Biomedical Researchers Research Articles

#Preeclampsiasurvivor and symbolic interactionism in women’s maternal health

A hypertensive disorder of pregnancy and leading cause of maternal mortality worldwide, preeclampsia (PE) impacts approximately one in 25 pregnancies. Biomedical researchers continue to look for concrete causes and effective treatments for PE, but the experience of PE—the personal and socially constructed meanings surrounding the condition—remains under-researched. Using a symbolic interactionism approach, we examined Instagram posts accompanying the #preeclampsiasurvivor hashtag during Preeclampsia Awareness Month. Themes emerging from interpretive analysis of 98 posts (160 images) included the role of PE in redefining a woman’s relationship to her body, reifying a woman’s connection to her child, and illuminating the transitive aspects of a childbearing woman’s identity. Additionally, PE survivors turned to Instagram to speak to an imagined, ‘generalized sisterhood’ of women sharing a common set of experiences. Our study is unique in its examination of the lived experiences of PE survivors.

PyTCR: A comprehensive and scalable solution for TCR-Seq data analysis to facilitate reproducibility and rigor of immunogenomics research.

T cell receptor (TCR) studies have grown substantially with the advancement in the sequencing techniques of T cell receptor repertoire sequencing (TCR-Seq). The analysis of the TCR-Seq data requires computational skills to run the computational analysis of TCR repertoire tools. However biomedical researchers with limited computational backgrounds face numerous obstacles to properly and efficiently utilizing bioinformatics tools for analyzing TCR-Seq data. Here we report pyTCR, a computational notebook-based solution for comprehensive and scalable TCR-Seq data analysis. Computational notebooks, which combine code, calculations, and visualization, are able to provide users with a high level of flexibility and transparency for the analysis. Additionally, computational notebooks are demonstrated to be user-friendly and suitable for researchers with limited computational skills. Our tool has a rich set of functionalities including various TCR metrics, statistical analysis, and customizable visualizations. The application of pyTCR on large and diverse TCR-Seq datasets will enable the effective analysis of large-scale TCR-Seq data with flexibility, and eventually facilitate new discoveries.

Overview of the COVID-19 text mining tool interactive demonstration track in BioCreative VII.

The coronavirus disease 2019 (COVID-19) pandemic has compelled biomedical researchers to communicate data in real time to establish more effective medical treatments and public health policies. Nontraditional sources such as preprint publications, i.e. articles not yet validated by peer review, have become crucial hubs for the dissemination of scientific results. Natural language processing (NLP) systems have been recently developed to extract and organize COVID-19 data in reasoning systems. Given this scenario, the BioCreative COVID-19 text mining tool interactive demonstration track was created to assess the landscape of the available tools and to gauge user interest, thereby providing a two-way communication channel between NLP system developers and potential end users. The goal was to inform system designers about the performance and usability of their products and to suggest new additional features. Considering the exploratory nature of this track, the call for participation solicited teams to apply for the track, based on their system’s ability to perform COVID-19-related tasks and interest in receiving user feedback. We also recruited volunteer users to test systems. Seven teams registered systems for the track, and >30 individuals volunteered as test users; these volunteer users covered a broad range of specialties, including bench scientists, bioinformaticians and biocurators. The users, who had the option to participate anonymously, were provided with written and video documentation to familiarize themselves with the NLP tools and completed a survey to record their evaluation. Additional feedback was also provided by NLP system developers. The track was well received as shown by the overall positive feedback from the participating teams and the users. Database URL: https://biocreative.bioinformatics.udel.edu/tasks/biocreative-vii/track-4/

A storm in a teacup -- A biomimetic lung microphysiological system in conjunction with a deep-learning algorithm to monitor lung pathological and inflammatory reactions

Creating a biomimetic in vitro lung model to recapitulate the infection and inflammatory reactions has been an important but challenging task for biomedical researchers. The 2D based cell culture models – culturing of lung epithelium – have long existed but lack multiple key physiological conditions, such as the involvement of different types of immune cells and the creation of connected lung models to study viral or bacterial infection between different individuals. Pioneers in organ-on-a-chip research have developed lung alveoli-on-a-chip and connected two lung chips with direct tubing and flow. Although this model provides a powerful tool for lung alveolar disease modeling, it still lacks interactions among immune cells, such as macrophages and monocytes, and the mimic of air flow and aerosol transmission between lung-chips is missing. Here, we report the development of an improved human lung physiological system (Lung-MPS) with both alveolar and pulmonary bronchial chambers that permits the integration of multiple immune cells into the system. We observed amplified inflammatory signals through the dynamic interactions among macrophages, epithelium, endothelium, and circulating monocytes. Furthermore, an integrated microdroplet/aerosol transmission system was fabricated and employed to study the propagation of pseudovirus particles containing microdroplets in integrated Lung-MPSs. Finally, a deep-learning algorithm was developed to characterize the activation of cells in this Lung-MPS. This Lung-MPS could provide an improved and more biomimetic sensory system for the study of COVID-19 and other high-risk infectious lung diseases.

Scientific rewards for biomedical specialization are large and persistent

BackgroundWhile specialization plays an essential role in how scientific research is pursued, we understand little about its effects on a researcher’s impact and career. In particular, the extent to which one specializes within their chosen fields likely has complex relationships with productivity, career stage, and eventual impact. Here, we develop a novel and fine-grained approach for measuring a researcher’s level of specialization at each point in their career and apply it to the publication data of almost 30,000 established biomedical researchers to measure the effect that specialization has on the impact of a researcher’s publications.ResultsUsing a within-researcher, panel-based econometric framework, we arrive at several important results. First, there are significant scientific rewards for specialization—25% more citations per standard deviation increase in specialization. Second, these benefits are much higher early in a researcher’s career—as large as 75% per standard deviation increase in specialization. Third, rewards are higher for researchers who publish few papers relative to their peers. Finally, we find that, all else equal, researchers who make large changes in their research direction see generally increased impact.ConclusionsThe extent to which one specializes, particularly at the early stages of a biomedical research career, appears to play a significant role in determining the citation-based impact of their publications. When this measure of impact is, implicitly or explicitly, an input into decision-making processes within the scientific system (for example, for job opportunities, promotions, or invited talks), these findings lead to some important implications for the system-level organization of scientific research and the incentives that exist therein. We propose several mechanisms within modern scientific systems that likely lead to the scientific rewards we observe and discuss them within the broader context of reward structures in biomedicine and science more generally.

Daphnetin: A bioactive natural coumarin with diverse therapeutic potentials.

Daphnetin (DAP), a coumarin derivative extracted from Daphne species, is biologically active phytochemical with copious bioactivities including anti-inflammatory, anti-oxidant, neuroprotective, analgesic, anti-pyretic, anti-malarial, anti-bacterial, anti-arthritic, neuroprotective, hepatoprotective, nephroprotective, and anti-cancer activities. A wide range of studies have been conducted exploring the significance and therapeutic potential of DAP. This study reviewed various databases such as NCBI, PubMed, Web of Science, Scopus and Google Scholar for published research articles regarding the sources, synthesis, and various bioactivities of DAP using different key words, including but not limited to “pharmacological activities,” “sources,” “neuroprotective effect,” “synthesis,” “cancer,” “anti-inflammatory effect” of “daphnetin.” Furthermore, this review encompasses both in-vivo and in-vitro studies on DAP for treating various diseases. A comprehensive review of the literature revealed that the DAP had a promising pharmacological and safety profile, and could be employed as a pharmaceutical moiety to treat a variety of illnesses including microbial infections, cancer, arthritis, hepatic damage, inflammation and neurological anomalies. The current review intends to provide an in-depth focus on all pharmacological activities and therapeutic approaches for the pharmaceutical and biomedical researchers.

Creating connections: developing an online space for cross-regional mentorship and network building in the dementia research field

Background Effective development and retention of talented early-career researchers (ECRs) is essential to the continued success of biomedical science research fields. To this end, formal mentorship programmes (where researchers are paired with one or more mentors beyond their direct manager) have proven to be successful in providing support and expanding career development opportunities. However, many programmes are limited to pools of mentors and mentees within one institute or geographical area, highlighting that cross-regional connections may be a missed opportunity in many mentorship schemes. Methods Here, we aimed to address this limitation through our pilot cross-regional mentorship scheme, creating reciprocal mentor-mentee pairings between two pre-established networks of Alzheimer’s Research UK (ARUK) Network-associated researchers. We carefully created 21 mentor-mentee pairings between the Scotland and University College London (UCL) networks in 2021, with surveys conducted to assess mentor/mentee satisfaction with the programme. Results Participants reported very high satisfaction with the nature of the pairings and the mentors’ contribution to the career development of mentees; a majority also reported that the mentorship scheme increased their connections outside of their home network. Our assessment of this pilot programme is that it supports the utility of cross-regional mentorship schemes for ECR development. At the same time, we highlight the limitations of our programme and recommend areas for improvement in future programmes, including greater consideration of support for minoritized groups and the need for additional training for mentors. Conclusions In conclusion, our pilot scheme generated successful and novel mentor-mentee pairings across pre-existing networks; both of which reported high satisfaction with pairings, ECR career and personal development, and the formation of new cross-network connections. This pilot may serve as a model for other networks of biomedical researchers, where existing networks within medical research charities can act as a scaffold to build new cross-regional career development opportunities for researchers.

Li-Doped Bioactive Ceramics: Promising Biomaterials for Tissue Engineering and Regenerative Medicine.

Lithium (Li) is a metal with critical therapeutic properties ranging from the treatment of bipolar depression to antibacterial, anticancer, antiviral and pro-regenerative effects. This element can be incorporated into the structure of various biomaterials through the inclusion of Li chloride/carbonate into polymeric matrices or being doped in bioceramics. The biocompatibility and multifunctionality of Li-doped bioceramics present many opportunities for biomedical researchers and clinicians. Li-doped bioceramics (capable of immunomodulation) have been used extensively for bone and tooth regeneration, and they have great potential for cartilage/nerve regeneration, osteochondral repair, and wound healing. The synergistic effect of Li in combination with other anticancer drugs as well as the anticancer properties of Li underline the rationale that bioceramics doped with Li may be impactful in cancer treatments. The role of Li in autophagy may explain its impact in regenerative, antiviral, and anticancer research. The combination of Li-doped bioceramics with polymers can provide new biomaterials with suitable flexibility, especially as bio-ink used in 3D printing for clinical applications of tissue engineering. Such Li-doped biomaterials have significant clinical potential in the foreseeable future.

Deep learning joint models for extracting entities and relations in biomedical: a survey and comparison.

The rapid development of biomedicine has produced a large number of biomedical written materials. These unstructured text data create serious challenges for biomedical researchers to find information. Biomedical named entity recognition (BioNER) and biomedical relation extraction (BioRE) are the two most fundamental tasks of biomedical text mining. Accurately and efficiently identifying entities and extracting relations have become very important. Methods that perform two tasks separately are called pipeline models, and they have shortcomings such as insufficient interaction, low extraction quality and easy redundancy. To overcome the above shortcomings, many deep learning-based joint name entity recognition and relation extraction models have been proposed, and they have achieved advanced performance. This paper comprehensively summarize deep learning models for joint name entity recognition and relation extraction for biomedicine. The joint BioNER and BioRE models are discussed in the light of the challenges existing in the BioNER and BioRE tasks. Five joint BioNER and BioRE models and one pipeline model are selected for comparative experiments on four biomedical public datasets, and the experimental results are analyzed. Finally, we discuss the opportunities for future development of deep learning-based joint BioNER and BioRE models.

Maneuvering through a Changing Funding Terrain: Biomedical University Scientists in Positive and Negative Feedback Loops

The mechanisms of research funding are in flux across the Organisation for Economic Co-operation and Development (OECD) countries. In Denmark the research system has experienced an increase in the concentration of research funding on individual researchers and topic areas. This article documents such concentration patterns in biomedical research and applies a case study methodology to explore some of its consequences. The study contrasts the markedly different funding environments of two sets of biomedical researchers at the same public university. One set of scientists has benefited significantly from working in specialized research centers sponsored by private funds. The other, located at a conventional university department has been adversely affected by the changing funding logic of the Danish research system. We compare the two sets of researchers with regard to: 1) how they perceive their funding conditions to have changed in recent times, 2) what coping strategies they rely on, and 3) how they perceive this to impact their “problem choice.” Our analysis shows how scientists, as a consequence of rising competition over funding and growing resource concentration on fewer research specialties (of particular interest to private funders), perceive considerable pressure to adapt their research activities. The perceived impact however differs substantially across informants.

Application of medical imaging methods and artificial intelligence in tissue engineering and organ-on-a-chip.

Organ-on-a-chip (OOC) is a new type of biochip technology. Various types of OOC systems have been developed rapidly in the past decade and found important applications in drug screening and precision medicine. However, due to the complexity in the structure of both the chip-body itself and the engineered-tissue inside, the imaging and analysis of OOC have still been a big challenge for biomedical researchers. Considering that medical imaging is moving towards higher spatial and temporal resolution and has more applications in tissue engineering, this paper aims to review medical imaging methods, including CT, micro-CT, MRI, small animal MRI, and OCT, and introduces the application of 3D printing in tissue engineering and OOC in which medical imaging plays an important role. The achievements of medical imaging assisted tissue engineering are reviewed, and the potential applications of medical imaging in organoids and OOC are discussed. Moreover, artificial intelligence - especially deep learning - has demonstrated its excellence in the analysis of medical imaging; we will also present the application of artificial intelligence in the image analysis of 3D tissues, especially for organoids developed in novel OOC systems.

Using Transfer Learning to Train a Binary Classifier for Lorrca Ektacytometery Microscopic Images of Sickle Cells and Healthy Red Blood Cells

Multiple blood images of stressed and sheared cells, taken by a Lorrca Ektacytometery microscope, needed a classification for biomedical researchers to assess several treatment options for blood-related diseases. The study proposes the design of a model capable of classifying these images, with high accuracy, into healthy Red Blood Cells (RBCs) or Sickle Cells (SCs) images. The performances of five Deep Learning (DL) models with two different optimizers, namely Adam and Stochastic Gradient Descent (SGD), were compared. The first three models consisted of 1, 2 and 3 blocks of CNN, respectively, and the last two models used a transfer learning approach to extract features. The dataset was first augmented, scaled, and then trained to develop models. The performance of the models was evaluated by testing on new images and was illustrated by confusion matrices, performance metrics (accuracy, recall, precision and f1 score), a receiver operating characteristic (ROC) curve and the area under the curve (AUC) value. The first, second and third models with the Adam optimizer could not achieve training, validation or testing accuracy above 50%. However, the second and third models with SGD optimizers showed good loss and accuracy scores during training and validation, but the testing accuracy did not exceed 51%. The fourth and fifth models used VGG16 and Resnet50 pre-trained models for feature extraction, respectively. VGG16 performed better than Resnet50, scoring 98% accuracy and an AUC of 0.98 with both optimizers. The study suggests that transfer learning with the VGG16 model helped to extract features from images for the classification of healthy RBCs and SCs, thus making a significant difference in performance comparing the first, second, third and fifth models.

Understanding the Usability of a Literature-Based Discovery System Among Clinical Researchers in Sarawak, Malaysia

The rapid increase in scientific publications makes it difficult for researchers to keep up with the latest literature and to explore new research directions. The literature-based discovery (LBD) systems aim to resolve this issue by bridging literatures from disparate fields to assist researchers in knowledge discovery and the formulation and testing of research hypotheses. Previous studies have focused mainly on evaluating the efficacy of LBD systems by replicating historical LBD events. The usability of LBD systems has been under-researched, which partly explains the low adoption of the systems. This paper presents a survey study that evaluates the usability of a LBD system for knowledge discovery and hypothesis refinement, and also investigates factors affecting its adoption among biomedical researchers in Sarawak, Malaysia. The findings suggest that the adoption of the LBD system is related to their perceived usefulness and perceived difficulty in interacting with the user interface features of the system.

Engaging People With Lived Experience of Dementia in Research: Perspectives From a Multi-disciplinary Research Network.

BackgroundPatient and public involvement/engagement in research on dementia is not new, but it is becoming increasingly common. The objective of this study was to describe researchers’ knowledge, attitudes, and activities related to engaging people with lived experience of dementia in research, and how these differ by research theme.MethodsData were from an online, anonymous survey of researchers within the Canadian Consortium on Neurodegeneration in Aging.ResultsOf the 84 researchers who completed the survey (response rate: 27%), 89% agreed they understood the meaning of engaging people with lived experience in research, although this was lower among biomedical researchers. Almost all (93%) agreed that people with lived experience could contribute meaningfully to research, and nearly two-thirds were already incorporating engagement in their research. Some engagement practices reported differed by research theme. Irrespective of the type of research they conduct, researchers were most often motivated by improving the relevance and quality of their research.ConclusionsThese findings support an optimistic outlook for engaging people with lived experience of dementia in research, but identify differences across research themes. Understanding approaches to incorporate, evaluate, and adapt engagement activities across research disciplines are needed to enable researchers, as well as others involved in research, to develop and target strategies for patient and public involvement/engagement in research on dementia.

Progress Update on the Epidemiology of COVID-19 Variants and the Assessment Status of Developed Vaccines

Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) has spread rapidly and diffused to more than 180 countries at varying severities. This pandemic has accounted for increased mortality and morbidity in developed as well as developing nations. The WHO has announced that there is a persistent need for the evaluation of the COVID-19 vaccine effectiveness (VE) against major outcomes, which include severe diseases, symptomatic COVID-19, and mortalities related to COVID-19. Therefore, mass vaccination programs using vaccines of high effectiveness are among the strategies that have been used by governments worldwide to impede the COVID-19 pandemic transmission. In this regard, massive efforts were made by governments, scientists, biomedical researchers, and healthcare professionals leading to the successful development of various vaccines to bring this pandemic under control. This editorial aims to shed light on the epidemiological status of COVID-19 variants, namely, Delta, Omicron, and Deltacron variants as well as discuss the effectiveness of the currently available COVID-19 vaccines.

A Mixed-Methods Analysis of Gender and Career Status Differences in the Impact of the COVID-19 Pandemic on Underrepresented Postdoctoral Fellows and Early-Career Faculty.

The lack of racial and ethnic diversity in the biomedical workforce is pronounced and those from underrepresented backgrounds encounter more challenges than their majority counterparts. The extent of the impact of the COVID-19 pandemic on early-career investigators from underrepresented backgrounds is not yet fully understood. To examine the impact of the pandemic on underrepresented early-career biomedical researchers, this study evaluated differences in productivity, research, and psychological well-being by gender and career status. This was a cross-sectional analysis of preintervention data, collected in September-October 2020, from 220 participants enrolled in the Building Up a Diverse Biomedical Research Workforce study. Participants were from 25 academic medical centers in the United States and were underrepresented early-career researchers. The primary outcomes were agreement on a 5-point Likert scale with pandemic impact statements (e.g., "The COVID-19 pandemic has impacted my ability to conduct research"). Thematic analysis was conducted on responses to 2 open-ended questions assessing the pandemic's impact. Most participants were female (79.9%), of non-Hispanic/Latinx/Spanish origin Black/African American (33.2%) or Hispanic/Latinx/Spanish origin (34.1%), and early-career faculty (53.4%). Over half of participants agreed or strongly agreed that the COVID-19 pandemic impacted their ability to work (55.7%) and conduct research (70.7%). Themes from qualitative analysis suggested lower research productivity, concerns about the academic job market and funding, and psychological distress due to the pandemic. Women were more likely to attribute lost productivity and psychological distress to homeschooling and childcare responsibilities. Postdoctoral fellows were concerned about more competition for fewer academic positions. In this study of early-career underrepresented biomedical researchers, the impact of the COVID-19 pandemic was widely felt by participants, varying by gender and career status. For those postdoctoral fellows and early-career faculty who are underrepresented, it is critical for institutions to offer flexibility in their positions.

Approximate Bayesian Techniques for Statistical Model Selection and Quantifying Model Uncertainty-Application to a Gait Study.

Frequently, biomedical researchers need to choose between multiple candidate statistical models. Several techniques exist to facilitate statistical model selection including adjusted R2, hypothesis testing and p-values, and information criteria among others. One particularly useful approach that has been slow to permeate the biomedical literature is the notion of posterior model probabilities. A major advantage of posterior model probabilities is that they quantify uncertainty in model selection by providing a direct, probabilistic comparison among competing models as to which is the "true" model that generated the observed data. Additionally, posterior model probabilities can be used to compute posterior inclusion probabilities which quantify the probability that individual predictors in a model are associated with the outcome in the context of all models considered given the observed data. Posterior model probabilities are typically derived from Bayesian statistical approaches which require specialized training to implement, but in this paper we describe an easy-to-compute version of posterior model probabilities and inclusion probabilities that rely on the readily-available Bayesian information criterion. We illustrate the utility of posterior model probabilities and inclusion probabilities by re-analyzing data from a published gait study investigating factors that predict required coefficient of friction between the shoe sole and floor while walking.

AIFM1 beyond cell death: An overview of this OXPHOS-inducing factor in mitochondrial diseases.

SummaryApoptosis-inducing factor (AIF) is a mitochondrial intermembrane space flavoprotein with diverse functions in cellular physiology. In this regard, a large number of studies have elucidated AIF's participation to chromatin condensation during cell death in development, cancer, cardiovascular and brain disorders. However, the discovery of rare AIFM1 mutations in patients has shifted the interest of biomedical researchers towards AIF's contribution to pathogenic mechanisms underlying inherited AIFM1-linked metabolic diseases. The functional characterization of AIF binding partners has rapidly advanced our understanding of AIF biology within the mitochondria and beyond its widely reported role in cell death. At the present time, it is reasonable to assume that AIF contributes to cell survival by promoting biogenesis and maintenance of the mitochondrial oxidative phosphorylation (OXPHOS) system. With this review, we aim to outline the current knowledge around the vital role of AIF by primarily focusing on currently reported human diseases that have been linked to AIFM1 deficiency.

In vitro and in silico approaches to engineering three-dimensional biological tissues and organoids

<i>In vitro</i> and <i>in silico</i> approaches to engineering three-dimensional biological tissues and organoids

A benchmark study of deep learning-based multi-omics data fusion methods for cancer

BackgroundA fused method using a combination of multi-omics data enables a comprehensive study of complex biological processes and highlights the interrelationship of relevant biomolecules and their functions. Driven by high-throughput sequencing technologies, several promising deep learning methods have been proposed for fusing multi-omics data generated from a large number of samples.ResultsIn this study, 16 representative deep learning methods are comprehensively evaluated on simulated, single-cell, and cancer multi-omics datasets. For each of the datasets, two tasks are designed: classification and clustering. The classification performance is evaluated by using three benchmarking metrics including accuracy, F1 macro, and F1 weighted. Meanwhile, the clustering performance is evaluated by using four benchmarking metrics including the Jaccard index (JI), C-index, silhouette score, and Davies Bouldin score. For the cancer multi-omics datasets, the methods’ strength in capturing the association of multi-omics dimensionality reduction results with survival and clinical annotations is further evaluated. The benchmarking results indicate that moGAT achieves the best classification performance. Meanwhile, efmmdVAE, efVAE, and lfmmdVAE show the most promising performance across all complementary contexts in clustering tasks.ConclusionsOur benchmarking results not only provide a reference for biomedical researchers to choose appropriate deep learning-based multi-omics data fusion methods, but also suggest the future directions for the development of more effective multi-omics data fusion methods. The deep learning frameworks are available at https://github.com/zhenglinyi/DL-mo.