The use of high-throughput genomic technologies has led to significant advances in the study of the molecular anatomy of the mammalian brain and the creation of the field known as neurogenomics. Microarray gene expression data has been utilized to identify genes associated with specific brain functions, behaviors and disease-related phenotypes. These gene expression datasets have shed light on the molecular organization of both the developing and adult mammalian brain, specifically in the mouse model. To investigate the molecular organization of the adult mammalian brain, a gene expression-based brain map was built. Gene expression patterns for 24 neural tissues covering the mouse central nervous system were measured and it was found, surprisingly, that the adult brain bears a transcriptional "imprint" consistent with both embryological origins and classic evolutionary relationships. Beyond simply analyzing gene expression patterns within the brain, it is now possible to analyze genomic sequence data, such as single nucleotide polymorphisms (SNPs), in parallel with large scale gene expression data in what has been called a genetical genomics approach to determine transcriptional regulatory networks. To further analyze the molecular organization of the mouse brain, we analyzed gene expression profiles of five brain regions from six inbred mouse strains and integrated these findings with SNP data available for the individual strains. We found that many transcriptional regulatory networks are highly specific to particular brain regions. The ability to query the rich, complementary data sources of gene expression and SNPs together offers tremendous inroads to start to unravel the genetic determinates of complex polygenic diseases and phenotypes. However, appropriate data analysis strategies must be developed that can accommodate the complexity and high-dimensional aspects of these disparate data sources. In order to address some of these analysis issues, we developed an algorithm to identify sequence variation in gene expression data which can artificially affect expression signals and lead to false positive results. We also expanded a new statistical technique termed multivariate distance matrix regression that tests the association of multivariate profiles arising from high- dimensional data sets common in neurogenomics. The body of work presented herein attempts to assimilate the distinct fields of neuroanatomy, genomics, bioinformatics, statistical genetics and biostatistics to create novel analysis tools and develop new insights into biological processes related to neurogenomics

Two HTML-based programs were developed to analyze and filter gene-expression data: 'Bullfrog' for Affymetrix oligonucleotide arrays and 'Spot' for custom cDNA arrays. The programs provide intuitive data-filtering tools through an easy-to-use interface. A background subtraction and normalization program for cDNA arrays was also built that provides an informative summary report with data-quality assessments. These programs are freeware to aid in the analysis of gene-expression results and facilitate the search for genes responsible for interesting biological processes and phenotypes.

Background

Premature infants are at risk for multiple types of intracranial injury with potentially significant long-term neurological impact. The number of screening head ultrasounds needed to detect such injuries remains controversial.

Objective

To determine the rate of abnormal findings on routine follow-up head ultrasound (US) performed in infants born at ≤ 32 weeks' gestational age (GA) after initial normal screening US.

Materials and methods

A retrospective study was performed on infants born at ≤ 32 weeks' GA with a head US at 3-5 weeks following a normal US at 3-10 days at a tertiary care pediatric hospital from 2014 to 2020. Exclusion criteria included significant congenital anomalies, such as congenital cardiac defects necessitating surgery, congenital diaphragmatic hernia or spinal dysraphism, and clinical indications for US other than routine screening, such as sepsis, other risk factors for intracranial injury besides prematurity, or clinical neurological abnormalities. Ultrasounds were classified as normal or abnormal based on original radiology reports. Images from initial examinations with abnormal follow-up were reviewed.

Results

Thirty-three (14.2%) of 233 infants had 34 total abnormal findings on follow-up head US after normal initial US. Twenty-seven infants had grade 1 germinal matrix hemorrhage, and four had grade 2 intraventricular hemorrhage. Two had periventricular echogenicity and one had a focus of cerebellar echogenicity that resolved and was determined to be artifactual.

Conclusion

When initial screening head ultrasounds in premature infants are normal, follow-up screening ultrasounds are typically also normal. Abnormal findings are usually limited to grade 1 germinal matrix hemorrhage.

The use of mouse blood as a model for human blood is often considered in the development of clinically relevant, gene expression-based disease biomarkers. However, the ability to derive biologically meaningful insights from microarray-based gene expression patterns in mouse whole blood, as in human whole blood, is hindered by high levels of globin mRNA. In order to characterize the effects of globin reduction on gene expression of peripheral mouse blood, we performed gene set enrichment analysis on genes identified as expressed in blood via microarray-based genome-wide transcriptome analysis. Depletion of globin mRNA enhanced the quality of microarray data as shown by improved gene expression detection and increased sensitivity. Compared to genes expressed in whole blood, genes detected as expressed in blood following globin reduction were enriched for low abundance transcripts implicated in many biological pathways, including development, g-protein signaling, and immune response. Broadly, globin reduction resulted in improved detection of expressed genes that serve as molecular binding proteins and enzymes in cellular metabolism, intracellular transport/localization, transcription, and translation, as well as genes that potentially could act as biomarkers for diseases such as schizophrenia. These significantly enriched pathways overlap considerably with those identified in globin-reduced human blood suggesting that globin-reduced mouse blood gene expression studies may be useful for identifying genes relevant to human disease. Overall, the results of this investigation provide a better understanding of the impact of reducing globin transcripts in mouse blood and highlight the potential of microarray-based, globin-reduced, mouse blood gene expression studies in biomarker development.

Background

Expression quantitative trait locus (eQTL) mapping is used to find loci that are responsible for the transcriptional activity of a particular gene. In recent eQTL studies, expression profiles were derived from either homogenized whole brain or collections of large brain regions. However, the brain is a very heterogeneous organ, and expression profiles of different brain regions vary significantly. Because of the importance and potential power of eQTL studies in identifying regulatory networks, we analyzed gene expression patterns in different brain regions from multiple inbred mouse strains and investigated the implications for the design and analysis of eQTL studies.

Results

Gene expression profiles of five brain regions in six inbred mouse strains were studied. Few genes exhibited a significant strain-specific expression pattern, whereas a large number of genes exhibited brain region-specific patterns. We constructed phylogenetic trees based on the expression relationships between the strains and compared them with a DNA-level relationship tree. The trees based on the expression of strain-specific genes were constant across brain regions and mirrored DNA-level variation. However, the trees based on region-specific genes exhibited a different set of strain relationships, depending on the brain region. An eQTL analysis showed enrichment of cis-acting regulators among strain-specific genes, whereas brain region-specific genes appear to be mainly regulated by trans-acting elements.

Conclusion

Our results suggest that many regulatory networks are highly brain region specific and indicate the importance of conducting eQTL mapping studies using data from brain regions or tissues that are physiologically and phenotypically relevant to the trait of interest.

Objectives

Intussusception is a pediatric medical emergency that can be difficult to diagnose. Radiology-performed ultrasound is the diagnostic study of choice but may lead to delays due to lack of availability. Point-of-care ultrasound for intussusception (POCUS-I) studies have shown excellent accuracy and reduced lengths of stay, but there are limited POCUS-I training materials for pediatric emergency medicine (PEM) providers.

Methods

We performed a prospective cohort study assessing PEM physicians undergoing a primarily Web-based POCUS-I curriculum. We developed the POCUS-I curriculum using Kern's six-step model. The curriculum included a Web-based module and a brief, hands-on practice that was developed with a board-certified pediatric radiologist. POCUS-I technical skill, knowledge, and confidence were determined by a direct observation checklist, multiple-choice test, and a self-reported Likert-scale survey, respectively. We assessed participants immediately pre- and postcourse as well as 3 months later to assess for retention of skill, knowledge, and confidence.

Results

A total of 17 of 17 eligible PEM physicians at a single institution participated in the study. For the direct observation skills test, participants scored well after the course with a median (interquartile range [IQR]) score of 20 of 22 (20-21) and maintained high scores even after 3 months (20 [20-21]). On the written knowledge test, there was significant improvement from 57.4% (95% CI = 49.8 to 65.2) to 75.3% (95% CI = 68.1 to 81.6; p < 0.001) and this improvement was maintained at 3 months at 81.2% (95% CI = 74.5 to 86.8). Physicians also demonstrated improved confidence with POCUS-I after exposure to the curriculum, with 5.9% reporting somewhat or very confident prior to the course to 76.5% both after the course and after 3 months (p < 0.001).

Conclusion

After a primarily Web-based curriculum for POCUS-I, PEM physicians performed well in technical skill in POCUS-I and showed improvement in knowledge and confidence, all of which were maintained over 3 months.

Neuroblastoma is one of the most common pediatric cancers. This study used machine learning (ML) to predict the mortality and a few other investigated intermediate outcomes of neuroblastoma patients non-invasively from CT images. Performances of multiple ML algorithms over retrospective CT images of 65 neuroblastoma patients are analyzed. An artificial neural network (ANN) is used on tumor radiomic features extracted from 3D CT images. A pre-trained 2D convolutional neural network (CNN) is used on slices of the same images. ML models are trained for various pathologically investigated outcomes of these patients. A subspecialty-trained pediatric radiologist independently reviewed the manually segmented primary tumors. Pyradiomics library is used to extract 105 radiomic features. Six ML algorithms are compared to predict the following outcomes: mortality, presence or absence of metastases, neuroblastoma differentiation, mitosis-karyorrhexis index (MKI), presence or absence of MYCN gene amplification, and presence of image-defined risk factors (IDRF). The prediction ranges over multiple experiments are measured using the area under the receiver operating characteristic (ROC-AUC) for comparison. Our results show that the radiomics-based ANN method slightly outperforms the other algorithms in predicting all outcomes except classification of the grade of neuroblastic differentiation, for which the elastic regression model performed the best. Contributions of the article are twofold: (1) noninvasive models for the prognosis from CT images of neuroblastoma, and (2) comparison of relevant ML models on this medical imaging problem.