ALBERT

Description: Background: In archaea and eukaryotes, ribonucleoprotein complexes containing small C/D box s(no)RNAs use base pair complementarity to target specific sites within ribosomal RNA for 2'-O-ribose methylation. These modifications aid in the folding and stabilization of nascent rRNA molecules and their assembly into ribosomal particles. The genomes of hyperthermophilic archaea encode large numbers of C/D box sRNA genes, suggesting an increased necessity for rRNA stabilization at extreme growth temperatures. Results: We have identified the complete sets of C/D box sRNAs from seven archaea using RNA-Seq methodology. In total, 489 C/D box sRNAs were identified, each containing two guide regions. A combination of computational and manual analyses predicts 719 guide interactions with 16S and 23S rRNA molecules. This first pan-archaeal description of guide sequences identifies (i) modified rRNA nucleotides that are frequently conserved between species and (ii) regions within rRNA that are hotspots for 2'-O-methylation. Gene duplication, rearrangement, mutational drift and convergent evolution of sRNA genes and guide sequences were observed. In addition, several C/D box sRNAs were identified that use their two guides to target locations distant in the rRNA sequence but close in the secondary and tertiary structure. We propose that they act as RNA chaperones and facilitate complex folding events between distant sequences. Conclusions: This pan-archaeal analysis of C/D box sRNA guide regions identified conserved patterns of rRNA 2'-O-methylation in archaea. The interaction between the sRNP complexes and the nascent rRNA facilitates proper folding and the methyl modifications stabilize higher order rRNA structure within the assembled ribosome.

Description: Intermolecular autophosphorylation at Tyr416 is a conserved mechanism of activation among the members of the Src family of nonreceptor tyrosine kinases. Like several other tyrosine kinases, Src can catalyze th...

Description: Background: Streptococcus pneumoniae is a leading cause of childhood morbidity and mortality worldwide, despite the availability of effective pneumococcal vaccines. Understanding the molecular interactions between the bacterium and the host will contribute to the control and prevention of pneumococcal disease. Results: We used a combination of adherence assays, mutagenesis and functional genomics to identify novel factors involved in adherence. By contrasting these processes in two pneumococcal strains, TIGR4 and G54, we showed that adherence and invasion capacities vary markedly by strain. Electron microscopy showed more adherent bacteria in association with membranous pseudopodia in the TIGR4 strain. Operons for cell wall phosphorylcholine incorporation (lic), manganese transport (psa) and phosphate utilization (phn) were up-regulated in both strains on exposure to epithelial cells. Pneumolysin, pili, stress protection genes (adhC-czcD) and genes of the type II fatty acid synthesis pathway were highly expressed in the naturally more invasive strain, TIGR4. Deletion mutagenesis of five gene regions identified as regulated in this study revealed attenuation in adherence. Most strikingly, [increment]SP_1922 which was predicted to contain a B-cell epitope and revealed significant attenuation in adherence, appeared to be expressed as a part of an operon that includes the gene encoding the cytoplasmic pore-forming toxin and vaccine candidate, pneumolysin. Conclusion: This work identifies a list of novel potential pneumococcal adherence determinants.

Description: Background: The recA/RAD51 gene family encodes a diverse set of recombinase proteins that affect homologous recombination, DNA-repair, and genome stability. The recA gene family is expressed across all three domains of life - Eubacteria, Archaea, and Eukaryotes - and even in some viruses. To date, efforts to resolve the deep evolutionary origins of this ancient protein family have been hindered by the high sequence divergence between paralogous groups (i.e. ~30% average pairwise identity). Results: Through large taxon sampling and the use of a phylogenetic algorithm designed for inferring evolutionary events in highly divergent paralogs, we obtained a robust, parsimonious and more refined phylogenetic history of the recA/RAD51 superfamily. Conclusions: In summary, our model for the evolution of recA/RAD51 family provides a better understanding of the ancient origin of recA proteins and the multiple events that lead to the diversification of recA homologs in eukaryotes, including the discovery of additional RAD51 sub-families.

Description: Background: Previous data using T1-weighted MRI demonstrated neck muscle fat infiltration (MFI) in patients with poor functional recovery following whiplash. Such findings do not occur in those with milder symptoms of whiplash, chronic non-traumatic neck pain or healthy controls, suggesting traumatic factors play a role. Muscle degeneration could potentially represent a quantifiable marker of poor recovery, but the temporal constraints of running a T1-weighted sequence and performing the subsequent analysis for muscle fat may be a barrier for clinical translation. The purpose of this preliminary study was to evaluate, quantify and compare MFI for the cervical multifidus muscles with T1-weighted imaging and a more rapid quantitative 3D multi-echo gradient echo (GRE) Dixon based method in healthy subjects. Methods: 5 asymptomatic participants with no history of neck pain underwent cervical spine MRI with a Siemens 3 Tesla system. The muscle and fat signal intensities on axial spin-echo T1-weighted images were quantitatively classified for the cervical multifidii from C3-C7, bilaterally. Additional axial GRE Dixon based data for fat and water quantification were used for comparison via paired t-tests. Inter-tester reliability for fat and water measures with GRE images were examined using 1) Pearson's Intra-class correlation coefficient 2) Bland-Altman Plots and 3) Lin's-Concordance Coefficient. P 〈 0.05 was used to indicate significance. Results: Total mean (SD) MFI (C3-C7) for the multifidii obtained with T1-weighted imaging and GRE were 18.4% (3.3) (range 14-22%) and 18.8% (2.9) (range 15-22%), respectively. The Pearson correlation coefficients for inter-tester reliability on the GRE sequences for the C3-C7 multifidii ranged from .83 - .99, indicating high levels of agreement with segmental MFI measures. Bland-Altman Plots revealed all data points were within 2 SDs and concordance was established between 2-blinded raters, suggesting good agreement between two raters measuring fat and water with GRE imaging. Conclusions: Results of this preliminary study demonstrate reliability between 2 raters of varying experience for MRI analysis of MFI with 3D GRE MRI. The quantification of MFI for healthy cervical musculature is comparable to T1-weighted images. Inclusion of larger samples of symptomatic data and histological comparison with the reference standard biopsy is warranted.

Description: Background: In this study we implemented and developed state-of-the-art machine learning (ML) and natural language processing (NLP) technologies and built a computerized algorithm for medication reconciliation. Our specific aims are: (1) to develop a computerized algorithm for medication discrepancy detection between patients’ discharge prescriptions (structured data) and medications documented in free-text clinical notes (unstructured data); and (2) to assess the performance of the algorithm on real-world medication reconciliation data. Methods: We collected clinical notes and discharge prescription lists for all 271 patients enrolled in the Complex Care Medical Home Program at Cincinnati Children’s Hospital Medical Center between 1/1/2010 and 12/31/2013. A double-annotated, gold-standard set of medication reconciliation data was created for this collection. We then developed a hybrid algorithm consisting of three processes: (1) a ML algorithm to identify medication entities from clinical notes, (2) a rule-based method to link medication names with their attributes, and (3) a NLP-based, hybrid approach to match medications with structured prescriptions in order to detect medication discrepancies. The performance was validated on the gold-standard medication reconciliation data, where precision (P), recall (R), F-value (F) and workload were assessed. Results: The hybrid algorithm achieved 95.0%/91.6%/93.3% of P/R/F on medication entity detection and 98.7%/99.4%/99.1% of P/R/F on attribute linkage. The medication matching achieved 92.4%/90.7%/91.5% (P/R/F) on identifying matched medications in the gold-standard and 88.6%/82.5%/85.5% (P/R/F) on discrepant medications. By combining all processes, the algorithm achieved 92.4%/90.7%/91.5% (P/R/F) and 71.5%/65.2%/68.2% (P/R/F) on identifying the matched and the discrepant medications, respectively. The error analysis on algorithm outputs identified challenges to be addressed in order to improve medication discrepancy detection. Conclusion: By leveraging ML and NLP technologies, an end-to-end, computerized algorithm achieves promising outcome in reconciling medications between clinical notes and discharge prescriptions.

Description: Background: Manual eligibility screening (ES) for a clinical trial typically requires a labor-intensive review of patient records that utilizes many resources. Leveraging state-of-the-art natural language processing (NLP) and information extraction (IE) technologies, we sought to improve the efficiency of physician decision-making in clinical trial enrollment. In order to markedly reduce the pool of potential candidates for staff screening, we developed an automated ES algorithm to identify patients who meet core eligibility characteristics of an oncology clinical trial. Methods: We collected narrative eligibility criteria from ClinicalTrials.gov for 55 clinical trials actively enrolling oncology patients in our institution between 12/01/2009 and 10/31/2011. In parallel, our ES algorithm extracted clinical and demographic information from the Electronic Health Record (EHR) data fields to represent profiles of all 215 oncology patients admitted to cancer treatment during the same period. The automated ES algorithm then matched the trial criteria with the patient profiles to identify potential trial-patient matches. Matching performance was validated on a reference set of 169 historical trial-patient enrollment decisions, and workload, precision, recall, negative predictive value (NPV) and specificity were calculated. Results: Without automation, an oncologist would need to review 163 patients per trial on average to replicate the historical patient enrollment for each trial. This workload is reduced by 85% to 24 patients when using automated ES (precision/recall/NPV/specificity: 12.6%/100.0%/100.0%/89.9%). Without automation, an oncologist would need to review 42 trials per patient on average to replicate the patient-trial matches that occur in the retrospective data set. With automated ES this workload is reduced by 90% to four trials (precision/recall/NPV/specificity: 35.7%/100.0%/100.0%/95.5%). Conclusion: By leveraging NLP and IE technologies, automated ES could dramatically increase the trial screening efficiency of oncologists and enable participation of small practices, which are often left out from trial enrollment. The algorithm has the potential to significantly reduce the effort to execute clinical research at a point in time when new initiatives of the cancer care community intend to greatly expand both the access to trials and the number of available trials.

Description: Background: Schools play an important role in promoting the health of children. However, little consideration is often given to the influence that headteachers’ and school staff’s prior beliefs have on the implementation of public health interventions. This study examined primary school headteachers’ and school health co-ordinators’ views regarding child health in order to provide greater insights on the school’s perspective for those designing future school-based health interventions. Methods: A qualitative study was conducted using 19 semi-structured interviews with headteachers, deputy headteachers and school health co-ordinators in the primary school setting. All transcripts were analysed using thematic analysis. Results: Whilst many participants in this study believed good health was vital for learning, wide variance was evident regarding the perceived health of school pupils and the magnitude of responsibility schools should take in addressing child health behaviours. Although staff in this study acknowledged the importance of their role, many believed the responsibility placed upon schools for health promotion was becoming too much; suggesting health interventions need to better integrate school, parental and societal components. With mental health highlighted as an increasing priority in many schools, incorporating wellbeing outcomes into future school based health interventions is advocated to ensure a more holistic understanding of child health is gained. Conclusion: Understanding the health beliefs of school staff when designing interventions is crucial as there appears to be a greater likelihood of interventions being successfully adopted if staff perceive a health issue as important among their pupils. An increased dependability on schools for addressing health was expressed by headteachers in this study, highlighting a need for better understanding of parental, child and key stakeholder perspectives on responsibility for child health. Without this understanding, there is potential for certain child health issues to be ignored.