9th Trends in Medical Mycology Held on 11-14 October 2019, Nice, France, Organized under the Auspices of EORTC-IDG and ECMM.
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ABSTRACT:
SUBMITTER: Gangneux JP
PROVIDER: S-EPMC7807382 | biostudies-literature | 2019 Oct
REPOSITORIES: biostudies-literature
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9th Trends in Medical Mycology Held on 11-14 October 2019, Nice, France, Organized under the Auspices of EORTC-IDG and ECMM.
Gangneux Jean-Pierre JP Lortholary Olivier O Cornely Oliver A OA Pagano Livio L
Journal of fungi (Basel, Switzerland) 20191008 4
Dear Friends and Colleagues,It is a great honor and pleasure for us to invite you cordially to participate in the 9th Congress on Trends in Medical Mycology (TIMM-9) [...]. ...[more]
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Project description:The Illuminating the Druggable Genome (IDG) consortium is a National Institutes of Health (NIH) Common Fund program designed to enhance our knowledge of under-studied proteins, more specifically, proteins unannotated within the three most commonly drug-targeted protein families: G-protein coupled receptors, ion channels, and protein kinases. Since 2014, the IDG Knowledge Management Center (IDG-KMC) has generated several open-access datasets and resources that jointly serve as a highly translational machine-learning-ready knowledgebase focused on human protein-coding genes and their products. The goal of the IDG-KMC is to develop comprehensive integrated knowledge for the druggable genome to illuminate the uncharacterized or poorly annotated portion of the druggable genome. The tools derived from the IDG-KMC provide either user-friendly visualizations or ways to impute the knowledge about potential targets using machine learning strategies. In the following protocols, we describe how to use each web-based tool to accelerate illumination in under-studied proteins. © 2022 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: Interacting with the Pharos user interface Basic Protocol 2: Accessing the data in Harmonizome Basic Protocol 3: The ARCHS4 resource Basic Protocol 4: Making predictions about gene function with PrismExp Basic Protocol 5: Using Geneshot to illuminate knowledge about under-studied targets Basic Protocol 6: Exploring under-studied targets with TIN-X Basic Protocol 7: Interacting with the DrugCentral user interface Basic Protocol 8: Estimating Anti-SARS-CoV-2 activities with DrugCentral REDIAL-2020 Basic Protocol 9: Drug Set Enrichment Analysis using Drugmonizome Basic Protocol 10: The Drugmonizome-ML Appyter Basic Protocol 11: The Harmonizome-ML Appyter Basic Protocol 12: GWAS target illumination with TIGA Basic Protocol 13: Prioritizing kinases for lists of proteins and phosphoproteins with KEA3 Basic Protocol 14: Converting PubMed searches to drug sets with the DrugShot Appyter.
| S-EPMC10789444 | biostudies-literature
Project description:The article presents an AI-based fungi species recognition system for a citizen-science community. The system's real-time identification too - FungiVision - with a mobile application front-end, led to increased public interest in fungi, quadrupling the number of citizens collecting data. FungiVision, deployed with a human-in-the-loop, reaches nearly 93% accuracy. Using the collected data, we developed a novel fine-grained classification dataset - Danish Fungi 2020 (DF20) - with several unique characteristics: species-level labels, a small number of errors, and rich observation metadata. The dataset enables the testing of the ability to improve classification using metadata, e.g., time, location, habitat and substrate, facilitates classifier calibration testing and finally allows the study of the impact of the device settings on the classification performance. The continual flow of labelled data supports improvements of the online recognition system. Finally, we present a novel method for the fungi recognition service, based on a Vision Transformer architecture. Trained on DF20 and exploiting available metadata, it achieves a recognition error that is 46.75% lower than the current system. By providing a stream of labeled data in one direction, and an accuracy increase in the other, the collaboration creates a virtuous cycle helping both communities.
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Project description: Not available
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Project description:For reasons poorly understood, and despite the availability of biological medications blocking IL-1 and IL-6 that have markedly improved overall disease control, children with Systemic Juvenile Idiopathic Arthritis (SJIA) are now increasingly diagnosed with life-threatening chronic complications, including hepatitis and lung disease (SJIA-LD). On October 3-4, 2019, a two-day meeting, NextGen Therapies for Systemic Juvenile Idiopathic Arthritis (SJIA) & macrophage activation syndrome (MAS) organized by the Systemic JIA Foundation ( www.systemicjia.org/ ) in Washington, DC brought together scientists, clinicians, parents and FDA representatives with the objectives (1) to integrate clinical and research findings in MAS and SJIA-LD, and (2) to develop a shared understanding of this seemingly new pulmonary complication of SJIA. The current manuscript summarizes discussions and conclusions of the meeting.
| S-EPMC7360380 | biostudies-literature
Project description:BackgroundRecent outbreaks of Candida auris further exemplify that invasive Candida infections are a substantial threat to patients and healthcare systems. Even short treatment delays are associated with higher mortality rates. Epidemiological shifts towards more resistant Candida spp. require careful surveillance.ObjectivesTriggered by the emergence of C auris and by increasing antifungal resistance rates the European Confederation of Medical Mycology developed an international Candida Registry (FungiScope™ CandiReg) to allow contemporary multinational surveillance.MethodsCandiReg serves as platform for international cooperation to enhance research regarding invasive Candida infections. CandiReg uses the General Data Protection Regulation compliant data platform ClinicalSurveys.net that holds the electronic case report forms (eCRF). Data entry is supported via an interactive macro created by the software that can be accessed via any Internet browser.ResultsCandiReg provides an eCRF for invasive Candida infections that can be used for a variety of studies from cohort studies on attributable mortality to evaluations of guideline adherence, offering to the investigators of the 28 ECMM member countries the opportunity to document their cases of invasive Candida infection. CandiReg allows the monitoring of epidemiology of invasive Candida infections, including monitoring of multinational outbreaks. Here, we describe the structure and management of the CandiReg platform.ConclusionCandiReg supports the collection of clinical information and isolates to improve the knowledge on epidemiology and eventually to improve management of invasive Candida infections. CandiReg promotes international collaboration, improving the availability and quality of evidence on invasive Candida infection and contributes to improved patient management.
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Project description:The 9th European Conference on Marine Natural Products (ECMNP) in Glasgow follows its predecessors in La Toja (2013), Tjärnö (2011), Porto (2009), Ischia (2007), Paris (2005), Elmau (2002), Santiago de Compostela (1999), and Athens (1997). [...].
| S-EPMC4698589 | biostudies-literature
Project description:IntroductionReports of SARS-CoV-2 coronavirus (COVID-19) vaccine-related myocarditis, particularly after mRNA vaccines, have raised concerns amongst the general public. This review examined the literature regarding myocarditis post COVID-19 vaccination, drawing from vaccine safety surveillance databases and case reports.MethodsCombinations of search terms were used in PubMed and COVID-19-specific repositories - LitCovid and the Cochrane COVID-19 Study Register - between 1 October 2020 and 31 October 2021. Manual searches of GoogleScholar and screening of article bibliographies were also performed.ResultsInformation was obtained from five vaccine safety surveillance databases. Fifty-two (52) case reports totalling 200 cases of possible COVID-19 vaccine-related myocarditis were summarised. Vaccine surveillance databases differed in reporting formats and vaccination rates; however, gross estimates suggested low overall incidence rates of 2-5 per million mRNA vaccines. The incidence appeared to be higher in younger male populations, with onset of symptoms within a few days, usually after the second dose. Some with prior COVID-19 infections had onset after the first dose. Cases with prior unrelated myocarditis were also noted. Almost all presented with chest pain (98.0%). Troponin elevation was universally described and cardiac magnetic resonance imaging was commonly reported based on the updated Lake Louise criteria. Clinical course was mild in the majority, with response to anti-inflammatory treatment.ConclusionCOVID-19 vaccine-related myocarditis is an important but rare adverse event. More research is needed into its pathogenesis and reasons for its predominance in young males, while gaps in data exist in those aged <16 years, as well as those with prior COVID-19 infections and prior myocarditis.
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Project description:BackgroundLINCS, "Library of Integrated Network-based Cellular Signatures", and IDG, "Illuminating the Druggable Genome", are both NIH projects and consortia that have generated rich datasets for the study of the molecular basis of human health and disease. LINCS L1000 expression signatures provide unbiased systems/omics experimental evidence. IDG provides compiled and curated knowledge for illumination and prioritization of novel drug target hypotheses. Together, these resources can support a powerful new approach to identifying novel drug targets for complex diseases, such as Parkinson's disease (PD), which continues to inflict severe harm on human health, and resist traditional research approaches.ResultsIntegrating LINCS and IDG, we built the Knowledge Graph Analytics Platform (KGAP) to support an important use case: identification and prioritization of drug target hypotheses for associated diseases. The KGAP approach includes strong semantics interpretable by domain scientists and a robust, high performance implementation of a graph database and related analytical methods. Illustrating the value of our approach, we investigated results from queries relevant to PD. Approved PD drug indications from IDG's resource DrugCentral were used as starting points for evidence paths exploring chemogenomic space via LINCS expression signatures for associated genes, evaluated as target hypotheses by integration with IDG. The KG-analytic scoring function was validated against a gold standard dataset of genes associated with PD as elucidated, published mechanism-of-action drug targets, also from DrugCentral. IDG's resource TIN-X was used to rank and filter KGAP results for novel PD targets, and one, SYNGR3 (Synaptogyrin-3), was manually investigated further as a case study and plausible new drug target for PD.ConclusionsThe synergy of LINCS and IDG, via KG methods, empowers graph analytics methods for the investigation of the molecular basis of complex diseases, and specifically for identification and prioritization of novel drug targets. The KGAP approach enables downstream applications via integration with resources similarly aligned with modern KG methodology. The generality of the approach indicates that KGAP is applicable to many disease areas, in addition to PD, the focus of this paper.
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