TY  - JOUR
AB  - BACKGROUND: Muscarinic receptor agonism and positive allosteric modulation is a promising mechanism of action for treating psychosis, not present in most D2R-blocking antipsychotics. Xanomeline, an M1/M4-preferring agonist, has shown efficacy in late-stage clinical trials, with more compounds being investigated. Therefore, we aim to synthesize evidence on the preclinical efficacy of muscarinic receptor agonists and positive allosteric modulators in animal models of psychosis to provide unique insights and evidence-based information to guide drug development. METHODS: We plan a systematic review and meta-analysis of in vivo animal studies comparing muscarinic receptor agonists or positive allosteric modulators with control conditions and existing D2R-blocking antipsychotics in animals subjected to any method that induces behavioural changes of relevance for psychosis. We will identify eligible studies by searching multiple electronic databases. At least two independent reviewers will conduct the study selection and data extraction using prespecified forms and assess the risk of bias with the SYRCLE's tool. Our primary outcomes include locomotor activity and prepulse inhibition measured with standardized mean differences. We will examine other behavioural readouts of relevance for psychosis as secondary outcomes, such as social interaction and cognitive function. We will synthesize the data using multi-level meta-analysis with a predefined random-effects structure, considering the non-independence of the data. In meta-regressions we will explore potential sources of heterogeneity from a predefined list of characteristics of the animal population, model, and intervention. We will assess the confidence in the evidence considering a self-developed instrument thatconsiders the internal and external validity of the evidence. PROTOCOL REGISTRATION: PROSPERO-ID: CRD42024520914.
AU  - Siafis, S.*
AU  - Nomura, N.*
AU  - Schneider-Thoma, J.*
AU  - Bighelli, I.*
AU  - Bannach-Brown, A.*
AU  - Ramage, F.J.*
AU  - Tinsdeall, F.*
AU  - Mantas, I.*
AU  - Jauhar, S.*
AU  - Natesan, S.*
AU  - Vernon, A.C.*
AU  - de Bartolomeis, A.*
AU  - Hölter, S.M.
AU  - Drude, N.I.*
AU  - Tölch, U.*
AU  - Hansen, W.P.*
AU  - Chiocchia, V.*
AU  - Howes, O.D.*
AU  - Priller, J.*
AU  - MacLeod, M.R.*
AU  - Salanti, G.*
AU  - Leucht, S.*
C1  - 73152
C2  - 56933
TI  - Muscarinic receptor agonists and positive allosteric modulators in animal models of psychosis: Protocol for a systematic review and meta-analysis.
JO  - F1000 Res.
VL  - 13
PY  - 2025
ER  - 

TY  - JOUR
AB  - In the last few decades, forward genetics approaches have been extensively used to identify gene function. Essentially, forward genetics is the elucidation of the genetic basis of a specific phenotype by screening a population containing random genomic modifications that alter gene function. These approaches have shed light on some essential gene functions in development and disease and have expanded the realm of understanding for genetic disorders. Due to the availability of efficient mutagenesis methods, phenotyping techniques, reliable validation, comprehensive sequence information and translational potential, mouse models are favored for forward genetics approaches. However, in this post-genomic CRISPR-Cas9 era, the relevance and future of forward genetics was brought into question. With more than 7300 mouse strains archived and close interactions with several leading mouse researchers around the world, INFRAFRONTIER - the European Research Infrastructure for mouse models organised a panel discussion on forward genetics at the International Mammalian Genome Conference 2018 to discuss the future of forward genetics as well as challenges faced by researchers using this approach in the current research environment. The commentary presents an overview of this discussion.
AU  - Ali Khan, A.
AU  - Raess, M.
AU  - Hrabě de Angelis, M.
C1  - 63771
C2  - 51517
TI  - Moving forward with forward genetics: A summary of the INFRAFRONTIER Forward Genetics Panel Discussion.
JO  - F1000 Res.
VL  - 10
PY  - 2021
ER  - 

TY  - JOUR
AB  - Background: Severe acute respiratory syndrome coronavirus 2 (SARSCoV-2) belongs to a subgroup of coronaviruses rampant in bats for centuries. It caused the coronavirus disease 2019 (COVID-19) pandemic. Most patients recover, but a minority of severe cases experience acute respiratory distress or an inflammatory storm devastating many organs that can lead to patient death. The spread of SARS-CoV-2 was facilitated by the increasing intensity of air travel, urban congestion and human contact during the past decades. Until therapies and vaccines are available, tests for virus exposure, confinement and distancing measures have helped curb the pandemic. Vision: The COVID-19 pandemic calls for safeguards and remediation measures through a systemic response. Self-organizing initiatives by scientists and citizens are developing an advanced collective intelligence response to the coronavirus crisis. Their integration forms Olympiads of Solidarity and Health. Their ability to optimize our response to COVID-19 could serve as a model to trigger a global metamorphosis of our societies with far-reaching consequences for attacking fundamental challenges facing humanity in the 21st century. Mission: For COVID-19 and these other challenges, there is no alternative but action. Meeting in Paris in 2003, we set out to "rethink research to understand life and improve health." We have formed an international coalition of academia and industry ecosystems taking a systems medicine approach to understanding COVID-19 by thoroughly characterizing viruses, patients and populations during the pandemic, using openly shared tools. All results will be publicly available with no initial claims for intellectual property rights. This World Alliance for Health and Wellbeing will catalyze the creation of medical and health products such as diagnostic tests, drugs and vaccines that become common goods accessible to all, while seeking further alliances with civil society to bridge with socio-ecological and technological approaches that characterise urban systems, for a collective response to future health emergencies.
AU  - Auffray, C.*
AU  - Balling, R.*
AU  - Blomberg, N.*
AU  - Bonaldo, M.C.*
AU  - Boutron, B.*
AU  - Brahmachari, S.*
AU  - Bréchot, C.*
AU  - Cesario, A.*
AU  - Chen, S.J.*
AU  - Clément, K.*
AU  - Danilenko, D.*
AU  - Meglio, A.D.*
AU  - Gelemanović, A.*
AU  - Goble, C.*
AU  - Gojobori, T.*
AU  - Goldman, J.D.*
AU  - Goldman, M.*
AU  - Guo, Y.K.*
AU  - Heath, J.*
AU  - Hood, L.*
AU  - Hunter, P.*
AU  - Jin, L.*
AU  - Kitano, H.*
AU  - Knoppers, B.M.*
AU  - Lancet, D.*
AU  - Larue, C.*
AU  - Lathrop, M.*
AU  - Laville, M.*
AU  - Lindner, A.B.*
AU  - Magnan, A.*
AU  - Metspalu, A.*
AU  - Morin, E.*
AU  - Ng, L.F.P.*
AU  - Nicod, L.*
AU  - Noble, D.*
AU  - Nottale, L.*
AU  - Nowotny, H.*
AU  - Ochoa, T.*
AU  - Okeke, I.N.*
AU  - Oni, T.*
AU  - Openshaw, P.*
AU  - Öztürk, M.*
AU  - Palkonen, S.*
AU  - Paweska, J.T.*
AU  - Pison, C.*
AU  - Polymeropoulos, M.H.*
AU  - Pristipino, C.*
AU  - Protzer, U.
AU  - Roca, J.*
AU  - Rozman, D.*
AU  - Santolini, M.*
AU  - Sanz, F.*
AU  - Scambia, G.*
AU  - Segal, E.*
AU  - Serageldin, I.*
AU  - Soares, M.B.*
AU  - Sterk, P.*
AU  - Sugano, S.*
AU  - Superti-Furga, G.*
AU  - Supple, D.*
AU  - Tegnér, J.*
AU  - Uhlén, M.*
AU  - Urbani, A.*
AU  - Valencia, A.*
AU  - Valentini, V.*
AU  - van der Werf, S.*
AU  - Vinciguerra, M.*
AU  - Wolkenhauer, O.*
AU  - Wouters, E.*
C1  - 63339
C2  - 51480
TI  - COVID-19 and beyond: A call for action and audacious solidarity to all the citizens and nations, it is humanity’s fight.
JO  - F1000 Res.
VL  - 9
PY  - 2021
ER  - 

TY  - JOUR
AB  - This article summarizes (1) the recent achievements to further improve symptomatic therapy of motor Parkinson's disease (PD) symptoms, (2) the still-few attempts to systematically search for symptomatic therapy of non-motor symptoms in PD, and (3) the advances in the development and clinical testing of compounds which promise to offer disease modification in already-manifest PD. However, prevention (that is, slowing or stopping PD in a prodromal stage) is still a dream and one reason for this is that we have no consensus on primary endpoints for clinical trials which reflect the progression in prodromal stages of PD, such as in rapid eye movement sleep behavior disorder (RBD) -a methodological challenge to be met in the future.
AU  - Oertel, W.H.
C1  - 50833
C2  - 42896
TI  - Recent advances in treating Parkinson's disease.
JO  - F1000 Res.
VL  - 6
PY  - 2017
ER  - 

TY  - JOUR
AB  - DREAM challenges are community competitions designed to advance computational methods and address fundamental questions in system biology and translational medicine. Each challenge asks participants to develop and apply computational methods to either predict unobserved outcomes or to identify unknown model parameters given a set of training data. Computational methods are evaluated using an automated scoring metric, scores are posted to a public leaderboard, and methods are published to facilitate community discussions on how to build improved methods. By engaging participants from a wide range of science and engineering backgrounds, DREAM challenges can comparatively evaluate a wide range of statistical, machine learning, and biophysical methods. Here, we describe DREAMTools, a Python package for evaluating DREAM challenge scoring metrics. DREAMTools provides a command line interface that enables researchers to test new methods on past challenges, as well as a framework for scoring new challenges. As of March 2016, DREAMTools includes more than 80% of completed DREAM challenges. DREAMTools complements the data, metadata, and software tools available at the DREAM website http://dreamchallenges.org and on the Synapse platform at https://www.synapse.org. Availability: DREAMTools is a Python package. Releases and documentation are available at http://pypi.python.org/pypi/dreamtools. The source code is available at http://github.com/dreamtools/dreamtools.
AU  - Cokelaer, T.*
AU  - Bansal, M.*
AU  - Bare, C.*
AU  - Bilal, E.*
AU  - Bot, B.M.*
AU  - Chaibub Neto, E.*
AU  - Eduati, F.*
AU  - de la Fuente, A.*
AU  - Gonen, M.*
AU  - Hill, S.M.*
AU  - Hoff, B.*
AU  - Karr, J.R.*
AU  - Küffner, R.
AU  - Menden, M.P.*
AU  - Meyer, P.*
AU  - Norel, R.*
AU  - Pratap, A.*
AU  - Prill, R.J.*
AU  - Weirauch, M.T.*
AU  - Costello, J.C.*
AU  - Stolovitzky, G.*
AU  - Saez-Rodriguez, J.*
C1  - 48715
C2  - 41297
TI  - DREAMTools: A Python package for scoring collaborative challenges.
JO  - F1000 Res.
VL  - 4
PY  - 2016
ER  - 

TY  - JOUR
AB  - In today's information age, the necessary means exist for clinical risk prediction to capitalize on a multitude of data sources, increasing the potential for greater accuracy and improved patient care. Towards this objective, the Prostate Cancer DREAM Challenge posted comprehensive information from three clinical trials recording survival for patients with metastatic castration-resistant prostate cancer treated with first-line docetaxel. A subset of an independent clinical trial was used for interim evaluation of model submissions, providing critical feedback to participating teams for tailoring their models to the desired target. Final submitted models were evaluated and ranked on the independent clinical trial. Our team, called "A Bavarian Dream", utilized many of the common statistical methods for data dimension reduction and summarization during the trial. Three general modeling principles emerged that were deemed helpful for building accurate risk prediction tools and ending up among the winning teams of both sub-challenges. These principles included: first, good data, encompassing the collection of important variables and imputation of missing data; second, wisdom of the crowd, extending beyond the usual model ensemble notion to the inclusion of experts on specific risk ranges; and third, recalibration, entailing transfer learning to the target source. In this study, we illustrate the application and impact of these principles applied to data from the Prostate Cancer DREAM Challenge.
AU  - Kondofersky, I.
AU  - Laimighofer, M.
AU  - Kurz, C.F.
AU  - Krautenbacher, N.
AU  - Söllner, J.F.
AU  - Dargatz, P.*
AU  - Scherb, H.
AU  - Ankerst, D.P.*
AU  - Fuchs, C.
C1  - 49963
C2  - 41943
TI  - Three general concepts to improve risk prediction: Good data, wisdom of the crowd, recalibration.
JO  - F1000 Res.
VL  - 5
PY  - 2016
ER  -