University of Maryland

Probiotics, despite their growing market, lack sufficient scientific backing for infant use, as highlighted by a 2023 case of a preterm baby's death from sepsis. The FDA has warned companies illegally selling probiotics for disease prevention in infants. Microbiome interventions like FMT and direct-to-consumer tests for infants raise ethical and safety concerns, emphasizing the need for rigorous FDA standards and caution in their application.

The University of Maryland ranks 18th among U.S. research institutions, with combined research expenditures of $1.4 billion in FY2023, reflecting faculty and student dedication to innovation. UMB, a pre-eminent public research university, received over $638 million in awards in FY2024 and collaborates with over 300 bioscience firms.

NIH developed TrialGPT, an AI algorithm matching potential volunteers to relevant clinical trials on ClinicalTrials.gov, aiding clinicians in navigating trial options efficiently. TrialGPT processes patient summaries, identifies eligible trials, and explains eligibility criteria, reducing clinician screening time by 40% while maintaining accuracy. The tool aims to improve clinical trial enrollment and accelerate medical research progress.

US originator biologic companies may increase rebates by up to 89% and reduce net prices by up to 51% to counter biosimilar competition, according to a study in *Expert Opinion on Biological Therapy*. The study highlights the impact of biosimilar entries on pricing, with significant net price reductions and rebate increases across biologics like trastuzumab, pegfilgrastim, infliximab, and rituximab. The authors suggest that promoting 1-2 biosimilars per market may be more effective for cost savings than supporting multiple biosimilars in fewer markets.

NIH developed AI algorithm TrialGPT to match volunteers to relevant clinical trials on ClinicalTrials.gov, streamlining the process and improving enrollment. TrialGPT uses large language models to process patient summaries, identify eligible trials, and explain eligibility criteria, achieving nearly the same accuracy as human clinicians while saving time. The tool aims to enhance clinical trial recruitment and reduce barriers for underrepresented populations.

NIH researchers developed TrialGPT, an AI algorithm to match potential volunteers to relevant clinical trials on ClinicalTrials.gov, enhancing trial enrollment efficiency and accuracy. The tool uses large language models to process patient summaries and identify eligible trials, reducing clinician screening time by 40% while maintaining accuracy. Selected for The Director's Challenge Innovation Award, TrialGPT aims to improve clinical trial recruitment and reduce participation barriers.

NIH developed AI algorithm TrialGPT matches potential volunteers to relevant clinical trials on ClinicalTrials.gov, improving enrollment efficiency and accuracy. TrialGPT processes patient summaries, identifies eligible trials, and explains eligibility criteria, saving clinicians 40% screening time. Selected for Director's Challenge Innovation Award to further assess real-world performance.

PSC genomic classifier predicts prognosis and benefits of LT-ADT in high-risk PCa, with basal subtype showing worse outcomes and greater benefit from LT-ADT, suggesting personalized ADT recommendations.

NIH researchers developed TrialGPT, an AI algorithm to match potential volunteers to clinical trials on ClinicalTrials.gov. TrialGPT processes patient summaries to identify and explain eligibility for relevant trials, reducing clinician screening time by 40% while maintaining accuracy. The tool aims to improve clinical trial enrollment and accelerate medical research.