The Quest for System-Theoretical Medicine in the COVID-19 Era

Precision medicine and molecular systems medicine (MSM) are highly utilized and successful approaches to improve understanding, diagnosis, and treatment of many diseases from bench-to-bedside. Especially in the COVID-19 pandemic, molecular techniques and biotechnological innovation have proven to be of utmost importance for rapid developments in disease diagnostics and treatment, including DNA and RNA sequencing technology, treatment with drugs and natural products and vaccine development. The COVID-19 crisis, however, has also demonstrated the need for systemic thinking and transdisciplinarity and the limits of MSM: the neglect of the bio-psycho-social systemic nature of humans and their context as the object of individual therapeutic and population-oriented interventions. COVID-19 illustrates how a medical problem requires a transdisciplinary approach in epidemiology, pathology, internal medicine, public health, environmental medicine, and socio-economic modeling. Regarding the need for conceptual integration of these different kinds of knowledge we suggest the application of general system theory (GST). This approach endorses an organism-centered view on health and disease, which according to Ludwig von Bertalanffy who was the founder of GST, we call Organismal Systems Medicine (OSM). We argue that systems science offers wider applications in the field of pathology and can contribute to an integrative systems medicine by (i) integration of evidence across functional and structural differentially scaled subsystems, (ii) conceptualization of complex multilevel systems, and (iii) suggesting mechanisms and non-linear relationships underlying the observed phenomena. We underline these points with a proposal on multi-level systems pathology including neurophysiology, endocrinology, immune system, genetics, and general metabolism. An integration of these areas is necessary to understand excess mortality rates and polypharmacological treatments. In the pandemic era this multi-level systems pathology is most important to assess potential vaccines, their effectiveness, short-, and long-time adverse effects. We further argue that these conceptual frameworks are not only valid in the COVID-19 era but also important to be integrated in a medicinal curriculum.

Prediction of Celiac Disease Severity and Associated Endocrine Morbidities through Deep Learning-based Image Analytics

ObjectiveDevelop a deep learning-based methodology using the foundations of systems pathology to generate highly accurate predictive tools for complex gastrointestinal diseases, using celiac disease (CD) as a prototype.DesignTo predict the severity of CD, defined by Marsh–Oberhüber classification, we used deep learning to develop a model based on histopathologic features.ResultsThe study was based on a pediatric cohort of 124 patients identified with different classes of CD severity. The model predicted CD with an overall 88.7% accuracy with the highest for Marsh IIIc (91.0%; 95% sensitivity; 91% specificity). The model identified EECs as a defining feature of children with Marsh IIIc CD and endocrinopathies which was confirmed using immunohistochemistry.ConclusionThis deep learning image analysis platform has broad applications in disease treatment, management, and prognostication and paves the way for precision medicine.SummaryWhat is already known about this subject?–Deep Learning has the potential to generate predictive models for complex gastrointestinal diseases.What are the new findings?–Our deep learning-based model used the foundations of systems pathology to generate a highly accurate predictive tool for complex gastrointestinal diseases, using a celiac disease (CD) pediatric cohort as a prototype.–The model predicated CD severity with high accuracy and identified enteroendocrine cells as a defining feature of children with severe CD and endocrinopathies.How might it impact on clinical practice in the foreseeable future?–Assessment of histopathological markers at the time of diagnosis that can predict risk of severity or complications can have broad applications in disease treatment, management, and prognostication and pave the way for precision medicine.

Molecular pathogenesis of rhegmatogenous retinal detachment

Rhegmatogenous retinal detachment (RRD) is an ophthalmic emergency, which usually requires prompt surgery to prevent further detachment and restore sensory function. Although several individual factors have been suggested, a systems level understanding of molecular pathomechanisms underlying this severe eye disorder is lacking. To address this gap in knowledge we performed the molecular level systems pathology analysis of the vitreous from 127 patients with RRD using state-of-the art quantitative mass spectrometry to identify the individual key proteins, as well as the biochemical pathways contributing to the development of the disease. RRD patients have specific vitreous proteome profiles compared to other diseases such as macular hole, pucker, or proliferative diabetic retinopathy eyes. Our data indicate that various mechanisms, including glycolysis, photoreceptor death, and Wnt and MAPK signaling, are activated during or after the RRD to promote retinal cell survival. In addition, platelet-mediated wound healing processes, cell adhesion molecules reorganization and apoptotic processes were detected during RRD progression or proliferative vitreoretinopathy formation. These findings improve the understanding of RRD pathogenesis, identify novel targets for treatment of this ophthalmic disease, and possibly affect the prognosis of eyes treated or operated upon due to RRD.