Using Artificial Intelligence-based Methods to Address the Placebo Response in Clinical Trials.

The placebo response is a highly complex psychosocial-biological phenomenon that has challenged drug development for decades, particularly in neurological and psychiatric disease. While decades of research have aimed to understand clinical trial factors that contribute to the placebo response, a comprehensive solution to manage the placebo response in drug development has yet to emerge. Advanced data analytic techniques, such as artificial intelligence (AI), might be needed to take the next leap forward in mitigating the negative consequences of high placebo-response rates. The objective of this review was to explore the use of techniques such as AI and the sub-discipline of machine learning (ML) to address placebo response in practical ways that can positively impact drug development. This examination focused on the critical factors that should be considered in applying AI and ML to the placebo response issue, examples of how these techniques can be used, and the regulatory considerations for integrating these approaches into clinical trials.

Using Health Information Technology and Data to Improve Chronic Disease Outcomes in Federally Qualified Health Centers in Maryland.

Federally Qualified Health Centers provide health care services to underserved communities and vulnerable populations. In Maryland, the burden of chronic disease is high among Federally Qualified Health Center patients. Electronic health records (EHRs) are becoming more widely used, and effective use of EHR data may improve chronic disease outcomes. This article describes the process of developing a data aggregation and analytics platform to support health centers in using population health data based on standardized clinical quality measures. This data warehouse, capable of aggregating EHR data across multiple health centers, provides opportunities for benchmarking and elicits a discussion of quality improvement, including identifying and sharing clinical best practices. Phase 1 of the project involved the strategic engagement of health center leadership and staff to get buy-in and to assess readiness. Phase 2 established the technological infrastructure and processes to support data warehouse implementation and began the process of information sharing and collaboration among 4 early adopters. Phase 3 will expand the project to additional health centers and continue quality improvement efforts. The health information technology marketplace is rapidly changing, and staying current will be a priority so that the data warehouse remains a useful quality improvement tool that continues to meet the demands of Maryland health centers. Ongoing efforts will also focus on ways to further add value to the system, such as incorporating new metrics to better inform health center decision making and allocation of resources. The data warehouse can inform and transform the quality of health care delivered to Maryland's most vulnerable populations, and future research should focus on the ability of health centers to translate this potential into actual improvements.

High-throughput analysis of algal crude oils using high resolution mass spectrometry.

Lipid analysis often needs to be specifically optimized for each class of compounds due to its wide variety of chemical and physical properties. It becomes a serious bottleneck in the development of algae-based next generation biofuels when high-throughput analysis becomes essential for the optimization of various process conditions. We propose a high-resolution mass spectrometry-based high-throughput assay as a 'quick-and-dirty' protocol to monitor various lipid classes in algal crude oils. Atmospheric pressure chemical ionization was determined to be most effective for this purpose to cover a wide range of lipid classes. With an autosampler-LC pump set-up, we could analyze algal crude samples every one and half minutes, monitoring several lipid species such as TAG, DAG, squalene, sterols, and chlorophyll a. High-mass resolution and high-mass accuracy of the orbitrap mass analyzer provides confidence in the identification of these lipid compounds. MS/MS and MS3 analysis could be performed in parallel for further structural information, as demonstrated for TAG and DAG. This high-throughput method was successfully demonstrated for semi-quantitative analysis of algal oils after treatment with various nanoparticles.