Development of the preterm gut microbiome in twins at risk of necrotising enterocolitis and sepsis

The preterm gut microbiome is a complex dynamic community influenced by genetic and environmental factors and is implicated in the pathogenesis of necrotising enterocolitis (NEC) and sepsis. We aimed to explore the longitudinal development of the gut microbiome in preterm twins to determine how shared environmental and genetic factors may influence temporal changes and compared this to the expressed breast milk (EBM) microbiome. Stool samples (n = 173) from 27 infants (12 twin pairs and 1 triplet set) and EBM (n = 18) from 4 mothers were collected longitudinally. All samples underwent PCR-DGGE (denaturing gradient gel electrophoresis) analysis and a selected subset underwent 454 pyrosequencing. Stool and EBM shared a core microbiome dominated by Enterobacteriaceae, Enterococcaceae, and Staphylococcaceae. The gut microbiome showed greater similarity between siblings compared to unrelated individuals. Pyrosequencing revealed a reduction in diversity and increasing dominance of Escherichia sp. preceding NEC that was not observed in the healthy twin. Antibiotic treatment had a substantial effect on the gut microbiome, reducing Escherichia sp. and increasing other Enterobacteriaceae. This study demonstrates related preterm twins share similar gut microbiome development, even within the complex environment of neonatal intensive care. This is likely a result of shared genetic and immunomodulatory factors as well as exposure to the same maternal microbiome during birth, skin contact and exposure to EBM. Environmental factors including antibiotic exposure and feeding are additional significant determinants of community structure, regardless of host genetics

PCPP: A MATLAB application for abnormal infant movement detection from video

PCPP is an application developed in MATLAB, for the detection of abnormal infant movements associated with cerebral palsy. This system uses 2D skeletal data extracted from videos, and consists of a full pipeline providing data pre-processing, data normalization, feature extraction and classification. Evaluation metrics, such as accuracy, sensitivity, specificity, F1 score and Matthews Correlation Coefficient (MCC), are computed to facilitate full assessment of performance and allow for comparison with other methods from the literature. These evaluations are conducted on the MINI-RGBD and RVI-38 datasets using the code and data provided

Characterising the metabolic functionality of the preterm neonatal gut microbiome prior to the onset of necrotising enterocolitis: a pilot study

\ua9 The Author(s) 2024. Background: Necrotising enterocolitis (NEC) is a devastating bowel disease that primarily occurs in infants born prematurely and is associated with abnormal gut microbiome development. While gut microbiome compositions associated with NEC have been well studied, there is a lack of experimental work investigating microbiota functions and their associations with disease onset. The aim of this pilot study was to characterise the metabolic functionality of the preterm gut microbiome prior to the onset of NEC compared with healthy controls. Results: Eight NEC infants were selected of median gestation 26.5 weeks and median day of life (DOL) of NEC onset 20, with one sample used per infant, collected within one to eight days (median four) before NEC onset. Each NEC case was matched to a control infant based on gestation and sample DOL, the main driver of microbiome composition in this population, giving a total cohort of 16 infants for this study. Dietary exposures were well matched. The microbiota of NEC and control infants showed similar wide-ranging metabolic functionalities. All 94 carbon sources were utilised to varying extents but NEC and control samples clustered separately by supervised ordination based on carbon source utilisation profiles. For a subset of eight samples (four NEC, four control) for which pre-existing metagenome data was available, microbiome composition was found to correlate significantly with metabolic activity measured on Biolog plates (p = 0.035). Comparisons across all 16 samples showed the NEC microbiota to have greater utilisation of carbon sources that are the products of proteolytic fermentation, specifically amino acids. In pairwise comparisons, L-methionine was highly utilised in NEC samples, but poorly utilised in controls (p = 0.043). Carbon sources identified as discriminatory for NEC also showed a greater enrichment for established markers of inflammatory disease, such as inflammatory bowel disease, irritable bowel syndrome and diverticular disease. Conclusions: Before NEC onset, the preterm gut microbiota showed greater metabolic utilisation of amino acids, potentially indicating a shift from predominantly saccharolytic to proteolytic fermentation. Products of amino acid breakdown could therefore act as biomarkers for NEC development. A larger study is warranted, ideally with infants from multiple sites

Gut microbiota and intestinal rehabilitation: A prospective childhood cohort longitudinal study of short bowel syndrome (the MIRACLS study): Study protocol

\ua9 Author(s) (or their employer(s)) 2024.Introduction Short bowel syndrome (SBS) is the predominant cause of paediatric intestinal failure. Although life-saving, parenteral nutrition (PN) is linked to complications and may impact quality of life (QoL). Most children will experience intestinal rehabilitation (IR), but the mechanisms underpinning this remain to be understood. SBS is characterised by abnormal microbiome patterns, which might serve as predictive indicators for IR. We aim to characterise the microbiome profiles of children with SBS during IR, concurrently exploring how parental perspectives of QoL relate to IR. Methods and analysis This study will enrol a minimum of 20 paediatric patients with SBS (0-18 years). Clinical data and biological samples will be collected over a 2-year study period. We will apply 16S rRNA gene sequencing to analyse the microbiome from faecal and gut tissue samples, with additional shotgun metagenomic sequencing specifically on samples obtained around the time of IR. Gas chromatography with flame ionisation detection will profile faecal short-chain fatty acids. Plasma citrulline and urinary intestinal fatty acid binding proteins will be measured annually. We will explore microbiome-clinical covariate interactions. Furthermore, we plan to assess parental perspectives on QoL during PN and post-IR by inviting parents to complete the Paediatric Quality of Life questionnaire at recruitment and after the completion of IR. Ethics and dissemination Ethical approval was obtained from the East Midlands-Nottingham 2 Research Ethics Committee (22/EM/0233; 28 November 2022). Recruitment began in February 2023. Outcomes of the study will be published in peer-reviewed scientific journals and presented at scientific meetings. A lay summary of the results will be made available to participants and the public. Trial registration number ISRCTN90620576

Identification of Abnormal Movements in Infants: A Deep Neural Network for Body Part-Based Prediction of Cerebral Palsy

The early diagnosis of cerebral palsy is an area which has recently seen significant multi-disciplinary research. Diagnostic tools such as the General Movements Assessment (GMA), have produced some very promising results, however these manual methods can be laborious. The prospect of automating these processes is seen as key in advancing this field of study. In our previous works, we examined the viability of using pose-based features extracted from RGB video sequences to undertake classification of infant body movements based upon the GMA. In this paper, we propose a new deep learning framework for this classification task. We also propose a visualization framework which identifies body-parts with the greatest contribution towards a classification decision. The inclusion of a visualization framework is an important step towards automation as it helps make the decisions made by the machine learning framework interpretable. We directly compare the proposed framework's classification with several other methods from the literature using two independent datasets. Our experimental results show that the proposed method performs more consistently and more robustly than our previous pose-based techniques as well as other features from related works in this setting. We also find that our visualization framework helps provide greater interpretability, enhancing the likelihood of the adoption of these technologies within the medical domain

The core phageome and its interrelationship with preterm human milk lipids

\ua9 2023 The AuthorsPhages and lipids in human milk (HM) may benefit preterm infant health by preventing gastrointestinal pathobiont overgrowth and microbiome modulation. Lipid association may promote vertical transmission of phages to the infant. Despite this, interrelationships between lipids and phages are poorly characterized in preterm HM. Shotgun metagenomics and untargeted lipidomics of phage and lipid profiles from 99 preterm HM samples reveals that phages are abundant and prevalent from the first week and throughout the first 100 days of lactation. Phage-host richness of preterm HM increases longitudinally. Core phage communities characterized by Staphylococcus- and Propionibacterium-infecting phages are significantly correlated with long-chain fatty acid abundances over lactational age. We report here a phage-lipid interaction in preterm HM, highlighting the potential importance of phage carriage in preterm HM. These results reveal possible strategies for phage carriage in HM and their importance in early-life microbiota development

Acquisition and Development of the Extremely Preterm Infant Microbiota Across Multiple Anatomical Sites

Microbial communities influencing health and disease are being increasingly studied in preterm neonates. There exists little data, however, detailing longitudinal microbial acquisition, especially in the most extremely preterm (<26 weeks' gestation). This study aims to characterize the development of the microbiota in this previously under-represented cohort.Methods:Seven extremely preterm infant-mother dyads (mean gestation 23.6 weeks) were recruited from a single neonatal intensive care unit. Oral and endotracheal secretions, stool, and breast milk (n = 157 total), were collected over the first 60 days of life. Targeted 16S rRNA gene sequencing identified bacterial communities present.Results:Microbiota of all body sites were most similar immediately following birth and diverged longitudinally. Throughout the sampling period Escherichia, Enterococcus, Staphylococcus, and an Enterobacteriaceae were dominant and well dispersed across all sites. Temporal divergence of the stool from other microbiota was driven by decreasing diversity and significantly greater proportional abundance of Bifidobacteriaceae compared to other sites.Conclusions:Four taxa dominated all anatomical sampling sites. Rare taxa promoted dissimilarity. Cross-seeding between upstream communities and the stool was demonstrated, possibly relating to buccal colostrum/breast milk exposure and indwelling tubes. Given the importance of dysbiosis in health and disease of extremely preterm infants, better understanding of microbial acquisition within this context may be of clinical benefit

A Pose-Based Feature Fusion and Classification Framework for the Early Prediction of Cerebral Palsy in Infants

The early diagnosis of cerebral palsy is an area which has recently seen significant multi-disciplinary research. Diagnostic tools such as the General Movements Assessment (GMA), have produced some very promising results. However, the prospect of automating these processes may improve accessibility of the assessment and also enhance the understanding of movement development of infants. Previous works have established the viability of using pose-based features extracted from RGB video sequences to undertake classification of infant body movements based upon the GMA. In this paper, we propose a series of new and improved features, and a feature fusion pipeline for this classification task. We also introduce the RVI-38 dataset, a series of videos captured as part of routine clinical care. By utilising this challenging dataset we establish the robustness of several motion features for classification, subsequently informing the design of our proposed feature fusion framework based upon the GMA. We evaluate our proposed framework’s classification performance using both the RVI-38 dataset and the publicly available MINI-RGBD dataset. We also implement several other methods from the literature for direct comparison using these two independent datasets. Our experimental results and feature analysis show that our proposed pose-based method performs well across both datasets. The proposed features afford us the opportunity to include finer detail than previous methods, and further model GMA specific body movements. These new features also allow us to take advantage of additional body-part specific information as a means of improving the overall classification performance, whilst retaining GMA relevant, interpretable, and shareable features