Announcing the PLOS ONE Collection: Early Diagnosis and Treatment of Alzheimer’s Disease

PLOS ONE is excited to publish the Early Diagnosis and Treatment of Alzheimer’s Disease Collection. As the global population ages, the impact and prevalence on Alzheimer’s disease is predicted to rise, making early diagnosis and treatment crucial. PLOS ONE launched a call for papers last year inviting submissions in this important area. This Collection showcases the submissions chosen for inclusion by the guest editors.

We received over 50 submissions, covering topics related to both the diagnosis and treatment of the disease using preclinical methods and clinical data.

Much of the preclinical work in the Collection provides insights that will be valuable for development of future therapeutics, with topics including gene delivery of modified antibodies and possible mechanisms for genes known to act as risk factors for Alzheimer’s. Further studies investigate the potential role of inflammatory responses as risk factors for the disease and the effects of modulation of the immune system in treating pre-symptomatic Alzheimer’s disease in an animal model.

These works are complemented by several clinical papers, many of which bring us closer to early detection of Alzheimer’s disease. This includes studies detecting Alzheimer’s disease with blood-based biomarkers and the correlation between Amyloid-? PET imaging results and cerebrospinal fluid biomarkers.

A number of the papers also make use of machine learning and other computational techniques to analyse genetic, PET imaging and MRI datasets, demonstrating how these can be used to improve detection of the disease.

At launch, the Collection includes 20 papers, with several more to be added as they reach publication. We invite you to revisit the Collection again to read the latest research on this topic.

To celebrate its launch the Guest Editors have selected some of their favourite papers featured in the Collection and provided a short summary.

 

Yona Levites, University of Florida, USA


Image credit: pone.0226245

This study by Elmer and Collegues assesses effects of single chain variable fragment coupled with mutated Fc domain retaining FcRn binding, but lacking Fc gamma receptor (Fc?R) binding (silent scFv-IgG) on amyloid accumulation and aggregation in Alzheimer’s disease transgenic mouse model. The molecule has increased affinity to amyloid as compared to scFv, but toxicity. Interestingly, peripheral AAV delivery resulted in detectable levels of scFv-IgG in the brain. Intracranial injection of anti-amyloid scFv IgG resulted in significant reduction of amyloid burden in TgAPP mice. Future studies will potentially focus on improving the effects of silent scFv-IgG delivered peripherally.

 

Jussi Tohka, University of Eastern Finland, Finland


Image credit: pone.0226784

The current diagnostic criteria allow the use of Cerebro Spinal Fluid (CSF) biomarkers to provide pathophysiological support for the diagnosis of AD. The paper  by Rhodius-Meester and colleagues argues that diagnostic guidelines do not specify which patients should receive CSF testing and the appropriate use criteria, which have been proposed as a guideline, are challenging to translate to clinical practice.  Hence, this study evaluated a computerized decision support system to select patients for CSF biomarker (CSF beta-amyloid 1–42 (AB42), total tau and tau phosphorylated at threonine 181 (p-tau)) determination. In the computerized decision support approach of the study, diagnosis is first attempted using only neuropsychology, MRI and APOE data.  Depending on the confidence of the first diagnosis, the clinician can decide either not to order CSF testing or visualize the effects of the simulated normal and AD-like CSF values on the workflow. Only if the potential change in the confidence of the diagnosis due to CSF is large enough, the clinician orders CSF testing.  The study indicated that this approach could support clinicians in making a balanced decision in ordering additional CSF testing. Specifically, the computerized decision support approach of the study restricted CSF testing to only 26% of cases, without compromising diagnostic accuracy.

Roberta Diaz Brinton, The University of Arizona


Image credit: pone.0222921

The computational analysis by Potashkin, Bottero, Santiago and Quinn yielded new insights regarding gene networks affected in Alzheimer’s while also strengthening the role of metabolic networks in the pathophysiology of the disease. Each of the gene networks identified by this team, glucose homeostasis, glucocorticoid signaling, sleep regulation, and memory, are familiar to those working in the field. The critical insight derived from this analysis is that multiple gene networks are involved in Alzheimer’s and by extension that a single therapeutic target does not address the complexity of the disease. The gene network analyses were conducted on postmortem brain tissue and thus represent the brain in the end stages of the disease. A challenge for the field is to develop accessible and predictive biomarkers of earlier stages of the disease when therapeutic interventions can potentially delay or reverse the disease process. The findings provided by Potashkin and colleagues provide a platform on which to build just such predictive biomarkers.

Michael Weiner, University of California San Francisco, USA


Image credit: pone.0221365

In this paper  comparing amyloid PET with Flutemetamol and measurements of A?42 in cerebrospinal fluid, Müller and co-workers found a significant correlation between 18F-Flutemetamol PET classification and the three CSF biomarkers. The highest correlation was between A?42 and 18F-Flutemetamol PET. Good correlations between CSF A?42   and amyloid PET have been previously reported in many studies.  An  “optimal cut-off value for A?42 “ was used and yielded an improvement in sensitivity, while maintaining a high specificity, for a positive 18F-Flutemetamol PET. 18F-Flutemetamol PET was found to be the best predictor of a clinical AD diagnosis. The authors stated that  “whether biomarkers are to be included in the clinical criteria to further improve their sensitivity is still under investigation.”

 

 

 

 

 

 

 

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Introducing the Mathematical Modelling of Infectious Disease Dynamics Collection

In recent months, the words “infection” and “outbreak” have not been far from anyone’s mind as we’ve faced the emergence of a new coronavirus, COVID-19. Across the globe, efforts are underway to control and limit the spread of the virus, and to find ways to treat those infected. As we watch these events unfurl, it is evident that there is still a lot that we, as a global community, do not yet understand about the dynamics of infectious diseases. The ways in which diseases spread are a concern that we all have a stake in?research that helps further our understanding of infectious diseases can influence each of our lives.

 

One distinct community of researchers working on understanding infectious disease dynamics is the mathematical modelling community, consisting of scientists from many different disciplines coming together to tackle a common problem through the use of mathematical models and computer simulations. Mathematics may sound like an unlikely hero to help us overcome a global epidemic; however, the insights we gain from studying the dynamics of infectious diseases by using equations describing fundamental variables are not to be underestimated. By approaching infectious diseases from a mathematical perspective, we can identify patterns and common systems in disease function, and it enables us to find some of the underlying structures that govern outbreaks and epidemics. Mathematical modellers make use of available data from current and previous outbreaks to predict who may get infected, where vaccination efforts will be most effective, and how to limit the spread of the disease.

 

Today at PLOS, we are launching a collection of new research papers submitted to a call for papers during the latter half of 2019 entitled “Mathematical Modelling of Infectious Disease Dynamics”, hosted by PLOS Biology, PLOS Computational Biology and PLOS ONE. The aim of this collection is to bring together different disciplines such as mathematics, biology, medicine and physics in order to shed light on the important topic of how mathematical models can help us understand infectious disease dynamics, and to present this research to the broad readership of these three journals and beyond. The accumulation of vital new research in a comprehensive collection will be a useful resource for understanding how infectious diseases operate, and how we can tackle them in real-time as well as in the future.

 

At PLOS we remain committed to our primary Open Access mission?ensuring that science is made as widely available as possible, and not locked behind paywalls. This is especially important in outbreak scenarios, such as the current COVID-19 epidemic, where it is critical that any new and relevant research be made easily accessible around the world, immediately at the time of publication.


Novel Coronavirus SARS-CoV-2 NIAID CC-BY

Several of the papers in this collection present new methods that can be utilized in a range of scenarios. For instance, Patel and Sprouge developed a new estimator for predicting the basic reproduction number R0, which is the expected number of host cells infected by a single infected cell. This can be used for instance to understand the early stages of HIV infections, and for assessing the effectiveness of various therapies.

 

If two pathogen species, strains, or clones don’t interact, surely we can estimate the proportion of coinfected hosts as the simple product of the individual prevalences? A paper in PLOS Biology by Frédéric Hamelin, Nik Cunniffe and co-workers shows that this assumption is false; even if pathogens don’t interact, death of coinfected hosts causes net prevalences of individual pathogens to decrease simultaneously. The authors reinterpret data from previous studies accordingly.

 

Unusually large outbreaks of mumps across the United States in 2016 and 2017 raised questions about the extent of mumps circulation and the relationship between these and prior outbreaks. In this PLOS Biology paper, Shirlee Wohl, Pardis Sabeti and co-authors paired epidemiological data from public health investigations with analysis of mumps virus whole-genome sequences from 201 infected individuals. This allowed them to reconstruct mumps transmission links not evident from more traditional approaches and also revealed connections between apparently unrelated mumps outbreaks.

 

Endo and colleagues present a model of a phenomenon to which we can all relate, but which is still not well understood – the spread of infection within the household. They modelled the fine structures of family life to understand how disease typically enters and spreads through the household. Their findings support the idea that children are the most likely culprits of bringing disease into the household, and showed that there is a high level of transmission within generations, as well as between mother and child.


FluShot NIAID CC-BY

Rotavirus, the leading cause of diarrhea globally in children under 5, shows a biennial pattern of emergence in the US, while in many other high-income countries it exhibits an annual pattern. Ai and colleagues modelled the effect that higher vaccine coverage may have on this phenomenon, and found that increasing vaccine coverage from the current 70-75% to 85% would not only reduce the number of rotavirus cases, but also shift occurance to a more predictable annual epidemic pattern.

 

Two of the papers published in the collection are concerned with malaria. Kim and colleagues modelled the effectiveness of relapse control methods for Plasmodium vivax, finding that current vector control methods may have a negative effect on controlling disease prevalence, but that a shift towards control at a higher vector control level may be more efficient. Meanwhile, Wang and colleagues have constructed a stacking model for malaria prediction by combining two traditional time series models and two deep learning methods. Utilising malaria incidence data from Yunnan Province, China, they find that the ensemble architecture outperforms each of the sub-structure models in predicting malaria cases.


Predicted dengue importations for August 2015 pone.0225193 CC-BY

There are two papers in the collection that look at improving prediction of dengue infections. Leibig and colleagues present a network model of how international air travel can affect the spread of dengue across the world. By modelling the number of dengue-infected passengers arriving at various airports each month, the authors were able to study how dengue may be imported into different countries, and which routes would be the most likely for dengue-infected passengers to arrive by. Secondly, Liu and colleagues developed a model for predicting the spread of dengue infections that incorporates climate factors such as mean temperature, relative humidity and precipitation and applied this to data from dengue infections in Guangzhou, China, in order to help inform best practices in the early stages of a dengue outbreak.

 

The development of diseases can be influenced by personal factors such as age, which two of the papers in the collection address. Ku and Dodd developed a model for accounting for population aging when looking at tuberculosis incidence, as the impact of demographic change on disease forecasting is still not well understood. They applied the model to historical data of TB cases in Taiwan from 2005-2018, and used this to forecast what the incidence may look like until 2035. On the other end of the age spectrum, Rostgaard and colleagues used a Markov model to study the relationship between Epstein-Barr virus and infectious mononucleosis. Most people are typically infected with Epstein-Barr virus in early childhood, while infectious mononucleosis can sometimes follow in adolescence or later in life. The authors developed a statistical model to probe some of the uncertainties surrounding the origin and dynamics of infectious mononucleosis.

 

Some of the papers in the collection address new and emerging diseases. Dodero-Rojas and colleagues used the SEIR model to study the last three Chikungunya outbreaks in Rio de Janeiro, Brazil, and estimated their respective Basic Reproduction Numbers, R0. They also expanded their findings to include predictions for the Mayaro virus, which is an emerging disease in South America, and found that it has the possibility to become an epidemic disease in Rio de Janeiro.


Aedes Mosquito NIAID CC-BY

The ability to accurately forecast disease patterns is crucial for ensuring that the right resources are in place to handle outbreaks. Morbey and colleagues looked at seasonal patterns in respiratory disease in England, and found that although syndromic indicators were affected by the timing of the peaks in seasonal disease, the demand for hospital beds was the highest on either 29th or 30th December, regardless of the timing of the syndromic peaks. Asadgol and colleagues also addressed seasonal patterns, this time in cholera in Iran, and predicted the effect of climate change on cholera incidence from 2020-2050 using an artificial neural network.

 

Given the interdisciplinary nature of the topic, we are grateful to countless authors, reviewers, Academic Editors and Guest Editors for making this collection a reality. We are especially grateful to our Guest Editor team, Konstantin Blyuss (University of Sussex), Sara Del Valle (Los Alamos National Laboratory), Jennifer Flegg (University of Melbourne), Louise Matthews (University of Glasgow) and Jane Heffernan (York University) for curating the collection. While 14 papers are included in this collection today, we’ll keep adding new papers as they are published, so please keep checking back for updates.

 

Guest Editor Konstantin Blyuss sums up the importance of this collection: “A recent and ongoing outbreak of coronavirus COVID-19 has highlighted the enormous significance of mathematical models for understanding the dynamics of infectious diseases and developing appropriate strategies for mitigating them. Mathematical models have helped identify the important factors affecting the spread of this infection both globally, and locally using country-specific information. They have also elucidated the effectiveness of different containment strategies and provided quantitative measures of disease severity”.

 

About the Guest Editors:

 

Konstantin Blyuss

Guest Editor, PLOS ONE, PLOS Biology, and PLOS Computational Biology

Konstantin Blyuss is a Reader in the Department of Mathematics at the University of Sussex, UK. He obtained his PhD in applied mathematics at the University of Surrey, which was followed by PostDocs at Universities of Exeter and Oxford. Before coming to Sussex in 2010, he was a Lecturer in Complexity at the University of Bristol. His main research interests are in the area of dynamical systems applied to biology, with particular interest in modelling various aspects of epidemiology, dynamics of immune responses and autoimmunity, as well as understanding mechanisms of interactions between plants and their pathogens

 

Sara del Valle

Guest Editor, PLOS ONE, PLOS Biology, and PLOS Computational Biology

Dr. Sara Del Valle is a scientist and deputy group leader in the Information Systems and Modeling Group at Los Alamos National Laboratory. She earned her Ph.D. in Applied Mathematics and Computational Science in 2005 from the University of Iowa. She works on developing, integrating, and analyzing mathematical, computational, and statistical models for the spread of infectious diseases such as smallpox, anthrax, HIV, influenza, malaria, Zika, Chikungunya, dengue, and Ebola. Most recently, she has been investigating the role of heterogeneous data streams such as satellite imagery, Internet data, and climate on detecting, monitoring, and forecasting diseases around the globe. Her research has generated new insights on the impact of behavioral changes on diseases spread as well as the role of non-traditional data streams on disease forecasting.

 

Jennifer Flegg

Guest Editor, PLOS ONE, PLOS Biology, and PLOS Computational Biology

Jennifer Flegg is a Senior Lecturer and DECRA fellow in the School of Mathematics and Statistics at the University of Melbourne. Her research focuses on mathematical biology in areas such as wound healing, tumour growth and epidemiology. She was awarded a PhD in 2009 from Queensland University of Technology on mathematical modelling of tissue repair. From 2010 – 2013, she was at the University of Oxford developing statistical models for the spread of resistance to antimalarial drugs. From 2014 – April 2017 she was a Lecturer in the School of Mathematical Sciences at Monash University. In May 2017 she joined the School of Mathematics and Statistics at the University of Melbourne as a Senior Lecturer in Applied Mathematics.

 

Louise Matthews

Guest Editor, PLOS ONE, PLOS Biology, and PLOS Computational Biology

Louise Matthews is Professor of Mathematical Biology and Infectious Disease Ecology at the Institute of Biodiversity, Animal Health and Comparative Medicine (BAHCM) at the University of Glasgow. She holds a degree and PhD in mathematics and has over 20 years research experience as an epidemiologist, with a particular focus on diseases of veterinary and zoonotic importance. Her current interests include a focus on drug resistance; antibiotic resistance in livestock; the community and the healthcare setting; anthelminthic resistance in livestock; and drug resistance in African Animal Trypanosomiasis. She is also interested in the integration of economic and epidemiological approaches such as game theory to understand farmer behaviour and micro-costing approaches to promote adoption of measures to reduce antibiotic resistance.

Jane Heffernan

Guest Editor, PLOS ONE, PLOS Biology, and PLOS Computational Biology

Jane Heffernan is a Professor in the Department of Mathematics and Statistics at York University, and York Research Chair (Tier II). She is also the Director of the Centre for Disease Modelling (CDM), and serves on the Board of Directors of the Canadian Applied and Industrial Mathematics Society (CAIMS). She is also very active in the Society for Mathematical Biology (SMB). Dr. Heffernan’s research program centers on understanding the spread and persistence of infectious diseases. Her Modelling Infection and Immunity Lab focuses on the development of new biologically motivated models of infectious diseases (deterministic and stochastic) that describe pathogen dynamics in-host (mathematical immunology) and in a population of hosts (mathematical epidemiology), as well as models in immuno-epidemiology, which integrate the in-host dynamics with population level models. More recently, Heffernan is focusing on applying mathematics and modelling to studying pollinator health and disease biology.

 

 

 

Featured Image : Spencer J. Fox, CC0

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Introducing PLOS ONE’s Science of Stories Collection

Stories have the power to shape our identities and worldviews. They can be factual or fictional, text-based or visual and can take many forms—from novels and non-fiction to conspiracy theories, rumors and disinformation. We can characterize stories by their plot, their characters, their audience, their style, their themes or their purpose. Given the massive power of stories to alter the course of society, innovative methods to understand them empirically and quantitatively are necessary.

Today, we are pleased to introduce PLOS ONE’s Science of Stories Collection, which  includes submissions invited through a Call for Papers last year. The Call for Papers welcomed primary research papers that propose solutions to real world, data-rich problems that use different empirical methods. The Guest Editors overseeing the scope and curating the Collection are Peter Dodds (University of Vermont), Mirta Galesic (Santa Fe Institute), Matthew Jockers (Washington State University), and Mohit Iyyer (University of Massachusetts Amherst).

At launch, the Collection includes over 15 papers illustrating data-driven approaches to understanding stories and their impact. Some articles explore the nature of narrative and narrative thinking in texts and other media, for instance, the role of similarity in narrative persuasion, the effects of choosing violence in narratives, the importance of characters in narratives communicating risk of natural disaster, the impact of storytelling in complex collaborative tasks such as food preparation, and the role of narrative in collaborative reasoning and intelligence analysis.

Other articles present new methods to extract stories from datasets and datasets from stories, including automated narrative analysis via machine learning, systematic modeling of narrative structure and dynamics, and large-scale analysis of gender stereotypes in movies and books.

A third group of papers analyze how narratives are transformed and how they can transform people, for example, looking at the co-evolution of contagion (e.g., disease, addiction, or rumor) and behavior, social media’s contribution to political misperceptions in US elections, how people’s intuitive theories of physics can partly account for how they think about imaginary worlds, how narrative can induce empathy for people engaging in negative health behaviors, and the impact of mental health recovery narratives on health outcomes.

A final group of papers explores the communication of data-rich narratives to the public, including the relative effectiveness of video abstracts and plain language summaries versus graphical abstracts and published abstracts, newly emerging platforms for writing and commenting on literary texts at unprecedented scale, and the role of narrative in perceived authenticity in science communication.

Papers will continue to be added to the Collection as they reach publication, so we invite you to revisit the Collection again for additional insights into the science of stories.

Guest Editors

Peter Sheridan Dodds

Peter Dodds is Professor at the University of Vermont’s Department of Mathematics and Statistics. He is Director of the Vermont Complex Systems Center and co-runs the center’s Computational Story Lab. Having a general interest in stories and narratives, complexification, contagion, and robustness, Dodd’s research focuses on system-level, big data problems of all kinds, often networked, sociotechnical ones. His work has been supported by an NSF CAREER award to study sociotechnical phenomena, the McDonnell Foundation, the Office of Naval Research, NASA, the MITRE Corporation, Computer Associates, and Mass Mutual.

Mirta Galesic

Mirta Galesic is Professor and Cowan Chair in Human Social Dynamics at the Santa Fe Institute, External Faculty at the Complexity Science Hub in Vienna, Austria, and Associate Researcher at the Harding Center for Risk Literacy at the Max Planck Institute for Human Development in Berlin, Germany. She studies how simple cognitive mechanisms interact with social and physical environments to produce seemingly complex social phenomena. She develops empirically grounded computational models of social judgments, social learning, collective problem solving, and opinion dynamics. She is also interested in how people understand and cope with uncertainty and complexity inherent in many everyday decisions.

Mohit Iyyer

Mohit Iyyer is an Assistant Professor in computer science at the University of Massachusetts, Amherst. Previously, he was a Young Investigator at the Allen Institute for Artificial Intelligence. Mohit obtained his PhD at the University of Maryland, College Park, advised by Jordan Boyd-Graber and Hal Daumé III. His research interests lie in natural language processing and machine learning. Much of his work uses deep learning to model language at the discourse level, tackling problems like generating long coherent units of text, answering questions about documents and understanding narratives in fictional text.

Matthew L. Jockers

Matthew L. Jockers is Dean of the College of Arts & Sciences and Professor of English and Data Analytics at Washington State University in Pullman, WA. Jockers has been leveraging computation to understand narrative and style since the early 1990s.  His books on the subject include Macroanalysis: Digital Methods and Literary History, Text Analysis with R for Students of Literature, and The Bestseller Code.  In addition to his academic work, Jockers helped launch two text mining startups and worked as Principal Research Scientist and Software Development Engineer in iBooks at Apple.

 

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Introducing the Autophagy and Proteostasis Collection

The importance of proteostasis is becoming increasingly apparent as disrupted proteostasis and dysregulation of proteostasis-associated networks has been linked with aging and many age-associated diseases such as Alzheimer’s, Parkinson’s and Huntington’s disorders. In recognition of

Introducing the Targeted Anticancer Therapies and Precision Medicine in Cancer Collection

  While the rate of death from cancer has been declining since the 1990s, an estimated 9.6 million people died from cancer in 2018, making it the second-leading cause of death worldwide [1]. According to