With the advent of effective immunotherapies to battle cancers and diseases, an obstacle in recovery has become the potential side effects, specifically cytokine release syndrome (CRS). As there is little quantitative understanding of risks for developing CRS and the degree of its severity, this work explored a model-based approach to produce a library of in silico patients through sensitivity analysis of cytokine interaction parameters and a Monte Carlo sampling. The objective of producing the in silico patients was to correlate a known grading system of cytokine release syndrome severity and thus design a new formula for grading CRS. Using our CRS grading system as the foundation, this work produced not only a formula which related the in silico patient data to the different grades, but we effectively demonstrated a selective approach to reduce the grade of CRS with sequential cytokine inhibition targets. We achieved the reduction of grades by applying the insight from the sensitivity analysis, beginning with the most sensitive targets. Cytokines IL-1, IL-8, TNF-α, INF-γ, IL-6, IL-2, IL-4, IL-10, and IL-12 were in turn the best targets for inhibition to alleviate CRS. Using this approach, patient cytokine time profiles in real-time can be related to the CRS grading system and if the grade is severe enough, action can be taken earlier during the treatment to prevent potentially life-threatening symptoms. What’s more, the identified inhibition sequence of the 9 cytokines provides guidance for clinical intervention of CRS.
Background/ObjectivesOwing to accelerated population aging, health in older adults is becoming increasingly important. Frailty can reflect the health status and disease risks of older adults; however, appropriate biomarkers for early screening of frailty have not been identified. Here, we applied metabolomics to identify frailty biomarkers and potential pathogenic mechanisms of frailty.MethodsSerum metabolic profiles from 25 frail and 49 non-frail (control) older adults were systematically investigated by liquid chromatography-mass spectrometry-based metabolomics.ResultsWe identified 349 metabolites of 46 classes, with four increased and seven decreased metabolites in frail older adults. Pearson correlation analysis identified 11 and 21 metabolites that were positively and negatively correlated with grip strength, and 7 and 76 metabolites that were positively and negatively correlated with gait speed, respectively. Pathway analysis identified 10 metabolite sets and 13 pathways significantly associated with one or more frailty phenotype criteria.ConclusionThese results revealed the metabolite characteristics of serum in frail older adults. Intermediates of carbohydrate metabolism (e.g., isocitrate, malate, fumarate, cis-aconitate, glucuronate, and pyruvate), saturated fatty acids (e.g., palmitic acid), unsaturated fatty acids (e.g., arachidonate and linoleic acid), and certain essential amino acids (e.g., tryptophan) may be candidate biomarkers for the early diagnosis of frailty. Mitochondrial function disorders, saturated fatty acid-mediated lipotoxicity, aberrant unsaturated fatty acid metabolism, and increased tryptophan degradation could be potential mechanisms of frailty.
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