Circulating non-esterified fatty acids as biomarkers for fat content and composition in pigs

Circulating non-esterified fatty acids (NEFA) can reflect the composition of dietary fat or adipose tissues depending on the fasting conditions. Therefore, circulating NEFA may be valuable as biomarkers for meat quality traits, such as intramuscular fat content and fatty acid composition in finishing pigs. Genetic variants that regulate lipid metabolism can also modulate the circulating NEFA. We conducted an experiment with 150 heavy Duroc pigs to evaluate fluctuations in the circulating NEFA composition due to age, fasting duration and two genetic polymorphisms, one in the leptin receptor (LEPR; rs709596309) and one in the stearoyl-CoA desaturase (SCD; rs80912566) gene. Circulating NEFA were more saturated and less monounsaturated than the subcutaneous and intramuscular adipose tissues. Absolute circulating NEFA content was more influenced by fasting duration than age. The SCD polymorphism did not impact NEFA content or composition. The LEPR polymorphism affected the content but not the fatty acid composition. Circulating oleic acid NEFA content after a short fasting was positively correlated with intramuscular fat content and, after a long fasting, with intramuscular oleic acid content. We conclude that circulating NEFA reflect environmental and genetic metabolic changes but are of limited value as biomarkers for intramuscular fat content and fatty acid composition.This Research was founded by the Spanish Ministry of Economy and Competitiveness and the European Union Regional Development Funds (grants AGL2012-33529, AGL2015-65846-R, and RTI2018-101346-B-I00)

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

Age-and genotype-related changes in intramuscular fat content and composition in pigs using longitudinal data

La present Tesi Doctoral s’emmarca en una línia d’investigació del Departament de Producció Animal de la Universitat de Lleida, dedicada a la millora genètica de la qualitat de la carn en bestiar porcí, en particular del contingut i composició del greix intramuscular. La Tesi es composa de quatre estudis, centrant-se el primer d’ells en el desenvolupament d’un mètode per a determinar el contingut i composició del greix intramuscular a partir de biòpsies i mostres post-mortem petites amb les que després es puguin portar a terme estudis en disseny longitudinal. La metodologia proposada ha resultat útil, demostrant-se que, especialment per al contingut de greix intramuscular, els espècimens petits del múscul objectiu són tan informatius com mostres grans d’altres músculs. En el segon estudi s’ha investigat, mitjançant un experiment amb dades longitudinals obtingudes segons la metodologia descrita anteriorment, l’efecte de l’edat sobre el contingut i composició del greix intramuscular i subcutani al llarg del cicle d’engreix en porcs de raça Duroc. Es conclou que un retard en l’edat de sacrifici implica un augment del contingut de greix intramuscular i d’àcid oleic, tot i que això s’aconsegueix a expenses de disminuir la velocitat de creixement magre. Per altra part es demostra que el greix intramuscular i el greix subcutani tenen comportaments diferents de creixement i composició i que la quantitat de greix per si mateix també influeix en la seva composició. El que un porc sigui més gras de l’esperat a una edat determinada és degut, en el cas del greix intramuscular, a que ha augmentat el contingut de greix monoinsaturat, en especial d’olèic, mentre que, en el del greix subcutani a que s’ha incrementat el contingut de saturat. En els dos últims estudis s’examina si la variació al·lèlica en els gens IGF-1 (insulin-like growth factor-1) i LEP (leptina), així com la concentració de IGF-1 i leptina en plasma, s’associen amb el contingut i la composició del greix intramuscular i, en cas que així fos, si aquesta associació és funció de l’edat. Es posa en evidència que els polimorfismes moleculars estudiats no són neutrals en relació al contingut de greix intramuscular, però també que els seus efectes no són constants al llarg del període de creixement. En aquest sentit, tant l’edat com l’estat d’engrassament poden modificar-los.La presente Tesis Doctoral se emmarca en una línea de investigación del Departamento de Producción Animal de la Universidad de Lleida dedicada a la mejora genética de la calidad de la carne en porcino, en particular del contenido y la composición de la grasa intramuscular. La Tesis se compone de cuatro estudios, centrándose el primero de ellos en el desarrollo de un método para determinar el contenido y la composición de la grasa intramuscular a partir de biopsias y muestras post-mortem pequeñas con las que luego poder realizar estudios mediante diseños longitudinales. La metodología propuesta ha resultado útil, demostrándose que, especialmente para el contenido de grasa intramuscular, los especímenes pequeños del músculo objetivo son tan informativos como muestras grandes de otros músculos. En el segundo estudio se ha investigado mediante un experimento con datos longitudinales, obtenidos según la metodología descrita anteriormente, el efecto de la edad sobre el contenido y la composición de la grasa intramuscular y subcutánea durante el engorde de cerdos Duroc. Se concluye que un retraso en la edad de sacrificio comporta un aumento del contenido de grasa intramuscular y de ácido oleico, aunque ello se consigue a costa de disminuir la velocidad de crecimiento magro. Por otra parte, se demuestra que la grasa intramuscular y la grasa subcutánea tienen patrones distintos de crecimiento y composición y que la cantidad de grasa por sí misma influye en su composición. El que un cerdo sea más graso de lo esperado a una edad determinada es debido, en el caso de la grasa intramuscular, a que ha aumentado el contenido de grasa monoinsaturada, en especial de oleico, mientras que, en el de la subcutánea, a que se ha incrementado el de la saturada. En los dos últimos estudios se examina si la variación alélica en los genes IGF-1 (insulin-like growth factor-1) y LEP (leptina), así como la concentración de IGF-1 y leptina en plasma, se asocian con el contenido y la composición de la grasa intramuscular y, en caso de que así fuera, si tal asociación es función de la edad. Se constata que los polimorfismos moleculares estudiados no son neutrales respecto al contenido de grasa intramuscular, pero, también, que sus efectos no son constantes a lo largo del crecimiento. En este sentido, tanto la edad como el estado de engrasamiento pueden modificarlos.This PhD is part of a line of research conducted in the Department of Animal Production of the Universitat de Lleida dedicated to the genetic improvement of pig meat quality, with particular reference to intramuscular fat content and composition. The PhD comprises four studies, with the first one focusing on the development of a method to jointly determine the content and composition of intramuscular fat from biopsies and small post-mortem samples and, in this way, to carry out studies with longitudinal data. It has been found that this particular methodology is useful and, in for intramuscular fat, small specimens of the target muscle are as informative as large samples of other muscles. In the second study the effect of age on the content and composition of the intramuscular and subcutaneous fat in the fattening period in Duroc pigs was investigated by an experiment using longitudinal data obtained following the methodology described above. It was concluded that a delay in the age of slaughter of the pig leads to an increase in intramuscular fat and oleic acid, although this comes at the cost of reducing the rate of lean growth. Moreover, it was proved that intramuscular and subcutaneous fat behaved differently in terms of fat accretion and composition and that the amount of fat itself affected composition. Whereas, for the intramuscular fat, values above the expected at a given age were because of increased monounsaturated fatty acid content, especially oleic acid, for the subcutaneous fat, they were due to the increased saturated fatty acid content. The final two studies considered whether allelic variation at the IGF-1 (insuline-like growth factor-1) and LEP (leptin) genes, as well as the concentration of IGF-1 and leptin in plasma, are associated to intramuscular fat content and composition and, if so, whether this is a function of age. It can be seen that the molecular polymorphisms studied are not neutral with regard to the content of intramuscular fat, but that their effects are not constant throughout the growing period. In this sense, both age and fatness can modify them

Nail Psoriasis Psoriasis ungueal

Nail involvement in psoriasis is common. It is seen in up to 80% of patients with psoriatic lesions and may be the only manifestation in 6% of cases. Nail psoriasis is correlated with more severe disease, characterized by earlier onset and a higher risk of psoriatic arthritis. Accordingly, it can also result in significant functional impairment and reduced quality of life. Psoriasis involving the nail matrix causes pitting, leukonychia, red lunula and nail dystrophy, while nail bed involvement causes splinter hemorrhages, onycholysis, oil spots (salmon patches), and subungual hyperkeratosis. Common evaluation tools are the Nail Psoriasis Severity Index (NAPSI), the modified NAPSI, and the f-PGA (Physician's Global Assessment of Fingernail Psoriasis). Treatment options include topical therapy, intralesional injections, and systemic and biologic agents. Treatment should therefore be assessed on an individualized basis according to the number of nails involved, the part of the nail or nails affected, and the presence of concomitant nail and/or joint involvement