ABSTRACT
Prenatal imprinting to interleukin 17A (IL-17A) triggers behavioral disorders in offspring. However, reported models of maternal immune activation utilizing immunostimulants, lack specificity to elucidate the anatomical compartments of IL-17A's action and the distinct behavioral disturbances it causes. By combining transgenic IL-17A overexpression with maternal deficiency in its receptor, we established a novel model of prenatal imprinting to maternal IL-17A (acronym: PRIMA-17 model). This model allowed us to study prenatal imprinting established exclusively through embryo-restricted IL-17A responses. We demonstrated a transfer of transgenic IL-17A across the placental barrier, which triggered the development of selected behavioral deficits in mouse offspring. More specifically, embryonic responses to IL-17A resulted in communicative impairment in early-life measured by reduced numbers of nest retrieval calls. In adulthood, IL-17A-imprinted offspring displayed an increase in anxiety-like behavior. We advocate our PRIMA-17 model as a useful tool to study neurological deficits in mice.
ABSTRACT
Artificial intelligence technology is trending in nearly every medical area. It offers the possibility for improving analytics, therapy outcome, and user experience during therapy. In dialysis, the application of artificial intelligence as a therapy-individualization tool is led more by start-ups than consolidated players, and innovation in dialysis seems comparably stagnant. Factors such as technical requirements or regulatory processes are important and necessary but can slow down the implementation of artificial intelligence due to missing data infrastructure and undefined approval processes. Current research focuses mainly on analyzing health records or wearable technology to add to existing health data. It barely uses signal data from treatment devices to apply artificial intelligence models. This article, therefore, discusses requirements for signal processing through artificial intelligence in health care and compares these with the status quo in dialysis therapy. It offers solutions for given barriers to speed up innovation with sensor data, opening access to existing and untapped sources, and shows the unique advantage of signal processing in dialysis compared to other health care domains. This research shows that even though the combination of different data is vital for improving patients' therapy, adding signal-based treatment data from dialysis devices to the picture can benefit the understanding of treatment dynamics, improving and individualizing therapy.