The serum lipoprotein pattern (HDL-, LDL- and VLDL-cholesterol) was determined in normal untreated rats, dogs and monkeys using the density gradient centrifugation method, and compared with that in humans. The serum of rats (Wistar) and dogs (Beagle) was found to contain mainly HDL-cholesterol, whereas the serum of monkeys (Macaca fascicularis) consists of similar amounts of HDL-cholesterol and LDL-cholesterol, as in humans. In contrast to rats and dogs, monkeys showed a very low amount of serum VLDL-cholesterol. Comparison of four different methods (density gradient centrifugation, FPLC, agarose gel electrophoresis and precipitation methods) for determination of HDL- and LDL-cholesterol in monkey serum showed that density gradient centrifugation and FPLC-methods have the highest accuracy and recovery rate. The FPLC-method offers the extra advantages that it requires less sample volume and is less laborious. In a separate experiment, the influence of the menstrual cycle on the serum lipoprotein pattern was investigated in female monkeys. No changes in total cholesterol, HDL- or LDL-cholesterol were detected in serum samples obtained in the follicular and luteal phases of the cycle. In conclusion, monkeys (Macaca fascicularis) may be more suitable than rats or dogs for the comparison of the effect of different compounds on serum lipoproteins in pharmacological and toxicological studies, because the lipoprotein pattern in this species is similar to that of humans.

Objectives: To assess the interpretability and acceptance of Shapley values for making artificial intelligence/machine learning (AI/ML) tools more transparent, interpretable, and useful to clinicians. Materials and Methods: Structured assessments were conducted with 30 clinicians (15 providers; 15 nurses; 8 assessments per clinician) to evaluate their ability to understand interventional Shapley Additive exPlanations (SHAP) values, a type of Shapley value that provides individualized variable importance scores and ascertain their perspective on SHAP value utility for the use of an AI/ML sepsis diagnostic. Participants were shown the diagnostic interface for real clinical scenarios with de-identified patient data with and without SHAP values. The primary outcomes were clinician ability to correctly interpret SHAP values and clinician self-reported improvement in their understanding of how the AI/ML algorithm produced its result. Results: Participants correctly interpreted SHAP values in 235 of 240 assessments (98%; CI, 95%-99%) and reported SHAP values improved their understanding of how the algorithm produced its result in every case (240/240; 100%; CI, 99%-100%). Participants were unanimous (30/30) in preferring the interface with SHAP values over the interface without. Discussion: Clinician participants strongly preferred the device interface with SHAP values, were unanimous in reporting SHAP values improved their understanding of the AI/ML diagnostic, and scored nearly perfectly when asked to interpret SHAP values. Conclusion: These results suggest health care providers value transparency into AI/ML algorithms designed for clinical use, and that Shapley values are a useful approach to providing that transparency, which in turn may improve tool adoption and clinical utility.

Suspected infection requiring hospitalisation has highly heterogenous presentation. Yet, variances in host response and its implications are largely unknown. In this multicentre cohort of 3802 individual patients presenting to the Emergency Department (ED) with suspected infection requiring hospitalisation, we apply uniform manifold approximation and projections and K-means clustering to 29 plasma proteins to identify biologically discrete host response clusters. In this work, we first describe two large clusters, called “Dysregulated” and “Undifferentiated”, with abnormal protein concentrations and adverse outcomes in the former. Through further clustering, we identify 4 sub-clusters in the Dysregulated cluster, each with discrete biological signatures, clinical correlates, and outcomes. Clusters 3 and 4 are characterised by renal impairment and viral infections respectively. Clusters 5 and 6 are associated with bacterial culture positivity, with the former consistent with an immunosuppressed signature and worse outcomes, and the latter with gram-negative bacteria, higher IL-6 and IL-8, and better outcomes despite higher vasopressor use. These clusters are a biologically driven approach to characterising acute suspected infection and may lead to more targeted therapeutics.

Background: Early and accurate risk stratification of patients suspected of serious infection is essential for improving outcomes, but existing diagnostic and predictive tools have limited accuracy. The objective was to compare the performance of an FDA-authorized AI diagnostic test, the Sepsis ImmunoScore, against widely available biomarkers and clinical tools for diagnosis of sepsis and prediction of in-hospital mortality and intensive care unit (ICU) admission. Methods: This multicenter observational study included 6027 adult patients suspected of infection across 7 U.S. hospital sites. The Sepsis ImmunoScore’s predictive performance was compared to the sequential organ failure assessment (SOFA) score, procalcitonin (PCT), C-reactive protein (CRP), Systemic Inflammatory Response Syndrome (SIRS) score, National Early Warning Score (NEWS), and quick SOFA (qSOFA). Primary outcomes included sepsis as defined by Sepsis-3 criteria, in-hospital mortality, and ICU admission. Predictive accuracy was assessed using area under the receiver operating characteristic curve (AUC), and 95% confidence intervals were generated and hypothesis testing conducted using the bootstrap method. Results: The Sepsis ImmunoScore demonstrated statistically significant superior performance across all outcomes. For sepsis prediction, the Sepsis ImmunoScore achieved an AUC of 0.82, compared to SOFA (0.72), procalcitonin (PCT) (0.70), C-reactive protein (CRP) (0.61), SIRS (0.59), NEWS (0.69), and qSOFA (0.67). For in-hospital mortality prediction, the Sepsis ImmunoScore achieved an AUC of 0.80, outperforming SOFA (0.72), PCT (0.67), CRP (0.58), SIRS (0.60), NEWS (0.72), and qSOFA (0.69). For ICU admission, the Sepsis ImmunoScore reached an AUC of 0.74, superior to SOFA (0.63), PCT (0.64), CRP (0.54), SIRS (0.60), NEWS (0.70), and qSOFA (0.65). All differences between the Sepsis ImmunoScore and comparators were statistically significant. Conclusions: The Sepsis ImmunoScore significantly improved predictive accuracy for sepsis, in-hospital mortality, and ICU admission compared to six conventional clinical scores and biomarkers. This AI-based tool may enhance risk stratification and clinical decision-making, potentially leading to more timely sepsis interventions and improved outcomes.