Epidemiology of Plasmodium infections in Flores Island, Indonesia using real-time PCR

BACKGROUND:\ud DNA-based diagnostic methods have been shown to be highly sensitive and specific for the detection of malaria. An 18S-rRNA-based, real-time polymerase chain reaction (PCR) was used to determine the prevalence and intensity of Plasmodium infections on Flores Island, Indonesia.\ud METHODS:\ud Microscopy and real-time multiplex PCR for the detection of Plasmodium species was performed on blood samples collected in a population-based study in Nangapanda Flores Island, Indonesia.\ud RESULTS:\ud A total 1,509 blood samples were analysed. Real-time PCR revealed prevalence for Plasmodium falciparum, Plasmodium vivax, and Plasmodium malariae to be 14.5%, 13.2%, and 1.9% respectively. Sub-microscopic parasitaemia were found in more than 80% of all positive cases. The prevalence of P. falciparum and P. vivax was significantly higher in subjects younger than 20 years (p <= 0.01). In the present study, among non-symptomatic healthy individuals, anaemia was strongly correlated with the prevalence and load of P. falciparum infections (p <= 0.01; p = 0.02) and with the load of P. vivax infections (p = 0.01) as detected with real-time PCR. Subjects with AB blood group tend to have a higher risk of being infected with P. falciparum and P. vivax when compared to other blood groups.\ud CONCLUSION:\ud The present study has shown that real-time PCR provides more insight in the epidemiology of Plasmodium infections and can be used as a monitoring tool in the battle against malaria. The unsurpassed sensitivity of real-time PCR reveals that sub microscopic infections are common in this area, which are likely to play an important role in transmission and control.Trial registration: Trials number ISRCTN83830814

Equity, diversity and inclusion (EDI) in organ transplantation: an ESOT survey about EDI within ESOT as an organization and its educational activities, and transplantation research and science

The European Society of Organ Transplantation (ESOT) strives to promote equity, diversity, and inclusion (EDI) across all its activities. We surveyed the transplant community’s experiences and perspectives regarding EDI within ESOT as an organization and its educational activities, and research in general. A total of 299 respondents completed the questionnaire. About half agreed that ESOT’s Executive Committee, Council, and Sections/Committees are diverse and inclusive (51%) and that ESOT promotes EDI in its live and digital educational activities (54%). Forty percent of respondents agreed that scientific and clinical trials in the field of transplantation are diverse and inclusive. Despite the wide distribution of the survey, most of the respondents self-identified as White and were either physician or surgeon. However, the results contribute a unique insight into the experiences and perspectives of the transplantation community regarding EDI. Whilst ESOT is committed to the principles of EDI, perceptions and the high number of proposals show the apparent need to prioritize efforts to embed EDI across ESOT and transplantation science. These data should constitute a starting point for change and provide guidance for future efforts to promote EDI within the transplantation community

Prevalence of disease related prion protein in anonymous tonsil specimens in Britain: cross sectional opportunistic survey

Objective To establish with improved accuracy the prevalence of disease related prion protein (PrPCJD) in the population of Britain and thereby guide a proportionate public health response to limit the threat of healthcare associated transmission of variant Creutzfeldt-Jakob disease (vCJD)

Community deworming alleviates geohelminth-induced immune hyporesponsiveness

In cross-sectional studies, chronic helminth infections have been associated with immunological hyporesponsiveness that can affect responses to unrelated antigens. To study the immunological effects of deworming, we conducted a cluster-randomized, double-blind, placebo-controlled trial in Indonesia and assigned 954 households to receive albendazole or placebo once every 3 mo for 2 y. Helminth-specific and nonspecific whole-blood cytokine responses were assessed in 1,059 subjects of all ages, whereas phenotyping of regulatory molecules was undertaken in 121 school-aged children. All measurements were performed before and at 9 and 21 mo after initiation of treatment. Anthelmintic treatment resulted in significant increases in proinflammatory cytokine responses to Plasmodium falciparum-infected red blood cells (PfRBCs) and mitogen, with the largest effect on TNF responses to PfRBCs at 9 mo—estimate [95% confidence interval], 0.37 [0.21–0.53], P value over time (Ptime) < 0.0001. Although the frequency of regulatory T cells did not change after treatment, there was a significant decline in the expression of the inhibitory molecule cytotoxic T lymphocyte-associated antigen 4 (CTLA-4) on CD4+ T cells of albendazole-treated individuals, –0.060 [–0.107 to –0.013] and –0.057 [–0.105 to –0.008] at 9 and 21 mo, respectively; Ptime = 0.017. This trial shows the capacity of helminths to up-regulate inhibitory molecules and to suppress proinflammatory immune responses in humans. This could help to explain the inferior immunological responses to vaccines and lower prevalence of inflammatory diseases in low- compared with high-income countries

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