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Handbook of Drug Monitoring Methods : Therapeutics and Drugs of Abuse
In Handbook of Drug Monitoring Methods: Therapeutics and Drug Abuse, authors discuss the different analytical techniques used in today’s practice of therapeutic drug monitoring and drugs of abuse as well as alcohol testing with relevant theory, mechanism, and in-depth scientific discussion on each topic.
Fetal compromise in labor
Sixty years ago, the purpose of introducing electronic fetal heart rate monitoring (EFM) was to reduce the incidence of intrapartum stillbirth. However, by the early 1980s, with falling stillbirth rates, fetal blood sampling had been widely abandoned, as many considered that EFM was sufficient on its own. Unfortunately, while the sensitivity of EFM for the detection of potential fetal compromise is high, specificity is low, and there is a high false positive rate which has been associated with a rising cesarean section rate. The authors suggest that EFM is considered and analyzed as a classic screening test and not a diagnostic test. Furthermore, it requires contextualization with other risk factors to achieve improved performance. A new proposed metric, the Fetal Reserve Index, takes into account additional risk factors and has demonstrated significantly improved performance metrics. It is going through the phases of further development, evaluation, and wider clinical implementation.
Layce (Image data poisoning) = لايس (تسميم بيانات الصور )
The ongoing growth of image generative artificial intelligence models was paved with existing drawings and art pieces by great artists both past and present, and while generative models are very useful and helpful, there is the issue of the origin of the datasets trained on, and the morality of usage regarding copyrights and artistic identity. A novel line of defense that helps artists and visual content creators actively protect their pieces emerged, dubbed Data Poisoning and it works by misleading Artificial Intelligence models that attempt to use a Poisoned Image for training, or as a reference, as the Poisoned Image will appear to the human eye identical to the original art piece, while the Artificial Intelligence model will be seeing a remarkably different image, causing generative models to generate false positive results when given a prompt poisoned by the author or when trained on data poisoned by the original owner. This study aims to study image data poisoning methods and technologies, and build an application containing multiple image models, and poisoning models as well, accompanied by a Community for artists to share art and interact with each other.
