Comparison of two extraction methods for ergosterol determination in vegetal feeds

Ergosterol is the principal sterol of fungi in which it plays an essential role in cell membrane and other cellular constituents. This sterol is considered as a good marker of fungal contamination and of mycotoxin production. After validation of ergosterol quantification by HPLC-UV system (linearity range: 0.2 to 20.0 mg/ml, repeatability: 3.27%, between day precision: 4.75%), 2 extraction methods of ergosterol from 3 vegetal matrixes (maize, barley and wheat) were compared: the first one, normalized by the AFNOR, is based on solid phase extraction (SPE), while the other is based on liquid/liquid extraction (LLE). The LLE procedure allowed ergosterol extraction gains of around 20% for high initial sterol contents (3 to 5 mg/kg) in naturally contaminated matrixes or in spiked samples, and of 86% for low initial sterol contents (1-2 mg/kg) in maize. Moreover, the precision of ergosterol determination was comparable for the 2 methods even if it was slightly lower using LLE and was more affected by the initial ergosterol contents in vegetal matrix than by its nature. These results suggest that ergosterol contents in vegetal feeds would be underestimated with the official method (SPE) and emphasize the importance of the extraction step

Trials

Background The risk/benefit ratio of using statins for primary prevention of cardiovascular (CV) events in elderly people has not been established. The main objectives of the present study are to assess the cost-effectiveness of statin cessation and to examine the non-inferiority of statin cessation in terms of mortality in patients aged 75 years and over, treated with statins for primary prevention of CV events. Methods The “Statins in the elderly” (SITE) study is an ongoing 3-year follow-up, open-label comparative multi-centre, randomized clinical trial that is being conducted in two parallel groups in outpatient primary care offices. Participants meeting the following criteria are included: people aged 75 years and older being treated with statins as primary prevention for CV events, who provide informed consent. After randomization, patients in the statin-cessation strategy are instructed to withdraw their treatment. In the comparison strategy, patients continue their statin treatment at the usual dosage. The cost-effectiveness of the statin-cessation strategy compared to continuing statins will be estimated through the incremental cost per quality-adjusted life years (QALYs) gained at 36 months, from the perspective of the French healthcare system. Overall mortality will be the primary clinical endpoint. We assumed that the mortality rate at 3 years will be 15%. The sample size was computed to achieve 90% power in showing the non-inferiority of statin cessation, assuming a non-inferiority margin of 5% of the between-group difference in overall mortality. In total, the SITE study will include 2430 individuals. Discussion There is some debate on the value of statins in people over 75 years old, especially for primary prevention of CV events, due to a lack of evidence of their efficacy in this population, potential compliance-related events, drug-drug interactions and side effects that could impair quality of life. Data from clinical trials guide the initiation of medication therapy for primary or secondary prevention of CV disease but do not define the timing, safety, or risks of discontinuing the agents. The SITE study is one of the first to examine whether treatment cessation is a cost-effective and a safe strategy in people of 75 years and over, formerly treated with statins

Crit Care Sci

We hypothesized that the use of mechanical insufflation-exsufflation can reduce the incidence of acute respiratory failure within the 48-hour post-extubation period in intensive care unit-acquired weakness patients. This was a prospective randomized controlled open-label trial. Patients diagnosed with intensive care unit-acquired weakness were consecutively enrolled based on a Medical Research Council score ≤ 48/60. The patients randomly received two daily sessions; in the control group, conventional chest physiotherapy was performed, while in the intervention group, chest physiotherapy was associated with mechanical insufflation-exsufflation. The incidence of acute respiratory failure within 48 hours of extubation was evaluated. Similarly, the reintubation rate, intensive care unit length of stay, mortality at 28 days, and survival probability at 90 days were assessed. The study was stopped after futility results in the interim analysis. We included 122 consecutive patients (n = 61 per group). There was no significant difference in the incidence of acute respiratory failure between treatments (11.5% control group versus 16.4%, intervention group; p = 0.60), the need for reintubation (3.6% versus 10.7%; p = 0.27), mean length of stay (3 versus 4 days; p = 0.33), mortality at Day 28 (9.8% versus 15.0%; p = 0.42), or survival probability at Day 90 (21.3% versus 28.3%; p = 0.41). Mechanical insufflation-exsufflation combined with chest physiotherapy seems to have no impact in preventing postextubation acute respiratory failure in intensive care unit-acquired weakness patients. Similarly, mortality and survival probability were similar in both groups. Nevertheless, given the early termination of the trial, further clinical investigation is strongly recommended. NCT01931228

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

Traumatic brain injury: integrated approaches to improve prevention, clinical care, and research

No abstract available

Variation in Structure and Process of Care in Traumatic Brain Injury: Provider Profiles of European Neurotrauma Centers Participating in the CENTER-TBI Study.

INTRODUCTION: The strength of evidence underpinning care and treatment recommendations in traumatic brain injury (TBI) is low. Comparative effectiveness research (CER) has been proposed as a framework to provide evidence for optimal care for TBI patients. The first step in CER is to map the existing variation. The aim of current study is to quantify variation in general structural and process characteristics among centers participating in the Collaborative European NeuroTrauma Effectiveness Research in Traumatic Brain Injury (CENTER-TBI) study. METHODS: We designed a set of 11 provider profiling questionnaires with 321 questions about various aspects of TBI care, chosen based on literature and expert opinion. After pilot testing, questionnaires were disseminated to 71 centers from 20 countries participating in the CENTER-TBI study. Reliability of questionnaires was estimated by calculating a concordance rate among 5% duplicate questions. RESULTS: All 71 centers completed the questionnaires. Median concordance rate among duplicate questions was 0.85. The majority of centers were academic hospitals (n = 65, 92%), designated as a level I trauma center (n = 48, 68%) and situated in an urban location (n = 70, 99%). The availability of facilities for neuro-trauma care varied across centers; e.g. 40 (57%) had a dedicated neuro-intensive care unit (ICU), 36 (51%) had an in-hospital rehabilitation unit and the organization of the ICU was closed in 64% (n = 45) of the centers. In addition, we found wide variation in processes of care, such as the ICU admission policy and intracranial pressure monitoring policy among centers. CONCLUSION: Even among high-volume, specialized neurotrauma centers there is substantial variation in structures and processes of TBI care. This variation provides an opportunity to study effectiveness of specific aspects of TBI care and to identify best practices with CER approaches

Finishing the euchromatic sequence of the human genome

The sequence of the human genome encodes the genetic instructions for human physiology, as well as rich information about human evolution. In 2001, the International Human Genome Sequencing Consortium reported a draft sequence of the euchromatic portion of the human genome. Since then, the international collaboration has worked to convert this draft into a genome sequence with high accuracy and nearly complete coverage. Here, we report the result of this finishing process. The current genome sequence (Build 35) contains 2.85 billion nucleotides interrupted by only 341 gaps. It covers ∼99% of the euchromatic genome and is accurate to an error rate of ∼1 event per 100,000 bases. Many of the remaining euchromatic gaps are associated with segmental duplications and will require focused work with new methods. The near-complete sequence, the first for a vertebrate, greatly improves the precision of biological analyses of the human genome including studies of gene number, birth and death. Notably, the human enome seems to encode only 20,000-25,000 protein-coding genes. The genome sequence reported here should serve as a firm foundation for biomedical research in the decades ahead