Ultrasound Evaluation of Gastric Emptying Time in Healthy Term Neonates after Formula Feeding.

BACKGROUND: The current American Society of Anesthesiologists fasting guideline for formula-fed infants in the periprocedural setting is 6 h. Prolonged fasting in very young infants is associated with an increased risk for hypoglycemia and dehydration as well as patient discomfort and patient/parental dissatisfaction. This study aimed to determine the time to gastric emptying in healthy neonates after formula feeding by serially evaluating the gastric antrum with ultrasound. The authors hypothesized that gastric emptying times in formula-fed neonates are significantly shorter than the current 6 h fasting recommendation. METHODS: After institutional review board approval and written informed parental consent, ultrasound examination was performed in healthy full-term neonates before and after formula feeding at 15-min intervals until return to baseline. Ultrasound images of the gastric antrum were measured to obtain cross-sectional areas, which were then used to estimate gastric antral volumes. RESULTS: Forty-six of 48 recruited neonates were included in the final analysis. Gastric emptying times ranged from 45 to 150 min and averaged 92.9 min (95% CI, 80.2 to 105.7 min; 99% CI, 76.0 to 109.8 min) in the overall study group. No significant differences were found in times to gastric emptying between male and female neonates (male: mean, 93.3 [95% CI, 82.4 to 104.2 min]; female: mean, 92.6 [95% CI, 82.0 to 103.2 min]; P = 0.930) or those delivered by vaginal versus cesarean routes (vaginal: mean, 93.9 [95% CI, 81.7 to 106.1 min]; cesarean: mean, 92.2 [95% CI, 82.5 to 101.9 min]; P = 0.819). CONCLUSIONS: These results demonstrate that gastric emptying times are substantially less than the current fasting guideline of 6 h for formula-fed, healthy term neonates.

Resting Heart Rate Variability Among Professional Baseball Starting Pitchers.

Cornell, DJ, Paxson, JL, Caplinger, RA, Seligman, JR, Davis, NA, and Ebersole, KT. Resting heart rate variability among professional baseball starting pitchers. J Strength Cond Res 31(3): 575-581, 2017-The purpose of this study was to examine the changes in resting heart rate variability (HRV) across a 5-day pitching rotation schedule among professional baseball starting pitchers. The HRV data were collected daily among 8 Single-A level professional baseball starting pitchers (mean ± SD, age = 21.9 ± 1.3 years; height = 185.4 ± 3.6 cm; weight = 85.2 ± 7.5 kg) throughout the entire baseball season with the participant quietly lying supine for 10 minutes. The HRV was quantified by calculating the natural log of the square root of the mean sum of the squared differences (lnRMSSD) during the middle 5 minutes of each R-R series data file. A split-plot repeated-measures analysis of variance was used to examine the influence of pitching rotation day on resting lnRMSSD. A statistically significant main effect of rotation day was identified (F4,706 = 3.139, p = 0.029). Follow-up pairwise analyses indicated that resting lnRMSSD on day 2 was significantly (p ≤ 0.05) lower than all other rotation days. In addition, a statistically significant main effect of pitcher was also identified (F7,706 = 83.388, p < 0.001). These results suggest that professional baseball starting pitchers display altered autonomic nervous system function 1 day after completing a normally scheduled start, as day 2 resting HRV was significantly lower than all other rotation days. In addition, the season average resting lnRMSSD varied among participants, implying that single-subject analysis of resting measures of HRV may be more appropriate when monitoring cumulative workload among this cohort population of athletes.

In-Game Heart Rate Responses Among Professional Baseball Starting Pitchers.

Cornell, DJ, Paxson, JL, Caplinger, RA, Seligman, JR, Davis, NA, Flees, RJ, and Ebersole, KT. In-game heart rate responses among professional baseball starting pitchers. J Strength Cond Res 31(1): 24-29, 2017-The purpose of the current study was to characterize the in-game heart rate (HR) responses of baseball pitching. In-game HR was recorded from 16 professional baseball starting pitchers (mean ± SD, age = 22.1 ± 1.3 years; height = 187.9 ± 4.4 cm; weight = 90.5 ± 9.5 kg) for a total of 682 innings (home = 381, away = 301). All analyzed HR data were then normalized to each pitcher's age-predicted maximal HR (%HRmax). The group mean ± SD in-game %HRmax among all pitchers was 84.8 ± 3.9%, suggesting that baseball pitching is predominantly an anaerobic task. A split-plot mixed-model repeated measures analysis of variance identified a significant interaction effect between inning and game location (p = 0.042). Follow-up simple effects indicated that the in-game %HRmax was significantly different across innings, but only during home starts (p < 0.001). Specifically, pairwise analyses indicated that the in-game %HRmax during home starts were significantly (p ≤ 0.05) higher in the first and second innings than in all other innings. In addition, follow-up simple effects indicated that the in-game %HRmax was significantly (p = 0.017) higher during home starts than away starts in the first inning (87.3 ± 3.6% vs. 85.8 vs. 3.8%, respectively). Thus, it is possible that inning-dependent psychological factors may have contributed to the observed changes in in-game physiological intensity across innings and that these factors are specific to game location. Consequently, strength and conditioning practitioners should prescribe high-intensity exercises when developing conditioning programs for professional baseball starting pitchers.

Induced ablation of Bmp1 and Tll1 produces osteogenesis imperfecta in mice.

Osteogenesis imperfecta (OI), or brittle bone disease, is most often caused by dominant mutations in the collagen I genes COL1A1/COL1A2, whereas rarer recessive OI is often caused by mutations in genes encoding collagen I-interacting proteins. Recently, mutations in the gene for the proteinase bone morphogenetic 1 (BMP1) were reported in two recessive OI families. BMP1 and the closely related proteinase mammalian tolloid-like 1 (mTLL1) are co-expressed in various tissues, including bone, and have overlapping activities that include biosynthetic processing of procollagen precursors into mature collagen monomers. However, early lethality of Bmp1- and Tll1-null mice has precluded use of such models for careful study of in vivo roles of their protein products. Here we employ novel mouse strains with floxed Bmp1 and Tll1 alleles to induce postnatal, simultaneous ablation of the two genes, thus avoiding barriers of Bmp1(-/-) and Tll1(-/-) lethality and issues of functional redundancy. Bones of the conditionally null mice are dramatically weakened and brittle, with spontaneous fractures-defining features of OI. Additional skeletal features include osteomalacia, thinned/porous cortical bone, reduced processing of procollagen and dentin matrix protein 1, remarkably high bone turnover and defective osteocyte maturation that is accompanied by decreased expression of the osteocyte marker and Wnt-signaling inhibitor sclerostin, and by marked induction of canonical Wnt signaling. The novel animal model presented here provides new opportunities for in-depth analyses of in vivo roles of BMP1-like proteinases in bone and other tissues, and for their roles, and for possible therapeutic interventions, in OI.

An Analysis of Medication Adherence of Sooner Health Access Network SoonerCare Choice Patients.

Medication adherence is a desirable but rarely available metric in patient care, providing key insights into patient behavior that has a direct effect on a patient's health. In this research, we determine the medication adherence characteristics of over 46,000 patients enrolled in the Sooner Health Access Network (HAN), based on Medicaid claims data from the Oklahoma Health Care Authority. We introduce a new measure called Specific Medication PDC (smPDC), based on the popular Proportion of Days Covered (PDC) method, using the last fill date for the end date of the measurement duration. The smPDC method is demonstrated by calculating medication adherence across the eligible patient population, for relevant subpopulations over a two-year period spanning 2012 - 2013. We leverage a clinical analytics platform to disseminate adherence measurements to providers. Aggregate results demonstrate that the smPDC method is relevant and indicates potential opportunities for health improvement for certain population segments.

Encore: Genetic Association Interaction Network centrality pipeline and application to SLE exome data.

Open source tools are needed to facilitate the construction, analysis, and visualization of gene-gene interaction networks for sequencing data. To address this need, we present Encore, an open source network analysis pipeline for genome-wide association studies and rare variant data. Encore constructs Genetic Association Interaction Networks or epistasis networks using two optional approaches: our previous information-theory method or a generalized linear model approach. Additionally, Encore includes multiple data filtering options, including Random Forest/Random Jungle for main effect enrichment and Evaporative Cooling and Relief-F filters for enrichment of interaction effects. Encore implements SNPrank network centrality for identifying susceptibility hubs (nodes containing a large amount of disease susceptibility information through the combination of multivariate main effects and multiple gene-gene interactions in the network), and it provides appropriate files for interactive visualization of a network using tools from our online Galaxy instance. We implemented these algorithms in C++ using OpenMP for shared-memory parallel analysis on a server or desktop. To demonstrate Encore's utility in analysis of genetic sequencing data, we present an analysis of exome resequencing data from healthy individuals and those with Systemic Lupus Erythematous (SLE). Our results verify the importance of the previously associated SLE genes HLA-DRB and NCF2, and these two genes had the highest gene-gene interaction degrees among the susceptibility hubs. An additional 14 genes previously associated with SLE emerged in our epistasis network model of the exome data, and three novel candidate genes, ST8SIA4, CMTM4, and C2CD4B, were implicated in the model. In summary, we present a comprehensive tool for epistasis network analysis and the first such analysis of exome data from a genetic study of SLE.

Comprehensive mass spectrometric mapping of the hydroxylated amino acid residues of the α1(V) collagen chain.

BACKGROUND: α1(V) is an extensively modified collagen chain important in disease. RESULTS: Comprehensive mapping of α1(V) post-translational modifications reveals unexpectedly large numbers of X-position hydroxyprolines in Gly-X-Y amino acid triplets. CONCLUSION: The unexpected abundance of X-position hydroxyprolines suggests a mechanism for differential modification of collagen properties. SIGNIFICANCE: Positions, numbers, and occupancy of modified sites can provide insights into α1(V) biological properties. Aberrant expression of the type V collagen α1(V) chain can underlie the connective tissue disorder classic Ehlers-Danlos syndrome, and autoimmune responses against the α1(V) chain are linked to lung transplant rejection and atherosclerosis. The α1(V) collagenous COL1 domain is thought to contain greater numbers of post-translational modifications (PTMs) than do similar domains of other fibrillar collagen chains, PTMs consisting of hydroxylated prolines and lysines, the latter of which can be glycosylated. These types of PTMs can contribute to epitopes that underlie immune responses against collagens, and the high level of PTMs may contribute to the unique biological properties of the α1(V) chain. Here we use high resolution mass spectrometry to map such PTMs in bovine placental α1(V) and human recombinant pro-α1(V) procollagen chains. Findings include the locations of those PTMs that vary and those PTMs that are invariant between these α1(V) chains from widely divergent sources. Notably, an unexpectedly large number of hydroxyproline residues were mapped to the X-positions of Gly-X-Y triplets, contrary to expectations based on previous amino acid analyses of hydrolyzed α1(V) chains from various tissues. We attribute this difference to the ability of tandem mass spectrometry coupled to nanoflow chromatographic separations to detect lower-level PTM combinations with superior sensitivity and specificity. The data are consistent with the presence of a relatively large number of 3-hydroxyproline sites with less than 100% occupancy, suggesting a previously unknown mechanism for the differential modification of α1(V) chain and type V collagen properties.

Real-world comparison of CPU and GPU implementations of SNPrank: a network analysis tool for GWAS.

MOTIVATION: Bioinformatics researchers have a variety of programming languages and architectures at their disposal, and recent advances in graphics processing unit (GPU) computing have added a promising new option. However, many performance comparisons inflate the actual advantages of GPU technology. In this study, we carry out a realistic performance evaluation of SNPrank, a network centrality algorithm that ranks single nucleotide polymorhisms (SNPs) based on their importance in the context of a phenotype-specific interaction network. Our goal is to identify the best computational engine for the SNPrank web application and to provide a variety of well-tested implementations of SNPrank for Bioinformaticists to integrate into their research. RESULTS: Using SNP data from the Wellcome Trust Case Control Consortium genome-wide association study of Bipolar Disorder, we compare multiple SNPrank implementations, including Python, Matlab and Java as well as CPU versus GPU implementations. When compared with naïve, single-threaded CPU implementations, the GPU yields a large improvement in the execution time. However, with comparable effort, multi-threaded CPU implementations negate the apparent advantage of GPU implementations. AVAILABILITY: The SNPrank code is open source and available at http://insilico.utulsa.edu/snprank.