Toxicology is undergoing a digital revolution, with mobile apps, sensors, artificial intelligence (AI), and machine learning enabling better record-keeping, data analysis, and risk assessment. Additionally, computational toxicology and digital risk assessment have led to more accurate predictions of chemical hazards, reducing the burden of laboratory studies. Blockchain technology is emerging as a promising approach to increase transparency, particularly in the management and processing of genomic data related with food safety. Robotics, smart agriculture, and smart food and feedstock offer new opportunities for collecting, analyzing, and evaluating data, while wearable devices can predict toxicity and monitor health-related issues. The review article focuses on the potential of digital technologies to improve risk assessment and public health in the field of toxicology. By examining key topics such as blockchain technology, smoking toxicology, wearable sensors, and food security, this article provides an overview of how digitalization is influencing toxicology. As well as highlighting future directions for research, this article demonstrates how emerging technologies can enhance risk assessment communication and efficiency. The integration of digital technologies has revolutionized toxicology and has great potential for improving risk assessment and promoting public health.
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10286258 | PMC |
http://dx.doi.org/10.1021/acsomega.3c00596 | DOI Listing |
Background: Availability of amyloid modifying therapies will dramatically increase the need for disclosure of Alzheimer's disease (AD) related genetic and/or biomarker test results. The 21st Century Cares Act requires the immediate return of most medical test results, including AD biomarkers. A shortage of genetic counselors and dementia specialists already exists, thus driving the need for scalable methods to responsibly communicate test results.
View Article and Find Full Text PDFAlzheimers Dement
December 2024
Karolinska Institute, Stockholm, Södermanland and Uppland, Sweden.
Background: Novel anti-amyloid therapies (AAT) for Alzheimer's Disease (AD) have recently been approved in the United States, Japan and China, and are under regulatory review in Europe. Questions remain regarding the long-term effectiveness and value of these drugs when used in routine clinical practice. Data from follow-up studies will be important to inform their optimal use, including criteria for treatment initiation, monitoring strategies, stopping rules, pricing and reimbursement considerations.
View Article and Find Full Text PDFBackground: Rater change is inevitable in often lengthy clinical trials in Alzheimer's disease. Other groups have previously assessed the impact of rater change on data variability. Their conclusions varied, possibly due to differing methodologies (e.
View Article and Find Full Text PDFBackground: Pivotal Alzheimer's Disease (AD) trials typically require thousands of participants, resulting in long enrollment timelines and substantial costs. We leverage deep learning predictive models to create prognostic scores (forecasted control outcome) of trial participants and in combination with a linear statistical model to increase statistical power in randomized clinical trials (RCT). This is a straightforward extension of the traditional RCT analysis, allowing for ease of use in any clinical program.
View Article and Find Full Text PDFBackground: Hypertension is a risk factor for cognitive impairment and dementia. Anti-hypertensives (AHT) are commonly used in old age, but their association with cognition and brain pathology is not well understood.
Method: To investigate the relation of AHT with change in cognitive function and postmortem brain pathology, we evaluated 4,207 older persons without known dementia at enrollment and a subset of 1880 participants who died and came to autopsy.
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