Transparency of COVID-19-related research : A meta-research study

Funding Information: The computational analyses were performed on servers provided by UEF Bioinformatics Center, University of Eastern Finland, Finland. Uribe was supported by European Union’s Horizon 2020 grant 857287 for the Baltic Biomaterials Centre of Excellence, Headquarters at Riga Technical University, Riga, Latvia and the Uzn¸ēmuma MikroTik līgumam Nr. UL8, 2021 RSU (toward implementing the RSU data repository and the FAIR data management principles). Raittio was supported by the Finnish Dental Society Apollonia and the Aarhus University Research Foundation (#AUFF-E 2019-7-3). Publisher Copyright: © 2023 Sofi-Mahmudi et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.BACKGROUND: We aimed to assess the adherence to five transparency practices (data availability, code availability, protocol registration and conflicts of interest (COI), and funding disclosures) from open access Coronavirus disease 2019 (COVID-19) related articles. METHODS: We searched and exported all open access COVID-19-related articles from PubMed-indexed journals in the Europe PubMed Central database published from January 2020 to June 9, 2022. With a validated and automated tool, we detected transparent practices of three paper types: research articles, randomized controlled trials (RCTs), and reviews. Basic journal- and article-related information were retrieved from the database. We used R for the descriptive analyses. RESULTS: The total number of articles was 258,678, of which we were able to retrieve full texts of 186,157 (72%) articles from the database Over half of the papers (55.7%, n = 103,732) were research articles, 10.9% (n = 20,229) were review articles, and less than one percent (n = 1,202) were RCTs. Approximately nine-tenths of articles (in all three paper types) had a statement to disclose COI. Funding disclosure (83.9%, confidence interval (CI): 81.7-85.8 95%) and protocol registration (53.5%, 95% CI: 50.7-56.3) were more frequent in RCTs than in reviews or research articles. Reviews shared data (2.5%, 95% CI: 2.3-2.8) and code (0.4%, 95% CI: 0.4-0.5) less frequently than RCTs or research articles. Articles published in 2022 had the highest adherence to all five transparency practices. Most of the reviews (62%) and research articles (58%) adhered to two transparency practices, whereas almost half of the RCTs (47%) adhered to three practices. There were journal- and publisher-related differences in all five practices, and articles that did not adhere to transparency practices were more likely published in lowest impact journals and were less likely cited. CONCLUSION: While most articles were freely available and had a COI disclosure, adherence to other transparent practices was far from acceptable. A much stronger commitment to open science practices, particularly to protocol registration, data and code sharing, is needed from all stakeholders.publishersversionPeer reviewe

A comparative study of the diagnostic methods for Group A streptococcal sore throat in two reference hospitals in Yaounde, Cameroon

Introduction: Sore throat is a common complaint in general practice which is more frequent in children. The most frequent pathogenic bacteriaassociated with this infection is Streptococcus pyogenes. Rapid Antigen Diagnostic Test (RADT) facilitates the rapid identification and consequentlyprompt treatment of patients, prevents complications, and also reduces the risk of spread of Group A Streptococcus (GAS). The main objective ofthis study was to assess the diagnostic value of a rapid streptococcal antigen detection test in patients with sore throat. Methods: A crosssectional descriptive study was carried out from January to April 2011 on patients aged 3 to 72 years consulting for pharyngitis or sore throat at the paediatric and Ear, Nose and Throat units of the University Teaching Hospital Yaounde and the Central Hospital Yaounde. Two throat swabs were collected per patient. One was used for the rapid test and the other for standard bacteriological analysis.Results: The prevalence of GAS in the study population was 22.5%. Out of the 71 samples collected, the RADT detected group A streptococcal  antigens in 12 of 16 positive cultures giving a sensitivity of 75%. The specificity of the rapid test was 96%, with positive predictive value of 85.7%, and negative predictive value of 93% respectively.Conclusion: Rapid test may have an additional value in the management of patients with high risk of having GAS infection. However, tests with a higher sensitivity are needed for accurate and reliable results for early  diagnosis of patients with sore throat caused by GAS

Evaluation of Cell Types for Assessment of Cytogenetic Damage in Arsenic Exposed Population

Background: Cytogenetic biomarkers are essential for assessing environmental exposure, and reflect adverse human health effects such as cellular damage. Arsenic is a potential clastogen and aneugen. In general, the majority of the studies on clastogenic effects of arsenic are based on frequency of micronuclei (MN) study in peripheral lymphocytes, urothelial and oral epithelial cells. To find out the most suitable cell type, here, we compared cytogenetic damage through MN assay in (a) various populations exposed to arsenic through drinking water retrieved from literature review, as also (b) arsenic-induced Bowen's patients from our own survey. Results: For literature review, we have searched the Pubmed database for English language journal articles using the following keywords: "arsenic", "micronuclei", "drinking water", and "human" in various combinations. We have selected 13 studies consistent with our inclusion criteria that measured micronuclei in either one or more of the above-mentioned three cell types, in human samples. Compared to urothelial and buccal mucosa cells, the median effect sizes measured by the difference between people with exposed and unexposed, lymphocyte based MN counts were found to be stronger. This general pattern pooled from 10 studies was consistent with our own set of three earlier studies. MN counts were also found to be stronger for lymphocytes even in arsenicinduced Bowen's patients (cases) compared to control individuals having arsenic-induced noncancerous skin lesions. Conclusion: Overall, it can be concluded that MN in lymphocytes may be superior to other epithelial cells for studying arsenic-induced cytogenetic damage

BI-LAVA: Biocuration with Hierarchical Image Labeling through Active Learning and Visual Analysis

In the biomedical domain, taxonomies organize the acquisition modalities of scientific images in hierarchical structures. Such taxonomies leverage large sets of correct image labels and provide essential information about the importance of a scientific publication, which could then be used in biocuration tasks. However, the hierarchical nature of the labels, the overhead of processing images, the absence or incompleteness of labeled data, and the expertise required to label this type of data impede the creation of useful datasets for biocuration. From a multi-year collaboration with biocurators and text-mining researchers, we derive an iterative visual analytics and active learning strategy to address these challenges. We implement this strategy in a system called BI-LAVA Biocuration with Hierarchical Image Labeling through Active Learning and Visual Analysis. BI-LAVA leverages a small set of image labels, a hierarchical set of image classifiers, and active learning to help model builders deal with incomplete ground-truth labels, target a hierarchical taxonomy of image modalities, and classify a large pool of unlabeled images. BI-LAVA's front end uses custom encodings to represent data distributions, taxonomies, image projections, and neighborhoods of image thumbnails, which help model builders explore an unfamiliar image dataset and taxonomy and correct and generate labels. An evaluation with machine learning practitioners shows that our mixed human-machine approach successfully supports domain experts in understanding the characteristics of classes within the taxonomy, as well as validating and improving data quality in labeled and unlabeled collections.Comment: 15 pages, 6 figure

Towards knowledge-based gene expression data mining

The field of gene expression data analysis has grown in the past few years from being purely data-centric to integrative, aiming at complementing microarray analysis with data and knowledge from diverse available sources. In this review, we report on the plethora of gene expression data mining techniques and focus on their evolution toward knowledge-based data analysis approaches. In particular, we discuss recent developments in gene expression-based analysis methods used in association and classification studies, phenotyping and reverse engineering of gene networks

An Ensemble Learning Method for Emotion Charting Using Multimodal Physiological Signals

publishedVersio

A cross-sectional evidence-based review of pharmaceutical promotional marketing brochures and their underlying studies: Is what they tell us important and true?

BACKGROUND: A major marketing technique used by pharmaceutical companies is direct-to-physician marketing. This form of marketing frequently employs promotional marketing brochures, based on clinical research, which may influence how a physician prescribes medicines. This study's objective was to investigate whether or not the information in promotional brochures presented to physicians by pharmaceutical representatives is accurate, consistent, and valid with respect to the actual studies upon which the promotional brochures are based. METHODS: Physicians in five clinics were asked to consecutively collect pharmaceutical promotional brochures and to send them all to a centralized location. The brochures for any class of medication were collected on a continuous basis until 20 distinct promotional brochures were received by a central location. Once the brochure was received, the corresponding original study was obtained. Two blinded reviewers performed an evidence-based review of the article, comparing data that was printed on the brochure to what was found in the original study. RESULTS: Among the 20 studies, 75% of the studies were found to be valid, 80% were funded by the pharmaceutical company, 60% of the studies and the corresponding brochures presented patient-oriented outcomes, and 40% were compared to another treatment regimen. Of the 19 brochures that presented the data as graphs, 4 brochures presented a relative risk reduction while only 1 brochure presented an absolute risk reduction. 15% of the promotional marketing brochures presented data that was different from what was in the original published study. CONCLUSION: Given the present findings, physicians should be cautious about drawing conclusions regarding a medication based on the marketing brochures provided by pharmaceutical companies

Data fusion by using machine learning and computational intelligence techniques for medical image analysis and classification

Data fusion is the process of integrating information from multiple sources to produce specific, comprehensive, unified data about an entity. Data fusion is categorized as low level, feature level and decision level. This research is focused on both investigating and developing feature- and decision-level data fusion for automated image analysis and classification. The common procedure for solving these problems can be described as: 1) process image for region of interest\u27 detection, 2) extract features from the region of interest and 3) create learning model based on the feature data. Image processing techniques were performed using edge detection, a histogram threshold and a color drop algorithm to determine the region of interest. The extracted features were low-level features, including textual, color and symmetrical features. For image analysis and classification, feature- and decision-level data fusion techniques are investigated for model learning using and integrating computational intelligence and machine learning techniques. These techniques include artificial neural networks, evolutionary algorithms, particle swarm optimization, decision tree, clustering algorithms, fuzzy logic inference, and voting algorithms. This work presents both the investigation and development of data fusion techniques for the application areas of dermoscopy skin lesion discrimination, content-based image retrieval, and graphic image type classification --Abstract, page v