Spectral features of non-nutritive suck dynamics in extremely preterm infants.

Background: Non-nutritive suck (NNS) is used to promote ororhythmic patterning and assess oral feeding readiness in preterm infants in the neonatal intensive care unit (NICU). While time domain measures of NNS are available in real time at cribside, our understanding of suck pattern generation in the frequency domain is limited. The aim of this study is to model the development of NNS in the frequency domain using Fourier and machine learning (ML) techniques in extremely preterm infants (EPIs). Methods: A total of 117 EPIs were randomized to a pulsed or sham orocutaneous intervention during tube feedings 3 times/day for 4 weeks, beginning at 30 weeks post-menstrual age (PMA). Infants were assessed 3 times/week for NNS dynamics until they attained 100% oral feeding or NICU discharge. Digitized NNS signals were processed in the frequency domain using two transforms, including the Welch power spectral density (PSD) method, and the Yule-Walker PSD method. Data analysis proceeded in two stages. Stage 1: ML longitudinal cluster analysis was conducted to identify groups (classes) of infants, each showing a unique pattern of change in Welch and Yule-Walker calculations during the interventions. Stage 2: linear mixed modeling (LMM) was performed for the Welch and Yule-Walker dependent variables to examine the effects of gestationally-aged (GA), PMA, sex (male, female), patient type [respiratory distress syndrome (RDS), bronchopulmonary dysplasia (BPD)], treatment (NTrainer, Sham), intervention phase [1, 2, 3], cluster class, and phase-by-class interaction. Results: ML of Welch PSD method and Yule-Walker PSD method measures revealed three membership classes of NNS growth patterns. The dependent measures peak_Hz, PSD amplitude, and area under the curve (AUC) are highly dependent on PMA, but show little relation to respiratory status (RDS, BPD) or somatosensory intervention. Thus, neural regulation of NNS in the frequency domain is significantly different for each identified cluster (classes A, B, C) during this developmental period. Conclusions: Efforts to increase our knowledge of the evolution of the suck central pattern generator (sCPG) in preterm infants, including NNS rhythmogenesis will help us better understand the observed phenotypes of NNS production in both the frequency and time domains. Knowledge of those features of the NNS which are relatively invariant vs. other features which are modifiable by experience will likewise inform more effective treatment strategies in this fragile population.

Rapid Whole-Genomic Sequencing and a Targeted Neonatal Gene Panel in Infants With a Suspected Genetic Disorder.

Importance: Genomic testing in infancy guides medical decisions and can improve health outcomes. However, it is unclear whether genomic sequencing or a targeted neonatal gene-sequencing test provides comparable molecular diagnostic yields and times to return of results. Objective: To compare outcomes of genomic sequencing with those of a targeted neonatal gene-sequencing test. Design, Setting, and Participants: The Genomic Medicine for Ill Neonates and Infants (GEMINI) study was a prospective, comparative, multicenter study of 400 hospitalized infants younger than 1 year of age (proband) and their parents, when available, suspected of having a genetic disorder. The study was conducted at 6 US hospitals from June 2019 to November 2021. Exposure: Enrolled participants underwent simultaneous testing with genomic sequencing and a targeted neonatal gene-sequencing test. Each laboratory performed an independent interpretation of variants guided by knowledge of the patient's phenotype and returned results to the clinical care team. Change in clinical management, therapies offered, and redirection of care was provided to families based on genetic findings from either platform. Main Outcomes and Measures: Primary end points were molecular diagnostic yield (participants with ≥1 pathogenic variant or variant of unknown significance), time to return of results, and clinical utility (changes in patient care). Results: A molecular diagnostic variant was identified in 51% of participants (n = 204; 297 variants identified with 134 being novel). Molecular diagnostic yield of genomic sequencing was 49% (95% CI, 44%-54%) vs 27% (95% CI, 23%-32%) with the targeted gene-sequencing test. Genomic sequencing did not report 19 variants found by the targeted neonatal gene-sequencing test; the targeted gene-sequencing test did not report 164 variants identified by genomic sequencing as diagnostic. Variants unidentified by the targeted genomic-sequencing test included structural variants longer than 1 kilobase (25.1%) and genes excluded from the test (24.6%) (McNemar odds ratio, 8.6 [95% CI, 5.4-14.7]). Variant interpretation by laboratories differed by 43%. Median time to return of results was 6.1 days for genomic sequencing and 4.2 days for the targeted genomic-sequencing test; for urgent cases (n = 107) the time was 3.3 days for genomic sequencing and 4.0 days for the targeted gene-sequencing test. Changes in clinical care affected 19% of participants, and 76% of clinicians viewed genomic testing as useful or very useful in clinical decision-making, irrespective of a diagnosis. Conclusions and Relevance: The molecular diagnostic yield for genomic sequencing was higher than a targeted neonatal gene-sequencing test, but the time to return of routine results was slower. Interlaboratory variant interpretation contributes to differences in molecular diagnostic yield and may have important consequences for clinical management.

Sex-specific inflammatory and white matter effects of prenatal opioid exposure: a pilot study.

BACKGROUND: Preclinical data demonstrate that opioids modulate brain reward signaling through an inflammatory cascade, but this relationship has yet to be studied in opioid-exposed neonates. METHODS: Saliva samples of 54 opioid-exposed and sex- and age-matched non-exposed neonates underwent transcriptomic analysis of inflammatory and reward genes. A subset of 22 neonates underwent brain magnetic resonance imaging (MRI) to evaluate white matter injury commonly associated with inflammatory response. Gene expression and brain MRI were compared between opioid- and non-exposed neonates and further stratified by sex and pharmacotherapy need. RESULTS: Opioid-exposed females regardless of pharmacotherapy need had higher expression of inflammatory genes than their male counterparts, with notable differences in the expression of CCL2 and CXCL1 in females requiring pharmacotherapy (p = 0.01 and 0.06, respectively). Opioid-exposed males requiring pharmacotherapy had higher expression of DRD2 than exposed females (p = 0.07), validating our prior research. Higher expression of IL1ß, IL6, TNFα, and IL10 was seen in opioid-exposed neonates with T1 white matter hyperintensity (WMH) compared to exposed neonates without WMH (p < 0.05). CONCLUSION: Prenatal opioid exposure may promote inflammation resulting in changes in reward signaling and white matter injury in the developing brain, with unique sex-specific effects. The actions of opioids through non-neuronal pathways need further investigation. IMPACT: Opioid-exposed neonates are at risk for punctate T1 white matter hyperintensity (WMH). Females carry a greater propensity for WMH. Salivary transcriptomic data showed significantly higher expression of inflammatory genes in opioid-exposed neonates with WMH than those without WMH, irrespective of pharmacotherapy need. Adding to prior studies, our findings suggest that prenatal opioid exposure may modulate white matter injury and reward signaling through a pro-inflammatory process that is sex specific. This novel study highlights the short-term molecular and structural effects of prenatal opioids and the need to elucidate the long-term impact of prenatal opioid exposure.