Restoring the Multiple Sclerosis Associated Imbalance of Gut Indole Metabolites Promotes Remyelination and Suppresses Neuroinflammation.

In multiple sclerosis (MS) the circulating metabolome is dysregulated, with indole lactate (ILA) being one of the most significantly reduced metabolites. We demonstrate that oral supplementation of ILA impacts key MS disease processes in two preclinical models. ILA reduces neuroinflammation by dampening immune cell activation/ infiltration; and promotes remyelination and oligodendrocyte differentiation through the aryl hydrocarbon receptor (AhR).

Biomedical knowledge graph-optimized prompt generation for large language models.

Motivation: Large language models (LLMs) are being adopted at an unprecedented rate, yet still face challenges in knowledge-intensive domains such as biomedicine. Solutions such as pretraining and domain-specific fine-tuning add substantial computational overhead, requiring further domain-expertise. Here, we introduce a token-optimized and robust Knowledge Graph-based Retrieval Augmented Generation (KG-RAG) framework by leveraging a massive biomedical KG (SPOKE) with LLMs such as Llama-2-13b, GPT-3.

Analytical Methods for Comparing Uncontrolled Trials With External Controls From Real-World Data: A Systematic Literature Review and Comparison With European Regulatory and Health Technology Assessment Practice.

Objectives: This study aimed to provide an overview of analytical methods in scientific literature for comparing uncontrolled medicine trials with external controls from individual patient data real-world data (IPD-RWD) and to compare these methods with recommendations made in guidelines from European regulatory and health technology assessment (HTA) organizations and with their evaluations described in assessment reports.

Methods: A systematic literature review (until March 1, 2023) in PubMed and Connected Papers was performed to identify analytical methods for comparing uncontrolled trials with external controls from IPD-RWD. These methods were compared descriptively with methods recommended in method guidelines and encountered in assessment reports of the European Medicines Agency (2015-2020) and 4 European HTA organizations (2015-2023).

Leveraging electronic health records and knowledge networks for Alzheimer's disease prediction and sex-specific biological insights.

Identification of Alzheimer's disease (AD) onset risk can facilitate interventions before irreversible disease progression. We demonstrate that electronic health records from the University of California, San Francisco, followed by knowledge networks (for example, SPOKE) allow for (1) prediction of AD onset and (2) prioritization of biological hypotheses, and (3) contextualization of sex dimorphism. We trained random forest models and predicted AD onset on a cohort of 749 individuals with AD and 250,545 controls with a mean area under the receiver operating characteristic of 0.