Cryopreservation – News and Features

Research from the University of Wisconsin–Madison reveals that AI tools can produce misleading conclusions in genetic studies, particularly concerning disease risks like Type 2 diabetes. The study highlights the importance of statistical rigor.

According to a new study, the amount of sugar a child consumes in their first 1,000 days could determine their chances of developing Type 2 diabetes decades later.

A study of over 250,000 individuals found that increased omega-3 and omega-6 fatty acids correlate with reduced cancer rates. Higher levels of these fats were linked to lower risks of colon, stomach, lung, and 14 other types of cancer.

A study by the University of Queensland identified inflammatory proteins, specifically elevated cytokines, in the blood of long COVID patients, which may explain persistent cardiovascular symptoms such as chest pain and heart palpitations.

A study from the Buck Institute reveals that threonine, an essential amino acid, could extend lifespan in both fruit flies and humans. By utilizing machine learning and systems biology, researchers linked key metabolites between species.

An international team has developed a new imaging method that analyzes cancer cells and their surrounding tissue at a single-cell level. This innovative approach identifies distinct patient groups based on prognosis.

Colossal reports significant advancements in thylacine de-extinction efforts, including the generation of the highest quality ancient genome and record-breaking multiplex genome editing.

Researchers found that specific genetic mutations in Black patients with acute myeloid leukemia (AML) significantly affect survival outcomes. Their findings stress the importance of including ancestry-specific factors in AML risk stratification.

A recent study found that people with any type of COVID-19 infection were twice as likely to have a major cardiac event, such as heart attack, stroke or even death, for up to three years after diagnosis.

Researchers developed the DRIVE method, which analyzes shared segments of the genome to identify undiagnosed cases of Long QT syndrome. The study highlights the potential for better detection of rare genetic variants in larger populations.