Personalized Medicine – News and Features

Synthetic biology techniques have emerged as transformative tools in the tug-of-war between humans and drug-resistant bacteria. Discover how researchers are utilizing synthetic biology techniques to engineer new antimicrobials.

Technology Networks spoke with Dr. Mohamed Alameri to explore how population-wide genome initiatives are addressing gaps in genomic data representation and advancing precision medicine in areas such as rare diseases and cancer.

A study suggests depression causes menstrual pain, supported by genetic evidence linking the two conditions. Researchers identified key pathways like the HPA and HPO axes, which connect mental and reproductive health.

Researchers have discovered the underlying biology that identifies a subset of patients with acute lymphoblastic leukemia who have a higher-risk version of the disease and are more likely to relapse despite treatment.

Researchers explore how established agricultural pest management strategies could be adapted to address cancer therapy. The pioneering method opens new possibilities for controlling drug resistance and improving patient survival.

UCLA researchers have developed a new approach to treating glioblastoma by combining genetic and functional profiling to predict tumor responses to therapies. The team showed promising results in tumor shrinkage.

Standardized tissue handling is crucial in cancer research, ensuring the preservation of sample integrity and data quality. This enables comprehensive analysis and enhances understanding of cancer biology, aiding therapeutic discoveries.

Researchers have identified a KIF18A gene variant that accelerates egg aging, increasing the risk of miscarriage and early infertility. The study highlights the causal relationship between the variant and embryonic abnormalities.

University researchers have developed a novel method to uncover low-abundance proteins in plasma, revealing critical biomarkers for early disease detection. This approach uses small molecules to reduce interference from common proteins.

This article delves into MS data analysis and its associated challenges, how AI/ML can aid analyses and exciting potential future developments in the field, with specific applications to proteomics and metabolomics research.