Category Archives: Literature Based Discovery

Probing Expert Opinions on the Future of Kidney Replacement Therapies

Patients with kidney failure can only survive with some form of kidney replacement (transplant or dialysis). Unfortunately, innovations in kidney replacement therapy lag behind many other medical fields. This study compiles expert opinions on candidate technologies for future kidney replacement therapies. A worldwide web‐based survey was conducted with 1,566 responding experts, identified via a text-mining process of scientific publications on kidney (renal) replacement therapy, indexed in the Web of Science Core Collection (period 2014‐2019). Candidate innovative approaches were categorized in line with the Kidney Health Initiative roadmap for innovative kidney replacement therapies. Most respondents expected a revolution in kidney replacement therapies: 68.59% before 2040 and 24.85% after 2040, while 6.56% expected none. Approaches anticipated as most likely were implantable artificial kidneys (38.6%) and wearable artificial kidneys (32.4%). A majority of experts expect that kidney replacement therapies can be significantly improved by innovative technologies.

Author(s): Bernardo Pereira Cabral, Joseph V. Bonventre , Fokko Wieringa , Fabio Batista Mota
Organization(s): Oswaldo Cruz Foundation, Harvard Medical School, Maastricht University
Source: Artificial Organs                                                                                              Year: 2020

What are the most promising treatments and vaccine candidates for COVID-19? A global survey of experts involved in virus research (FULL-TEXT)

The Coronavirus Disease 2019 (COVID-19) pandemic presents a great public health challenge around the world, especially given the urgency to identify effective drugs and develop a vaccine in a short period of time. Globally, there are several drug and vaccine candidates currently in clinical trials, yet it is not yet clear which will prove successful. This study addresses this gap by mapping the treatments and vaccine candidates currently in clinical trials and assessing the opinions on these candidates of virus-related researchers from all over the world. Clinical trial data were obtained from and the survey’s respondents were authors of recent scientific publications related to viruses, SARS virus, coronavirus, and COVID-19 indexed in the Web of Science Core Collection. The results show that remdesivir, immunoglobulin from cured patients and plasma are considered the most promising treatments, and ChAdOx1 and mRNA-1273 the most promising vaccine candidates. They also indicate that a vaccine could be available within eighteen months.

For a copy of a pre-print go to

Author(s): Bernardo Pereira Cabral, Luiza Braga, Fabio Batista Mota
Organization(s): Oswaldo Cruz Foundation, Federal University of Bahia
Source: pre-print Journal of Medical Internet Research
Year: 2020

Nano-enabled Drug Delivery in Cancer Therapy: Literature Analysis Using the MeSH System

Background: Biomedical literature provides abundant knowledge on R&D development and emerging themes and techniques to researchers and to enhance clinical treatment. Tracing research topic activity and researcher connections, and understanding evolving research landscapes, support identification of research domain potential and informs R&D portfolio management.

Methods: We offer a systematic approach to summarize biomedical research information compiled from the MEDLINE database. Selected MeSH qualifiers are applied as properties for clustering terms. Linkages among clusters are measured based on an object-attributevalue, relative research concentration. By arraying selected technical dimensions against each other, we enable identification and evaluation of latent connections.

Results: 10354 MEDLINE records from 2000 to 2013 on nano-enabled drug delivery (NEDD) for cancer treatment are retrieved and analyzed. Seven topical clusters are generated with relatively clear boundaries. Elements with high relative research concentration but low number of records show emerging trends. And the concentrations’ decline indicates the universalization of drugs and nano components in cancer treatment.

Conclusion: This systematic topical analysis process helps explore particular technological trends and potentials in biomedical areas. It combines an algorithm to reveal latent connections hidden in literature text content with expert judgement. From the standpoint of technology assessment, it provides researchers and administrators the ability to capture biomedical research dynamics.

Author(s): Jing Ma, Alan L. Porter, Tejraj M. Aminabhavi
Organization(s): Beijing Institute of Technology, Georgia Institute of Technology
Source: Pharmaceutical Nanotechnology
Year: 2016

Nano-enabled drug delivery systems for brain cancer and Alzheimer’s disease: research patterns and opportunities

“Tech mining” applies bibliometric and text analytic methods to scientific literature of a target field. In this study, we compare the evolution of nano-enabled drug delivery (NEDD) systems for two different applications – viz., brain cancer (BC) and Alzheimer’s disease (AD) – using this approach. In this process, we derive research intelligence from papers indexed in MEDLINE. Review by domain specialists helps understand the macro-level disease problems and pathologies to identify commonalities and differences between BC and AD. Results provide a fresh perspective on the developmental pathways for NEDD approaches that have been used in the treatment of BC and AD. Results also point toward finding future solutions to drug delivery issues that are critical to medical practitioners and pharmaceutical scientists addressing the brain.

FROM THE CLINICAL EDITOR: Drug delivery to brain cells has been very challenging due to the presence of the blood-brain barrier (BBB). Suitable and effective nano-enabled drug delivery (NEDD) system is urgently needed. In this study, the authors utilized “tech-mining” tools to describe and compare various choices of delivery system available for the diagnosis, as well as treatment, of brain cancer and Alzheimer’s disease.

Author(s): Jing Ma, Alan Porter, TM Aminabhavi, Donghua Zhu
Organization(s): Beijing Institute of Technology, Georgi
Source: Nanomedicine
Year: 2015

Literature-related discovery and innovation: Chronic kidney disease

Different approaches for preventing, reducing, halting, and reversing chronic kidney disease (CKD) have been described in the medical literature. However, all related factors have not been identified together. To overcome these limitations, we used an LRDI-based methodology (potentially applicable to any disease) based on the following holistic principle: a necessary, but not sufficient, condition for restorative treatment effectiveness is that potential causes must be removed initially or in parallel with treatment. Literature-Related Discovery and Innovation (LRDI) is a text mining approach that integrates discovery generation from disparate literatures with the wealth of knowledge contained in prior scientific publications. To support the central requirement of the holistic principle above, LRDI seeks to identify foundational causes that, if eliminated, could potentially reverse chronic and infectious diseases.

The LRDI findings would be implemented in three steps by: 1) identifying major symptoms of CKD, 2) identifying and removing foundational causes that drive the symptoms identified, then 3) identifying treatment(s) to reduce, halt, or reverse the progression of CKD and eliminate the remaining symptoms and damage caused by CKD (if not irreversible). We presumed that identifying and eliminating all of the foundational causes as comprehensively, thoroughly, and rapidly as possible may potentially achieve the desired medical goals and obviate the need for any pharmacologic treatments in selected patients. If treatments are required, eliminating the wide spectrum of potential causes identified in this study should enhance their effectiveness.

There were two major types of advances made in this study: information technology and medical. The major information technology advance was development of a query to identify the full-spectrum of foundational causes for CKD, and substantially upgrading a query used previously to identify the full spectrum of treatments. The major medical advance was identification of over 900 potential CKD direct and indirect foundational causes that encompass discovery and innovation, along with over 900 CKD direct and indirect treatments that encompass discovery and innovation. The foundational causes were comprised of environmental and occupational exposures, biotoxins, iatrogenic, and lifestyle factors. The myriad treatments ranged from foods, food extracts, drugs, biological, biophysical, and lifestyle changes. A limitation of the LRDI method is that the magnitude of these associations cannot be determined. Nonetheless, after prioritizing potentially relevant factors, eliminating as many upstream or foundational causes as possible may provide benefits to patients with CKD beyond the current emphasis on downstream pharmacologic approaches.

Author(s): Ronald Neil Kostoff and Uptal Patel
Organization(s): Georgia Institute of Technology and  Duke Clinical Research Institute
Source: Technological Forecasting and Social Change
Year: 2015

Combined biological and health effects of electromagnetic fields and other agents in the published literature

Electromagnetic field (EMF) radiation exerts both stand-alone and combined effects on biological systems. The present study examines the scope of the combined effects; i.e., identify effects on biological systems from combined exposure to electromagnetic fields/radiation and at least one other agent. Only articles in which the presence of EMF had some effect (beneficial or adverse) on the biological system were selected. Continue reading Combined biological and health effects of electromagnetic fields and other agents in the published literature

A knowledge discovery method based on analysis of multiple co-occurrence relationships in collections of journal papers

This paper explores a method of knowledge discovery by visualizing and analyzing co-occurrence relations among three or more entities in collections of journal articles. Continue reading A knowledge discovery method based on analysis of multiple co-occurrence relationships in collections of journal papers

Literature-related discovery (LRD): Potential treatments for Multiple Sclerosis

Literature-related discovery (LRD) is the linking of two or more literature concepts that have heretofore not been linked (i.e., disjoint), in order to produce novel, interesting, plausible, and intelligible knowledge (i.e., potential discovery). The open discovery systems (ODS) component of LRD starts with a problem to be solved, and generates solutions to that problem through potential discovery. Continue reading Literature-related discovery (LRD): Potential treatments for Multiple Sclerosis