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Introduction: Treating extensive burn injury requires an individually tailored resuscitation protocol that includes hourly-titrated intravenous fluid infusion to avert both hypovolemic shock and edema. Due to the complexity of burn pathophysiology and significant variability in treatment protocols, there is an ongoing effort to optimize burn resuscitation. The goal of this work is to contribute to this effort by developing a mathematical model of burn pathophysiology and resuscitation for in silico testing of burn resuscitation protocols and decision-support systems. Methods: In our previous work, we developed and validated a mathematical model consisting of volume kinetics, burn-induced perturbations, and kidney function. In this work, we expanded our previous mathematical model to incorporate novel mathematical models of cardiovascular system and hormonal system (renin-angiotensin-aldosterone (RAAS) system and antidiuretic hormone) which affect blood volume and pressure regulation. We also developed a detailed mathematical model of kidney function to regulate blood volume, pressure, and sodium levels, including components for glomerular filtration rate, reabsorption rates in nephron tubules, Tubuglomerular feedback, and myogenic mechanisms. We trained and validated the expanded mathematical model using experimental data from 15 pigs and 9 sheep with extensive burns to quantitatively evaluate its prediction accuracy for hematocrit, cardiac output, mean arterial pressure, central venous pressure, serum sodium levels, and urinary output. We then trained and tested the mathematical model using a clinical dataset of 233 human burn patients with demographic data and urinary output measurements. Results: The mathematical model could predict all tested variables very well, while internal variables and estimated parameters were consistent with the literature. Discussion: To the best of our knowledge, this is the first mathematical model of burn injury and resuscitation which is extensively validated to replicate actual burn patients. Hence, this in silico platform may complement large animal preclinical testing of burn resuscitation protocols. Beyond its primary purpose, the mathematical model can be used as a training tool for healthcare providers delivering insight into the pathophysiology of burn shock, and offering novel mathematical models of human physiology which can be independently used for other purposes and contexts. [ABSTRACT FROM AUTHOR]

Large language models (LLMs) are sophisticated AI-driven models trained on vast sources of natural language data. They are adept at generating responses that closely mimic human conversational patterns. One of the most notable examples is OpenAI's ChatGPT, which has been extensively used across diverse sectors. Despite their flexibility, a significant challenge arises as most users must transmit their data to the servers of companies operating these models. Utilizing ChatGPT or similar models online may inadvertently expose sensitive information to the risk of data breaches. Therefore, implementing LLMs that are open source and smaller in scale within a secure local network becomes a crucial step for organizations where ensuring data privacy and protection has the highest priority, such as regulatory agencies. As a feasibility evaluation, we implemented a series of open-source LLMs within a regulatory agency's local network and assessed their performance on specific tasks involving extracting relevant clinical pharmacology information from regulatory drug labels. Our research shows that some models work well in the context of few- or zero-shot learning, achieving performance comparable, or even better than, neural network models that needed thousands of training samples. One of the models was selected to address a real-world issue of finding intrinsic factors that affect drugs' clinical exposure without any training or fine-tuning. In a dataset of over 700 000 sentences, the model showed a 78.5% accuracy rate. Our work pointed to the possibility of implementing open-source LLMs within a secure local network and using these models to perform various natural language processing tasks when large numbers of training examples are unavailable. [ABSTRACT FROM AUTHOR]

Burn patients require high-volume intravenous resuscitation with the goal of restoring global tissue perfusion to make up for burn-induced loss of fluid from the vasculature. Clinical standards of burn resuscitation are predominantly based on urinary output, which is not context-aware because it is not a trustworthy indicator of tissue perfusion. This paper investigates the initial development and analysis of a context-aware decision-support algorithm for burn resuscitation. In this context, we hypothesized that the use of a more context-aware surrogate of tissue perfusion may enhance the efficacy of burn resuscitation in normalizing cardiac output. Toward this goal, we exploited the arterial pulse wave analysis to discover novel surrogates of cardiac output. Then, we developed the cardiac output-enabled burn resuscitation decision-support (CaRD) algorithm. Using experimental data collected from animals undergoing burn injury and resuscitation, we conducted an initial evaluation and analysis of the CaRD algorithm in comparison with the commercially available Burn NavigatorTM algorithm. Combining a surrogate of cardiac output with urinary output in the CaRD algorithm has the potential to improve the efficacy of burn resuscitation. However, the improvement achieved in this work was only marginal, which is likely due to the suboptimal tuning of the CaRD algorithm with the limited available dataset. In this way, the results showed both promise and challenges that are crucial to future algorithm development. [ABSTRACT FROM AUTHOR]