TY  - JOUR
A1  - Solimando, Antonio G.
A1  - Bittrich, Max
A1  - Shahini, Endrit
A1  - Albanese, Federica
A1  - Fritz, Georg
A1  - Krebs, Markus
T1  - Determinants of COVID-19 disease severity – lessons from primary and secondary immune disorders including cancer
JF  - International Journal of Molecular Sciences
N2  - At the beginning of the COVID-19 pandemic, patients with primary and secondary immune disorders — including patients suffering from cancer — were generally regarded as a high-risk population in terms of COVID-19 disease severity and mortality. By now, scientific evidence indicates that there is substantial heterogeneity regarding the vulnerability towards COVID-19 in patients with immune disorders. In this review, we aimed to summarize the current knowledge about the effect of coexistent immune disorders on COVID-19 disease severity and vaccination response. In this context, we also regarded cancer as a secondary immune disorder. While patients with hematological malignancies displayed lower seroconversion rates after vaccination in some studies, a majority of cancer patients’ risk factors for severe COVID-19 disease were either inherent (such as metastatic or progressive disease) or comparable to the general population (age, male gender and comorbidities such as kidney or liver disease). A deeper understanding is needed to better define patient subgroups at a higher risk for severe COVID-19 disease courses. At the same time, immune disorders as functional disease models offer further insights into the role of specific immune cells and cytokines when orchestrating the immune response towards SARS-CoV-2 infection. Longitudinal serological studies are urgently needed to determine the extent and the duration of SARS-CoV-2 immunity in the general population, as well as immune-compromised and oncological patients.
KW  - COVID-19
KW  - SARS-CoV-2
KW  - disorder of immunity
KW  - cancer
Y1  - 2023
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-319412
SN  - 1422-0067
VL  - 24
IS  - 10
ER  - 
TY  - JOUR
A1  - Solimando, Antonio G.
A1  - Palumbo, Carmen
A1  - Pragnell, Mary Victoria
A1  - Bittrich, Max
A1  - Argentiero, Antonella
A1  - Krebs, Markus
T1  - Aplastic anemia as a roadmap for bone marrow failure: an overview and a clinical workflow
JF  - International Journal of Molecular Sciences
N2  - In recent years, it has become increasingly apparent that bone marrow (BM) failures and myeloid malignancy predisposition syndromes are characterized by a wide phenotypic spectrum and that these diseases must be considered in the differential diagnosis of children and adults with unexplained hematopoiesis defects. Clinically, hypocellular BM failure still represents a challenge in pathobiology-guided treatment. There are three fundamental topics that emerged from our review of the existing data. An exogenous stressor, an immune defect, and a constitutional genetic defect fuel a vicious cycle of hematopoietic stem cells, immune niches, and stroma compartments. A wide phenotypic spectrum exists for inherited and acquired BM failures and predispositions to myeloid malignancies. In order to effectively manage patients, it is crucial to establish the right diagnosis. New theragnostic windows can be revealed by exploring BM failure pathomechanisms.
KW  - hematopoietic stem cells
KW  - bone marrow immune-microenvironment
KW  - bone marrow failure
KW  - cytopenia
KW  - aplastic anemia
Y1  - 2022
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-290440
SN  - 1422-0067
VL  - 23
IS  - 19
ER  - 
TY  - JOUR
A1  - Loda, Sophia
A1  - Krebs, Jonathan
A1  - Danhof, Sophia
A1  - Schreder, Martin
A1  - Solimando, Antonio G.
A1  - Strifler, Susanne
A1  - Rasche, Leo
A1  - Kortüm, Martin
A1  - Kerscher, Alexander
A1  - Knop, Stefan
A1  - Puppe, Frank
A1  - Einsele, Hermann
A1  - Bittrich, Max
T1  - Exploration of artificial intelligence use with ARIES in multiple myeloma research
JF  - Journal of Clinical Medicine
N2  - Background: Natural language processing (NLP) is a powerful tool supporting the generation of Real-World Evidence (RWE). There is no NLP system that enables the extensive querying of parameters specific to multiple myeloma (MM) out of unstructured medical reports. We therefore created a MM-specific ontology to accelerate the information extraction (IE) out of unstructured text. Methods: Our MM ontology consists of extensive MM-specific and hierarchically structured attributes and values. We implemented “A Rule-based Information Extraction System” (ARIES) that uses this ontology. We evaluated ARIES on 200 randomly selected medical reports of patients diagnosed with MM. Results: Our system achieved a high F1-Score of 0.92 on the evaluation dataset with a precision of 0.87 and recall of 0.98. Conclusions: Our rule-based IE system enables the comprehensive querying of medical reports. The IE accelerates the extraction of data and enables clinicians to faster generate RWE on hematological issues. RWE helps clinicians to make decisions in an evidence-based manner. Our tool easily accelerates the integration of research evidence into everyday clinical practice.
KW  - natural language processing
KW  - ontology
KW  - artificial intelligence
KW  - multiple myeloma
KW  - real world evidence
Y1  - 2019
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-197231
SN  - 2077-0383
VL  - 8
IS  - 7
ER  -