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Identifying biomarkers distinguishing sepsis after trauma from trauma-induced SIRS based on metabolomics data: a retrospective study

Abstract Sepsis after trauma and trauma-induced SIRS have similar symptoms, making their differentiation challenging. Therefore, biomarkers are needed to differentiate between sepsis after trauma and trauma-induced SIRS. We hypothesized that sepsis following trauma induces distinct alterations in blood metabolism compared to trauma-induced SIRS and sought to identify metabolite biomarkers in blood that could differentiate between the two. In this retrospective study, the existing blood metabolomics data from 60 patients without trauma-induced SIRS, 40 patients with trauma-induced SIRS, and 50 non-trauma control cases were analyzed. Among 40 traumatic patients with SIRS, 16 developed sepsis (SDS group), 24 did not develop sepsis (SDDS group) within the subsequent two-week period after trauma. A pairwise comparison between SDS group and SDDS group was used to screen the differential metabolites as biomarkers distinguishing sepsis after trauma from trauma-induced SIRS. Using partial least‑squares discriminant analysis, we demonstrated that SDS group was metabolically distinct from the SDDS group. A total of 37 differential metabolites were found between SDS group and SDDS group. We selected 5 most significantly different metabolites between SDS and SDDS groups as biomarkers to discriminate sepsis after trauma from trauma-induced SIRS, which were 7-alpha-carboxy-17-alpha-carboxyethylandrostan lactone phenyl ester, docosatrienoic acid, SM 8:1;2O/26:1, SM 34:2;2O, and N1-[1-(3-isopropenylphenyl)-1-methylethyl]-3-oxobutanamide. Our study has identified the potential of these biomarkers for differentiating sepsis after trauma from trauma-induced SIRS. This not only provides a new approach for the early diagnosis of sepsis after trauma but also lays a solid foundation for further research based on targeted metabolomics, which may lead to the development of more effective treatment strategies in the future.

The impact of mobile internet development on firm labor demands in China

Effect of blast furnace slag on the fresh and hardened properties of volcanic tuff-based geopolymer mortars

Explaining basketball game performance with SHAP: insights from Chinese Basketball Association

A systematic review and network meta-analysis of surgical interventions for glaucoma following penetrating keratoplasty

Comparison of phage and plasmid populations in the gut microbiota between Parkinson’s disease patients and controls

Mechanical characteristics and calculation method of static pressure pile installation for PHC pipe piles in sandy soil foundation with pebble interlayer

Characterization of neutralizing versus binding antibody and T cell responses to varicella-zoster virus in the elderly

Investigation of fracture properties of mode I fracture in heat-treated granite

The development of CC-TF-BiGRU model for enhancing accuracy in photovoltaic power forecasting

A segment-based framework for explainability in animal affective computing

Pharmacological agents and injection stress, but not social isolation, alter cognitive judgement bias in the mouse touchscreen operant chamber

Correlation between upper limb proprioception and stroke effect of table tennis players

Prediction of tablet disintegration time based on formulations properties via artificial intelligence by comparing machine learning models and validation

Research on target detection for autonomous driving based on ECS-spiking neural networks

Abstract In response to the increasing demands for improved model performance and reduced energy consumption in object detection tasks relevant to autonomous driving, this research presents an advanced YOLO model, designated as ECSLIF-YOLO, which is based on the Leaky Integrate-and-Fire with Extracellular Space (ECS-LIF) framework. The primary aim of this model is to tackle the issues associated with the high energy consumption of traditional artificial neural networks (ANNs) and the suboptimal performance of existing spiking neural networks (SNNs). Empirical findings demonstrate that ECSLIF-YOLO achieves a peak mean Average Precision (mAP) of 0.917 on the BDD100K and KITTI datasets, thereby aligning with the accuracy levels of conventional ANNs while exceeding the performance of current direct-training SNN approaches without incurring additional energy costs. These findings suggest that ECSLIF-YOLO is particularly well-suited to assist the development of efficient and reliable systems for autonomous driving.

MHCII reduction is insufficient to protect mice from alpha-synuclein-induced degeneration and the Parkinson’s HLA locus exhibits epigenetic regulation

Abstract Major histocompatibility complex class II (MHCII) molecules are antigen presentation proteins and increased in post-mortem Parkinson’s disease (PD) brain. Attempts to decrease MHCII expression have led to neuroprotection in PD mouse models. Our group reported that a single nucleotide polymorphism (SNP) at rs3129882 in the MHCII gene Human Leukocyte Antigen (HLA) DRA is associated with increased MHCII transcripts and surface protein and increased risk for late-onset idiopathic PD. We therefore hypothesized that decreased MHCII may mitigate dopaminergic degeneration. During an ongoing α-synuclein lesion, mice with MHCII reduction in systemic and brain innate immune cells (LysMCre + I-Ab fl/fl or CRE+) displayed brain T cell repertoire shifts and greater preservation of the dopaminergic phenotype in nigrostriatal terminals. Next, we investigated a human cohort to characterize the immunophenotype of subjects with and without the high-risk GG genotype at the rs3129882 SNP. We confirmed that the high-risk GG genotype is associated with peripheral changes in MHCII inducibility, frequency of CD4 + T cells, and differentially accessible chromatin regions within the MHCII locus. Although our mouse studies indicate that myeloid MHCII reduction coinciding with an intact adaptive immune system is insufficient to fully protect dopamine neurons from α-synuclein-induced degeneration, our data are consistent with the overwhelming evidence implicating antigen presentation in PD pathophysiology.

Rapid detection of soybean nutrient deficiencies with YOLOv8s for precision agriculture advancement

A weakly-supervised follicle segmentation method in ultrasound images

Improving generalization in slope movement prediction using sequential models and hierarchical transformer predictor autoencoder

A comparative study on TB incidence and HIVTB coinfection using machine learning models on WHO global TB dataset