Metabolic profiles derived from residual blood spot samples: A longitudinal analysis [version 1; referees: 2 approved]

Background: Secondary use of newborn screening dried blood spot samples include use for biomedical or epidemiological research. However, the effects of storage conditions on archival samples requires further examination. The objective of this study was to determine the utility of residual newborn samples for deriving reliable metabolic gestational age estimates. Methods: Residual newborn dried blood spot samples that had been stored for 2-, 4-, 6-, or 12-months in temperature controlled (21°C) conditions were re-analyzed for the full panel of newborn screening analytes offered by a provincial newborn screening lab in Ottawa, Canada. Data from re-analyzed samples were compared to corresponding baseline newborn screening values for absolute agreement, and Pearson and intraclass correlation. Performance of a gestational age estimation algorithm originally developed from baseline newborn screening values was then validated on data derived from stored samples. Results: A total of 307 samples were used for this study. 17-hydroxyprogesterone and newborn hemoglobin profiles measured by immunoassay and high-performance liquid chromatography, respectively, were among the most stable markers across all time points of analysis. Acylcarnitines exhibited the greatest degree of variation in stability upon repeat measurement. The largest shifts in newborn analyte profiles and the poorest performance of metabolic gestational age algorithms were observed when samples were analyzed 12-months after sample collection. Conclusions: Duration of sample storage, independent of temperature and humidity, affects newborn screening profiles and gestational age estimates derived from metabolic gestational dating algorithms. When considering use of dried blood spot samples either for clinical or research purposes, care should be taken when interpreting data stemming from secondary use

Families\u27 healthcare experiences for children with inherited metabolic diseases: Protocol for a mixed methods cohort study

Introduction Children with inherited metabolic diseases (IMDs) often have complex and intensive healthcare needs and their families face challenges in receiving high-quality, family centred health services. Improvement in care requires complex interventions involving multiple components and stakeholders, customised to specific care contexts. This study aims to comprehensively understand the healthcare experiences of children with IMDs and their families across Canada. Methods and analysis A two-stage explanatory sequential mixed methods design will be used. Stage 1: quantitative data on healthcare networks and encounter experiences will be collected from 100 parent/guardians through a care map, 2 baseline questionnaires and 17 weekly diaries over 5-7 months. Care networks will be analysed using social network analysis. Relationships between demographic or clinical variables and ratings of healthcare experiences across a range of family centred care dimensions will be analysed using generalised linear regression. Other quantitative data related to family experiences and healthcare experiences will be summarised descriptively. Ongoing analysis of quantitative data and purposive, maximum variation sampling will inform sample selection for stage 2: a subset of stage 1 participants will participate in one-on-one videoconference interviews to elaborate on the quantitative data regarding care networks and healthcare experiences. Interview data will be analysed thematically. Qualitative and quantitative data will be merged during analysis to arrive at an enhanced understanding of care experiences. Quantitative and qualitative data will be combined and presented narratively using a weaving approach (jointly on a theme-by-theme basis) and visually in a side-by-side joint display. Ethics and dissemination The study protocol and procedures were approved by the Children\u27s Hospital of Eastern Ontario\u27s Research Ethics Board, the University of Ottawa Research Ethics Board and the research ethics boards of each participating study centre. Findings will be published in peer-reviewed journals and presented at scientific conferences

Experiences of caregivers of children with inherited metabolic diseases: a qualitative study

Background: We sought to understand the experiences of parents/caregivers of children with inherited metabolic diseases (IMD) in order to inform strategies for supporting patients and their families. We investigated their experiences regarding the management of disease, its impact on child and family life, and interactions with the health care system. Methods: From four Canadian centres, we conducted semi-structured telephone interviews with parents/caregivers of children with an IMD who were born between 2006 and 2015 and who were participating in a larger cohort study. Participants were selected with the aim of achieving a diverse sample with respect to treatment centre, IMD, and age of the child. Interviews emphasized the impacts of the disease and its treatment on the child and family and explicitly queried perceptions of interactions with the health care system. We identified emergent themes from the interview data. Results: We completed interviews with 21 parents/caregivers. The 21 children were aged \u3c1 to 7 years old with IMD that included amino acid disorders, urea cycle disorders, fatty acid oxidation disorders, and organic acid disorders or \u27other\u27 IMD. Most parents reported that they and their families had adapted well to their child\u27s diagnosis. Parents used proactive coping strategies to integrate complex disease management protocols into routine family life. An important source of stress was concern about the social challenges faced by their children. Participants reported positive interactions with their most involved health care providers within the metabolic clinic. However, they reported challenges associated with the health care system outside of disease-specific metabolic care, when encountering systems and providers unfamiliar with the child\u27s disease. Conclusions: The successful use of proactive coping strategies among parents of children with IMD in this study suggests the potential value of promoting positive coping and is an important direction for future study. Parents\u27 social concerns for their children were important stressors that warrant consideration by health care providers positioned to support families. Our results with respect to experiences with care highlight the important role of specialized metabolic clinics and point to a need for better coordination of the care that takes place outside the disease-specific management of IMD

Combining macula clinical signs and patient characteristics for age-related macular degeneration diagnosis: a machine learning approach

Background: To investigate machine learning methods, ranging from simpler interpretable techniques to complex (non-linear) “black-box” approaches, for automated diagnosis of Age-related Macular Degeneration (AMD). Methods: Data from healthy subjects and patients diagnosed with AMD or other retinal diseases were collected during routine visits via an Electronic Health Record (EHR) system. Patients’ attributes included demographics and, for each eye, presence/absence of major AMD-related clinical signs (soft drusen, retinal pigment epitelium, defects/ pigment mottling, depigmentation area, subretinal haemorrhage, subretinal fluid, macula thickness, macular scar, subretinal fibrosis). Interpretable techniques known as white box methods including logistic regression and decision trees as well as less interpreitable techniques known as black box methods, such as support vector machines (SVM), random forests and AdaBoost, were used to develop models (trained and validated on unseen data) to diagnose AMD. The gold standard was confirmed diagnosis of AMD by physicians. Sensitivity, specificity and area under the receiver operating characteristic (AUC) were used to assess performance. Results: Study population included 487 patients (912 eyes). In terms of AUC, random forests, logistic regression and adaboost showed a mean performance of (0.92), followed by SVM and decision trees (0.90). All machine learning models identified soft drusen and age as the most discriminating variables in clinicians’ decision pathways to diagnose AMD. C Conclusions: Both black-box and white box methods performed well in identifying diagnoses of AMD and their decision pathways. Machine learning models developed through the proposed approach, relying on clinical signs identified by retinal specialists, could be embedded into EHR to provide physicians with real time (interpretable) support

Searching for early breast cancer biomarkers by serum protein profiling of pre-diagnostic serum; a nested case-control study

<p>Abstract</p> <p>Background</p> <p>Serum protein profiles have been investigated frequently to discover early biomarkers for breast cancer. So far, these studies used biological samples collected <it>at </it>or <it>after </it>diagnosis. This may limit these studies' value in the search for cancer biomarkers because of the often advanced tumor stage, and consequently risk of reverse causality. We present for the first time pre-diagnostic serum protein profiles in relation to breast cancer, using the Prospect-EPIC (European Prospective Investigation into Cancer and nutrition) cohort.</p> <p>Methods</p> <p>In a nested case-control design we compared 68 women diagnosed with breast cancer within three years after enrollment, with 68 matched controls for differences in serum protein profiles. All samples were analyzed with SELDI-TOF MS (surface enhanced laser desorption/ionization time-of-flight mass spectrometry). In a subset of 20 case-control pairs, the serum proteome was identified and relatively quantified using isobaric Tags for Relative and Absolute Quantification (iTRAQ) and online two-dimensional nano-liquid chromatography coupled with tandem MS (2D-nanoLC-MS/MS).</p> <p>Results</p> <p>Two SELDI-TOF MS peaks with m/z 3323 and 8939, which probably represent doubly charged apolipoprotein C-I and C3a des-arginine anaphylatoxin (C3a_desArg), were higher in pre-diagnostic breast cancer serum (p = 0.02 and p = 0.06, respectively). With 2D-nanoLC-MS/MS, afamin, apolipoprotein E and isoform 1 of inter-alpha trypsin inhibitor heavy chain H4 (ITIH4) were found to be higher in pre-diagnostic breast cancer (p < 0.05), while alpha-2-macroglobulin and ceruloplasmin were lower (p < 0.05). C3a_desArgand ITIH4 have previously been related to the presence of symptomatic and/or mammographically detectable breast cancer.</p> <p>Conclusions</p> <p>We show that serum protein profiles are already altered up to three years before breast cancer detection.</p

Temporal Signal Pattern Recognition in Mass Spectrometry: A Method for Rapid Identification and Accurate Quantification of Biomarkers for Inborn Errors of Metabolism with Quality Assurance

Mass spectrometry (MS)-based metabolomic initiatives that use conventional separation techniques are limited by low sample throughput and complicated data processing that contribute to false discoveries. Herein, we introduce a new strategy for unambiguous identification and accurate quantification of biomarkers for inborn errors of metabolism (IEM) from dried blood spots (DBS) with quality assurance. A multiplexed separation platform based on multisegment injection-capillary electrophoresis-mass spectrometry (MSI-CE-MS) was developed to provide comparable sample throughput to flow injection analysis-tandem MS (FIA-MS/MS) but with greater selectivity as required for confirmatory testing and discovery-based metabolite profiling of volume-restricted biospecimens. Mass spectral information is encoded temporally within a separation by serial injection of three or more sample pairs, each having a unique dilution pattern, alongside a quality control (QC) that serves as a reference in every run to facilitate between-sample comparisons and/or batch correction due to system drift. Optimization of whole blood extraction conditions on DBS filter paper cut-outs was first achieved to maximize recovery of a wide range of polar metabolites from DBS extracts. An interlaboratory comparison study was also conducted using a proficiency test and retrospective neonatal DBS that demonstrated good agreement between MSI-CE-MS and validated FIA-MS/MS methods within an accredited facility. Our work demonstrated accurate identification of various IEM based on reliable measurement of a panel of primary or secondary biomarkers above an upper cutoff concentration limit for presumptive screen-positive cases without stable isotope-labeled reagents. Additionally, nontargeted metabolite profiling by MSI-CE-MS with temporal signal pattern recognition revealed new biomarkers for early detection of galactosemia, such as <i>N</i>-galactated amino acids, that are a novel class of pathognomonic marker due to galactose stress in affected neonates

Les dictionnaires Larousse

Il y a fort à parier que le Petit Larousse ait été notre premier dictionnaire, comme pour nos parents et nos grands-parents. À la fois familiers et méconnus, anciens et contemporains, les dictionnaires Larousse sont nés du génie de Pierre Larousse, un instituteur doublé d'un visionnaire dont la devise était : instruire tout le monde sur toutes choses. Ce livre nous invite à la découverte de Pierre Larousse et de son œuvre, guidés par des experts qui en sont passionnés. Chacun prospecte la région laroussienne à la recherche des richesses qu'elle recèle. On y fait des découvertes étonnantes. Ainsi, on apprend que le lexicographe barbu qu'on nous présente toujours était en outre un entrepreneur dynamique capable de brasser non seulement des mots, mais des chiffres et des stratégies. Grand maître du dictionnaire d'apprentissage, Pierre Larousse a produit une œuvre à laquelle ses successeurs ont réussi à garder toute la force d'information, au point de gagner l'ensemble de la francophonie

Incidental screen positive findings in a prospective cohort study in Matlab, Bangladesh: insights into expanded newborn screening for low-resource settings

Abstract Background Newborn screening programs are essential preventative public health initiatives but are not widely available in low-resource settings. The objective of this study was to describe the frequency and nature of screen positive determinations as made by a Canadian newborn screening program in a cohort of infants born in Matlab, Bangladesh. Dried newborn cord and heel-prick blood spot samples collected as part of a validation study nested within a preterm birth research cohort were collected between January 2017 and July 2018 and analyzed in a Canadian newborn screening laboratory where the laboratory’s disease panel and screening thresholds were applied. Results A total of 1661 newborn samples (520 heel-prick and 1141 cord blood samples) were available for analysis. Based on the applied screening thresholds, 61 samples (22 by heel-prick and 39 by cord blood) were screen positive for conditions included in the Canadian disease panel. Congenital hypothyroidism was the most common determination for heel-prick (n = 17) and cord blood (n = 12) samples. Carriers of hemoglobinopathy variants were identified in 6.9% of both tested heel-prick and cord blood samples. Conclusions This study provides insight into the nature and frequency of treatable congenital conditions in a rural Bangladesh community where such data were previously unavailable. As comment to the feasibility of newborn screening in the region we confirm that screening based on cord blood sampling continues to be the most acceptable modality to parents in such settings. Acknowledged barriers include early infant discharge, which may affect the reliability of initial screening thresholds to determine disease risk. We further highlight the importance of continuing efforts in the country to identify infants with congenital hypothyroidism

Real world external validation of metabolic gestational age assessment in Kenya.

Using data from Ontario Canada, we previously developed machine learning-based algorithms incorporating newborn screening metabolites to estimate gestational age (GA). The objective of this study was to evaluate the use of these algorithms in a population of infants born in Siaya county, Kenya. Cord and heel prick samples were collected from newborns in Kenya and metabolic analysis was carried out by Newborn Screening Ontario in Ottawa, Canada. Postnatal GA estimation models were developed with data from Ontario with multivariable linear regression using ELASTIC NET regularization. Model performance was evaluated by applying the models to the data collected from Kenya and comparing model-derived estimates of GA to reference estimates from early pregnancy ultrasound. Heel prick samples were collected from 1,039 newborns from Kenya. Of these, 8.9% were born preterm and 8.5% were small for GA. Cord blood samples were also collected from 1,012 newborns. In data from heel prick samples, our best-performing model estimated GA within 9.5 days overall of reference GA [mean absolute error (MAE) 1.35 (95% CI 1.27, 1.43)]. In preterm infants and those small for GA, MAE was 2.62 (2.28, 2.99) and 1.81 (1.57, 2.07) weeks, respectively. In data from cord blood, model accuracy slightly decreased overall (MAE 1.44 (95% CI 1.36, 1.53)). Accuracy was not impacted by maternal HIV status and improved when the dating ultrasound occurred between 9 and 13 weeks of gestation, in both heel prick and cord blood data (overall MAE 1.04 (95% CI 0.87, 1.22) and 1.08 (95% CI 0.90, 1.27), respectively). The accuracy of metabolic model based GA estimates in the Kenya cohort was lower compared to our previously published validation studies, however inconsistency in the timing of reference dating ultrasounds appears to have been a contributing factor to diminished model performance