Recent studies demonstrate that blood-based biomarkers combined with AI can predict dementia risk 25 years early in women, identify liver cancer-prone environments with 93% accuracy, and classify prediabetes risk groups with 90% accuracy.
Researchers at RIKEN Center for Integrative Medical Sciences in Japan have developed a machine-learning algorithm that predicts hepatocellular carcinoma risk by analyzing the MYCN protein and tumor-promoting microenvironments in liver tissue before malignancy manifests.
Researchers develop machine-learned biomarker predicting liver cancer risk with 93% accuracy, while AI-driven drug repurposing offers faster, cost-effective alternatives to address cancer survivorship gaps.
A newly published study in Int. J. Mol. Sci. confirms that deuterium concentration directly influences gene expression in lung adenocarcinoma cells, validating HYD LLC's three-decade research on deuterium-depleted water as a complementary cancer therapy.
