flux, consistent with increased glutaminolysis, and these results were reproducible in vitro. Finally, induction of ALT2 and post-treatment with dichloroacetate both reduced APAP-induced liver injury, suggesting new therapeutic avenues. Conclusions: Increased susceptibility to APAP toxicity requires loss of both the MPC and ALT2 in vivo, indicating that MPC inhibition alone is insufficient to disrupt redox balance. Furthermore, the results from ALT2 induction and dichloroacetate in the APAP model suggest new metabolic approaches to the treatment of liver damage

Dermatologist-like explainable AI enhances trust and confidence in diagnosing melanoma

Abstract Artificial intelligence (AI) systems have been shown to help dermatologists diagnose melanoma more accurately, however they lack transparency, hindering user acceptance. Explainable AI (XAI) methods can help to increase transparency, yet often lack precise, domain-specific explanations. Moreover, the impact of XAI methods on dermatologists’ decisions has not yet been evaluated. Building upon previous research, we introduce an XAI system that provides precise and domain-specific explanations alongside its differential diagnoses of melanomas and nevi. Through a three-phase study, we assess its impact on dermatologists’ diagnostic accuracy, diagnostic confidence, and trust in the XAI-support. Our results show strong alignment between XAI and dermatologist explanations. We also show that dermatologists’ confidence in their diagnoses, and their trust in the support system significantly increase with XAI compared to conventional AI. This study highlights dermatologists’ willingness to adopt such XAI systems, promoting future use in the clinic