Interindividual variabilities in cognitive performance degradation after alcohol consuption and sleep loss are related

Introduction The sleep inducing effects of alcohol as well as the increase in sleep propensity and sleepiness after sleep loss have been linked to the adenosinergic system in the brain. While the performance impairing effects of ethanol have partly been related to the inhibitory effects of cerebral adenosine, sleep loss has been found to increase adenosine receptor density. The interindividual variability of cognitive performance impairments after alcohol intake as well as after sleep loss is extensive. Thus, we examined in humans whether performance degradations resulting from sleep loss and alcohol consumption are related. Methods Performance in a 10-min Psychomotor Vigilance Task (PVT) was tested in 47 healthy volunteers (mean age 27 ± 5 (SD) years, 21 females) at 6 pm 1) after an 8 hour control night, 2) after alcohol consumption (aiming at a blood alcohol concentration (BAC) of 0.08%), and 3) after 35 hours of total sleep deprivation. After alcohol intake, 35 of the participants reached a BAC of more than 0.06% prior to the performance testing (mean BAC 0.074%, SD 0.009%, min. 0.063%, max. 0.095%) and were included in the analyses. Two recovery nights were scheduled between conditions. Results Performance impairments due to acute alcohol intake and due to 35 hours of sustained wakefulness were calculated as differences from performance under control conditions. The degree in performance degradation correlated highly between both conditions (i.e. 10% slowest reaction times: Pearson’s r=0.73, p<0.0001; standard deviation of reaction times: r=0.75, p<0.0001; mean reaction time: r=0.59, p=0.0002). Conclusions Participants whose PVT performance proved to be vulnerable to the effects of alcohol consumption were also vulnerable to sleep loss, whereas individuals who were resilient against the effects of alcohol were also less susceptible to the impact of sleep deprivation. These results suggest that the effects of alcohol and sleep deprivation on performance are mediated – at least in part – by a common pathway that may involve the adenosinergic system in the brain

Hepatitis B Immunoglobulin discontinuation in long‐term liver transplant patients

Background: Hepatitis B immunoglobulin (HBIG)-as a monotherapy or combined with nucleos(t)ide analogs (NUCs)-has effectively lowered Hepatitis B virus (HBV) reinfection after liver transplantation. However, it is associated with high costs and viral resistance. HBIG-free prophylaxis with novel NUCs (tenofovir, entecavir) composes a viable alternative. We evaluated reinfection rate, histological changes, and outcome associated with HBIG discontinuation. Methods: A retrospective analysis was performed of patients undergoing liver transplantation due to HBV-induced liver disease at our center since 1988. A controlled HBIG discontinuation was conducted between 2015 and 2017 in 65 patients. Recurrent infection was determined by HbsAg values. Fibrosis and inflammation were evaluated by routine biopsy. The survival of patients after HBIG discontinuation was compared to a control population on HBIG for prophylaxis. Results: From 1988 to 2013, 352 patients underwent liver transplantation due to HBV-induced liver disease. 169 patients could be included for analysis. 104 (51.5%) patients continued a prophylaxis containing HBIG. HBIG was discontinued in 65 (38.5%) patients in a controlled manner, maintaining an oral NUC. None of those patients showed HBV reinfection or graft dysfunction. No significant changes of inflammation grades (P = .067) or fibrosis stages (P = .051) were detected. The survival of patients after HBIG discontinuation was comparable to the control (P = .95). Conclusion: HBIG withdrawal under continuation of oral NUC therapy is safe and not related to graft dysfunction, based on blood tests and histology. HBIG-free prophylaxis is not associated with a worse outcome and displays a financial relief as well as a logistic simplification during long-term follow-up

Self‐limited HBV infection of the recipient does not reactivate after liver transplantation: Observations from a 30‐year liver transplant program

Background: A self-limited hepatitis B infection can reactivate in patients under immunosuppression or chemotherapy (reappearance of hepatitis B surface antigen (HBsAg) or HBV-DNA). Exact circumstances of HBV reactivation in patients undergoing liver transplantation (LT) for end-stage liver diseases (ESLD) unrelated to HBV are unknown, and recommendations on HBV prophylaxis remain unclear. Patients and methods: Among 1273 liver transplants, 168 patients with a self-limited HBV hepatitis B infection prior to LT were identified from our prospective liver transplant database. Patients with underlying chronic HBV infection and recipients of an anti-HBc-positive liver were not included in the analysis. Demographic, laboratory, serological, and virological data were analyzed retrospectively. Appearance of HBsAg or HBV-DNA was defined as reactivation. Results: The median follow-up after LT was 12.0 years (0.6-30.7 years). The rate of HBV reactivation was 0% independent of antiviral prophylaxis (n = 7; 4.2%), the etiology of ESLD, hepatitis C treatment, or the anti-HBs concentration. The overall patient survival with a history of a self-limited HBV infection before LT did not significantly differ from the rest of the cohort. Conclusion: Antiviral treatment with nucleos(t)ide analogues post-liver transplantation in order to prevent HBV reactivation in patients with a resolved self-limited hepatitis B infection prior to LT seems to be omittable since the main viral reservoir is removed by the hepatectomy. These findings may clarify the current uncertainty in the recommendations regarding the risk of HBV reactivation in patients with self-limited hepatitis B prior to LT

ATPase activity of DFCP1 controls selective autophagy

Cellular homeostasis is governed by removal of damaged organelles and protein aggregates by selective autophagy mediated by cargo adaptors such as p62/SQSTM1. Autophagosomes can assemble in specialized cup-shaped regions of the endoplasmic reticulum (ER) known as omegasomes, which are characterized by the presence of the ER protein DFCP1/ZFYVE1. The function of DFCP1 is unknown, as are the mechanisms of omegasome formation and constriction. Here, we demonstrate that DFCP1 is an ATPase that is activated by membrane binding and dimerizes in an ATP-dependent fashion. Whereas depletion of DFCP1 has a minor effect on bulk autophagic flux, DFCP1 is required to maintain the autophagic flux of p62 under both fed and starved conditions, and this is dependent on its ability to bind and hydrolyse ATP. While DFCP1 mutants defective in ATP binding or hydrolysis localize to forming omegasomes, these omegasomes fail to constrict properly in a size-dependent manner. Consequently, the release of nascent autophagosomes from large omegasomes is markedly delayed. While knockout of DFCP1 does not affect bulk autophagy, it inhibits selective autophagy, including aggrephagy, mitophagy and micronucleophagy. We conclude that DFCP1 mediates ATPase-driven constriction of large omegasomes to release autophagosomes for selective autophagy

Science journalism and a multi-directional science-policy-society dialogue are needed to foster public awareness for biodiversity and its conservation

Biodiversity is the manifestation of life on our planet and provides manifold benefits for humans. Yet we destroy ecosystems and drive species to extinction. We submit that anthropogenic biodiversity loss does not yet receive sufficient public attention, although biodiversity conservation and its sustainable use are key to mitigate global crises. Effective communication of biodiversity-related knowledge with diverse audiences is therefore crucial and should contribute to ensuring that evidence guides environmental decision-making. In this context, it is essential to stimulate multi-directional dialogues between science, policy, and society. Here, we suggest Dos and Don’ts that can guide science communication for scientists working in biodiversity research and beyond. Moreover, we emphasize the role of science journalism and other institutions specialized in science communication in critically mediating the complexity of scientific knowledge

Cholesterol transfer via endoplasmic reticulum contacts mediates lysosome damage repair

Lysosome integrity is essential for cell viability, and lesions in lysosome membranes are repaired by the ESCRT machinery. Here, we describe an additional mechanism for lysosome repair that is activated independently of ESCRT recruitment. Lipidomic analyses showed increases in lysosomal phosphatidylserine and cholesterol after damage. Electron microscopy demonstrated that lysosomal membrane damage is rapidly followed by the formation of contacts with the endoplasmic reticulum (ER), which depends on the ER proteins VAPA/B. The cholesterol-binding protein ORP1L was recruited to damaged lysosomes, accompanied by cholesterol accumulation by a mechanism that required VAP-ORP1L interactions. The PtdIns 4-kinase PI4K2A rapidly produced PtdIns4P on lysosomes upon damage, and knockout of PI4K2A inhibited damage-induced accumulation of ORP1L and cholesterol and led to the failure of lysosomal membrane repair. The cholesterol-PtdIns4P transporter OSBP was also recruited upon damage, and its depletion caused lysosomal accumulation of PtdIns4P and resulted in cell death. We conclude that ER contacts are activated on damaged lysosomes in parallel to ESCRTs to provide lipids for membrane repair, and that PtdIns4P generation and removal are central in this response.Peer reviewe

Future-ai:International consensus guideline for trustworthy and deployable artificial intelligence in healthcare

Despite major advances in artificial intelligence (AI) for medicine and healthcare, the deployment and adoption of AI technologies remain limited in real-world clinical practice. In recent years, concerns have been raised about the technical, clinical, ethical and legal risks associated with medical AI. To increase real world adoption, it is essential that medical AI tools are trusted and accepted by patients, clinicians, health organisations and authorities. This work describes the FUTURE-AI guideline as the first international consensus framework for guiding the development and deployment of trustworthy AI tools in healthcare. The FUTURE-AI consortium was founded in 2021 and currently comprises 118 inter-disciplinary experts from 51 countries representing all continents, including AI scientists, clinicians, ethicists, and social scientists. Over a two-year period, the consortium defined guiding principles and best practices for trustworthy AI through an iterative process comprising an in-depth literature review, a modified Delphi survey, and online consensus meetings. The FUTURE-AI framework was established based on 6 guiding principles for trustworthy AI in healthcare, i.e. Fairness, Universality, Traceability, Usability, Robustness and Explainability. Through consensus, a set of 28 best practices were defined, addressing technical, clinical, legal and socio-ethical dimensions. The recommendations cover the entire lifecycle of medical AI, from design, development and validation to regulation, deployment, and monitoring. FUTURE-AI is a risk-informed, assumption-free guideline which provides a structured approach for constructing medical AI tools that will be trusted, deployed and adopted in real-world practice. Researchers are encouraged to take the recommendations into account in proof-of-concept stages to facilitate future translation towards clinical practice of medical AI

FUTURE-AI: International consensus guideline for trustworthy and deployable artificial intelligence in healthcare

Despite major advances in artificial intelligence (AI) for medicine and healthcare, the deployment and adoption of AI technologies remain limited in real-world clinical practice. In recent years, concerns have been raised about the technical, clinical, ethical and legal risks associated with medical AI. To increase real world adoption, it is essential that medical AI tools are trusted and accepted by patients, clinicians, health organisations and authorities. This work describes the FUTURE-AI guideline as the first international consensus framework for guiding the development and deployment of trustworthy AI tools in healthcare. The FUTURE-AI consortium was founded in 2021 and currently comprises 118 inter-disciplinary experts from 51 countries representing all continents, including AI scientists, clinicians, ethicists, and social scientists. Over a two-year period, the consortium defined guiding principles and best practices for trustworthy AI through an iterative process comprising an in-depth literature review, a modified Delphi survey, and online consensus meetings. The FUTURE-AI framework was established based on 6 guiding principles for trustworthy AI in healthcare, i.e. Fairness, Universality, Traceability, Usability, Robustness and Explainability. Through consensus, a set of 28 best practices were defined, addressing technical, clinical, legal and socio-ethical dimensions. The recommendations cover the entire lifecycle of medical AI, from design, development and validation to regulation, deployment, and monitoring. FUTURE-AI is a risk-informed, assumption-free guideline which provides a structured approach for constructing medical AI tools that will be trusted, deployed and adopted in real-world practice. Researchers are encouraged to take the recommendations into account in proof-of-concept stages to facilitate future translation towards clinical practice of medical AI

FUTURE-AI: International consensus guideline for trustworthy and deployable artificial intelligence in healthcare

Despite major advances in artificial intelligence (AI) for medicine and healthcare, the deployment and adoption of AI technologies remain limited in real-world clinical practice. In recent years, concerns have been raised about the technical, clinical, ethical and legal risks associated with medical AI. To increase real world adoption, it is essential that medical AI tools are trusted and accepted by patients, clinicians, health organisations and authorities. This work describes the FUTURE-AI guideline as the first international consensus framework for guiding the development and deployment of trustworthy AI tools in healthcare. The FUTURE-AI consortium was founded in 2021 and currently comprises 118 inter-disciplinary experts from 51 countries representing all continents, including AI scientists, clinicians, ethicists, and social scientists. Over a two-year period, the consortium defined guiding principles and best practices for trustworthy AI through an iterative process comprising an in-depth literature review, a modified Delphi survey, and online consensus meetings. The FUTURE-AI framework was established based on 6 guiding principles for trustworthy AI in healthcare, i.e. Fairness, Universality, Traceability, Usability, Robustness and Explainability. Through consensus, a set of 28 best practices were defined, addressing technical, clinical, legal and socio-ethical dimensions. The recommendations cover the entire lifecycle of medical AI, from design, development and validation to regulation, deployment, and monitoring. FUTURE-AI is a risk-informed, assumption-free guideline which provides a structured approach for constructing medical AI tools that will be trusted, deployed and adopted in real-world practice. Researchers are encouraged to take the recommendations into account in proof-of-concept stages to facilitate future translation towards clinical practice of medical AI

FUTURE-AI: International consensus guideline for trustworthy and deployable artificial intelligence in healthcare

Despite major advances in artificial intelligence (AI) for medicine and healthcare, the deployment and adoption of AI technologies remain limited in real-world clinical practice. In recent years, concerns have been raised about the technical, clinical, ethical and legal risks associated with medical AI. To increase real world adoption, it is essential that medical AI tools are trusted and accepted by patients, clinicians, health organisations and authorities. This work describes the FUTURE-AI guideline as the first international consensus framework for guiding the development and deployment of trustworthy AI tools in healthcare. The FUTURE-AI consortium was founded in 2021 and currently comprises 118 inter-disciplinary experts from 51 countries representing all continents, including AI scientists, clinicians, ethicists, and social scientists. Over a two-year period, the consortium defined guiding principles and best practices for trustworthy AI through an iterative process comprising an in-depth literature review, a modified Delphi survey, and online consensus meetings. The FUTURE-AI framework was established based on 6 guiding principles for trustworthy AI in healthcare, i.e. Fairness, Universality, Traceability, Usability, Robustness and Explainability. Through consensus, a set of 28 best practices were defined, addressing technical, clinical, legal and socio-ethical dimensions. The recommendations cover the entire lifecycle of medical AI, from design, development and validation to regulation, deployment, and monitoring. FUTURE-AI is a risk-informed, assumption-free guideline which provides a structured approach for constructing medical AI tools that will be trusted, deployed and adopted in real-world practice. Researchers are encouraged to take the recommendations into account in proof-of-concept stages to facilitate future translation towards clinical practice of medical AI