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

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

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

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

Response: Commentary: Reducing Viability Bias in Analysis of Gut Microbiota in Preterm Infants at Risk of NEC and Sepsis

A Commentary on Commentary: Reducing Viability Bias in Analysis of Gut Microbiota in Preterm Infants at Risk of NEC and Sepsis by Agustí, G., and Codony, F. (2018). Front. Cell. Infect. Microbiol. 8:212. doi: 10.3389/fcimb.2018.0021

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

Time to full enteral feeds in hospitalised preterm and very low birth weight infants in Nigeria and Kenya

\ua9 2024 Imam et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.Background Preterm (born &lt; 37 weeks’ gestation) and very low birthweight (VLBW; &lt;1.5kg) infants are at the greatest risk of morbidity and mortality within the first 28 days of life. Establishing full enteral feeds is a vital aspect of their clinical care. Evidence predominantly from high income countries shows that early and rapid advancement of feeds is safe and reduces length of hospital stay and adverse health outcomes. However, there are limited data on feeding practices and factors that influence the attainment of full enteral feeds among these vulnerable infants in sub-Saharan Africa. Aim To identify factors that influence the time to full enteral feeds, defined as tolerance of 120ml/ kg/day, in hospitalised preterm and VLBW infants in neonatal units in two sub-Saharan African countries. Methods Demographic and clinical variables were collected for newborns admitted to 7 neonatal units in Nigeria and Kenya over 6-months. Multiple linear regression analysis was conducted to identify factors independently associated with time to full enteral feeds. Results Of the 2280 newborn infants admitted, 484 were preterm and VLBW. Overall, 222/484 (45.8%) infants died with over half of the deaths (136/222; 61.7%) occurring before the first feed. The median (inter-quartile range) time to first feed was 46 (27, 72) hours of life and time to full enteral feeds (tFEF) was 8 (4.5,12) days with marked variation between neonatal units. Independent predictors of tFEF were time to first feed (unstandardised coefficient B 1.69; 95% CI 1.11 to 2.26; p value &lt;0.001), gestational age (1.77; 0.72 to 2.81; &lt;0.001), the occurrence of respiratory distress (-1.89; -3.50 to -0.79; &lt;0.002) and necrotising enterocolitis (4.31; 1.00 to 7.62; &lt;0.011). Conclusion The use of standardised feeding guidelines may decrease variations in clinical practice, shorten tFEF and thereby improve preterm and VLBW outcomes

Probiotics and Preterm Infants: A Position Paper by the ESPGHAN Committee on Nutrition and the ESPGHAN Working Group for Probiotics and Prebiotics

More than 10,000 preterm infants have participated in randomised controlled trials on probiotics worldwide, suggesting that probiotics in general could reduce rates of necrotising enterocolitis (NEC), sepsis, and mortality. However, answers to relevant clinical questions as to which strain to use, at what dosage, and how long to supplement, are not available. On the other hand, an increasing number of commercial products containing probiotics are available from sometimes suboptimal quality. Also, a large number of units around the world are routinely offering probiotic supplementation as the standard of care despite lacking solid evidence. Our recent network meta-analysis identified probiotic strains with greatest efficacy regarding relevant clinical outcomes for preterm neonates. Efficacy in reducing mortality and morbidity was found for only a minority of the studied strains or combinations. In the present position paper, we aim to provide advice which specific strains might potentially be used and which strains should not be used. Besides, we aim to address safety issues of probiotic supplementation to preterm infants, who have reduced immunological capacities and occasional indwelling catheters. For example, quality reassurance of the probiotic product is essential, probiotic strains should be devoid of transferable antibiotic resistance genes, and local microbiologists should be able to routinely detect probiotic sepsis. Provided all safety issues are met, there is currently a conditional recommendation (with low certainty of evidence) to provide either L. rhamnosus GG ATCC53103 or the combination of B. infantis Bb-02, B. lactis Bb-12, and Str. thermophilus TH-4 in order to reduce NEC rates