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Category: biotech/medical – Page 122

New gene tool leads to better treatments for complex diseases

Genetic changes can signal evidence of disease, but pinpointing which genes and what’s changed can be difficult.

But in a study of traits that offer clues to a person’s —such as lipid and and inflammation—a team of researchers at Case Western Reserve University devised a and tool to improve how genes and genetic changes that cause diseases are identified.

Their new approach could allow doctors to detect and treat so-called cardiometabolic diseases earlier in their development. Their findings were recently published in the journal Nature Communications.

Vertex Presents Positive Data for Zimislecel in Type 1 Diabetes at the American Diabetes Association 85th Scientific Sessions

– Results from the study continue to demonstrate the transformative potential of zimislecel with consistent and durable patient benefit – – All 12 patients with at least one year of follow-up who received a full dose of zimislecel as a single infusion achieved ADA –recommended target HbA1c levels…

Antibiotic resistance predicts higher mortality risk in 17-year follow-up—linked to diet and gender

A population-based study led by the University of Turku, Finland, investigated factors associated with the prevalence of antibiotic resistance. In addition to antibiotic use, diet, gender, living environment, income level and certain gut bacteria were associated with a higher burden of resistance. A higher resistance burden was associated with a 40% higher risk of all-cause mortality during the follow-up.

Antibiotic-resistant bacteria cause more than one million deaths per year worldwide, and the number is rising fast.

A recent study shows that an increase in relative mortality risk can be predicted by high resistance burden as well as by elevated blood pressure or type 2 diabetes. The number of antibiotic resistance genes found in gut bacteria predicted the risk of sepsis or death during a long follow-up period of almost two decades.

New AI tool deciphers mysteries of nanoparticle motion in liquid environments

Nanoparticles—the tiniest building blocks of our world—are constantly in motion, bouncing, shifting, and drifting in unpredictable paths shaped by invisible forces and random environmental fluctuations.

Better understanding their movements is key to developing better medicines, materials, and sensors. But observing and interpreting their motion at the atomic scale has presented scientists with major challenges.

Researchers in Georgia Tech’s School of Chemical and Biomolecular Engineering (ChBE) have developed an (AI) model that learns the underlying physics governing those movements.

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