Biomedical Articles Research Articles

Improving transparency-A call to include social housing information in biomedical research articles involving nonhuman primates.

The social setting of animal subjects in the research environment has known effects on a variety of dependent measures used in biomedical research. Proper evaluation of the robustness of published research is dependent upon transparent, detailed, and accurate reporting of research methods, including the animals' social housing conditions. However, to date, most research articles utilizing nonhuman primates (NHPs) provide only partial data on this topic, hampering transparency, and reproducibility. Therefore, we call for the inclusion of information pertaining to the social aspects of the animals' housing conditions in publications involving NHPs to improve transparency. We argue that including this information in scientific publications is crucial for the interpretation of research findings in the appropriate contextand for understanding unexplained variability in study findings. Finally, the inclusion of this information in publications will additionally familiarize scientists with how other researchers conducting similar studies are housing their animals and will encourage them to consider the implications of various housing conditions on their research outcomes.

Survey of BERT-Base Models for Scientific Text Classification: COVID-19 Case Study

On 30 January 2020, the World Health Organization announced a new coronavirus, which later turned out to be very dangerous. Since that date, COVID-19 has spread to become a pandemic that has now affected practically all regions in the world. Since then, many researchers in medicine have contributed to fighting COVID-19. In this context and given the great growth of scientific publications related to this global pandemic, manual text and data retrieval has become a challenging task. To remedy this challenge, we are proposing CovBERT, a pre-trained language model based on the BERT model to automate the literature review process. CovBERT relies on prior training on a large corpus of scientific publications in the biomedical domain and related to COVID-19 to increase its performance on the literature review task. We evaluate CovBERT on the classification of short text based on our scientific dataset of biomedical articles on COVID-19 entitled COV-Dat-20. We demonstrate statistically significant improvements by using BERT.

DocR-BERT: Document-Level R-BERT for Chemical-Induced Disease Relation Extraction via Gaussian Probability Distribution.

Chemical-induced disease (CID) relation extraction from biomedical articles plays an important role in disease treatment and drug development. Existing methods are insufficient for capturing complete document level semantic information due to ignoring semantic information of entities in different sentences. In this work, we proposed an effective document-level relation extraction model to automatically extract intra-/inter-sentential CID relations from articles. Firstly, our model employed BERT to generate contextual semantic representations of the title, abstract and shortest dependency paths (SDPs). Secondly, to enhance the semantic representation of the whole document, cross attention with self-attention (named cross2self-attention) between abstract, title and SDPs was proposed to learn the mutual semantic information. Thirdly, to distinguish the importance of the target entity in different sentences, the Gaussian probability distribution was utilized to compute the weights of the co-occurrence sentence and its adjacent entity sentences. More complete semantic information of the target entity is collected from all entities occurring in the document via our presented document-level R-BERT (DocR-BERT). Finally, the related representations were concatenated and fed into the softmax function to extract CIDs. We evaluated the model on the CDR corpus provided by BioCreative V. The proposed model without external resources is superior in performance as compared with other state-of-the-art models (our model achieves 53.5%, 70%, and 63.7% of the F1-score on inter-/intra-sentential and overall CDR dataset). The experimental results indicate that cross2self-attention, the Gaussian probability distribution and DocR-BERT can effectively improve the CID extraction performance. Furthermore, the mutual semantic information learned by the cross self-attention from abstract towards title can significantly influence the extraction performance of document-level biomedical relation extraction tasks.

Pancreaticoduodenectomy (Whipple Procedure) research output: A 30-year bibliometric analysis

Background: Pancreaticoduodenectomy (PD) is the most common procedure performed for pancreatic and periampullary tumors. The aim of this study is to describe and characterize global PD research activity between 1989 and 2019. Methods: The PubMed database was used to collect publications on PD from 1989 to 2019. Research output was analyzed with respect to the total biomedical articles produced, GDP, population size of each global geographic region, publication venues, authorship, language, institution and funding. Results: 10,700 articles were included. Eastern Asia was the most productive in crude number of publications (3,787), percentage of total biomedical publications (0.1%) and per GDP (0.34). Postoperative outcomes were the most studied theme, and post-operative pancreatic fistula was the primary endpoint in 1,002 articles. There was a significant increase in PD articles from 1989 to 2019. Johns Hopkins School of Medicine produced the highest number of publications with 437, and the National Cancer Institute funded the highest number of projects with 210. Conclusion: PD is a heavily studied surgical procedure worldwide with a significantly increasing annual trend. Recognizing barriers facing PD research such as funding, socioeconomic issues and lack of proper academic infrastructure is needed to improve research output and advance surgical outcomes associated with PD.

Expert-recommended biomedical journal articles: Their retractions or corrections, and post-retraction citing

Faculty Opinions has provided recommendations of important biomedical publications by domain experts (FMs) since 2001. The purpose of this study is two-fold: (1) identify the characteristics of the expert-recommended articles that were subsequently retracted and (2) investigate what happened after retraction. We examined a set of 232 recommended, later retracted or corrected articles. These articles were classified as New Finding (43%), Interesting Hypothesis (16%), and so on. More than 71% of the articles acknowledged funding support; the National Institutes of Health, USA (NIH) was a top funder (64%). The top reasons for retractions were Errors of various types (28%); Falsification/fabrication of data, image, or results (20%); Unreliable data, image, or results (16%); and Results not reproducible (16%). Retractions took from less than 2 months to more than 15 years. Only 15% of recommendations were withdrawn either after dissents were made by other FMs or after retractions. Most of the retracted articles continue to be cited post-retraction, especially those published in Nature, Science, and Cell. Significant positive correlations were observed between post-retraction citations and pre-retraction citations, between post-retraction citations and peak citations, and between post-retraction citations and the post-retraction citing span. A significant negative correlation was also observed between the post-retraction citing span and years taken to reach peak citations. Literature recommendation systems need to update the changing status of the recommended articles in a timely manner; invite the recommending experts to update their recommendations; and provide a personalised mechanism to alert users who have accessed the recommended articles on their subsequent retractions, concerns, or corrections.

HbA1c as a marker for glycemic control and dyslipidemia in diabetes mellitus patients in a tertiary care hospital

Aim: To establish HbA1c as a marker for glycemic control and dyslipidemia in diabetics in a tertiary care hospital. Material And Method: A single centered study of eighteen-month duration conducted in the general medicine department in a tertiary care teaching hospital of north India. A systematic protocol followed for obtaining permission from IEC by submitting a detailed Performa of study which include data collection form, patient information and consent form and evaluated biomedical literature or articles. The study was approved by the research ethical committee.Result: A substantial association was also found between HbA1c and lipid profile markers. Study was conducted on 170 patient. Raised triglycerides, total cholesterol and LDL levels were seen in 69.4%, 29.4% and 19.4% cases of type 2 diabetics while low HDL was observed in 50% cases. Conclusion: A high prevalence of dyslipidemia was observed among cases of diabetes. We also observed a significant correlation between HbA1c and lipid profile parameters. Thus better glycemic control reflected by HbA1c would also reflect better lipidemic state and vice versa.

Free MEDLINE Access Worldwide.

When Donald A.B. Lindberg M.D. was sworn in as Director of the National Library of Medicine (NLM) in 1984, MEDLINE, NLM's online database of citations and abstracts to biomedical journal articles, was searched primarily by librarians trained to use its command language interface. There were fees for searching, primarily to recover the cost of using commercial value-added telecommunications networks. Thirteen years later, in 1997, MEDLINE became free to anyone with an Internet connection and a Web browser. This chapter provides an insider's view of how Dr. Lindberg's vision and leadership - combined with new technology, astute handling of policy issues, and key help from political supporters and influential advocates - enabled a tremendous expansion in access to biomedical and health information for scientists, health professionals, patients, and the public.

Prediction of Age-Adjusted Mortality From Stroke in Japanese Prefectures: Ecological Study Using Search Engine Queries.

BackgroundStroke is a major cause of death and the need for nursing care in Japan, with large regional disparities.ObjectiveThe purpose of this study was to clarify the association between stroke-related information retrieval behavior and age-adjusted mortality in each prefecture in Japan.MethodsAge-adjusted mortality from stroke and aging rates were obtained from publicly available Japanese government statistics. A total of 9476 abstracts of Japanese articles related to symptoms and signs of stroke were identified in Ichushi-Web, a Japanese web-based database of biomedical articles, and 100 highly frequent words (hereafter referred to as the Stroke 100) were extracted. Using data from 2014 to 2019, a random forest analysis was carried out using the age-adjusted mortality from stroke in 47 prefectures as the outcome variable and the standardized retrieval numbers of the Stroke 100 words in the log data of Yahoo! JAPAN Search as predictive variables. Regression analysis was performed using a generalized linear mixed model (GLMM) with the number of standardized searches for Stroke 100 words with high importance scores in the random forest model as the predictive variable. In the GLMM, the aging rate and data year were used as control variables, and the random slope of data year and random intercept were calculated by prefecture.ResultsThe mean age-adjusted mortality from stroke was 28.07 (SD 4.55) deaths per 100,000 for all prefectures in all data years. The accuracy score of the random forest analysis was 89.94%, the average error was 2.79 degrees, and the mean squared error was 13.57 degrees. The following 9 variables with high importance scores in the random forest analysis were selected as predictive variables for the regression analysis: male, age, hospitalization, enforcement, progress, stroke, abnormal, use, and change. As a result of the regression analysis with GLMM, the standardized partial regression coefficients (β) and 95% confidence intervals showed that the following internet search terms were significantly associated with age-adjusted mortality from stroke: male (β=−5.83, 95% CI −8.67 to −3.29), age (β=−5.83, 95% CI −8.67 to −3.29), hospitalization (β=−5.83, 95% CI −8.67 to −3.29), and abnormal (β=3.83, 95% CI 1.14 to 6.56).ConclusionsStroke-related search behavior was associated with age-adjusted mortality from stroke in each prefecture in Japan. Query terms that were strongly associated with age-adjusted mortality rates of stroke suggested the possibility that individual characteristics, such as sex and age, have an impact on stroke-associated mortality and that it is important to receive medical care early after stroke onset. Further studies on the criteria and timing of alerting are needed by monitoring information-seeking behavior to identify queries that are strongly associated with stroke mortality.

Predicting the impact of online news articles – is information necessary?

We exploit the Twitter platform to create a dataset of news articles derived from tweets concerning COVID-19, and use the associated tweets to define a number of popularity measures. The focus on (potentially) biomedical news articles allows the quantity of biomedically valid information (as extracted by biomedical relation extraction) to be included in the list of explored features. Aside from forming part of a systematic correlation exploration, the features – ranging from the semantic relations through readability measures to the article’s digital content – are used within a number of machine learning classifier and regression algorithms. Unsurprisingly, the results support that for more complex articles (as determined by a readability measure) more sophisticated syntactic structure may be expected. A weak correlation is found with information within an article suggesting that other factors, such as numbers of videos, have a notable impact on the popularity of a news article. The best popularity prediction performance is obtained using a random forest machine learning algorithm, and the feature describing the quantity of biomedical information is in the top 3 most important features in almost a third of the experiments performed. Additionally, this feature is found to be more valuable than the widely used named entity recognition.

An annotated corpus from biomedical articles to construct a drug-food interaction database

MotivationWhile drug-food interaction (DFI) may undermine the efficacy and safety of drugs, DFI detection has been difficult because a well-organized database for DFI did not exist. To construct a DFI database and build a natural language processing system extracting DFI from biomedical articles, we formulated the DFI extraction tasks and manually annotated texts that could have contained DFI information. In this article, we introduced a new annotated corpus for extracting DFI, the DFI corpus. ResultsThe DFI corpus contains 2270 abstracts of biomedical articles accessible through PubMed and 2498 sentences that contain DFI and/or drug-drug information (DDI), a substantial amount of information about drug/food entities, evidence-levels of abstracts and relations between named entities. BERT models pre-trained on the biomedical domain achieved a F1 score 55.0% in extracting DFI key-sentences. To the best of our knowledge, the DFI corpus is the largest public corpus for drug-food interaction. Availability and implementationOur corpus is available at https://github.com/ccadd-snu/corpus-for-DFI-extraction.

Biomedical Literature Mining and Its Components.

The published biomedical articles are the best source of knowledge to understand the importance of biomedical entities such as disease, drugs, and their role in different patient population groups. The number of biomedical literature available and being published is increasing at an exponential rate with the use of large scale experimental techniques. Manual extraction of such information is becoming extremely difficult because of the huge number of biomedical literature available. Alternatively, text mining approaches receive much interest within biomedicine by providing automatic extraction of such information in more structured format from the unstructured biomedical text. Here, a text mining protocol to extract the patient population information, to identify the disease and drug mentions in PubMed titles and abstracts, and a simple information retrieval approach to retrieve a list of relevant documents for a user query are presented. The text mining protocol presented in this chapter is useful for retrieving information on drugs for patients with a specific disease. The protocol covers three major text mining tasks, namely, information retrieval, information extraction, and knowledge discovery.

The evolution of knowledge on genes associated with human diseases

SummaryThousands of biomedical scientific articles, including those describing genes associated with human diseases, are published every week. Computational methods such as text mining and machine learning algorithms are now able to automatically detect these associations. In this study, we used a cognitive computing text-mining application to construct a knowledge network comprising 3,723 genes and 99 diseases. We then tracked the yearly changes on these networks to analyze how our knowledge has evolved in the past 30 years. Our systems approach helped to unravel the molecular bases of diseases and detect shared mechanisms between clinically distinct diseases. It also revealed that multi-purpose therapeutic drugs target genes that are commonly associated with several psychiatric, inflammatory, or infectious disorders. By navigating this knowledge tsunami, we were able to extract relevant biological information and insights about human diseases.

Linguistic changes in the transition from summaries to abstracts: The case of the Journal of Experimental Medicine

Linguistic changes in the transition from summaries to abstracts: The case of the <scp> <i>Journal of Experimental Medicine</i> </scp>

Paolo Gava, a professional engineer, who has become a Master athlete, an amateur scientist and a lifelong friend.

Paolo Gava, (Conegliano, Treviso, September 1, 1946 – Stra, Venezia, Italy, July 19, 2021) was a sustainable resources engineer, who worked in Italy, France and England, leading research programs well before the current international interest in countering global warming. Passionate about Tango, Paolo kept himself in shape for many decades by running or pedaling or roller-skating, after years of training as a semi-professional athlete, competing and winning Italian and European short distance races in the Master classes. Then, Paolo applied his engineering skills to optimize comparisons between the results of the different Classes of Master Athletes, questioning the rules used by Italian and World Master Sports Associations. Friendly discussing during an after-dinner, he shocked us claiming that, in absence of diseases and trauma (Early Aging), the aging decay is a linear process from 30 to 110 years. Under our friendly pressure he was able to publish his first biomedical article, detailing his mathematical approaches and results in a 2015 issue of Experimental Aging Research, titled: Age-associated power decline from running, jumping and throwing male master world records. To honor his other legacies during his last six years of life, we add here further examples of Paolo's scientific studies and his relationships with senior colleagues and young students of sports and aging sciences.

Instance-Based Zero-Shot learning for semi-Automatic MeSH indexing

• Propose an instance-based Zero-shot learning method for facilitating the semi-automatic indexing of biomedical articles. • Novel labels of complex ontologies are ranked through exploiting similarity functions on an arbitrary semantic space. • We design a hybrid criterion based on underlying label dependencies, semantic similarities and pattern matching rules. • Robust ranking performance of the novel labels is obtained for ideal, artificially noisy and realistic oracles. • We obtain promising multi-label ranking performance regarding the novel labels on the last version of the PubMed dataset. Zero-shot learning constitutes a variant of the broader category of weakly supervised learning algorithms. Its main asset is the possibility of identifying entities for which no training data are provided in advance. Under this extreme scenario, conventional supervised learning methods cannot operate properly, while consumption of human resources for obtaining even limited instances may be highly restricted, especially when the label space is quite complex because of its cardinality and the underlying semantic dependencies. However, removing the human factor from the learning loop under complicated tasks cannot guarantee robust performance. Thus, semi-automated solutions are widely accepted by both the research and industrial communities, favoring cooperation of human and machine, mainly for alleviating the spent effort of the former, and for acquiring safer predictions. In contrast with the majority of existing Zero-shot learning approaches, we propose a generalized instance-based method oriented towards tackling the Multi-label classification task without performing any transductive operations over the test instances. Instead, we aim to provide a label ranking of the unseen classes exploiting sentence-based semantic embeddings and label similarities, through a dedicated fine-tuned language representational model. We also use a pattern matching rule to further boost the ranking of our method. Some realistic assumptions are made in order for our approach to work correctly and provide said ranking. Results on a biomedical database with a semantically rich fine-grained label space are really promising, rendering its utilization as a helpful and computationally inexpensive tool for facilitating semi-automated indexing.

Towards Good Statistical Practice. CORSTAN Validated Questionnaire for Assessing the Correctness of Statistical Analysis in Medical Research.

In evidence-based medicine, the research methodology is determined by the risks of systematic biases and incorrect data analysis. Minimizing both risks increases the internal validity of the study. There are numerous recommendations and guidelines for data analysis and reporting, but the international community has not yet developed a questionnaire for reviewers to assess the quality of statistical analysis. To develop a tool for formalized assessment of the quality of statistical analysis presented in scientific medical publications. The questionnaire was developed based on the authors' decades of experience in statistical data analysis and reviewing the statistical aspects of biomedical articles and dissertations. The SAMPL guidelines, ICH E9, and other guidelines were taken into account when developing the questionnaire. Internal validation of the questionnaire was based on an independent assessment by two experts of 20 randomly selected articles on randomized controlled trials (RCTs) from elibrary.ru, and further statistical analysis of the agreement of experts' conclusions. The CORSTAN (CORrect STatistical ANalysis) questionnaire was developed, which consists of two parts: the first part (10 questions) is intended for evaluating studies of any designs, while the second (following eight questions) is for additional assessment of RCTs. A stratification of the risk of incorrect statistical analysis is proposed. The evaluation of the questionnaire's internal validity showed its substantial and almost perfect agreement for each question and each article both in the sum of points and risk level. The use of the questionnaire will simplify and harmonize the statistical review of publications and manuscripts in various institutions - scientific journals, dissertation boards, etc. The questionnaire can also be helpful for authors during preparing manuscripts; it will also help improve the quality of publications and research itself. We plan to improve the questionnaire as we gain experience in its application.

Role of herbs for elevating immunity against SARS-CoV-2: a concise review

COVID-19 has emerged as a dreadful pandemic caused by the virus SARS-CoV-2. Though the number of patients recovering from infection is encouraging, sturdy cure is still being researched upon and the effective vaccines are yet to be rigidly resolute. COVID-19 has so far instigated a massive loss of lives globally and even the countries with the most advanced healthcare systems have come under its grasp. Health experts suggest in such case “Prevention is better than cure” which includes an appropriate daily lifestyle and a rugged immune system that could help mankind surmount this peril. This review highlights the importance of influential herbs and their medicinal significance. Biomedical articles were explored from PubMed, Google Scholar, Web of Science for the period of 2010-2021. Guidelines from the Ministry of Ayush (India) were also considered. The Ayurveda does not mention any cure for the novel coronavirus; however, it emphasizes the means of boosting one’s immunity. Accordingly, this article highlights some of the role of prevalent Ayurvedic herbs in promoting immunity for combating the novel coronavirus. The herbs in combination with drugs could be utilized for the sake of curtailing the side effects as well as the malefic repercussions of strong synthetic drugs used for treating COVID-19 patients, which would be a paramount leap in the field of Ayurveda as well as western-style medicine. However, the beneficial effects of these traditional medicines and their clinical trials remained to be known. We reviewed the latest updates on traditional medicines proposed for promoting immunity towards COVID-19.

Smart watches: A review of evolution in bio-medical sector

Smartwatch (SW) is a wearable gadget used in everyday life. It is equivalent to a customary wristwatch and offers features similar to a smartphone. These features include access to the internet, making a call, weather updates, text or video messages, GPS navigation, health, physical fitness information, etc. The market for wearable gadgets and SWs is increasing day by day. There are many benefits and drawbacks of smartwatches in medical environments. This article aims to review the existing literature on smartwatch applications in the biomedical sector. As a result, this paper compiles all the information regarding SW technology in the biomedical sector and related journal articles, conference proceedings, web materials, market survey reports, and history in one place. The review shows that the demand rate of SWs in India is less as compared to other countries. It is increasing with the years, but the overall rate of demand is not as expected. It varies with the user’s choice. The vital author keyword and index keywords are presented. The abstract and title words are also analyzed with Vosviewer 1.6.16 software. The attributes/criteria associated with SWs are also discussed, which helps in purchasing them. The paper also discusses the flaws and problems that need to be resolved in the present situation and several potential alternatives for prospective research projects.

Post-publication expert recommendations in faculty opinions (F1000Prime): Recommended articles and citations

• Experts recommended published articles were cited more than other articles of the same biomedical journals except for cell. • Although correlations between recommendation level and citedness were observed, a linear relationship was not found between the two measurements. • The recommended articles were mostly classified as new finding, interesting hypothesis, and technical advance. • Experts’ star ratings correlate with their sentiment comments for top recommended articles. • Experts used hedged sentences to praise or criticize the articles in their comments. This exploratory study of the post-publication expert recommendations (PPER) of biomedical articles in Faculty Opinions observed whether the recommended articles were cited differently from other articles in the same journal. The collected data include 830 research articles published in Cell, Nature Genetics, Nature Medicine , and PLoS Biology in 2010 and their 205,976 citations in Web of Science (WoS) from 2010 to 2019. Of the 830 articles, 417 were recommended in Faculty Opinions. A recommendation made by a Faculty Member (FM) includes a star rating and optional classification and commentary. For Nature Genetics, Nature Medicine , and PLoS Biology , the recommended articles (dataset.FM) were cited significantly more than other articles (dataset.other). Certain correlations were found between recommendation level and citedness , but a scaled mapping showed no linear relationship between the two measurements. The majority of the articles reached a citation peak two years after publication. The most assigned classification tags are New Finding, Interesting Hypothesis, Technical Advance, and Novel Drug Target. Sentiment analysis of the 118 recommendations of the 30 top articles found that FM ratings were correlated with sentiment intensity level. The repeated measures ANOVA did not show the Matthew effect of citations. Suggestions include refining Faculty Opinions’ rating schema.

Educating the future generation of researchers: A cross-disciplinary survey of trends in analysis methods.

Methods for data analysis in the biomedical, life, and social (BLS) sciences are developing at a rapid pace. At the same time, there is increasing concern that education in quantitative methods is failing to adequately prepare students for contemporary research. These trends have led to calls for educational reform to undergraduate and graduate quantitative research method curricula. We argue that such reform should be based on data-driven insights into within- and cross-disciplinary use of analytic methods. Our survey of peer-reviewed literature analyzed approximately 1.3 million openly available research articles to monitor the cross-disciplinary mentions of analytic methods in the past decade. We applied data-driven text mining analyses to the "Methods" and "Results" sections of a large subset of this corpus to identify trends in analytic method mentions shared across disciplines, as well as those unique to each discipline. We found that the t test, analysis of variance (ANOVA), linear regression, chi-squared test, and other classical statistical methods have been and remain the most mentioned analytic methods in biomedical, life science, and social science research articles. However, mentions of these methods have declined as a percentage of the published literature between 2009 and 2020. On the other hand, multivariate statistical and machine learning approaches, such as artificial neural networks (ANNs), have seen a significant increase in the total share of scientific publications. We also found unique groupings of analytic methods associated with each BLS science discipline, such as the use of structural equation modeling (SEM) in psychology, survival models in oncology, and manifold learning in ecology. We discuss the implications of these findings for education in statistics and research methods, as well as within- and cross-disciplinary collaboration.