A UMLS-based spell checker for natural language processing in vaccine safety

BACKGROUND: The Institute of Medicine has identified patient safety as a key goal for health care in the United States. Detecting vaccine adverse events is an important public health activity that contributes to patient safety. Reports about adverse events following immunization (AEFI) from surveillance systems contain free-text components that can be analyzed using natural language processing. To extract Unified Medical Language System (UMLS) concepts from free text and classify AEFI reports based on concepts they contain, we first needed to clean the text by expanding abbreviations and shortcuts and correcting spelling errors. Our objective in this paper was to create a UMLS-based spelling error correction tool as a first step in the natural language processing (NLP) pipeline for AEFI reports. METHODS: We developed spell checking algorithms using open source tools. We used de-identified AEFI surveillance reports to create free-text data sets for analysis. After expansion of abbreviated clinical terms and shortcuts, we performed spelling correction in four steps: (1) error detection, (2) word list generation, (3) word list disambiguation and (4) error correction. We then measured the performance of the resulting spell checker by comparing it to manual correction. RESULTS: We used 12,056 words to train the spell checker and tested its performance on 8,131 words. During testing, sensitivity, specificity, and positive predictive value (PPV) for the spell checker were 74% (95% CI: 74–75), 100% (95% CI: 100–100), and 47% (95% CI: 46%–48%), respectively. CONCLUSION: We created a prototype spell checker that can be used to process AEFI reports. We used the UMLS Specialist Lexicon as the primary source of dictionary terms and the WordNet lexicon as a secondary source. We used the UMLS as a domain-specific source of dictionary terms to compare potentially misspelled words in the corpus. The prototype sensitivity was comparable to currently available tools, but the specificity was much superior. The slow processing speed may be improved by trimming it down to the most useful component algorithms. Other investigators may find the methods we developed useful for cleaning text using lexicons specific to their area of interest

Bias and heteroscedastic memory error in self-reported health behavior: an investigation using covariance structure analysis

BACKGROUND: Frequent use of self-reports for investigating recent and past behavior in medical research requires statistical techniques capable of analyzing complex sources of bias associated with this methodology. In particular, although decreasing accuracy of recalling more distant past events is commonplace, the bias due to differential in memory errors resulting from it has rarely been modeled statistically. METHODS: Covariance structure analysis was used to estimate the recall error of self-reported number of sexual partners for past periods of varying duration and its implication for the bias. RESULTS: Results indicated increasing levels of inaccuracy for reports about more distant past. Considerable positive bias was found for a small fraction of respondents who reported ten or more partners in the last year, last two years and last five years. This is consistent with the effect of heteroscedastic random error where the majority of partners had been acquired in the more distant past and therefore were recalled less accurately than the partners acquired more recently to the time of interviewing. CONCLUSIONS: Memory errors of this type depend on the salience of the events recalled and are likely to be present in many areas of health research based on self-reported behavior

Motivators of and barriers to becoming a COVID-19 convalescent plasma donor: A survey study

Objectives: To determine the motivators and barriers to COVID-19 convalescent plasma donation by those in the United Kingdom who have been diagnosed with or who have had symptoms of SARS-CoV-2 (COVID-19) but who have not donated. Background: Convalescent plasma from people recovered from COVID-19 with sufficient antibody titres is a potential option for the treatment and prevention of COVID-19. However, to date, recruiting and retaining COVID-19 convalescent plasma donors has been challenging. Understanding why those eligible to donate COVID-19 convalescent plasma have not donated is critical to developing recruitment campaigns. Methods/Materials: A total of 419 UK residents who indicated that they had been infected with COVID-19 and who lived within 50 km of sites collecting COVID-19 convalescent plasma completed an online survey between 25th June and 5th July 2020. Respondents completed items assessing their awareness of convalescent plasma, motivations and barriers to donation and intention to donate COVID-19 convalescent plasma. Results: Awareness of COVID-19 convalescent plasma was low. Exploratory factor analysis identified six motivations and seven barriers to donating. A stronger sense of altruism through adversity and moral and civic duty were positively related to intention to donate, whereas generic donation fears was negatively related. Conclusions: Once potential donors are aware of convalescent plasma, interventions should focus on the gratitude and reciprocity that those eligible to donate feel, along with a focus on (potentially) helping family and norms of what people ought to do. Fears associated with donation should not be neglected, and strategies that have been successfully used tor recruit whole-blood donors should be adapted and deployed to recruit COVID-19 convalescent plasma donors

RH5.1-CyRPA-Ripr antigen combination vaccine shows little improvement over RH5.1 in a preclinical setting

Background: RH5 is the leading vaccine candidate for the Plasmodium falciparum blood stage and has shown impact on parasite growth in the blood in a human clinical trial. RH5 binds to Ripr and CyRPA at the apical end of the invasive merozoite form, and this complex, designated RCR, is essential for entry into human erythrocytes. RH5 has advanced to human clinical trials, and the impact on parasite growth in the blood was encouraging but modest. This study assessed the potential of a protein-in-adjuvant blood stage malaria vaccine based on a combination of RH5, Ripr and CyRPA to provide improved neutralizing activity against P. falciparum in vitro. Methods: Mice were immunized with the individual RCR antigens to down select the best performing adjuvant formulation and rats were immunized with the individual RCR antigens to select the correct antigen dose. A second cohort of rats were immunized with single, double and triple antigen combinations to assess immunogenicity and parasite neutralizing activity in growth inhibition assays. Results: The DPX® platform was identified as the best performing formulation in potentiating P. falciparum inhibitory antibody responses to these antigens. The three antigens derived from RH5, Ripr and CyRPA proteins formulated with DPX induced highly inhibitory parasite neutralising antibodies. Notably, RH5 either as a single antigen or in combination with Ripr and/or CyRPA, induced inhibitory antibodies that outperformed CyRPA, Ripr. Conclusion: An RCR combination vaccine may not induce substantially improved protective immunity as compared with RH5 as a single immunogen in a clinical setting and leaves the development pathway open for other antigens to be combined with RH5 as a next generation malaria vaccine

De novo assembly and characterization of a maternal and developmental transcriptome for the emerging model crustacean Parhyale hawaiensis

<p>Abstract</p> <p>Background</p> <p>Arthropods are the most diverse animal phylum, but their genomic resources are relatively few. While the genome of the branchiopod <it>Daphnia pulex </it>is now available, no other large-scale crustacean genomic resources are available for comparison. In particular, genomic resources are lacking for the most tractable laboratory model of crustacean development, the amphipod <it>Parhyale hawaiensis</it>. Insight into shared and divergent characters of crustacean genomes will facilitate interpretation of future developmental, biomedical, and ecological research using crustacean models.</p> <p>Results</p> <p>To generate a transcriptome enriched for maternally provided and zygotically transcribed developmental genes, we created cDNA from ovaries and embryos of <it>P. hawaiensis</it>. Using 454 pyrosequencing, we sequenced over 1.1 billion bases of this cDNA, and assembled them <it>de novo </it>to create, to our knowledge, the second largest crustacean genomic resource to date. We found an unusually high proportion of C2H2 zinc finger-containing transcripts, as has also been reported for the genome of the pea aphid <it>Acyrthosiphon pisum</it>. Consistent with previous reports, we detected trans-spliced transcripts, but found that they did not noticeably impact transcriptome assembly. Our assembly products yielded 19,067 unique BLAST hits against <b>nr </b>(E-value cutoff e-10). These included over 400 predicted transcripts with significant similarity to <it>D. pulex </it>sequences but not to sequences of any other animal. Annotation of several hundred genes revealed <it>P. hawaiensis </it>homologues of genes involved in development, gametogenesis, and a majority of the members of six major conserved metazoan signaling pathways.</p> <p>Conclusions</p> <p>The amphipod <it>P. hawaiensis </it>has higher transcript complexity than known insect transcriptomes, and trans-splicing does not appear to be a major contributor to this complexity. We discuss the importance of a reliable comparative genomic framework within which to consider findings from new crustacean models such as <it>D. pulex </it>and <it>P. hawaiensis</it>, as well as the need for development of further substantial crustacean genomic resources.</p

Comparison of generalized estimating equations and quadratic inference functions using data from the National Longitudinal Survey of Children and Youth (NLSCY) database

<p>Abstract</p> <p>Background</p> <p>The generalized estimating equations (GEE) technique is often used in longitudinal data modeling, where investigators are interested in population-averaged effects of covariates on responses of interest. GEE involves specifying a model relating covariates to outcomes and a plausible correlation structure between responses at different time periods. While GEE parameter estimates are consistent irrespective of the true underlying correlation structure, the method has some limitations that include challenges with model selection due to lack of absolute goodness-of-fit tests to aid comparisons among several plausible models. The quadratic inference functions (QIF) method extends the capabilities of GEE, while also addressing some GEE limitations.</p> <p>Methods</p> <p>We conducted a comparative study between GEE and QIF via an illustrative example, using data from the "National Longitudinal Survey of Children and Youth (NLSCY)" database. The NLSCY dataset consists of long-term, population based survey data collected since 1994, and is designed to evaluate the determinants of developmental outcomes in Canadian children. We modeled the relationship between hyperactivity-inattention and gender, age, family functioning, maternal depression symptoms, household income adequacy, maternal immigration status and maternal educational level using GEE and QIF. Basis for comparison include: (1) ease of model selection; (2) sensitivity of results to different working correlation matrices; and (3) efficiency of parameter estimates.</p> <p>Results</p> <p>The sample included 795, 858 respondents (50.3% male; 12% immigrant; 6% from dysfunctional families). QIF analysis reveals that gender (male) (odds ratio [OR] = 1.73; 95% confidence interval [CI] = 1.10 to 2.71), family dysfunctional (OR = 2.84, 95% CI of 1.58 to 5.11), and maternal depression (OR = 2.49, 95% CI of 1.60 to 2.60) are significantly associated with higher odds of hyperactivity-inattention. The results remained robust under GEE modeling. Model selection was facilitated in QIF using a goodness-of-fit statistic. Overall, estimates from QIF were more efficient than those from GEE using AR (1) and Exchangeable working correlation matrices (Relative efficiency = 1.1117; 1.3082 respectively).</p> <p>Conclusion</p> <p>QIF is useful for model selection and provides more efficient parameter estimates than GEE. QIF can help investigators obtain more reliable results when used in conjunction with GEE.</p

Adherent Monomer-Misfolded SOD1

Background: Multiple cellular functions are compromised in amyotrophic lateral sclerosis (ALS). In familial ALS (FALS) with Cu/Zn superoxide dismutase (SOD1) mutations, the mechanisms by which the mutation in SOD1 leads to such a wide range of abnormalities remains elusive. Methodology/Principal Findings: To investigate underlying cellular conditions caused by the SOD1 mutation, we explored mutant SOD1-interacting proteins in the spinal cord of symptomatic transgenic mice expressing a mutant SOD1, SOD1 Leu126delTT with a FLAG sequence (DF mice). This gene product is structurally unable to form a functional homodimer. Tissues were obtained from both DF mice and disease-free mice expressing wild-type with FLAG SOD1 (WF mice). Both FLAG-tagged SOD1 and cross-linking proteins were enriched and subjected to a shotgun proteomic analysis. We identified 34 proteins (or protein subunits) in DF preparations, while in WF preparations, interactions were detected with only 4 proteins. Conclusions/Significance: These results indicate that disease-causing mutant SOD1 likely leads to inadequate proteinprotein interactions. This could be an early and crucial process in the pathogenesis of FALS

Feasibility and acceptability of artemisinin-based combination therapy for the home management of malaria in four African sites

BACKGROUND: The Home Management of Malaria (HMM) strategy was developed using chloroquine, a now obsolete drug, which has been replaced by artemisinin-based combination therapy (ACT) in health facility settings. Incorporation of ACT in HMM would greatly expand access to effective antimalarial therapy by the populations living in underserved areas in malaria endemic countries. The feasibility and acceptability of incorporating ACT in HMM needs to be evaluated. METHODS: A multi-country study was performed in four district-size sites in Ghana (two sites), Nigeria and Uganda, with populations ranging between 38,000 and 60,000. Community medicine distributors (CMDs) were trained in each village to dispense pre-packaged ACT to febrile children aged 6-59 months, after exclusion of danger signs. A community mobilization campaign accompanied the programme. Artesunate-amodiaquine (AA) was used in Ghana and artemether-lumefantrine (AL) in Nigeria and Uganda. Harmonized qualitative and quantitative data collection methods were used to evaluate CMD performance, caregiver adherence and treatment coverage of febrile children with ACTs obtained from CMDs. RESULTS: Some 20,000 fever episodes in young children were treated with ACT by CMDs across the four study sites. Cross-sectional surveys identified 2,190 children with fever in the two preceding weeks, of whom 1,289 (59%) were reported to have received ACT from a CMD. Coverage varied from 52% in Nigeria to 75% in Ho District, Ghana. Coverage rates did not appear to vary greatly with the age of the child or with the educational level of the caregiver. A very high proportion of children were reported to have received the first dose on the day of onset or the next day in all four sites (range 86-97%, average 90%). The proportion of children correctly treated in terms of dose and duration was also high (range 74-97%, average 85%). Overall, the proportion of febrile children who received prompt treatment and the correct dose for the assigned duration of treatment ranged from 71% to 87% (average 77%). Almost all caregivers perceived ACT to be effective, and no severe adverse events were reported. CONCLUSION: ACTs can be successfully integrated into the HMM strategy

Bayesian shrinkage mapping of quantitative trait loci in variance component models

<p>Abstract</p> <p>Background</p> <p>In this article, I propose a model-selection-free method to map multiple quantitative trait loci (QTL) in variance component model, which is useful in outbred populations. The new method can estimate the variance of zero-effect QTL infinitely to zero, but nearly unbiased for non-zero-effect QTL. It is analogous to Xu's Bayesian shrinkage estimation method, but his method is based on allelic substitution model, while the new method is based on the variance component models.</p> <p>Results</p> <p>Extensive simulation experiments were conducted to investigate the performance of the proposed method. The results showed that the proposed method was efficient in mapping multiple QTL simultaneously, and moreover it was more competitive than the reversible jump MCMC (RJMCMC) method and may even out-perform it.</p> <p>Conclusions</p> <p>The newly developed Bayesian shrinkage method is very efficient and powerful for mapping multiple QTL in outbred populations.</p