Identification of a novel modulator of thyroid hormone receptor-mediated action

Diabetes is characterized by reduced thyroid function and altered myogenesis after muscle injury. Here we identify a novel component of thyroid hormone action that is repressed in diabetic rat muscle. Methodology/Principal Findings. We have identified a gene, named DOR, abundantly expressed in insulin-sensitive tissues such as skeletal muscle and heart, whose expression is highly repressed in muscle from obese diabetic rats. DOR expression is up-regulated during muscle differentiation and its loss-of-function has a negative impact on gene expression programmes linked to myogenesis or driven by thyroid hormones. In agreement with this, DOR enhances the transcriptional activity of the thyroid hormone receptor TRa1. This function is driven by the N-terminal part of the protein. Moreover, DOR physically interacts with TR a1 and to T3-responsive promoters, as shown by ChIP assays. T3 stimulation also promotes the mobilization of DOR from its localization in nuclear PML bodies, thereby indicating that its nuclear localization and cellular function may be related. Conclusions/Significance. Our data indicate that DOR modulates thyroid hormone function and controls myogenesis. DOR expression is down-regulated in skeletal muscle in diabetes. This finding may be of relevance for the alterations in muscle function associated with this disease

Identification of a Novel Modulator of Thyroid Hormone Receptor-Mediated Action

Diabetes is characterized by reduced thyroid function and altered myogenesis after muscle injury. Here we identify a novel component of thyroid hormone action that is repressed in diabetic rat muscle. Methodology/Principal Findings. We have identified a gene, named DOR, abundantly expressed in insulin-sensitive tissues such as skeletal muscle and heart, whose expression is highly repressed in muscle from obese diabetic rats. DOR expression is up-regulated during muscle differentiation and its loss-of-function has a negative impact on gene expression programmes linked to myogenesis or driven by thyroid hormones. In agreement with this, DOR enhances the transcriptional activity of the thyroid hormone receptor TRa1. This function is driven by the N-terminal part of the protein. Moreover, DOR physically interacts with TR a1 and to T3-responsive promoters, as shown by ChIP assays. T3 stimulation also promotes the mobilization of DOR from its localization in nuclear PML bodies, thereby indicating that its nuclear localization and cellular function may be related. Conclusions/Significance. Our data indicate that DOR modulates thyroid hormone function and controls myogenesis. DOR expression is down-regulated in skeletal muscle in diabetes. This finding may be of relevance for the alterations in muscle function associated with this disease

Abdominal aortic aneurysm screening program using hand-held ultrasound in primary healthcare

We determined the feasibility of abdominal aortic aneurysm (AAA) screening program led by family physicians in public primary healthcare setting using hand-held ultrasound device. The potential study population was 11,214 men aged ≥ 60 years attended by three urban, public primary healthcare centers. Participants were recruited by randomly-selected telephone calls. Ultrasound examinations were performed by four trained family physicians with a hand-held ultrasound device (Vscan®). AAA observed were verified by confirmatory imaging using standard ultrasound or computed tomography. Cardiovascular risk factors were determined. The prevalence of AAA was computed as the sum of previously-known aneurysms, aneurysms detected by the screening program and model-based estimated undiagnosed aneurysms. We screened 1,010 men, with mean age of 71.3 (SD 6.9) years; 995 (98.5%) men had normal aortas and 15 (1.5%) had AAA on Vscan®. Eleven out of 14 AAA-cases (78.6%) had AAA on confirmatory imaging (one patient died). The total prevalence of AAA was 2.49% (95%CI 2.20 to 2.78). The median aortic diameter at diagnosis was 3.5 cm in screened patients and 4.7 cm (p<0.001) in patients in whom AAA was diagnosed incidentally. Multivariate logistic regression analysis identified coronary heart disease (OR = 4.6, 95%CI 1.3 to 15.9) as the independent factor with the highest odds ratio. A screening program led by trained family physicians using hand-held ultrasound was a feasible, safe and reliable tool for the early detection of AAA

Impact of COVID-19 Lockdown in Eating Disorders: A Multicentre Collaborative International Study

Background. The COVID-19 lockdown has had a significant impact on mental health. Patients with eating disorders (ED) have been particularly vulnerable. Aims. (1) To explore changes in eating-related symptoms and general psychopathology during lockdown in patients with an ED from various European and Asian countries; and (2) to assess differences related to diagnostic ED subtypes, age, and geography. Methods. The sample comprised 829 participants, diagnosed with an ED according to DSM-5 criteria from specialized ED units in Europe and Asia. Participants were assessed using the COVID-19 Isolation Scale (CIES). Results. Patients with binge eating disorder (BED) experienced the highest impact on weight and ED symptoms in comparison with other ED subtypes during lockdown, whereas individuals with other specified feeding and eating disorders (OFSED) had greater deterioration in general psychological functioning than subjects with other ED subtypes. Finally, Asian and younger individuals appeared to be more resilient. Conclusions. The psychopathological changes in ED patients during the COVID-19 lockdown varied by cultural context and individual variation in age and ED diagnosis. Clinical services may need to target preventive measures and adapt therapeutic approaches for the most vulnerable patients

Métodos para la investigación de la loque americana

American foulbrood is one of the most devastating diseases of the honey bee. It is caused by the spore-forming, Gram-positive rod-shaped bacterium Paenibacillus larvae. The recent updated genome assembly and annotation for this pathogen now permits in-depth molecular studies. In this paper, selected techniques and protocols for American foulbrood research are provided, mostly in a recipe-like format that permits easy implementation in the laboratory. Topics covered include: working with Paenibacillus larvae, basic microbiological techniques, experimental infection, and “’omics” and other sophisticated techniques. Further, this chapter covers other technical information including biosafety measures to guarantee the safe handling of this pathogen.La loque americana es una de las enfermedades más devastadoras de la abeja melífera, causada por el bacilo, formador de esporas Grampositivo Paenibacillus larvae. El reciente ensamblaje y anotación del genoma de este patógeno permite actualmente la realización de profundos estudios moleculares. En este trabajo, se proporcionan técnicas y protocolos seleccionados para la investigación de la loque americana, principalmente bajo la forma de protocolos de trabajo con una estructura similar al de las recetas, para facilitar su implementación en el laboratorio. Los temas desarrollados incluyen: el trabajo con Paenibacillus larvae, técnicas básicas microbiológicas, la infección experimental, y "'ómicas" y otras técnicas sofisticadas. Además, este capítulo abarca otro tipo de información técnica, incluyendo medidas de bioseguridad para garantizar la seguridad en el manejo de este patógeno.Trabajo publicado en Dietemann, V.; Ellis, J. D.; Neumann, P. (eds.) The Coloss Beebook, Volume II: standard methods for Apis mellifera pest and pathogen research. Journal of Apicultural Research, 52(1).Facultad de Ciencias Agrarias y Forestale

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