Introducing a drift and diffusion framework for childhood growth research

Acknowledgements We thank the participants and staff of the MAL-ED study for their vital contributions and we thank Prof. Laura Caulfield for her insightful and constructive input. The findings and conclusions in this report are those of the authors and do not necessarily represent the official position of the U.S. National Institutes of Health or Department of Health and Human Services. Publisher Copyright: © 2020 Lewis FI et al.Peer reviewedPublisher PD

Effects of Child and Maternal Histo-Blood Group Antigen Status on Symptomatic and Asymptomatic Enteric Infections in Early Childhood

Funding Information: Financial support. This work was funded by the Etiology, Risk Factors, and Interactions of Enteric Infections and Malnutrition and the Consequences for Child Health and Development Project (MAL-ED) is carried out as a collaborative project funded by the Bill & Melinda Gates Foundation (BMGF) (BMGF-47075), the Foundation for the National Institutes of Health, and the National Institutes of Health, Fogarty International Center, whereas additional support was obtained from BMGF for the examination of host innate factors on enteric disease risk and enteropathy (Grants OPP1066146 and OPP1152146; to M. N. K.). Additional funding was obtained from teh Sherrilyn and Ken Fisher Center for Environmental Infectious Diseases, Johns Hopkins School of Medicine (to M. N. K) and the National Center for Research Resources and the National Center for Advancing Translational Sciences, National Institues of health 1UL1TR001079. Acknowledgments. We thank the participants, their families, and the study community for their dedicated time and effort to better the understanding the transmission and more enduring impact of enteric infections in early childhood. We also thank the following: Jan Vinje (Centers for Disease Control and Prevention) for critical input and manuscript review; Dr. Leah Jager for consultation regarding the statistical analysis; Dr. Ben Jann (University of Bern, Switzerland) for guidance in generating the figures; Christine Szymanski for insight and encouragement, particularly regarding Campylobacter infection and disease patency; Chris Damman and Anita Zaidi for input on early iterations of the analysis; and Dick Guerrant for final reflections.Peer reviewe

Epidemiology and Risk Factors for Cryptosporidiosis in Children from 8 Low-income Sites : Results from the MAL-ED Study

Funding Information: The MAL-ED study is carried out as a collaborative project supported by the Bill & Melinda Gates Foundation, the Foundation for the National Institutes of Health (NIH), and the NIH Fogarty International Center. This work was also supported by the National Institute of Allergy and Infectious Diseases of the NIH (grant numbers K23 AI087910 to P. K. and K23 AI087910 to W. A. P.) and by the Sherrilyn and Ken Fisher Center for Environmental Infectious Diseases Discovery Program (to P. D.).Peer reviewedPublisher PD

Automatic prediction of therapeutic activities during newborn resuscitation combining video and signal data

Newborn mortality is a global challenge with around 2.4 million neonatal deaths in 2019. One third of these occur within the first-and-only day of life with labour complications and birth asphyxia being the primary causes. Existing guidelines for newborn resuscitation are based on limited scientific evidence, and evidens based research is sought for. To increase our knowledge on resuscitation of newborns, it is crucial to first quantify what is currently being done in terms of therapeutic activities, such as ventilation and stimulation, and how they affect resuscitation outcomes. In the current study, the therapeutic activities during newborn resuscitation are quantified by estimating a timeline describing the start and stop of activities. The proposed approach is combining methods using both video and time series data recorded during resuscitation, where the predictions are based on the available sources. From video the activity recognition is done by a 3D CNN method. For the signal data feature extraction is performed on ECG and accelerometer signals and thereafter machine learning is done to perform stimulation detection. We show that best results are achieved with all signals and video available, for the activity ‘‘stimulation’’ we get an AUC of 0.86, sensitivity of 82.32%, specificity of 82.23%, and precision of 57.59%. If only signals or video is available we still get good results with AUC at 0.80, and 0.84 respectivel

Genotypic antimicrobial resistance assays for use on E. coli isolates and stool specimens.

Antimicrobial resistance (AMR) is an emerging public health problem and methods for surveillance are needed. We designed 85 sequence-specific PCR reactions to detect 79 genes or mutations associated with resistance across 10 major antimicrobial classes, with a focus on E. coli. The 85 qPCR assays demonstrated >99.9% concordance with sequencing. We evaluated the correlation between genotypic resistance markers and phenotypic susceptibility results on 239 E. coli isolates. Both sensitivity and specificity exceeded 90% for ampicillin, ceftriaxone, cefepime, imipenem, ciprofloxacin, azithromycin, gentamicin, amikacin, trimethoprim/sulfamethoxazole, tetracycline, and chloramphenicol phenotypic susceptibility results. We then evaluated the assays on direct stool specimens and observed a sensitivity of 97% ± 5 but, as expected, a lower specificity of 75% ± 31 versus the genotype of the E. coli cultured from stool. Finally, the assays were incorporated into a convenient TaqMan Array Card (TAC) format. These assays may be useful for tracking AMR in E. coli isolates or directly in stool for targeted testing of the fecal antibiotic resistome

Optimization of Quantitative PCR Methods for Enteropathogen Detection.

Detection and quantification of enteropathogens in stool specimens is useful for diagnosing the cause of diarrhea but is technically challenging. Here we evaluate several important determinants of quantification: specimen collection, nucleic acid extraction, and extraction and amplification efficiency. First, we evaluate the molecular detection and quantification of pathogens in rectal swabs versus stool, using paired flocked rectal swabs and whole stool collected from 129 children hospitalized with diarrhea in Tanzania. Swabs generally yielded a higher quantification cycle (Cq) (average 29.7, standard deviation 3.5 vs. 25.3 ± 2.9 from stool, P<0.001) but were still able to detect 80% of pathogens with a Cq < 30 in stool. Second, a simplified total nucleic acid (TNA) extraction procedure was compared to separate DNA and RNA extractions and showed 92% (318/344) sensitivity and 98% (951/968) specificity, with no difference in Cq value for the positive results (ΔCq(DNA+RNA-TNA) = -0.01 ± 1.17, P = 0.972, N = 318). Third, we devised a quantification scheme that adjusts pathogen quantity to the specimen's extraction and amplification efficiency, and show that this better estimates the quantity of spiked specimens than the raw target Cq. In sum, these methods for enteropathogen quantification, stool sample collection, and nucleic acid extraction will be useful for laboratories studying enteric disease

Primer and probe sequences of the real time PCR panels.

<p>Primer and probe sequences of the real time PCR panels.</p

Multiplex real time PCR panels to identify fourteen colonization factors of enterotoxigenic <i>Escherichia coli</i> (ETEC)

<div><p>Enterotoxigenic <i>Escherichia coli</i> (ETEC) is a leading cause of childhood diarrhea in low income countries and in travelers to those areas. Inactivated enterotoxins and colonization factors (CFs) are leading vaccine candidates, therefore it is important to determine the prevailing CF types in different geographic locations and populations. Here we developed real time PCR (qPCR) assays for 14 colonization factors, including the common vaccine targets. These assays, along with three enterotoxin targets (STh, STp, and LT) were formulated into three 5-plex qPCR panels, and validated on 120 ETEC isolates and 74 <i>E</i>. <i>coli</i> colony pools. The overall sensitivity and specificity was 99% (199/202) and 99% (2497/2514), respectively, compared to the CF results obtained with conventional PCR. Amplicon sequencing of discrepant samples revealed that the qPCR was 100% accurate. qPCR panels were also performed on nucleic acid extracted from stool and compared to the results of the ETEC isolates or E. coli colony pools cultured from them. 95% (105/110) of the CF detections in the cultures were confirmed in the stool. Additionally, direct testing of stool yielded 30 more CF detections. Among 74 randomly selected <i>E</i>. <i>coli</i> colony pools with paired stool, at least one CF was detected in 63% (32/51) of the colony pools while at least one CF was detected in 78% (47/60) of the stool samples (P = NS). We conclude that these ETEC CF assays can be used on both cultures and stool samples to facilitate better understanding of CF distribution for ETEC epidemiology and vaccine development.</p></div

Correlation of Cqs between enterotoxin and CFs on cultures (A) and stool (B).

<p>(A) On culture (N = 194), the overall correlation R² = 0.78 (<i>P</i> < 0.001). Each symbol represents one CF type. (B) On stool, the Cq correlation between enterotoxin and primary/sole CF (the most abundant CF type measured by Cq values), secondary CF (the second most abundant, if present), tertiary CF (the third most abundant, if present), and quaternary CF (the fourth abundant, if present) CF were all statistically significant (P < 0.05, with R² = 0.66 (N = 81), 0.45 (N = 39), 0.46 (N = 17), and 0.82 (N = 7), respectively. The dotted line shows the diagonal line.</p