LIVIVO - Das Suchportal für Lebenswissenschaften

switch to English language
Erweiterte Suche

Suchergebnis

Treffer 1 - 8 von insgesamt 8

Suchoptionen

  1. Buch ; Online ; Dissertation / Habilitation: Identifying genome-wide transcription units from histone modifications using EPIGENE

    Sahu, Anshupa [Verfasser] / Chung, Ho-Ryun [Akademischer Betreuer]

    2021  

    Verfasserangabe Anshupa Sahu ; Betreuer: Ho-Ryun Chung
    Schlagwörter Biowissenschaften, Biologie ; Life Science, Biology
    Thema/Rubrik (Code) sg570
    Sprache Englisch
    Verlag Philipps-Universität Marburg
    Erscheinungsort Marburg
    Dokumenttyp Buch ; Online ; Dissertation / Habilitation
    Datenquelle Digitale Dissertationen im Internet

    Volltext online

    Zusatzmaterialien

    Kategorien

    Fernleihe an ZB MED

    Sie können sich den gewünschten Titel als lokale Nutzerin oder lokaler Nutzer von ZB MED direkt an den Standort Köln schicken lassen.

  2. Artikel: Neuropilin2 in Mesenchymal Stromal Cells as a Potential Novel Therapeutic Target in Myelofibrosis.

    Vosbeck, Karla / Förster, Sarah / Mayr, Thomas / Sahu, Anshupa / Haddouti, El-Mustapha / Al-Adilee, Osamah / Körber, Ruth-Miriam / Bisht, Savita / Muders, Michael H / Nesic, Svetozar / Buness, Andreas / Kristiansen, Glen / Schildberg, Frank A / Gütgemann, Ines

    Cancers

    2024  Band 16, Heft 10

    Abstract: Bone marrow fibrosis in myeloproliferative neoplasm (MPN), myelodysplastic syndromes (MDS), MPN/MDS overlap syndromes and acute myeloid leukemia (AML) is associated with poor prognosis and early treatment failure. Myelofibrosis (MF) is accompanied by ... ...

    Abstract Bone marrow fibrosis in myeloproliferative neoplasm (MPN), myelodysplastic syndromes (MDS), MPN/MDS overlap syndromes and acute myeloid leukemia (AML) is associated with poor prognosis and early treatment failure. Myelofibrosis (MF) is accompanied by reprogramming of multipotent bone marrow mesenchymal stromal cells (MSC) into osteoid and fiber-producing stromal cells. We demonstrate NRP2 and osteolineage marker NCAM1 (neural cell adhesion molecule 1) expression within the endosteal niche in normal bone marrow and aberrantly in MPN, MDS MPN/MDS overlap syndromes and AML (
    Sprache Englisch
    Erscheinungsdatum 2024-05-18
    Erscheinungsland Switzerland
    Dokumenttyp Journal Article
    ZDB-ID 2527080-1
    ISSN 2072-6694
    ISSN 2072-6694
    DOI 10.3390/cancers16101924
    Datenquelle MEDical Literature Analysis and Retrieval System OnLINE

    Zusatzmaterialien

    Kategorien

    Über subito bestellen

    Dieser Service ist kostenpflichtig (siehe Lieferbedingungen von subito). Bestellungen, die einen Artikel nebst Supplementary Material umfassen, werden grundsätzlich wie mehrfache Bestellungen bearbeitet. Gebühren fallen in diesen Fällen für jede einzelne Bestellung an.

  3. Artikel ; Online: EPIGENE: genome-wide transcription unit annotation using a multivariate probabilistic model of histone modifications.

    Sahu, Anshupa / Li, Na / Dunkel, Ilona / Chung, Ho-Ryun

    Epigenetics & chromatin

    2020  Band 13, Heft 1, Seite(n) 20

    Abstract: Background: Understanding the transcriptome is critical for explaining the functional as well as regulatory roles of genomic regions. Current methods for the identification of transcription units (TUs) use RNA-seq that, however, require large quantities ...

    Abstract Background: Understanding the transcriptome is critical for explaining the functional as well as regulatory roles of genomic regions. Current methods for the identification of transcription units (TUs) use RNA-seq that, however, require large quantities of mRNA rendering the identification of inherently unstable TUs, e.g. miRNA precursors, difficult. This problem can be alleviated by chromatin-based approaches due to a correlation between histone modifications and transcription.
    Results: Here, we introduce EPIGENE, a novel chromatin segmentation method for the identification of active TUs using transcription-associated histone modifications. Unlike the existing chromatin segmentation approaches, EPIGENE uses a constrained, semi-supervised multivariate hidden Markov model (HMM) that models the observed combination of histone modifications using a product of independent Bernoulli random variables, to identify active TUs. Our results show that EPIGENE can identify genome-wide TUs in an unbiased manner. EPIGENE-predicted TUs show an enrichment of RNA Polymerase II at the transcription start site and in gene body indicating that they are indeed transcribed. Comprehensive validation using existing annotations revealed that 93% of EPIGENE TUs can be explained by existing gene annotations and 5% of EPIGENE TUs in HepG2 can be explained by microRNA annotations. EPIGENE outperformed the existing RNA-seq-based approaches in TU prediction precision across human cell lines. Finally, we identified 232 novel TUs in K562 and 43 novel cell-specific TUs all of which were supported by RNA Polymerase II ChIP-seq and Nascent RNA-seq data.
    Conclusion: We demonstrate the applicability of EPIGENE to identify genome-wide active TUs and to provide valuable information about unannotated TUs. EPIGENE is an open-source method and is freely available at: https://github.com/imbbLab/EPIGENE.
    Mesh-Begriff(e) Chromatin Immunoprecipitation Sequencing/methods ; Epigenomics/methods ; Hep G2 Cells ; Histone Code ; Humans ; K562 Cells ; Markov Chains ; Molecular Sequence Annotation/methods ; Software ; Transcription Initiation Site ; Transcriptome
    Sprache Englisch
    Erscheinungsdatum 2020-04-07
    Erscheinungsland England
    Dokumenttyp Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 2462129-8
    ISSN 1756-8935 ; 1756-8935
    ISSN (online) 1756-8935
    ISSN 1756-8935
    DOI 10.1186/s13072-020-00341-z
    Datenquelle MEDical Literature Analysis and Retrieval System OnLINE

    Zusatzmaterialien

    Kategorien

    Über subito bestellen

    Dieser Service ist kostenpflichtig (siehe Lieferbedingungen von subito). Bestellungen, die einen Artikel nebst Supplementary Material umfassen, werden grundsätzlich wie mehrfache Bestellungen bearbeitet. Gebühren fallen in diesen Fällen für jede einzelne Bestellung an.

  4. Artikel: Reconstruction of the origin of the first major SARS-CoV-2 outbreak in Germany

    Korencak, Marek / Sivalingam, Sugirthan / Sahu, Anshupa / Dressen, Dietmar / Schmidt, Axel / Brand, Fabian / Krawitz, Peter / Hart, Libor / Maria Eis-Hübinger, Anna / Buness, Andreas / Streeck, Hendrik

    Computational and Structural Biotechnology Journal. 2022, v. 20

    2022  

    Abstract: The first major COVID-19 outbreak in Germany occurred in Heinsberg in February 2020 with 388 officially reported cases. Unexpectedly, the first outbreak happened in a small town with little to no travelers. We used phylogenetic analyses to investigate ... ...

    Abstract The first major COVID-19 outbreak in Germany occurred in Heinsberg in February 2020 with 388 officially reported cases. Unexpectedly, the first outbreak happened in a small town with little to no travelers. We used phylogenetic analyses to investigate the origin and spread of the virus in this outbreak. We sequenced 90 (23%) SARS-CoV-2 genomes from the 388 reported cases including the samples from the first documented cases. Phylogenetic analyses of these sequences revealed mainly two circulating strains with 74 samples assigned to lineage B.3 and 6 samples assigned to lineage B.1. Lineage B.3 was introduced first and probably caused the initial spread. Using phylogenetic analysis tools, we were able to identify closely related strains in France and hypothesized the possible introduction from France.
    Schlagwörter COVID-19 infection ; Severe acute respiratory syndrome coronavirus 2 ; biotechnology ; genome ; phylogeny ; viruses ; France ; Germany
    Sprache Englisch
    Umfang p. 2292-2296.
    Erscheinungsort Elsevier B.V.
    Dokumenttyp Artikel
    ZDB-ID 2694435-2
    ISSN 2001-0370
    ISSN 2001-0370
    DOI 10.1016/j.csbj.2022.05.011
    Datenquelle NAL Katalog (AGRICOLA)

    Zusatzmaterialien

    Kategorien

    Über subito bestellen

    Dieser Service ist kostenpflichtig (siehe Lieferbedingungen von subito). Bestellungen, die einen Artikel nebst Supplementary Material umfassen, werden grundsätzlich wie mehrfache Bestellungen bearbeitet. Gebühren fallen in diesen Fällen für jede einzelne Bestellung an.

  5. Artikel ; Online: Machine Learning for Decision-Support in Acute Abdominal Pain - Proof of Concept and Central Considerations.

    Henn, Jonas / Hatterscheidt, Simon / Sahu, Anshupa / Buness, Andreas / Dohmen, Jonas / Arensmeyer, Jan / Feodorovici, Philipp / Sommer, Nils / Schmidt, Joachim / Kalff, Jörg C / Matthaei, Hanno

    Zentralblatt fur Chirurgie

    2023  Band 148, Heft 4, Seite(n) 376–383

    Abstract: Acute abdominal pain is a common presenting symptom in the emergency department and represents heterogeneous causes and diagnoses. There is often a decision to be made regarding emergency surgical care. Machine learning (ML) could be used here as a ... ...

    Titelübersetzung Maschinelles Lernen als Entscheidungshilfe bei akuten Bauchschmerzen – Proof-of-Concept und zentrale Überlegungen.
    Abstract Acute abdominal pain is a common presenting symptom in the emergency department and represents heterogeneous causes and diagnoses. There is often a decision to be made regarding emergency surgical care. Machine learning (ML) could be used here as a decision-support and relieve the time and personnel resource shortage.Patients with acute abdominal pain presenting to the Department of Surgery at Bonn University Hospital in 2020 and 2021 were retrospectively analyzed. Clinical parameters as well as laboratory values were used as predictors. After randomly splitting into a training and test data set (ratio 80 to 20), three ML algorithms were comparatively trained and validated. The entire procedure was repeated 20 times.A total of 1357 patients were identified and included in the analysis, with one in five (n = 276, 20.3%) requiring emergency abdominal surgery within 24 hours. Patients operated on were more likely to be male (p = 0.026), older (p = 0.006), had more gastrointestinal symptoms (nausea: p < 0.001, vomiting p < 0.001) as well as a more recent onset of pain (p < 0.001). Tenderness (p < 0.001) and guarding (p < 0.001) were more common in surgically treated patients and blood analyses showed increased inflammation levels (white blood cell count: p < 0.001, CRP: p < 0.001) and onset of organ dysfunction (creatinine: p < 0.014, quick p < 0.001). Of the three trained algorithms, the tree-based methods (h2o random forest and cforest) showed the best performance. The algorithms classified patients, i.e., predicted surgery, with a median AUC ROC of 0.81 and 0.79 and AUC PRC of 0.56 in test sets.A proof-of-concept was achieved with the development of an ML model for predicting timely surgical therapy for acute abdomen. The ML algorithm can be a valuable tool in decision-making. Especially in the context of heavily used medical resources, the algorithm can help to use these scarce resources more effectively. Technological progress, especially regarding artificial intelligence, increasingly enables evidence-based approaches in surgery but requires a strictly interdisciplinary approach. In the future, the use and handling of ML should be integrated into surgical training.
    Mesh-Begriff(e) Humans ; Abdomen, Acute ; Artificial Intelligence ; Retrospective Studies ; Machine Learning ; Algorithms
    Sprache Englisch
    Erscheinungsdatum 2023-08-10
    Erscheinungsland Germany
    Dokumenttyp Journal Article
    ZDB-ID 200935-3
    ISSN 1438-9592 ; 0044-409X
    ISSN (online) 1438-9592
    ISSN 0044-409X
    DOI 10.1055/a-2125-1559
    Datenquelle MEDical Literature Analysis and Retrieval System OnLINE

    Zusatzmaterialien

    Kategorien

    Verfügbar in ZB MED Köln/Königswinter

    Uk I Zs.25: Hefte anzeigen Standort:
    Je nach Verfügbarkeit (siehe Angabe bei Bestand)
    bis Jg. 2021: Bestellungen von Artikeln über das Online-Bestellformular
    ab Jg. 2022: Lesesaal (EG)
    Zs.MO 95: Hefte anzeigen

    Über subito bestellen

    Dieser Service ist kostenpflichtig (siehe Lieferbedingungen von subito). Bestellungen, die einen Artikel nebst Supplementary Material umfassen, werden grundsätzlich wie mehrfache Bestellungen bearbeitet. Gebühren fallen in diesen Fällen für jede einzelne Bestellung an.

  6. Artikel ; Online: Induction of Interferon-Stimulated Genes Correlates with Reduced Growth of Influenza A Virus in Lungs after RIG-I Agonist Treatment of Ferrets.

    Schwab, Lara S U / Londrigan, Sarah L / Brooks, Andrew G / Hurt, Aeron C / Sahu, Anshupa / Deng, Yi-Mo / Moselen, Jean / Coch, Christoph / Zillinger, Thomas / Hartmann, Gunther / Reading, Patrick C

    Journal of virology

    2022  Band 96, Heft 16, Seite(n) e0055922

    Abstract: Intracellular RIG-I receptors represent key innate sensors of RNA virus infection, and RIG-I activation results in the induction of hundreds of host effector genes, including interferon-stimulated genes (ISGs). Synthetic RNA agonists targeting RIG-I have ...

    Abstract Intracellular RIG-I receptors represent key innate sensors of RNA virus infection, and RIG-I activation results in the induction of hundreds of host effector genes, including interferon-stimulated genes (ISGs). Synthetic RNA agonists targeting RIG-I have shown promise as antivirals against a broad spectrum of viruses, including influenza A virus (IAV), in both
    Mesh-Begriff(e) Animals ; Antiviral Agents/pharmacology ; Ferrets/metabolism ; Humans ; Immunity, Innate ; Influenza A virus/genetics ; Influenza, Human ; Interferons/metabolism ; Leukocytes, Mononuclear/metabolism ; Lung ; Mice ; Virus Replication/genetics
    Chemische Substanzen Antiviral Agents ; Interferons (9008-11-1)
    Sprache Englisch
    Erscheinungsdatum 2022-08-02
    Erscheinungsland United States
    Dokumenttyp Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 80174-4
    ISSN 1098-5514 ; 0022-538X
    ISSN (online) 1098-5514
    ISSN 0022-538X
    DOI 10.1128/jvi.00559-22
    Datenquelle MEDical Literature Analysis and Retrieval System OnLINE

    Zusatzmaterialien

    Kategorien

    Verfügbar in ZB MED Köln/Königswinter

    Zs.A 609: Hefte anzeigen Standort:
    Je nach Verfügbarkeit (siehe Angabe bei Bestand)
    bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular
    Jg. 1995 - 2021: Lesesall (1.OG)
    ab Jg. 2022: Lesesaal (EG)

    Über subito bestellen

    Dieser Service ist kostenpflichtig (siehe Lieferbedingungen von subito). Bestellungen, die einen Artikel nebst Supplementary Material umfassen, werden grundsätzlich wie mehrfache Bestellungen bearbeitet. Gebühren fallen in diesen Fällen für jede einzelne Bestellung an.

  7. Artikel: Reconstruction of the origin of the first major SARS-CoV-2 outbreak in Germany.

    Korencak, Marek / Sivalingam, Sugirthan / Sahu, Anshupa / Dressen, Dietmar / Schmidt, Axel / Brand, Fabian / Krawitz, Peter / Hart, Libor / Maria Eis-Hübinger, Anna / Buness, Andreas / Streeck, Hendrik

    Computational and structural biotechnology journal

    2022  Band 20, Seite(n) 2292–2296

    Abstract: The first major COVID-19 outbreak in Germany occurred in Heinsberg in February 2020 with 388 officially reported cases. Unexpectedly, the first outbreak happened in a small town with little to no travelers. We used phylogenetic analyses to investigate ... ...

    Abstract The first major COVID-19 outbreak in Germany occurred in Heinsberg in February 2020 with 388 officially reported cases. Unexpectedly, the first outbreak happened in a small town with little to no travelers. We used phylogenetic analyses to investigate the origin and spread of the virus in this outbreak. We sequenced 90 (23%) SARS-CoV-2 genomes from the 388 reported cases including the samples from the first documented cases. Phylogenetic analyses of these sequences revealed mainly two circulating strains with 74 samples assigned to lineage B.3 and 6 samples assigned to lineage B.1. Lineage B.3 was introduced first and probably caused the initial spread. Using phylogenetic analysis tools, we were able to identify closely related strains in France and hypothesized the possible introduction from France.
    Sprache Englisch
    Erscheinungsdatum 2022-05-10
    Erscheinungsland Netherlands
    Dokumenttyp Journal Article
    ZDB-ID 2694435-2
    ISSN 2001-0370
    ISSN 2001-0370
    DOI 10.1016/j.csbj.2022.05.011
    Datenquelle MEDical Literature Analysis and Retrieval System OnLINE

    Zusatzmaterialien

    Kategorien

    Über subito bestellen

    Dieser Service ist kostenpflichtig (siehe Lieferbedingungen von subito). Bestellungen, die einen Artikel nebst Supplementary Material umfassen, werden grundsätzlich wie mehrfache Bestellungen bearbeitet. Gebühren fallen in diesen Fällen für jede einzelne Bestellung an.

  8. Artikel: Machine Learning for Decision-Support in Acute Abdominal Pain – Proof of Concept and Central Considerations

    Henn, Jonas / Hatterscheidt, Simon / Sahu, Anshupa / Buness, Andreas / Dohmen, Jonas / Arensmeyer, Jan / Feodorovici, Philipp / Sommer, Nils / Schmidt, Joachim / Kalff, Jörg C. / Matthaei, Hanno

    Zentralblatt für Chirurgie - Zeitschrift für Allgemeine, Viszeral-, Thorax- und Gefäßchirurgie

    2023  Band 148, Heft 04, Seite(n) 376–383

    Abstract: Acute abdominal pain is a common presenting symptom in the emergency department and represents heterogeneous causes and diagnoses. There is often a decision to be made regarding emergency surgical care. Machine learning (ML) could be used ... ...

    Abstract Acute abdominal pain is a common presenting symptom in the emergency department and represents heterogeneous causes and diagnoses. There is often a decision to be made regarding emergency surgical care. Machine learning (ML) could be used here as a decision-support and relieve the time and personnel resource shortage. Patients with acute abdominal pain presenting to the Department of Surgery at Bonn University Hospital in 2020 and 2021 were retrospectively analyzed. Clinical parameters as well as laboratory values were used as predictors. After randomly splitting into a training and test data set (ratio 80 to 20), three ML algorithms were comparatively trained and validated. The entire procedure was repeated 20 times. A total of 1357 patients were identified and included in the analysis, with one in five (n = 276, 20.3%) requiring emergency abdominal surgery within 24 hours. Patients operated on were more likely to be male (p = 0.026), older (p = 0.006), had more gastrointestinal symptoms (nausea: p < 0.001, vomiting p < 0.001) as well as a more recent onset of pain (p < 0.001). Tenderness (p < 0.001) and guarding (p < 0.001) were more common in surgically treated patients and blood analyses showed increased inflammation levels (white blood cell count: p < 0.001, CRP: p < 0.001) and onset of organ dysfunction (creatinine: p < 0.014, quick p < 0.001). Of the three trained algorithms, the tree-based methods (h2o random forest and cforest) showed the best performance. The algorithms classified patients, i.e., predicted surgery, with a median AUC ROC of 0.81 and 0.79 and AUC PRC of 0.56 in test sets. A proof-of-concept was achieved with the development of an ML model for predicting timely surgical therapy for acute abdomen. The ML algorithm can be a valuable tool in decision-making. Especially in the context of heavily used medical resources, the algorithm can help to use these scarce resources more effectively. Technological progress, especially regarding artificial intelligence, increasingly enables evidence-based approaches in surgery but requires a strictly interdisciplinary approach. In the future, the use and handling of ML should be integrated into surgical training.
    Schlagwörter maschinelles Lernen ; künstliche Intelligenz ; klinische Entscheidungsfindung ; Entscheidungshilfe ; akutes Abdomen ; machine learning ; artificial intelligence ; acute abdominal pain ; clinical decision making ; decision support
    Sprache Englisch
    Erscheinungsdatum 2023-08-01
    Verlag Georg Thieme Verlag KG
    Erscheinungsort Stuttgart ; New York
    Dokumenttyp Artikel
    ZDB-ID 200935-3
    ISSN 1438-9592 ; 0044-409X
    ISSN (online) 1438-9592
    ISSN 0044-409X
    DOI 10.1055/a-2125-1559
    Datenquelle Thieme Verlag

    Zusatzmaterialien

    Kategorien

    Verfügbar in ZB MED Köln/Königswinter

    Uk I Zs.25: Hefte anzeigen Standort:
    Je nach Verfügbarkeit (siehe Angabe bei Bestand)
    bis Jg. 2021: Bestellungen von Artikeln über das Online-Bestellformular
    ab Jg. 2022: Lesesaal (EG)
    Zs.MO 95: Hefte anzeigen

    Über subito bestellen

    Dieser Service ist kostenpflichtig (siehe Lieferbedingungen von subito). Bestellungen, die einen Artikel nebst Supplementary Material umfassen, werden grundsätzlich wie mehrfache Bestellungen bearbeitet. Gebühren fallen in diesen Fällen für jede einzelne Bestellung an.

Zum Seitenanfang