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Determining the duration of protective immunity requires quantifying the magnitude and rate of loss of antibodies to different virus and vaccine antigens. A key complication is heterogeneity in both the magnitude and decay rate of responses of different individuals to a given vaccine, as well as of a given individual to different vaccines. We analyzed longitudinal data on antibody titers in 45 individuals to characterize the extent of this heterogeneity and used models to determine how it affected the longevity of protective immunity to measles, rubella, vaccinia, tetanus, and diphtheria. Our analysis showed that the magnitude of responses in different individuals varied between 12- and 200-fold (95% coverage) depending on the antigen. Heterogeneity in the magnitude and decay rate contribute comparably to variation in the longevity of protective immunity between different individuals. We found that some individuals have, on average, slightly longer-lasting memory than others-on average, they have higher antibody levels with slower decay rates. We identified different patterns for the loss of protective levels of antibodies to different vaccine and virus antigens. Specifically, we found that for the first 25 to 50 years, virtually all individuals have protective antibody titers against diphtheria and tetanus, respectively, but about 10% of the population subsequently lose protective immunity per decade. In contrast, at the outset, not all individuals had protective titers against measles, rubella, and vaccinia. However, these antibody titers wane much more slowly, with a loss of protective immunity in only 1% to 3% of the population per decade. Our results highlight the importance of long-term longitudinal studies for estimating the duration of protective immunity and suggest both how vaccines might be improved and how boosting schedules might be reevaluated.

In recent years, the explosion of genomic data and bioinformatic tools has been accompanied by a growing conversation around reproducibility of results and usability of software. However, the actual state of the body of bioinformatics software remains largely unknown. The purpose of this paper is to investigate the state of source code in the bioinformatics community, specifically looking at relationships between code properties, development activity, developer communities, and software impact. To investigate these issues, we curated a list of 1,720 bioinformatics repositories on GitHub through their mention in peer-reviewed bioinformatics articles. Additionally, we included 23 high-profile repositories identified by their popularity in an online bioinformatics forum. We analyzed repository metadata, source code, development activity, and team dynamics using data made available publicly through the GitHub API, as well as article metadata. We found key relationships within our dataset, including: certain scientific topics are associated with more active code development and higher community interest in the repository; most of the code in the main dataset is written in dynamically typed languages, while most of the code in the high-profile set is statically typed; developer team size is associated with community engagement and high-profile repositories have larger teams; the proportion of female contributors decreases for high-profile repositories and with seniority level in author lists; and, multiple measures of project impact are associated with the simple variable of whether the code was modified at all after paper publication. In addition to providing the first large-scale analysis of bioinformatics code to our knowledge, our work will enable future analysis through publicly available data, code, and methods. Code to generate the dataset and reproduce the analysis is provided under the MIT license at https://github.com/pamelarussell/github-bioinformatics. Data are available at https://doi.org/10.17605/OSF.IO/UWHX8.

In this study, humoral immunity after vaccination or natural infection was assessed for several common pathogens. Antibodies against varicella–zoster virus, measles, and mumps were long-lived (estimated half-lives, ≥50 years), and antibodies against tetanus and diphtheria were short-lived (estimated half-lives, 11 and 19 years, respectively). Antibody titers were poorly correlated with peripheral-blood memory B cells. Antibodies against varicella–zoster virus, measles, and mumps were long-lived, and antibodies against tetanus and diphtheria were short-lived. Recovery from acute viral or microbial infection often results in long-term or even lifelong immunity. 1 – 3 Although the importance of sustaining protective humoral immunity is widely recognized, the mechanisms involved in this maintenance remain unclear. 3 – 10 To address the issue of antibody maintenance after infection or vaccination, we conducted a longitudinal analysis of antibody titers against multiple antigens with the use of serum samples banked from a combination of scheduled (annual) and event-based collections. On the basis of these studies, we determined whether antigen-specific antibody responses were measurably boosted through environmental exposure, infection, or vaccination. Moreover, we determined the duration . . .

COVID-19 patients have experienced worry, altered provider-patient interactions, and options to use novel treatments, initially with neutralizing monoclonal antibodies (mAbs). Limited research has been performed on these aspects of the COVID-19 outpatient experience. This study aimed to evaluate the experiences of outpatients recently diagnosed with COVID-19, who were eligible for use of mAbs, during the diagnosis and treatment process based on sociodemographic and clinical factors. This was a self-reported cohort study performed via telephone surveys. Participants included COVID-19 outpatients who met at least one emergency use criterion for mAbs during the first 120 days after a SARS-CoV-2 positive test. We analyzed survey results using multivariable logistic regression for non-scale outcomes and adjusted proportional odds logistic regression for scaled outcomes. Greater worry about their COVID-19 diagnosis was reported by younger, female, and Hispanic patients and those with Medicaid insurance, two or more comorbid conditions, BMI > 25, and at least 2 COVID-19 vaccinations. Greater provider trust was reported by patients with ≥ 2 years of college education, one or more comorbid conditions, and one or more COVID-19 vaccinations; whereas less provider trust was reported by patients ages 45-64 years, with usual place of care in a walk-in clinic, and those without Commercial, Medicare, or Medicaid insurance. In patients who did not receive mAbs, patients with Medicaid and those without Commercial/Medicare insurance were among the factors that were less likely be offered mAbs by a provider. This report describes factors associated with multiple aspects of outpatients' experience of COVID-19. This study demonstrated that there are important differences in the experience of outpatient COVID-19 patients based on sociodemographic factors and clinical factors, as well as where additional strategies are needed to improve this experience and associated outcomes.

Quantitative assessment of emphysema in CT scans has mostly focused on calculating the percentage of lung tissue that is deemed abnormal based on a density thresholding strategy. However, this overall measure of disease burden discards virtually all the spatial information encoded in the scan that is implicitly utilized in a visual assessment. This simplification is likely grouping heterogenous disease patterns and is potentially obscuring clinical phenotypes and variable disease outcomes. To overcome this, several methods that attempt to quantify heterogeneity in emphysema distribution have been proposed. Here, we compare three of those: one based on estimating a power law for the size distribution of contiguous emphysema clusters, a second that looks at the number of emphysema-to-emphysema voxel adjacencies, and a third that applies a parametric spatial point process model to the emphysema voxel locations. This was done using data from 587 individuals from Phase 1 of COPDGene that had an inspiratory CT scan and plasma protein abundance measurements. The associations between these imaging metrics and visual assessment with clinical measures (FEV 1 , FEV 1 -FVC ratio, etc.) and plasma protein biomarker levels were evaluated using a variety of regression models. Our results showed that a selection of spatial measures had the ability to discern heterogeneous patterns among CTs that had similar emphysema burdens. The most informative quantitative measure, average cluster size from the point process model, showed much stronger associations with nearly every clinical outcome examined than existing CT-derived emphysema metrics and visual assessment. Moreover, approximately 75% more plasma biomarkers were found to be associated with an emphysema heterogeneity phenotype when accounting for spatial clustering measures than when they were excluded.

Background A trial performed among unvaccinated, high-risk outpatients with COVID-19 during the delta period showed remdesivir reduced hospitalization. We used our real-world data platform to determine the effectiveness of remdesivir on reducing 28-day hospitalization among outpatients with mild-moderate COVID-19 during an Omicron period including BQ.1/BQ.1.1/XBB.1.5. Methods We did a propensity-matched, retrospective cohort study of non-hospitalized adults with SARS-CoV-2 infection between April 7, 2022, and February 7, 2023. Electronic healthcare record data from a large health system in Colorado were linked to statewide vaccination and mortality data. We included patients with a positive SARS-CoV-2 test or outpatient remdesivir administration. Exclusion criteria were other SARS-CoV-2 treatments or positive SARS-CoV-2 test more than seven days before remdesivir. The primary outcome was all-cause hospitalization up to day 28. Secondary outcomes included 28-day COVID-related hospitalization and 28-day all-cause mortality. Results Among 29,270 patients with SARS-CoV-2 infection, 1,252 remdesivir-treated patients were matched to 2,499 untreated patients. Remdesivir was associated with lower 28-day all-cause hospitalization (1.3% vs. 3.3%, adjusted hazard ratio (aHR) 0.39 [95% CI 0.23–0.67], p  < 0.001) than no treatment. All-cause mortality at 28 days was numerically lower among remdesivir-treated patients (0.1% vs. 0.4%; aOR 0.32 [95% CI 0.03–1.40]). Similar benefit of RDV treatment on 28-day all-cause hospitalization was observed across Omicron periods, aOR (95% CI): BA.2/BA2.12.1 (0.77[0.19–2.41]), BA.4/5 (0.50[95% CI 0.50–1.01]), BQ.1/BQ.1.1/XBB.1.5 (0.21[95% CI 0.08–0.57]. Conclusion Among outpatients with SARS-CoV-2 during recent Omicron surges, remdesivir was associated with lower hospitalization than no treatment, supporting current National Institutes of Health Guidelines.

Background Effectiveness of remdesivir (RDV) treatment on short-term mortality and other outcomes has been well-studied, yet the impact of RDV on long-term outcomes is less well-known. The objective of this study was to determine if inpatient RDV use in survivors of COVID-19 hospitalization is associated with reduced mortality after discharge. Methods This is a retrospective observational cohort study of patients hospitalized with COVID-19 between November 2020 and October 2022 in three health systems in Colorado and Utah. Real-world data were identified from electronic health records and state-level vaccination and mortality records. Our primary cohort were patients hospitalized with COVID-19, either treated or not treated with RDV, who survived to hospital discharge. Unadjusted and adjusted Cox proportional hazard models were used to estimate the hazard ratio of all-cause mortality following hospital discharge for those administered vs. not administered inpatient RDV. Sensitivity analyses included propensity-matching the primary cohort with in-hospital mortality as a competing risk. Secondary outcomes, including hospital and ED readmissions respectively, within 28 days after index hospitalization discharge, were also evaluated using Cox proportional hazard models. Results The primary cohort consisted of 9760 patients who survived index hospitalization and had between 6 and 29 months of post-hospital follow up. Of the primary cohort, 4771 (48.8%) were treated with inpatient RDV, inpatient RDV was associated with a decreased mortality hazard (aHR 0.73; 95% confidence interval (CI) 0.61–0.87) among survivors with up to two and a half years of follow-up. Results from a sensitivity analysis using in-hospital mortality as a competing risk were similar to the primary model (aHR 0.76; CI 0.63–0.92). RDV treatment was also associated with decreased re-hospitalization (aHR 0.77; CI 0.67–0.89) and ED readmission rates (aHR 0.79; CI 0.67–0.92). Most subgroups appear to benefit from RDV, with possible exceptions for patients infected during the first Omicron wave, having received at least 1 vaccine dose, and those not requiring supplemental oxygen during index hospitalization. Conclusions In this real-world analysis of three large health systems in Colorado and Utah, RDV use was associated with decreased long-term mortality among survivors of initial COVID-19 hospitalization. Inpatient RDV treatment may provide a mortality benefit after COVID-19 hospitalization.

Background Most phenotyping paradigms in sarcoidosis are based on expert opinion; however, no paradigm has been widely adopted because of the subjectivity in classification. We hypothesized that cluster analysis could be performed on common clinical variables to define more objective sarcoidosis phenotypes. Methods We performed a retrospective cohort study of 554 sarcoidosis cases to identify distinct phenotypes of sarcoidosis based on 29 clinical features. Model-based clustering was performed using the VarSelLCM R package and the Integrated Completed Likelihood (ICL) criteria were used to estimate number of clusters. To identify features associated with cluster membership, features were ranked based on variable importance scores from the VarSelLCM model, and additional univariate tests (Fisher’s exact test and one-way ANOVA) were performed using q-values correcting for multiple testing. The Wasfi severity score was also compared between clusters. Results Cluster analysis resulted in 6 sarcoidosis phenotypes. Salient characteristics for each cluster are as follows: Phenotype (1) supranormal lung function and majority Scadding stage 2/3; phenotype (2) supranormal lung function and majority Scadding stage 0/1; phenotype (3) normal lung function and split Scadding stages between 0/1 and 2/3; phenotype (4) obstructive lung function and majority Scadding stage 2/3; phenotype (5) restrictive lung function and majority Scadding stage 2/3; phenotype (6) mixed obstructive and restrictive lung function and mostly Scadding stage 4. Although there were differences in the percentages, all Scadding stages were encompassed by all of the phenotypes, except for phenotype 1, in which none were Scadding stage 4. Clusters 4, 5, 6 were significantly more likely to have ever been on immunosuppressive treatment and had higher Wasfi disease severity scores. Conclusions Cluster analysis produced 6 sarcoidosis phenotypes that demonstrated less severe and severe phenotypes. Phenotypes 1, 2, 3 have less lung function abnormalities, a lower percentage on immunosuppressive treatment and lower Wasfi severity scores. Phenotypes 4, 5, 6 were characterized by lung function abnormalities, more parenchymal abnormalities, an increased percentage on immunosuppressive treatment and higher Wasfi severity scores. These data support using cluster analysis as an objective and clinically useful way to phenotype sarcoidosis subjects and to empower clinicians to identify those with more severe disease versus those who have less severe disease, independent of Scadding stage.

Background Dropout is a common problem in longitudinal clinical trials and cohort studies, and is of particular concern when dropout occurs for reasons that may be related to the outcome of interest. This paper reviews common parametric models to account for dropout and introduces a Bayesian semi-parametric varying coefficient model for exponential family longitudinal data with non-ignorable dropout. Methods To demonstrate these methods, we present results from a simulation study and estimate the impact of drug use on longitudinal CD4 + T cell count and viral load suppression in the Women’s Interagency HIV Study. Sensitivity analyses are performed to consider the impact of model assumptions on inference. We compare results between our semi-parametric method and parametric models to account for dropout, including the conditional linear model and a parametric frailty model. We also compare results to analyses that fail to account for dropout. Results In simulation studies, we show that semi-parametric methods reduce bias and mean squared error when parametric model assumptions are violated. In analyses of the Women’s Interagency HIV Study data, we find important differences in estimates of changes in CD4 + T cell count over time in untreated subjects that report drug use between different models used to account for dropout. We find steeper declines over time using our semi-parametric model, which makes fewer assumptions, compared to parametric models. Failing to account for dropout or to meet parametric assumptions of models to account for dropout could lead to underestimation of the impact of hard drug use on CD4 + cell count decline in untreated subjects. In analyses of subjects that initiated highly active anti-retroviral treatment, we find that the estimated probability of viral load suppression is lower in models that account for dropout. Conclusions Non-ignorable dropout is an important consideration when analyzing data from longitudinal clinical trials and cohort studies. While methods that account for non-ignorable dropout must make some unavoidable assumptions that cannot be verified from the observed data, many methods make additional parametric assumptions. If these assumptions are not met, inferences can be biased, making more flexible methods with minimal assumptions important.

Background Neutralizing monoclonal antibodies (mAbs) are highly effective in reducing hospitalization and mortality among early symptomatic COVID-19 patients in clinical trials and real-world data. While resistance to some mAbs has since emerged among new variants, characteristics associated with treatment failure of mAbs remain unknown. Methods This multicenter, observational cohort study included patients with COVID-19 who received mAb treatment between November 20, 2020, and December 9, 2021. We utilized electronic health records from a statewide health system plus state-level vaccine and mortality data. The primary outcome was mAb treatment failure, defined as hospitalization or death within 28 days of a positive SARS-CoV-2 test. Results COVID-19 mAb was administered to 7406 patients. Hospitalization within 28 days of positive SARS-CoV-2 test occurred in 258 (3.5%) of all patients who received mAb treatment. Ten patients (0.1%) died within 28 days, and all but one were hospitalized prior to death. Characteristics associated with treatment failure included having two or more comorbidities excluding obesity and immunocompromised status (adjusted odds ratio [OR] 3.71, 95% confidence interval [CI] 2.52–5.56), lack of SARS-CoV-2 vaccination (OR 2.73, 95% CI 2.01–3.77), non-Hispanic black race/ethnicity (OR 2.21, 95% CI 1.20–3.82), obesity (OR 1.79, 95% CI 1.36–2.34), one comorbidity (OR 1.68, 95% CI 1.11–2.57), age ≥ 65 years (OR 1.62, 95% CI 1.13–2.35), and male sex (OR 1.56, 95% CI 1.21–2.02). Immunocompromised status (none, mild, or moderate/severe), pandemic phase, and type of mAb received were not associated with treatment failure (all p > 0.05). Conclusions Comorbidities, lack of prior SARS-CoV-2 vaccination, non-Hispanic black race/ethnicity, obesity, age ≥ 65 years, and male sex are associated with treatment failure of mAbs.