Markov chain Monte Carlo and expectation maximization approaches for estimation of haplotype frequencies for multiply infected human blood samples

Background Haplotypes are important in anti-malarial drug resistance because genes encoding drug resistance may accumulate mutations at several codons in the same gene, each mutation increasing the level of drug resistance and, possibly, reducing the metabolic costs of previous mutation. Patients often have two or more haplotypes in their blood sample which may make it impossible to identify exactly which haplotypes they carry, and hence to measure the type and frequency of resistant haplotypes in the malaria population. Results This study presents two novel statistical methods expectation–maximization (EM) and Markov chain Monte Carlo (MCMC) algorithms to investigate this issue. The performance of the algorithms is evaluated on simulated datasets consisting of patient blood characterized by their multiplicity of infection (MOI) and malaria genotype. The datasets are generated using different resistance allele frequencies (RAF) at each single nucleotide polymorphisms (SNPs) and different limit of detection (LoD) of the SNPs and the MOI. The EM and the MCMC algorithm are validated and appear more accurate, faster and slightly less affected by LoD of the SNPs and the MOI compared to previous related statistical approaches. Conclusions The EM and the MCMC algorithms perform well when analysing malaria genetic data obtained from infected human blood samples. The results are robust to genotyping errors caused by LoDs and function well even in the absence of MOI data on individual patients

Comparing the Efficacy of Drug Regimens for Pulmonary Tuberculosis: Meta-analysis of Endpoints in Early-Phase Clinical Trials

Background A systematic review of early clinical outcomes in tuberculosis was undertaken to determine ranking of efficacy of drugs and combinations, define variability of these measures on different endpoints, and to establish the relationships between them. Methods Studies were identified by searching PubMed, Medline, Embase, LILACS (Latin American and Caribbean Health Sciences Literature), and reference lists of included studies. Outcomes were early bactericidal activity results over 2, 7, and 14 days, and the proportion of patients with negative culture at 8 weeks. Results One hundred thirty-three trials reporting phase 2A (early bactericidal activity) and phase 2B (culture conversion at 2 months) outcomes were identified. Only 9 drug combinations were assessed on >1 phase 2A endpoint and only 3 were assessed in both phase 2A and 2B trials. Conclusions The existing evidence base supporting phase 2 methodology in tuberculosis is highly incomplete. In future, a broader range of drugs and combinations should be more consistently studied across a greater range of phase 2 endpoints

Reconceptualizing Open Access to Theses and Dissertations

The global COVID-19 crisis has turned public attention to the special need for accessing those cutting-edge studies that are needed for further scientific innovation. Theses and dissertations (TDs) are prominent examples of such studies. TDs are academic research projects conducted by graduate students to acquire a high academic degree, such as a PhD. They encompass not only knowledge about basic science but also knowledge that generates social and economic value for society. Therefore, access to TDs is imperative for promoting science and innovation. Open access to scientific publications has been in the focus of public policy discourse for two decades, but progress toward this end has been limited. As part of this discourse, there has been no systematic discussion of the special case of TDs and of the justification for adopting an open access publication policy toward them. The present study aims to fill this gap. We argue that the essence of TDs as unique outputs of academic research merits a special policy mandating the publication of these studies in open access format, subject to certain exceptions. This policy is underpinned by several arguments, which we develop in our study, based on historic and normative analysis. These considerations support reconceiving access to TDs using an open access approach designated particularly for them. To better understand current open access policies toward TDs, we conducted a limited semi-empirical investigation to collect information. Our findings confirm that–despite the growing awareness of the importance of an open access TDs policy–no standard policy exists. Therefore, we propose to establish a mandatory global policy and standardization regarding the publication of TDs in designated repositories, open to the public, that would generate together an “open world wide web of TDs.” Such a global framework would facilitate the progress of science and promote the public good worldwide. In the aftermath of the global COVID-19 crisis, it seems that the time is ripe for such a move at both international and national levels

Reconceptualizing Open Access to Theses and Dissertations

Theses and dissertations (TD) are academic research projects that are conducted by graduate students to acquire a high academic degree, such as a PhD. The perception of the written TD has evolved over the years, following changes concerning the purpose of advanced academic studies. Today, these academic fruits should meet a high standard of academic innovation, which is understood broadly as encompassing not only knowledge concerning basic science but also the knowledge that generates social and economic value for society. The modern perception of TD has generated a call for their greater accessibility, as part of the Open Science movement. Nevertheless, in many countries around the world TD are not published in an open access format. While the normative basis for open access approach to publicly funded academic research is extensively discussed in the literature, there is a lack of legal and normative discussion concerning the special case of TD. The present study aims at filling this gap. We argue that the essence of TD as unique outputs of academic research merits a special stance compelling the publication of these studies in open access format, subject to certain exceptions. This stance is underpinned by several arguments, which we develop in our study, based on historic and normative analysis. Moreover, we propose to establish a mandatory global policy and standardization regarding the publication of TD in designated repositories, open to the public, that would generate together an open world wide web of TD. Such a global framework will facilitate the progress of science and promote the public good worldwide

Prediction of Long-Term Poor Clinical Outcomes in Cerebral Venous Thrombosis Using Neural Networks Model:The BEAST Study

IntroductionRisk prediction models are commonly performed with logistic regression analysis but are limited by skewed datasets. We utilised neural networks (NNs) model to identify independent predictors of poor outcomes in cerebral venous thrombosis (CVT) due to the limitations of logistic regression (LR) analysis with complex datasets.MethodsWe evaluated 1309 adult CVT patients from the prospective BEAST (Biorepository to Establish the Aetiology of Sinovenous Thrombosis) study. The area under the receiver operating characteristic (AUROC) curve confirmed the goodness-of-fit of prediction models. The normalised importance (NI) of the NNs determines the significance of independent predictors.ResultsThe stepwise logistic regression model found thrombolysis (OR 32.1; 95% CI 3.6–287.0; P=0.002), craniotomy (OR 6.9; 95% CI 1.3–36.8; P=0.02), and cerebral haemorrhage (OR 4.5; 95% CI 1.3–15.4; P=0.01) as predictors of poor clinical outcome with the AUROC of 0.71. Conversely, the NNs model identified major independent predictors of long-term poor clinical outcomes as cerebral haemorrhage (NI 100%) and thrombolysis (NI 98%), as well as trivial predictors of age (NI 2.8%) and altered mental status (NI 3.5%). The accuracy of the NNs model was 95.1% and 94.1% for self-learned randomly selected training and testing samples with an AUROC of 0.82. Positive and negative predictive values for poor outcomes were 13.2% and 97.1% for the LR model, compared with the NNs model of 18.8% and 98.7%, respectively.ConclusionCerebral haemorrhage and thrombolysis was a strong independent predictor, whereas age merely impacts the long-term poor clinical outcome in adult CVT. Integrating unorthodox neural networks risk prediction model can improve decision-making as it outperforms conventional logistic regression with complex datasets

Prediction of Long-Term Poor Clinical Outcomes in Cerebral Venous Thrombosis Using Neural Networks Model: The BEAST Study

Redoy Ranjan,1 Gie Ken-Dror,1 Pankaj Sharma1,2 On behalf of the BEAST Collaborators1Department of Biological Sciences, Institute of Cardiovascular Research, Royal Holloway University of London (ICR2UL), Egham, Greater London, UK; 2Department of Clinical Neuroscience, Imperial College Healthcare NHS Trust, London, UKCorrespondence: Pankaj Sharma, Institute of Cardiovascular Research, Royal Holloway University of London (ICR2UL), Egham, Greater London, TW20 0EX, UK, Email [email protected]: Risk prediction models are commonly performed with logistic regression analysis but are limited by skewed datasets. We utilised neural networks (NNs) model to identify independent predictors of poor outcomes in cerebral venous thrombosis (CVT) due to the limitations of logistic regression (LR) analysis with complex datasets.Methods: We evaluated 1309 adult CVT patients from the prospective BEAST (Biorepository to Establish the Aetiology of Sinovenous Thrombosis) study. The area under the receiver operating characteristic (AUROC) curve confirmed the goodness-of-fit of prediction models. The normalised importance (NI) of the NNs determines the significance of independent predictors.Results: The stepwise logistic regression model found thrombolysis (OR 32.1; 95% CI 3.6– 287.0; P=0.002), craniotomy (OR 6.9; 95% CI 1.3– 36.8; P=0.02), and cerebral haemorrhage (OR 4.5; 95% CI 1.3– 15.4; P=0.01) as predictors of poor clinical outcome with the AUROC of 0.71. Conversely, the NNs model identified major independent predictors of long-term poor clinical outcomes as cerebral haemorrhage (NI 100%) and thrombolysis (NI 98%), as well as trivial predictors of age (NI 2.8%) and altered mental status (NI 3.5%). The accuracy of the NNs model was 95.1% and 94.1% for self-learned randomly selected training and testing samples with an AUROC of 0.82. Positive and negative predictive values for poor outcomes were 13.2% and 97.1% for the LR model, compared with the NNs model of 18.8% and 98.7%, respectively.Conclusion: Cerebral haemorrhage and thrombolysis was a strong independent predictor, whereas age merely impacts the long-term poor clinical outcome in adult CVT. Integrating unorthodox neural networks risk prediction model can improve decision-making as it outperforms conventional logistic regression with complex datasets.Keywords: cerebral venous thrombosis, neural network, stroke, predictors, outcom