Search

Background: The European & Developing Countries Clinical Trials Partnership (EDCTP), like many other research funders, requires its grantees to make papers available via open access (OA). This article investigates the effect of publishing in OA journals and international collaboration within and between European and sub-Saharan African countries on citation impact and likelihood of falling into the top 1% and top 10% most cited papers in poverty-related disease (PRD) research., Methods: Disease-specific research publications were identified in the Web of Science™ and MEDLINE using Medical Subject Heading (MeSH) terms. Data on the open accessibility of scientific literature were derived from 1science oaFindr. Publication data, including relative citation counts, were extracted for 2003-2015. Regression models were applied to quantify the relationship between relative citations and presence in the 1% and top 10% most cited papers versus OA and international collaboration., Results: The results show that since 2003 papers on PRDs have become increasingly available in OA. Among all PRD areas, malaria research is most frequently published in OA and in international collaboration. The adjusted regression analyses show that holding other factors constant, publishing research in OA and in international collaboration has a significant and meaningful citation advantage over non-OA or non-international collaborative research. Publishing papers as part of a European-wide or European- sub-Saharan African collaboration increases research impact. In contrast, such collaboration advantage is not observed for research output involving sub-Saharan Africa only which seems to decrease research impact., Conclusions: Our results indicate that there is a real, measurable citation advantage for publishing PRD research in OA and international collaboration. However, the international collaboration advantage seems to be region-specific with increased research impact for European-wide and European-sub-Saharan African collaborations but a decrease in research impact of collaborations confined to sub-Saharan African research institutions. Further research is required to further verify this finding and to understand the underlying factors related to this observed decrease in research impact. To target future research capacity building activities in sub-Saharan Africa it is important to assess whether the observed decreased impact reflects the scientific competencies and geographic distribution of individual researchers or institutional-, national- or funder-specific research requirements., Competing Interests: JGB and MMM have no competing interests to declare. CT, GR, MD and DBS have the following interests to declare: CT, GR, MD and DBS are employed by Science-Metrix. Financial support was provided by Science-Metrix for the manuscript preparation (over and above the contracted study) in the form of salaries for CT, GR, MD and DBS. There are no patents, products in development or marketed products to declare. This does not alter our adherence to all the PLOS ONE policies on sharing data and materials.

Rather than confining the categories health and sickness to a biomedical conception of the biological organism, there is growing recognition of epistemological and ontological multiplicity in the realm of diagnosis and, indeed, in the very realm of disease itself. In short, the empirical manifestations of health and illness as well as the processes thought to cause them are now understood to assume a much wider variety of both biological and other forms. This essay considers the underlying epistemological and ontological opportunities and challenges of taking what we are calling this diffusion of diagnosis seriously. By diffusion we mean the movement from a concentrated understanding of diagnostic authority as confined to scientific biomedicine to a less concentrated appreciation of the diverse approaches to diagnosis throughout the world. We consider the extent to which, and the manner in which, we as sociologists of diagnosis might not only critique these various processes but perhaps also take them seriously in an ethnographic sense as locally produced, evaluated and legitimated forms of health care., (© 2023 The Authors. Sociology of Health & Illness published by John Wiley & Sons Ltd on behalf of Foundation for the Sociology of Health & Illness.)

Contemporary clinical practice increasingly functions within a disease management paradigm aimed at finding and implementing therapeutic interventions that demonstrate efficacy in clinical trials. Disease prevention, elucidation of illness aetiology and proactive health promotion have taken a back seat. Current clinical care often includes a 'fast-food' type of medical encounter, which frequently neglects disease causality. The medical community is presently challenged by unique administrative and professional adversities as well as undue commercial influence; these factors contribute to a lethargic response to escalating rates of chronic illness and to mainstream medicine's relative inattention to emerging research about disease aetiology. Individual medical practitioners and the medical establishment must strategically seek to advance patient health and maintain professional relevancy by a renewed emphasis on the following principles: patient-based clinical care, aetiology-centred medicine, and proactive health promotion.

Paper and pencil screening tests for depression have never become widely used in the medically ill, despite consensus that a sizeable pool of depression exists in this population. The performance of several self-rating scales in these patients is reviewed, demonstrating that satisfactory screening of depression as defined by standard case criteria can be achieved. It is proposed that it is primarily the absence of clear guidelines for treatment to be used in conjunction with these scales which will make clinicians sceptical of their value. Treatment validation of case criteria is required to demonstrate that screening for depression in medical patients is worthwhile.

Aquaporins (AQPs), as transmembrane proteins, were primarily identified as water channels with the ability of regulating the transmission of water, glycerol, urea, and other small-sized molecules. The classic view of AQPs involvement in therapeutic plan restricted them and their regulators into managing only a narrow spectrum of the diseases such as diabetes insipidus and the syndrome of inappropriate ADH secretion. However, further investigations performed, especially in the third millennium, has found that their cooperation in water transmission control can be manipulated to handle other burden-imposing diseases such as cirrhosis, heart failure, Meniere's disease, cancer, bullous pemphigoid, eczema, and Sjögren's syndrome., (© 2021 Wiley Periodicals LLC.)

The genomes of virtually all organisms contain repetitive sequences that are generated by the activity of transposable elements (transposons). Transposons are mobile genetic elements that can move from one genomic location to another; in this process, they amplify and increase their presence in genomes, sometimes to very high copy numbers. In this Review we discuss new evidence and ideas that the activity of retrotransposons, a major subgroup of transposons overall, influences and even promotes the process of ageing and age-related diseases in complex metazoan organisms, including humans. Retrotransposons have been coevolving with their host genomes since the dawn of life. This relationship has been largely competitive, and transposons have earned epithets such as 'junk DNA' and 'molecular parasites'. Much of our knowledge of the evolution of retrotransposons reflects their activity in the germline and is evident from genome sequence data. Recent research has provided a wealth of information on the activity of retrotransposons in somatic tissues during an individual lifespan, the molecular mechanisms that underlie this activity, and the manner in which these processes intersect with our own physiology, health and well-being., (© 2021. The Author(s), under exclusive licence to Springer Nature Limited.)

Antisense technology is now beginning to deliver on its promise to treat diseases by targeting RNA. Nine single-stranded antisense oligonucleotide (ASO) drugs representing four chemical classes, two mechanisms of action and four routes of administration have been approved for commercial use, including the first RNA-targeted drug to be a major commercial success, nusinersen. Although all the approved drugs are for use in patients with rare diseases, many of the ASOs in late- and middle-stage clinical development are intended to treat patients with very common diseases. ASOs in development are showing substantial improvements in potency and performance based on advances in medicinal chemistry, understanding of molecular mechanisms and targeted delivery. Moreover, the ASOs in development include additional mechanisms of action and routes of administration such as aerosol and oral formulations. Here, we describe the key technological advances that have enabled this progress and discuss recent clinical trials that illustrate the impact of these advances on the performance of ASOs in a wide range of therapeutic applications. We also consider strategic issues such as target selection and provide perspectives on the future of the field.

Nearly all genetic variants that influence disease risk have human-specific origins; however, the systems they influence have ancient roots that often trace back to evolutionary events long before the origin of humans. Here, we review how advances in our understanding of the genetic architectures of diseases, recent human evolution and deep evolutionary history can help explain how and why humans in modern environments become ill. Human populations exhibit differences in the prevalence of many common and rare genetic diseases. These differences are largely the result of the diverse environmental, cultural, demographic and genetic histories of modern human populations. Synthesizing our growing knowledge of evolutionary history with genetic medicine, while accounting for environmental and social factors, will help to achieve the promise of personalized genomics and realize the potential hidden in an individual's DNA sequence to guide clinical decisions. In short, precision medicine is fundamentally evolutionary medicine, and integration of evolutionary perspectives into the clinic will support the realization of its full potential.

Discovery and development of biopeptides are time-consuming, laborious, and dependent on various factors. Data-driven computational methods, especially machine learning (ML) approach, can rapidly and efficiently predict the utility of therapeutic peptides. ML methods offer an array of tools that can accelerate and enhance decision making and discovery for well-defined queries with ample and sophisticated data quality. Various ML approaches, such as support vector machines, random forest, extremely randomized tree, and more recently deep learning methods, are useful in peptide-based drug discovery. These approaches leverage the peptide data sets, created via high-throughput sequencing and computational methods, and enable the prediction of functional peptides with increased levels of accuracy. The use of ML approaches in the development of peptide-based therapeutics is relatively recent; however, these techniques are already revolutionizing protein research by unraveling their novel therapeutic peptide functions. In this review, we discuss several ML-based state-of-the-art peptide-prediction tools and compare these methods in terms of their algorithms, feature encodings, prediction scores, evaluation methodologies, and software utilities. We also assessed the prediction performance of these methods using well-constructed independent data sets. In addition, we discuss the common pitfalls and challenges of using ML approaches for peptide therapeutics. Overall, we show that using ML models in peptide research can streamline the development of targeted peptide therapies., (© 2020 Wiley Periodicals, Inc.)

Most human genes have multiple sites at which RNA 3' end cleavage and polyadenylation can occur, enabling the expression of distinct transcript isoforms under different conditions. Novel methods to sequence RNA 3' ends have generated comprehensive catalogues of polyadenylation (poly(A)) sites; their analysis using innovative computational methods has revealed how poly(A) site choice is regulated by core RNA 3' end processing factors, such as cleavage factor I and cleavage and polyadenylation specificity factor, as well as by other RNA-binding proteins, particularly splicing factors. Here, we review the experimental and computational methods that have enabled the global mapping of mRNA and of long non-coding RNA 3' ends, quantification of the resulting isoforms and the discovery of regulators of alternative cleavage and polyadenylation (APA). We highlight the different types of APA-derived isoforms and their functional differences, and illustrate how APA contributes to human diseases, including cancer and haematological, immunological and neurological diseases.

Philosophers of medicine have formulated different accounts of the concept of disease. Which concept of disease one assumes has implications for what conditions count as diseases and, by extension, who may be regarded as having a disease (disease judgements) and for who may be accorded the social privileges and personal responsibilities associated with being sick (sickness judgements). In this article, we consider an ideal diagnostic test for coronavirus disease 2019 (COVID-19) infection with respect to four groups of people-positive and asymptomatic; positive and symptomatic; negative; and untested-and show how different concepts of disease impact on the disease and sickness judgements for these groups. The suggestion is that sickness judgements and social measures akin to those experienced during the current COVID-19 outbreak presuppose a concept of disease containing social (risk of) harm as a component. We indicate the problems that arise when adopting this kind of disease concept beyond a state of emergency.

The structural information and spatial distribution of molecules in biological tissues are closely related to the potential molecular mechanisms of disease origin, transfer, and classification. Ambient ionization mass spectrometry imaging is an effective tool that provides molecular images while describing in situ information of biomolecules in complex samples, in which ionization occurs at atmospheric pressure with the samples being analyzed in the native state. Ambient ionization mass spectrometry imaging can directly analyze tissue samples at a fairly high resolution to obtain molecules in situ information on the tissue surface to identify pathological features associated with a disease, resulting in the wide applications in pharmacy, food science, botanical research, and especially clinical research. Herein, novel ambient ionization techniques, such as techniques based on spray and solid-liquid extraction, techniques based on plasma desorption, techniques based on laser desorption ablation, and techniques based on acoustic desorption were introduced, and the data processing of ambient ionization mass spectrometry imaging was briefly reviewed. Besides, we also highlight recent applications of this imaging technology in clinical researches and discuss the challenges in this imaging technology and the perspectives on the future of the clinical research., (© 2020 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.)

Public health policymakers face increasingly complex questions and decisions and need to deal with an increasing quantity of data and information. For policy advisors to make use of scientific evidence and to assess available intervention options effectively and therefore indirectly for those deciding on and implementing public health policies, mathematical modeling has proven to be a useful tool. In some areas, the use of mathematical modeling for public health policy support has become standard practice at various levels of decision-making. To make use of this tool effectively within public health organizations, it is necessary to provide good infrastructure and ensure close collaboration between modelers and policymakers. Based on experience from a national public health institute, we discuss the strategic requirements for good modeling practice for public health. For modeling to be of maximal value for a public health institute, the organization and budgeting of mathematical modeling should be transparent, and a long-term strategy for how to position and develop mathematical modeling should be in place.

Considerable controversy exists concerning the assessment of thyroidal state in critically ill patients with decreased serum T4 and T3 concentrations, in part because serum free T4 values are often low in such patients no matter what method of measurement is used. We developed an ultrafiltration method to measure free T4 and free T3 in undiluted serum and compared the results with those obtained using a standard equilibrium dialysis method to measure free T4 and T3. In 30 consecutive intensive care unit (ICU) patients, serum free T4 values were similar to or higher than those in 12 normal subjects by both methods in most patients and were clearly distinguishable from those in hypothyroid patients. The serum total T4 concentrations in these patients ranged from 12.9-131.3 nmol/L (mean, 68.2; normal mean, 115.8). Free T4 by equilibrium dialysis was highly correlated with free T4 by ultrafiltration in the ICU group (r = 0.91; P less than 0.001). Serum free T3 levels, however, whether measured by equilibrium dialysis or ultrafiltration, were decreased in the ICU patients, confirming other reports of lowered free T3 in critically ill clinically euthyroid patients. Our findings suggest that the use of equilibrium dialysis of undiluted serum or ultrafiltration to measure serum free T4 concentrations will distinguish euthyroid hypothyroxinemic ICU patients from those with hypothyroidism.

Bacteria in the human body, particularly in the large intestine, are known to be associated with various diseases. To identify disease-associated bacteria (markers), a typical method is to statistically compare the relative abundance of bacteria between healthy subjects and diseased patients. However, since bacteria do not necessarily cause diseases in isolation, it is also important to focus on the interactions and relationships among bacteria when examining their association with diseases. In fact, although there are common approaches to represent and analyze bacterial interaction relationships as networks, there are limited methods to find bacteria associated with diseases through network-driven analysis. In this paper, we focus on rewiring of the bacterial network and propose a new method for quantifying the rewiring. We then apply the proposed method to a group of colorectal cancer patients. We show that it can identify and detect bacteria that cannot be detected by conventional methods such as abundance comparison. Furthermore, the proposed method is implemented as a general-purpose tool and made available to the general public., (© 2024. The Author(s).)

MicroRNAs have a significant role in the emergence of various human disorders. Consequently, it is essential to understand the existing interactions between miRNAs and diseases, as this will help scientists better study and comprehend the diseases' biological mechanisms. Findings can be employed as biomarkers or drug targets to advance the detection, diagnosis, and treatment of complex human disorders by foretelling possible disease-related miRNAs. This study proposed a computational model for predicting potential miRNA-disease associations called the Collaborative Filtering Neighborhood-based Classification Model (CFNCM), in light of the shortcomings of conventional and biological experiments, which are expensive and time-consuming. The model generated integrated miRNA and disease similarity matrices using the validated associations and miRNA and disease similarity information and used them as the input features for CFNCM. To produce class labels, we first determined the association scores for brand-new pairs using user-based collaborative filtering. With zero as the threshold, the associations with scores >0 were labelled 1, indicating a potential positive association, otherwise, it is marked as 0. Then, we developed classification models using various machine-learning algorithms. By comparison, we discovered that the support vector machine (SVM) produced the best AUC of 0.96 with 10-fold cross-validation through the GridSearchCV technique for identifying optimal parameter values. In addition, the models were evaluated and verified by analyzing the top 50 breast and lung neoplasms-related miRNAs, of which 46 and 47 associations were verified in two authoritative databases, dbDEMC and miR2Disease., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

Background: Muscle damage leads to increased serum creatine kinase (CK) levels in diseases such as acute myocardial infarction. Still, many individuals have abnormal serum CK activities lacking muscle-related diagnoses. The current study hypothesized that failed or overactivated CK clearance by non-muscle organs/tissues might be responsible for increased or decreased CK activities in blood., Methods: We analyzing 37,081 independent CK test results in 36 human diseases during the past 5 y., Results: We found that 33 out of 36 diseases were associated with decreased median CK activities compared to healthy controls. Besides muscle damage-related conditions, the highest mean CK activities were observed in hepatitis and cirrhosis. In contrast, 6 blood cell-related illnesses had the lowest mean CK values. ROC analysis showed that CK activities were the best biomarkers (AUC: 0.80-0.94) for the 6 blood-related diseases, especially myeloproliferative disorders. The principal component analysis revealed that the same category of diseases, such as liver-, blood -, kidney-, cancers, and vascular-related diseases, had clustered CK distributions., Conclusions: We proposed that the liver and blood cells were mainly responsible for CK clearance in blood circulation based on overall results. The testable mechanisms were presented and discussed., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier B.V. All rights reserved.)