Articles

  • Jan 14, 2025 | medicalxpress.com | Miles Martin

    A partnership between Kick It California, a tobacco quitline operated by University of California San Diego, and local 211 information and referral agencies resulted in more than 55,000 new referrals for tobacco cessation services between 2021 and 2023, report researchers at the UC San Diego Herbert Wertheim School of Public Health and Human Longevity Science and UC San Diego Moores Cancer Center.

  • Jan 8, 2025 | medicalxpress.com | Miles Martin

    A study analyzing data from the National Survey on Drug Use and Health (NSDUH) found that past-year recreational ketamine use among adults has increased dramatically since 2015, including significant shifts in associations with depression and sociodemographic characteristics such as race, age and education status.

  • Jan 8, 2025 | today.ucsd.edu | Miles Martin

    The CHARM Network will leverage expertise from across the country to help the nation better prepare for respiratory virus outbreaks Published Date January 08, 2025 Article Content The Centers for Disease Control and Prevention (CDC) has selected University of California San Diego as one of three partner institutions to establish a groundbreaking pandemic preparedness initiative, the Community and Household Acute Respiratory Illness Monitoring (CHARM) Network. The new five-year cooperative...

  • Jan 6, 2025 | today.ucsd.edu | Miles Martin

    Article Content A recent study analyzing data from the National Survey on Drug Use and Health (NSDUH) found that past-year recreational ketamine use among adults has increased dramatically since 2015, including significant shifts in associations with depression and sociodemographic characteristics such as race, age and education status.

  • Dec 9, 2024 | today.ucsd.edu | Miles Martin

    Study of malaria parasite genomes paves the way for new, more effective treatments Published Date December 09, 2024 Article Content Researchers at University of California San Diego analyzed the genomes of hundreds of malaria parasites to determine which genetic variants are most likely to confer drug resistance. The findings, published in Science, could help scientists use machine learning to predict antimalarial drug resistance and more effectively prioritize the most promising experimental...

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