Gene filtering strategies for machine learning guided biomarker discovery using neonatal sepsis RNA-seq data.

Machine learning (ML) algorithms are powerful tools that are increasingly being used for sepsis biomarker discovery in RNA-Seq data. RNA-Seq datasets contain multiple sources and types of noise (operator, technical and non-systematic) that may bias ML classification. Normalisation and independent gene filtering approaches described in RNA-Seq workflows account for some of this variability and are typically only targeted at differential expression analysis rather than ML applications. Pre-processing normalisation steps significantly reduce the number of variables in the data and thereby increase the power of statistical testing, but can potentially discard valuable and insightful classification features. A systematic assessment of applying transcript level filtering on the robustness and stability of ML based RNA-seq classification remains to be fully explored. In this report we examine the impact of filtering out low count transcripts and those with influential outliers read counts on downstream ML analysis for sepsis biomarker discovery using elastic net regularised logistic regression, L1-reguarlised support vector machines and random forests. We demonstrate that applying a systematic objective strategy for removal of uninformative and potentially biasing biomarkers representing up to 60% of transcripts in different sample size datasets, including two illustrative neonatal sepsis cohorts, leads to substantial improvements in classification performance, higher stability of the resulting gene signatures, and better agreement with previously reported sepsis biomarkers. We also demonstrate that the performance uplift from gene filtering depends on the ML classifier chosen, with L1-regularlised support vector machines showing the greatest performance improvements with our experimental data.

Immune and metabolic markers for identifying and investigating severe Coronavirus disease and Sepsis in children and young people (pSeP/COVID ChYP study): protocol for a prospective cohort study.

INTRODUCTION: Early recognition and appropriate management of paediatric sepsis are known to improve outcomes. A previous system's biology investigation of the systemic immune response in neonates to sepsis identified immune and metabolic markers that showed high accuracy for detecting bacterial infection. Further gene expression markers have also been reported previously in the paediatric age group for discriminating sepsis from control cases. More recently, specific gene signatures were identified to discriminate between COVID-19 and its associated inflammatory sequelae. Through the current prospective cohort study, we aim to evaluate immune and metabolic blood markers which discriminate between sepses (including COVID-19) from other acute illnesses in critically unwell children and young persons, up to 18 years of age. METHODS AND ANALYSIS: We describe a prospective cohort study for comparing the immune and metabolic whole-blood markers in patients with sepsis, COVID-19 and other illnesses. Clinical phenotyping and blood culture test results will provide a reference standard to evaluate the performance of blood markers from the research sample analysis. Serial sampling of whole blood (50 µL each) will be collected from children admitted to intensive care and with an acute illness to follow time dependent changes in biomarkers. An integrated lipidomics and RNASeq transcriptomics analyses will be conducted to evaluate immune-metabolic networks that discriminate sepsis and COVID-19 from other acute illnesses. This study received approval for deferred consent. ETHICS AND DISSEMINATION: The study has received research ethics committee approval from the Yorkshire and Humber Leeds West Research Ethics Committee 2 (reference 20/YH/0214; IRAS reference 250612). Submission of study results for publication will involve making available all anonymised primary and processed data on public repository sites. TRIAL REGISTRATION NUMBER: NCT04904523.

mSep: investigating physiological and immune-metabolic biomarkers in septic and healthy pregnant women to predict feto-maternal immune health - a prospective observational cohort study protocol.

INTRODUCTION: Maternal sepsis remains a leading cause of death in pregnancy. Physiological adaptations to pregnancy obscure early signs of sepsis and can result in delays in recognition and treatment. Identifying biomarkers that can reliably diagnose sepsis will reduce morbidity and mortality and antibiotic overuse. We have previously identified an immune-metabolic biomarker network comprising three pathways with a >99% accuracy for detecting bacterial neonatal sepsis. In this prospective study, we will describe physiological parameters and novel biomarkers in two cohorts-healthy pregnant women and pregnant women with suspected sepsis-with the aim of mapping pathophysiological drivers and evaluating predictive biomarkers for diagnosing maternal sepsis. METHODS AND ANALYSIS: Women aged over 18 with an ultrasound-confirmed pregnancy will be recruited to a pilot and two main study cohorts. The pilot will involve blood sample collection from 30 pregnant women undergoing an elective caesarean section. Cohort A will follow 100 healthy pregnant women throughout their pregnancy journey, with collection of blood samples from participants at routine time points in their pregnancy: week 12 'booking', week 28 and during labour. Cohort B will follow 100 pregnant women who present with suspected sepsis in pregnancy or labour and will have at least two blood samples taken during their care pathway. Study blood samples will be collected during routine clinical blood sampling. Detailed medical history and physiological parameters at the time of blood sampling will be recorded, along with the results of routine biochemical tests, including C reactive protein, lactate and white blood cell count. In addition, study blood samples will be processed and analysed for transcriptomic, lipidomic and metabolomic analyses and both qualitative and functional immunophenotyping. ETHICS AND DISSEMINATION: Ethical approval has been obtained from the Wales Research Ethics Committee 2 (SPON1752-19, 30 October 2019). TRIAL REGISTRATION NUMBER: NCT05023954.

Learning from previous lockdown measures and minimising harmful biopsychosocial consequences as they end: A systematic review.

BACKGROUND: Infectious outbreaks, most recently coronavirus disease 2019 (COVID-19), have required pervasive public health strategies, termed lockdown measures, including quarantine, social distancing, and closure of workplaces and educational establishments. Although evidence analysing immediate effects is expanding, repercussions following lockdown measures remain poorly understood. This systematic review aims to analyse biopsychosocial consequences after lockdown measures end according to short, medium, and long-term impacts. METHODS: PubMed, Ovid MEDLINE, Embase, PsycInfo, Web of Science, and Scopus databases were searched from inception to January 12, 2021. Reference lists were manually reviewed. Eligible studies analysed biopsychosocial functioning after lockdown measures secondary to recent infectious outbreaks ended. Lockdown measures were defined as quarantine, isolation, workplace or educational closures, social or physical distancing, and national or local closure of public institutions deemed non-essential. Studies exclusively researching outcomes during lockdown measures, examined infectious participants, or analysed lockdown measures not pertaining to an infectious outbreak were excluded. Two independent reviewers extracted data and assessed bias with a third resolving discrepancies. Data was extracted from published reports with further information requested from authors where necessary. The mixed methods appraisal tool assessed study quality, languages were restricted to English, German, Italian, and French and narrative synthesis was applied. RESULTS: Of 5149 identified studies, 40 were eligible for inclusion. Psychological distress, economic repercussions, social, biological, and behavioural ramifications were observed. Short to medium-term effects comprised reactions relating to early trauma processing whereas medium to long-term repercussions manifested in maladaptive behaviours and mental health deterioration. Increased alcohol intake, stigmatisation, and economic effects were also identified consequences. High-risk groups included health care workers, children, elderly, inpatients, those with pre-existing psychiatric diagnoses, and socially isolated individuals. CONCLUSIONS: Supporting vulnerable groups and offering education, workplace modifications, financial, and social assistance may mitigate negative repercussions. Establishing a rapid and comprehensive evidence base appraising the efficacy of such interventions and identifying areas for development is essential. This review was limited by study heterogeneity and lack of randomisation in available literature. Given the unprecedented nature and progression of COVID-19, the relevance of previous outcomes remains uncertain. PROTOCOL REGISTRATION: PROSPERO registration CRD42020181134.