Leveraging implementation science in clinical trials of vaccines in the global south
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Leveraging the collaborative power of AI and citizen science for sustainable development
Both artificial intelligence (AI) and citizen science hold immense potential for addressing major sustainability challenges from health to climate change. Alongside their individual benefits, when combined, they offer considerable synergies that can aid in both better monitoring of, and achieving, sustainable development. While AI has already been integrated into citizen science projects such as through automated classification and identification, the integration of citizen science approaches into AI is lacking. This integration has, however, the potential to address some of the major challenges associated with AI such as social bias, which could accelerate progress towards achieving sustainable development.
Optimising the mainstreaming of renal genomics: Complementing empirical and theoretical strategies for implementation
To identify and develop complementary implementation strategies that support nephrologists in mainstreaming renal genomic testing. Interviews were conducted with individuals nominated as ‘genomics champions’ and ‘embedded genomics experts’ as part of a mainstreaming project to identify initial barriers and investigate empirical strategies for delivering the project at initial stage. Data were mapped onto implementation science framework to identify complementary theoretical strategies. Interviews with 14 genomics champions and embedded genomics experts (genetic counsellors, nephrologists, renal nurses), identified 34 barriers to incorporating genomic testing into routine care, e.g., lack of long-term multidisciplinary team support and role clarity. In total, 25 empirical implementation strategies were identified such as creating new clinical teams. Using the Consolidated Framework for Implementation Research, 10 complementary theoretical implementation strategies were identified. Our study presents a novel approach complementing empirical strategies with theoretical strategies to support nephrologists in incorporating genomic testing into routine practice. Complementary strategies can potentially address barriers and inform future studies when mainstreaming renal genomics. This process underscored the need for integrating collaborative efforts among health professionals, patients, implementation scientists and the health system to overcome identified challenges to mainstream genomic testing. Future research should explore the applicability of these strategies to support mainstreaming genomic testing in different clinical settings.
Addressing myocardial infarction in South-Asian populations: risk factors and machine learning approaches
Cardiovascular diseases, especially myocardial infarction (MI), are an important and up-trending public health challenge in the South Asian population. With urbanization and economic development, there has been a rise in obesity, dyslipidemia, diabetes mellitus, and hypertension in these regions, which, combined with genetic predisposition, create a unique cardiovascular risk profile among South Asians. Traditional risk assessment tools often underestimate the cardiovascular risk in South Asians due to a lack of phenotypic representation in their development. In this review, we explore the risk factors for MI in South Asians and highlight the potential role of machine learning (ML) and deep learning (DL) in enhancing diagnostic and predictive accuracy. These ML algorithms, including convolutional neural networks (CNNs) and transformer-based models, show potential in analyzing complex information from clinical characteristics, electrocardiograms (ECG), and cardiac biomarkers while integrating multimodal data. We also explore the challenges in accessing high-quality datasets and enabling applicability in clinical settings. We believe that future research should focus on developing comprehensive cardiovascular risk scores that incorporate South Asian-specific risk factors and leverage advanced ML models to enhance risk prediction, diagnosis, and management.
Bridging gene therapy and next-generation vaccine technologies
Contributions of the COVID-19 pandemic to gene-based vaccination Dealing with the serious problems of COVID-19 has propelled vaccine research and the importance of pandemic preparedness…
Trust in scientists and their role in society across 68 countries
Science is crucial for evidence-based decision-making. Public trust in scientists can help decision makers act on the basis of the best available evidence, especially during crises. However, in recent years the epistemic authority of science has been challenged, causing concerns about low public trust in scientists. We interrogated these concerns with a preregistered 68-country survey of 71,922 respondents and found that in most countries, most people trust scientists and agree that scientists should engage more in society and policymaking. We found variations between and within countries, which we explain with individual- and country-level variables, including political orientation. While there is no widespread lack of trust in scientists, we cannot discount the concern that lack of trust in scientists by even a small minority may affect considerations of scientific evidence in policymaking. These findings have implications for scientists and policymakers seeking to maintain and increase trust in scientists.
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