Late-onset neonatal sepsis: genetic differences by sex and involvement of the NOTCH pathway

Quality improvement in neonatal care through enhanced patient safety and clinical risk management: a before-and-after study about neonatal sepsis

Introduction

Neonatal sepsis, classified into early-onset and late-onset based on symptom timing, poses significant risks of morbidity and mortality, especially in low birth weight infants. Effective clinical risk management protocols are crucial in reducing these risks.

Methods

This before-and-after study evaluated the impact of a newly implemented clinical risk management protocol in the Neonatology and Neonatal Intensive Care Unit (NICU) at Policlinico Hospital-University of Bari. The study included 399 neonates over three years, comparing pre- and post-protocol outcomes. Data collection focused on maternal and neonatal demographics, infection rates, and hospital stay lengths. Statistical analysis included t-tests, Wilcoxon-Mann-Whitney tests, and logistic regression models.

Results

The study found no significant differences in neonatal pathologies or demographics between pre- and post-protocol groups. However, post-protocol implementation showed a notable reduction in umbilical venous catheter (UVC) infections (p = 0.018) and improved hospital stay lengths. Blood and urine cultures did not show significant changes in microbial patterns post-protocol.

Discussion

The findings underscore the effectiveness of structured clinical risk management protocols in enhancing neonatal outcomes, particularly in reducing specific infection risks. Despite the study's limitations, including its observational nature and sample size, the results advocate for broader adoption and further research on these protocols in diverse healthcare settings. The positive outcomes highlight the importance of continuous clinical risk management efforts in high-risk neonatal environments.

Bioinformatics in Neonatal/Pediatric Medicine-A Literature Review

Bioinformatics is a scientific field that uses computer technology to gather, store, analyze, and share biological data and information. DNA sequences of genes or entire genomes, protein amino acid sequences, nucleic acid, and protein-nucleic acid complex structures are examples of traditional bioinformatics data. Moreover, proteomics, the distribution of proteins in cells, interactomics, the patterns of interactions between proteins and nucleic acids, and metabolomics, the types and patterns of small-molecule transformations by the biochemical pathways in cells, are further data streams. Currently, the objectives of bioinformatics are integrative, focusing on how various data combinations might be utilized to comprehend organisms and diseases. Bioinformatic techniques have become popular as novel instruments for examining the fundamental mechanisms behind neonatal diseases. In the first few weeks of newborn life, these methods can be utilized in conjunction with clinical data to identify the most vulnerable neonates and to gain a better understanding of certain mortalities, including respiratory distress, bronchopulmonary dysplasia, sepsis, or inborn errors of metabolism. In the current study, we performed a literature review to summarize the current application of bioinformatics in neonatal medicine. Our aim was to provide evidence that could supply novel insights into the underlying mechanism of neonatal pathophysiology and could be used as an early diagnostic tool in neonatal care.

eQTLs identify regulatory networks and drivers of variation in the individual response to sepsis

Sepsis is a clinical syndrome of life-threatening organ dysfunction caused by a dysregulated response to infection, for which disease heterogeneity is a major obstacle to developing targeted treatments. We have previously identified gene-expression-based patient subgroups (sepsis response signatures [SRS]) informative for outcome and underlying pathophysiology. Here, we aimed to investigate the role of genetic variation in determining the host transcriptomic response and to delineate regulatory networks underlying SRS. Using genotyping and RNA-sequencing data on 638 adult sepsis patients, we report 16,049 independent expression (eQTLs) and 32 co-expression module (modQTLs) quantitative trait loci in this disease context. We identified significant interactions between SRS and genotype for 1,578 SNP-gene pairs and combined transcription factor (TF) binding site information (SNP2TFBS) and predicted regulon activity (DoRothEA) to identify candidate upstream regulators. Overall, these approaches identified putative mechanistic links between host genetic variation, cell subtypes, and the individual transcriptomic response to infection.

Sepsis research: Heterogeneity as a foundation rather than an afterthought

Our understanding of sepsis has been hampered by the implicit assumption that sepsis is a homogeneous disease. In this issue of Cell Genomics, Burnham et al.1 have started to characterize the genetic variants and regulatory networks that underlie variations in the individual response to sepsis; this may eventually enable targeted intervention development.

Analysis of Immune and Prognostic-Related lncRNA PRKCQ-AS1 for Predicting Prognosis and Regulating Effect in Sepsis

Background

Sepsis was a high mortality and great harm systemic inflammatory response syndrome caused by infection. lncRNAs were potential prognostic marker and therapeutic target. Therefore, we expect to screen and analyze lncRNAs with potential prognostic markers in sepsis.

Methods

Transcriptome sequencing and limma was used to screen dysregulated RNAs. Key RNAs were screened by correlation analysis, lncRNA-mRNA co-expression and weighted gene co-expression network analysis. Immune infiltration, gene set enrichment analysis and gene set variation analysis were used to analyze the immune correlation. Kaplan-Meier curve, receiver operator characteristic curve, Cox regression analysis and nomogram were used to analyze the correlation between key RNAs and prognosis. Sepsis model was established by lipopolysaccharide-induced HUVECs injury, and then cell viability and migration ability were detected by cell counting kit-8 and wound healing assay. The levels of apoptosis-related proteins and inflammatory cytokines were detected by RT-qPCR and Western blot. Reactive Oxygen Species and superoxide dismutase were detected by commercial kit.

Results

Fourteen key differentially expressed lncRNAs and 663 key differentially expressed genes were obtained. And these lncRNAs were closely related to immune cells, especially T cell activation, immune response and inflammation. Subsequently, Subsequently, lncRNA PRKCQ-AS1 was identified as the regulator for further investigation in sepsis. RT-qPCR results showed that PRKCQ-AS1 expression was up-regulated in clinical samples and sepsis model cells, which was an independent prognostic factor in sepsis patients. Immune correlation analysis showed that PRKCQ-AS1 was involved in the immune response and inflammatory process of sepsis. Cell function tests confirmed that PRKCQ-AS1 could inhibit sepsis model cells viability and promote cell apoptosis, inflammatory damage and oxidative stress.

Conclusion

We constructed immune-related lncRNA-mRNA regulatory networks in the progression of sepsis and confirmed that PRKCQ-AS1 is an important prognostic factor affecting the progression of sepsis and is involved in immune response.

MiRNAs as biomarkers for diagnosis of neonatal sepsis: a systematic review and meta-analysis

BACKGROUND

The relationship between circulating miRNAs and neonatal sepsis and the mechanism of action are still unclear at this time. Therefore, the potential diagnostic role of miRNAs in neonatal sepsis (NS) was studied through meta-analysis.

METHOD

Web of Science, Cochrane Library, PubMed, and Embase are retrieved, supplemented by manual search, and the search was conducted to find related studies without time limit until May 2022.The quality of the literature was assessed via QUADAS criteria and meta-analyzed via Stata 11.0 software, including the assessment of specificity, sensitivity, likelihood ratio and diagnostic odds ratio. Then, sensitivity analysis and heterogeneity testing were conducted, and finally, the summary receiver operating characteristics (SROC) curve was drawn.

RESULT

This study included 14 articles, including 20 miRNAs and 1597 newborns(control group: 727 and case group: 870). Among them, one article was of low quality, three articles were of high quality, and the rest were of medium quality. According to the results of random effects model analysis, the pooled specificity and sensitivity of miRNA for the diagnosis of NS were 0.83 (95%CI: 0.79-0.87) and 0.76 (95%CI: 0.72-0.80), respectively. And negative likelihood ratio, positive likelihood ratio, and diagnostic odds ratio were 0.29 (95%CI: 0.24-0.34), 4.51 (95%CI: 3.52-5.78), and 15.81 (95%CI: 10.71-23.35), respectively. The area under the SROC curve was 0.86, and there was no evidence publication bias detected in the funnel plot.

CONCLUSION

Circulating miRNAs may be very useful in the development of early diagnostic strategies for neonatal sepsis.

Genetic polymorphisms associated with sepsis incidence, severity, and outcomes among neonates: A mini-review

Genetic variation remains a topic of great interest due to its potential as a risk factor for various diseases. Interactions between genes contribute to diverse phenotypes in response to factors such as infection. The impact of genetic background on susceptibility and clinical outcomes, particularly in neonatal sepsis, has gained recognition. The variability in sepsis susceptibility and outcomes can be attributed to the genetic diversity in coding regions and regulatory elements of genes related to innate immune response. Recent advances in genomics and technology have shed light on genetic polymorphisms among humans, often represented by single-nucleotide polymorphisms (SNPs). These SNPs encode proteins crucial for recognizing and responding to pathogenic bacteria, including Toll-like receptor 4, CD14, tumor necrosis factor-alpha, as well as interleukin-1-10. This literature review specifically discusses the involvement of genetic polymorphism during the pathogenesis stage of sepsis, with an emphasis on previous research findings in neonatal sepsis cases, aiming to discuss the implications of polymorphism in sepsis susceptibility and outcomes.

Identification of an Immune-Related Gene Diagnostic Model and Potential Drugs in Sepsis Using Bioinformatics and Pharmacogenomics Approaches

Purpose

Sepsis is an organ dysfunction with high mortality. Early identification, diagnosis, and effective treatment of sepsis are beneficial to the survival of patients. This study aimed to find potential diagnosis and immune-related genes, and drug targets, which could provide novel diagnostic and therapeutic markers for sepsis.

Patients and Methods

The GSE69063, GSE154918 and GSE28750 datasets were integrated to evaluate immune infiltration and identify differentially expressed genes (DEGs) and immune-related genes. Weighted gene co-expression network analysis (WGCNA) was applied to find the hub module related to immune score and sepsis. Immune-related key genes were screened out by taking interaction of DEGs, immune-related genes, and genes in hub module. Protein-protein interaction (PPI) analysis was used to further screen immune-related hub genes, followed by construction of a diagnostic model based on immune-related hub genes. Functional analysis and drug prediction of immune-related hub genes were, respectively, performed by David software and DGIdb database, followed by expression validation by reverse transcriptase polymerase chain reaction (RT-PCR).

Results

Totally, 93 immune-related key genes were identified between 561 DEGs, 1793 immune-related genes and 12,459 genes in the hub module of WGCNA. Through PPI analysis, a total of 5 diagnose and immune-related hub genes were further obtained, including IL7R, IL10, CD40LG, CD28 and LCN2. Relationship pairs between these 5 genes and immune cell were identified, including LCN2/IL7R/CD28-activated dendritic cell and IL10-immature B cell. Based on pharmacogenomics, 17 candidate drugs might interact with IL 10, including CYCLOSPORINE. Six candidate drugs might interact with CD28 and 11 with CD40LG, CD40LG and CD28 were drug targets of ALDESLEUKIN. Four significantly enriched signaling pathways were identified, such as T cell receptor signaling pathway, NF-kappa B signaling pathway and JAK-STAT signaling pathway.

Conclusion

The 5-gene diagnostic model could be used to diagnose and guide clinical immunotherapy for sepsis.

Effect of neonatal and adult sepsis on inflammation-related diseases in multiple physiological systems: a Mendelian randomization study

Background

Long-term impact of sepsis on whole body systems is not well investigated. The aim of the study was to explore the potential association of neonatal/adult sepsis with several inflammation-related diseases in multiple physiological systems.

Methods

Instrumental variables for neonatal and adult sepsis were collected from the public genome-wide association studies, which must satisfy the correlation, exclusivity and independence assumptions. Mendelian randomization methods (including random-effect inverse-variance weighted, MR-PRESSO, weighted median and MR-Egger) were used to determine the genetic association of neonatal/adult sepsis with asthma, allergy, rheumatoid arthritis, body mass index/obesity, type 1/type 2 diabetes and intelligence/dementia. Sensitivity analyses were conducted to assess heterogeneity and horizontal pleiotropy. The study was performed by TwoSampleMR in R software.

Results

The inverse-variance weighted method reported that neonatal sepsis was related to the decreased level of body mass index (OR = 0.988, 95%CI = 0.980 ~ 0.997, P = 0.007), and adult sepsis was related to the decreased risk of obesity (OR = 0.785, 95%CI = 0.655 ~ 0.940, P = 0.009). These results were supported by the other Mendelian randomization methods. In addition, the study did not find any association of neonatal/adult sepsis with the other inflammation-related diseases. No heterogeneity and horizontal pleiotropy were found using sensitivity analyses.

Conclusion

Sepsis had the potential to reduce the risk of obesity or body mass index level at a genetic level, both in neonates and in adults.