Empowering Discovery,
Enhancing Knowledge

Embodiments of the present disclosure provide for contrast agents, methods of making contrast agents, and methods of using contrast agents, and the like.

Clinical reasoning refers to the cognitive process that physicians employ in evaluating and managing patients. This process typically involves suggesting necessary examinations, diagnosing patients’ diseases, and deciding on appropriate therapies, etc. Accurate clinical reasoning requires extensive medical knowledge and rich clinical experience, setting a high bar for physicians. This is particularly challenging in developing countries due to the overwhelming number of patients and limited physician resources, contributing significantly to global health inequity and necessitating automated clinical reasoning approaches. Recently, the emergence of large language models (LLMs) such as ChatGPT and GPT-4 have demonstrated their potential in clinical reasoning. However, these LLMs are prone to hallucination problems, and the reasoning process of LLMs may not align with the clinical decision path of physicians. In this study, we introduce a novel framework, In-Context Padding (ICP), designed to enhance LLMs with medical knowledge. Specifically, we infer critical clinical reasoning elements (referred to as knowledge seeds) and use these as anchors to guide the generation process of LLMs. Experiments on two clinical question datasets demonstrate that ICP significantly improves the clinical reasoning ability of LLMs.

With appropriate data selection and training techniques, Large Language Models (LLMs) have demonstrated exceptional success in various medical examinations and multiple-choice questions. However, the application of LLMs in medical dialogue generation-a task more closely aligned with actual medical practice-has been less explored. This gap is attributed to the insufficient medical knowledge of LLMs, which leads to inaccuracies and hallucinated information in the generated medical responses. In this work, we introduce the Medical dialogue with Knowledge enhancement and clinical Pathway encoding (MedKP) framework, which integrates an external knowledge enhancement module through a medical knowledge graph and an internal clinical pathway encoding via medical entities and physician actions. Evaluated with comprehensive metrics, our experiments on two large-scale, real-world online medical consultation datasets (MedDG and KaMed) demonstrate that MedKP surpasses multiple baselines and mitigates the incidence of hallucinations, achieving a new state-of-the-art. Extensive ablation studies further reveal the effectiveness of each component of MedKP. This enhancement advances the development of reliable, automated medical consultation responses using LLMs, thereby broadening the potential accessibility of precise and real-time medical assistance.

Background
N6-methyladenosine (m6A) is the most frequently occurring interior modification in eukaryotic messenger RNA (mRNA), and abnormal mRNA modifications can affect many biological processes. However, m6A’s effect on the metabolism of antiplatelet drugs for the prevention of ischemic stroke (IS) remains largely unclear.

Methods
We analyzed the m6A enzymes and m6A methylation in peripheral blood samples of IS patients with/without clopidogrel resistance (CR), and the peripheral blood and liver of rat models with/without CR. We also compared the effect of m6A methylation on the expression of the drug-metabolizing enzymes (CYP2C19 and CYP2C6v1) in CR and non-CR samples.
Results

Methyltransferase-like 3 (METTL3), an m6A enzyme, was highly expressed in the peripheral blood of patients with CR …