{"title":"A Crossroad for Physician-Led Care in Missouri.","authors":"Jacob Scott","doi":"","DOIUrl":"","url":null,"abstract":"","PeriodicalId":74203,"journal":{"name":"Missouri medicine","volume":"123 1","pages":"26"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12931574/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147313352","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"The Empty Promises of Medical Marijuana.","authors":"Gary A Salzman","doi":"","DOIUrl":"","url":null,"abstract":"","PeriodicalId":74203,"journal":{"name":"Missouri medicine","volume":"123 1","pages":"12-14"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12931603/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147313171","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"A 20th Century Historical Perspective on Congenital Syphilis and Lessons for Today.","authors":"Jane F Knapp, Robert D Schremmer","doi":"","DOIUrl":"","url":null,"abstract":"","PeriodicalId":74203,"journal":{"name":"Missouri medicine","volume":"123 1","pages":"15-17"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12931602/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147313319","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"A History of Biostatistics and Data Science at Washington University School of Medicine.","authors":"Charles W Goss, Daberru C Rao","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>Biostatistics at Washington University School of Medicine has evolved from a modest component of pharmacology in the 1940s into a nationally recognized leader in quantitative biomedical research. In this perspective we trace the development of the biostatistics program from its early incorporation into medical education through the formal establishment of the Division of Biostatistics in 1966 and its subsequent expansion in scope and impact. The Division's transformation into the Center for Biostatistics and Data Science (CBDS) marks a new era that integrates traditional biostatistics with modern data science, including machine learning and other advanced computational methods. Guided by three strategic pillars-team science and collaborative research, innovative methods, and education and training-CBDS continues to advance biomedical discovery through rigorous quantitative science. This historical reflection connects the early foundations to the questions and methods that define biostatistics and data science at Washington University today.</p>","PeriodicalId":74203,"journal":{"name":"Missouri medicine","volume":"123 1","pages":"36-39"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12931573/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147313360","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Yiming Shi, Ke Xie, Zhichen Xu, Jingyi Zhang, Lei Liu
{"title":"Trends and Determinants of Blood Pressure Control in US Hypertensive Patients: Insights from NHANES 1999-2021.","authors":"Yiming Shi, Ke Xie, Zhichen Xu, Jingyi Zhang, Lei Liu","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>This study analyzes trends and determinants of blood pressure control among hypertensive patients in the United States (US) using National Health and Nutrition Examination Survey NHANES data from 1999 to 2021. Blood pressure control rates improved through 2013 but declined thereafter. Applying machine learning methods such as XGBoost, we identify hypertension awareness and antihypertensive medication use as the strongest predictors of successful control. In contrast, resistant hypertension, chronic kidney disease, and elevated low-density lipoprotein (LDL) levels are associated with poorer control. The shift from JNC7 to JNC8 hypertension guidelines reclassified some individuals from uncontrolled to controlled, influencing observed trends. Additionally, a reduction in antihypertensive medication use among female patients may have contributed to the recent decline. These findings highlight the critical role of medication adherence and patient awareness in maintaining blood pressure control.</p>","PeriodicalId":74203,"journal":{"name":"Missouri medicine","volume":"123 1","pages":"50-54"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12931589/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147313264","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Advancing Decision Support Systems with Data, Informatics, and Learning Health Systems in a Post-Meaningful Use Era.","authors":"Adam Wilcox","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>The HITECH Act and Meaningful Use (MU) successfully drove widespread EHR adoption but largely missed the strategic goal of healthcare transformation. While EHRs are now ubiquitous, few institutions have transitioned to a true Learning Health System (LHS). To support achieving transformative impact, this article proposes an extension of a previously-described quality improvement life cycle model, aligning it with modern informatics and artificial intelligence (AI) capabilities. We detail how the stages of DMAIC (Define, Measure, Analyze, Improve, Control) provide the necessary structured framework for advancing decision support within a post-MU environment. We argue that by integrating AI-driven tools into each step of the DMAIC cycle, healthcare organizations can effectively leverage their vast stores of electronic clinical data. This structured approach offers a pathway to establishing agile, data-driven learning health systems necessary to fulfill the latent promise of health information technology.</p>","PeriodicalId":74203,"journal":{"name":"Missouri medicine","volume":"123 1","pages":"40-44"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12931598/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147313322","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Fuhai Li, Heming Zhang, Di Huang, Hao Li, Wenyu Li, Tianqi Xu, Yixin Chen, Michael Province, Philip R O Payne
{"title":"AI for Scientific Discovery in Omics Data-Driven Precision Medicine.","authors":"Fuhai Li, Heming Zhang, Di Huang, Hao Li, Wenyu Li, Tianqi Xu, Yixin Chen, Michael Province, Philip R O Payne","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>In recent years, the rapid advancement of high-throughput technologies has led to the generation of vast and complex multi-omics datasets that are valuable for characterizing and understanding complex cell signaling network systems. On the other hand, large language models (LLMs), domain-specific foundation models (FMs) and AI agents, have achieved significant breakthroughs and have been revolutionizing scientific research. The convergence of these two trends is catalyzing a new era for biomedical research to augment and speed up scientific discovery and the development of precision medicine. In this study, we examine the large-scale omics datasets, emerging applications, and challenges at the intersection of massive omic datasets and related AI models and agents, highlighting how their integration is reshaping the landscape of biomedical research and precision medicine.</p>","PeriodicalId":74203,"journal":{"name":"Missouri medicine","volume":"123 1","pages":"55-66"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12931591/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147313284","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Artificial Intelligence as a Catalyst for Quality, Safety, and Value in Healthcare Delivery.","authors":"Philip R O Payne","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>Artificial intelligence (AI) has the potential to transform healthcare by enhancing quality, improving safety, and increasing value. However, achieving these benefits requires navigating a complex interplay between data quality, interoperability, computability, workflow integration, and governance, as well as the need to create and sustain what have been described as learning health systems. The full promise of AI will only be realized through rigorous data stewardship, the design and demonstration of novel computational methods, cross-disciplinary collaboration, and a commitment to responsible and equitable technology implementation. Substantial work remains to address these challenges and capitalize on these opportunities, which can and should lead to significant improvements in human health and well-being.</p>","PeriodicalId":74203,"journal":{"name":"Missouri medicine","volume":"123 1","pages":"45-49"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12931601/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147313289","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}