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The central question of systems biology is to understand how individual components of a biological system such as genes or proteins cooperate in emerging phenotypes resulting in the evolution of diseases. As living cells are open systems in quasi-steady state type equilibrium in continuous exchange with their environment, computational techniques that have been successfully applied in statistical thermodynamics to describe phase transitions may provide new insights to the emerging behavior of biological systems. Here we systematically evaluate the translation of computational techniques from solid-state physics to network models that closely resemble biological networks and develop specific translational rules to tackle problems unique to living systems. We focus on logic models exhibiting only two states in each network node. Motivated by the apparent asymmetry between biological states where an entity exhibits boolean states i.e. is active or inactive, we present an adaptation of symmetric Ising model towards an asymmetric one fitting to living systems here referred to as the modified Ising model with gene-type spins. We analyze phase transitions by Monte Carlo simulations and propose a mean-field solution of a modified Ising model of a network type that closely resembles a real-world network, the Barabási–Albert model of scale-free networks. We show that asymmetric Ising models show similarities to symmetric Ising models with the external field and undergoes a discontinuous phase transition of the first-order and exhibits hysteresis. The simulation setup presented herein can be directly used for any biological network connectivity dataset and is also applicable for other networks that exhibit similar states of activity. The method proposed here is a general statistical method to deal with non-linear large scale models arising in the context of biological systems and is scalable to any network size. [ABSTRACT FROM AUTHOR]

Background: Classification of binary data arises naturally in many clinical applications, such as patient risk stratification through ICD codes. One of the key practical challenges in data classification using machine learning is to avoid overfitting. Overfitting in supervised learning primarily occurs when a model learns random variations from noisy labels in training data rather than the underlying patterns. While traditional methods such as regularization and early stopping have demonstrated effectiveness in interpolation tasks, addressing overfitting in the classification of binary data, in which predictions always amount to extrapolation, demands extrapolation-enhanced strategies. One such approach is hybrid mechanistic/data-driven modeling, which integrates prior knowledge on input features into the learning process, enhancing the model's ability to extrapolate. Results: We present NoiseCut, a Python package for noise-tolerant classification of binary data by employing a hybrid modeling approach that leverages solutions of defined max-cut problems. In a comparative analysis conducted on synthetically generated binary datasets, NoiseCut exhibits better overfitting prevention compared to the early stopping technique employed by different supervised machine learning algorithms. The noise tolerance of NoiseCut stems from a dropout strategy that leverages prior knowledge of input features and is further enhanced by the integration of max-cut problems into the learning process. Conclusions: NoiseCut is a Python package for the implementation of hybrid modeling for the classification of binary data. It facilitates the integration of mechanistic knowledge on the input features into learning from data in a structured manner and proves to be a valuable classification tool when the available training data is noisy and/or limited in size. This advantage is especially prominent in medical and biomedical applications where data scarcity and noise are common challenges. The codebase, illustrations, and documentation for NoiseCut are accessible for download at https://pypi.org/project/noisecut/. The implementation detailed in this paper corresponds to the version 0.2.1 release of the software. [ABSTRACT FROM AUTHOR]

Acute Respiratory Distress Syndrome (ARDS) is a condition that endangers the lives of many Intensive Care Unit patients through gradual reduction of lung function. Due to its heterogeneity, this condition has been difficult to diagnose and treat, although it has been the subject of continuous research, leading to the development of several tools for modeling disease progression on the one hand, and guidelines for diagnosis on the other, mainly the "Berlin Definition". This paper describes the development of a deep learning-based surrogate model of one such tool for modeling ARDS onset in a virtual patient: the Nottingham Physiology Simulator. The model-development process takes advantage of current machine learning and data-analysis techniques, as well as efficient hyperparameter-tuning methods, within a high-performance computing-enabled data science platform. The lightweight models developed through this process present comparable accuracy to the original simulator (per-parameter R2 > 0.90). The experimental process described herein serves as a proof of concept for the rapid development and dissemination of specialised diagnosis support systems based on pre-existing generalised mechanistic models, making use of supercomputing infrastructure for the development and testing processes and supported by open-source software for streamlined implementation in clinical routines. [ABSTRACT FROM AUTHOR]

Electronic health records (EHRs) are used in hospitals to store diagnoses, clinician notes, examinations, lab results, and interventions for each patient. Grouping patients into distinct subsets, for example, via clustering, may enable the discovery of unknown disease patterns or comorbidities, which could eventually lead to better treatment through personalized medicine. Patient data derived from EHRs is heterogeneous and temporally irregular. Therefore, traditional machine learning methods like PCA are ill-suited for analysis of EHR-derived patient data. We propose to address these issues with a new methodology based on training a gated recurrent unit (GRU) autoencoder directly on health record data. Our method learns a low-dimensional feature space by training on patient data time series, where the time of each data point is expressed explicitly. We use positional encodings for time, allowing our model to better handle the temporal irregularity of the data. We apply our method to data from the Medical Information Mart for Intensive Care (MIMIC-III). Using our data-derived feature space, we can cluster patients into groups representing major classes of disease patterns. Additionally, we show that our feature space exhibits a rich substructure at multiple scales. [ABSTRACT FROM AUTHOR]

Background: The impact of biometric covariates on risk for adverse outcomes of COVID-19 disease was assessed by numerous observational studies on unstratified cohorts, which show great heterogeneity. However, multilevel evaluations to find possible complex, e.g. non-monotonic multi-variate patterns reflecting mutual interference of parameters are missing. We used a more detailed, computational analysis to investigate the influence of biometric differences on mortality and disease evolution among severely ill COVID-19 patients.Methods: We analyzed a group of COVID-19 patients requiring Intensive care unit (ICU) treatment. For further analysis, the study group was segmented into six subgroups according to Body mass index (BMI) and age. To link the BMI/age derived subgroups with risk factors, we performed an enrichment analysis of diagnostic parameters and comorbidities. To suppress spurious patterns, multiple segmentations were analyzed and integrated into a consensus score for each analysis step.Results: We analyzed 81 COVID-19 patients, of whom 67 required mechanical ventilation (MV). Mean mortality was 35.8%. We found a complex, non-monotonic interaction between age, BMI and mortality. A subcohort of patients with younger age and intermediate BMI exhibited a strongly reduced mortality risk (p < 0.001), while differences in all other groups were not significant. Univariate impacts of BMI or age on mortality were missing. Comparing MV with non-MV patients, we found an enrichment of baseline CRP, PCT and D-Dimers within the MV group, but not when comparing survivors vs. non-survivors within the MV patient group.Conclusions: The aim of this study was to get a more detailed insight into the influence of biometric covariates on the outcome of COVID-19 patients with high degree of severity. We found that survival in MV is affected by complex interactions of covariates differing to the reported covariates, which are hidden in generic, non-stratified studies on risk factors. Hence, our study suggests that a detailed, multivariate pattern analysis on larger patient cohorts reflecting the specific disease stages might reveal more specific patterns of risk factors supporting individually adapted treatment strategies. [ABSTRACT FROM AUTHOR]

• Introduce HybridML, an open source hybrid modeling platform. • Train hybrid models including neural networks and differential equations in Tensorflow. • Build a hybrid model to predict the drug concentration in patients' blood over time. Hybrid modelling, i.e., the combination of data-driven modelling with mechanistic model components, reduces the data demand and enables extrapolation of data-driven models. However, building, training and evaluation of hybrid models is cumbersome with current frameworks. We developed HybridML, an open-source modeling platform, in which hybrid models can be trained, i.e., combinations of artificial neural networks, arithmetic expressions, and differential equations. We employ TensorFlow for artificial neural network training and Casadi to integrate ordinary differential equations and provide gradients of differential model equations enabling continuous time representations. HybridML provides also a JSON interface for the model development. We apply HybridML to an industrial case study, in which the trained model is used to predict drug concentrations over time, based on physiological information about the patients. To demonstrate its versatility, we also present a nonlinear application, where HybridML is used to model the spread of the COVID-19 pandemic in German federal states based on the state's socio-economic attributes. [ABSTRACT FROM AUTHOR]

Abstract Background Identifying stable gene lists for diagnosis, prognosis prediction, and treatment guidance of tumors remains a major challenge in cancer research. Microarrays measuring differential gene expression are widely used and should be versatile predictors of disease and other phenotypic data. However, gene expression profile studies and predictive biomarkers are often of low power, requiring numerous samples for a sound statistic, or vary between studies. Given the inconsistency of results across similar studies, methods that identify robust biomarkers from microarray data are needed to relay true biological information. Here we present a method to demonstrate that gene list stability and predictive power depends not only on the size of studies, but also on the clinical phenotype. Results Our method projects genomic tumor expression data to a lower dimensional space representing the main variation in the data. Some information regarding the phenotype resides in this low dimensional space, while some information resides in the residuum. We then introduce an information ratio (IR) as a metric defined by the partition between projected and residual space. Upon grouping phenotypes such as tumor tissue, histological grades, relapse, or aging, we show that higher IR values correlated with phenotypes that yield less robust biomarkers whereas lower IR values showed higher transferability across studies. Our results indicate that the IR is correlated with predictive accuracy. When tested across different published datasets, the IR can identify information-rich data characterizing clinical phenotypes and stable biomarkers. Conclusions The IR presents a quantitative metric to estimate the information content of gene expression data with respect to particular phenotypes.

• Shows an example of utilizing dynamic optimization for QbD in biopharmaceutics. • Examines problem formulations for high process performance considering product quality. • Derives optimal feeding profiles for mAb production with desired glycosylation ranges. Compliance with Quality by Design (QbD) constitutes a major challenge in biopharmaceuticals. Monoclonal antibodies (mAbs) represent a significant biopharmaceutical product class, typically produced in mammalian cell cultures. A key quality attribute for mAb production is glycosylation. We examine how process intensification affects glycosylation via dynamic optimization using different problem formulations. We maximize process performance with simultaneous control of product quality. For these, we utilize a mechanistic dynamic model for mAb production in mammalian cell cultures including glycosylation presented by Ehsani et al. in Computer Aided Chemical Engineering (2017). To achieve target glycan distribution in the final product, we incorporate constraints for the acceptable glycosylation ranges into the dynamic optimization problem. As a result, we derive optimal supplementation profiles of nutrients and/or nucleotide sugars. This work successfully illustrates an example of how model-based dynamic optimization can be employed for implementation of the QbD approach in biopharmaceutics. [ABSTRACT FROM AUTHOR]

Patients with diabetes mellitus (DM) and chronic kidney disease (CKD) exhibit an elevated risk for cardiac arrhythmias, such as bradycardia, which may potentially lead to sudden cardiac death (SCD). While hypoglycemia, defined as a critical drop in glucose levels below the normal range, has long been associated with adverse cardiovascular events, recent studies have highlighted the need for a comprehensive reevaluation of its direct impact on cardiovascular outcomes, particularly in high-risk populations such as those with DM and CKD. In this study, we investigated the association between glucose levels and bradycardia by simultaneously monitoring interstitial glucose (IG) and ECG for 7 days in insulin-treated patients with DM and CKD. We identified bradycardia episodes in 19 of 85 patients (22%) and associated these episodes with personalized low, medium, and high relative glucose levels. Our analysis revealed a significant increase in bradycardia frequency during periods of lowest relative glucose, particularly between 06:00-09:00 and 12:00-15:00. Furthermore, leveraging a Random Forests classifier, we achieved a promising area under the curve (AUC) of 0.94 for predicting bradyarrhythmias using glucose levels and heart rate variability features. Contrary to previous findings, only 4% of bradycardia episodes in our study population occurred at glucose levels of 70 mg/dL or lower, with 28% observed at levels exceeding 180 mg/dL. Our findings not only highlight the strong correlation between relative glucose levels, heart rate parameters, and bradycardia onset but also emphasize the need for a more personalized definition of hypoglycemia to understand its relationship with bradyarrhythmias in high-risk DM and CKD patient populations., Competing Interests: Declarations. Competing interests: The authors declare no competing interests., (© 2024. The Author(s).)

Amperometry is a commonly used electrochemical method for studying the process of exocytosis in real-time. Given the high precision of recording that amperometry procedures offer, the volume of data generated can span over several hundreds of megabytes to a few gigabytes and therefore necessitates systematic and reproducible methods for analysis. Though the spike characteristics of amperometry traces in the time domain hold information about the dynamics of exocytosis, these biochemical signals are, more often than not, characterized by time-varying signal properties. Such signals with time-variant properties may occur at different frequencies and therefore analyzing them in the frequency domain may provide statistical validation for observations already established in the time domain. This necessitates the use of time-variant, frequency-selective signal processing methods as well, which can adeptly quantify the dominant or mean frequencies in the signal. The Fast Fourier Transform (FFT) is a well-established computational tool that is commonly used to find the frequency components of a signal buried in noise. In this work, we outline a method for spike-based frequency analysis of amperometry traces using FFT that also provides statistical validation of observations on spike characteristics in the time domain. We demonstrate the method by utilizing simulated signals and by subsequently testing it on diverse amperometry datasets generated from different experiments with various chemical stimulations. To our knowledge, this is the first fully automated open-source tool available dedicated to the analysis of spikes extracted from amperometry signals in the frequency domain., (© 2024. The Author(s).)

Purpose: Treatment of patients undergoing prolonged weaning from mechanical ventilation includes repeated spontaneous breathing trials (SBTs) without respiratory support, whose duration must be balanced critically to prevent over- and underload of respiratory musculature. This study aimed to develop a machine learning model to predict the duration of unassisted spontaneous breathing., Materials and Methods: Structured clinical data of patients from a specialized weaning unit were used to develop (1) a classifier model to qualitatively predict an increase of duration, (2) a regressor model to quantitatively predict the precise duration of SBTs on the next day, and (3) the duration difference between the current and following day. 61 features, known to influence weaning, were included into a Histogram-based gradient boosting model. The models were trained and evaluated using separated data sets., Results: 18.948 patient-days from 1018 individual patients were included. The classifier model yielded an ROC-AUC of 0.713. The regressor models displayed a mean absolute error of 2:50 h for prediction of absolute durations and 2:47 h for day-to-day difference., Conclusions: The developed machine learning model showed informed results when predicting the spontaneous breathing capacity of a patient in prolonged weaning, however lacking prognostic quality required for direct translation to clinical use., Competing Interests: Declaration of competing interest The authors have no conflicts of interest to declare., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)

Induced pluripotent stem cells (iPSCs) and their derivatives have been described to display epigenetic memory of their founder cells, as well as de novo reprogramming-associated alterations. In order to selectively explore changes due to the reprogramming process and not to heterologous somatic memory, we devised a circular reprogramming approach where somatic stem cells are used to generate iPSCs, which are subsequently re-differentiated into their original fate. As somatic founder cells, we employed human embryonic stem cell-derived neural stem cells (NSCs) and compared them to iPSC-derived NSCs derived thereof. Global transcription profiling of this isogenic circular system revealed remarkably similar transcriptomes of both NSC populations, with the exception of 36 transcripts. Amongst these we detected a disproportionately large fraction of X chromosomal genes, all of which were upregulated in iPSC-NSCs. Concurrently, we detected differential methylation of X chromosomal sites spatially coinciding with regions harboring differentially expressed genes. While our data point to a pronounced overall reinstallation of autosomal transcriptomic and methylation signatures when a defined somatic lineage is propagated through pluripotency, they also indicate that X chromosomal genes may partially escape this reinstallation process. Considering the broad application of iPSCs in disease modeling and regenerative approaches, such reprogramming-associated alterations in X chromosomal gene expression and DNA methylation deserve particular attention.

The development of reliable mortality risk stratification models is an active research area in computational healthcare. Mortality risk stratification provides a standard to assist physicians in evaluating a patient's condition or prognosis objectively. Particular interest lies in methods that are transparent to clinical interpretation and that retain predictive power once validated across diverse datasets they were not trained on. This study addresses the challenge of consolidating numerous ICD codes for predictive modeling of ICU mortality, employing a hybrid modeling approach that integrates mechanistic, clinical knowledge with mathematical and machine learning models . A tree-structured network connecting independent modules that carry clinical meaning is implemented for interpretability. Our training strategy utilizes graph-theoretic methods for data analysis, aiming to identify the functions of individual black-box modules within the tree-structured network by harnessing solutions from specific max-cut problems. The trained model is then validated on external datasets from different hospitals, demonstrating successful generalization capabilities, particularly in binary-feature datasets where label assessment involves extrapolation., (© 2024. The Author(s).)

In July 2023, the Center of Excellence in Respiratory Pathogens organized a two-day workshop on infectious diseases modelling and the lessons learnt from the Covid-19 pandemic. This report summarizes the rich discussions that occurred during the workshop. The workshop participants discussed multisource data integration and highlighted the benefits of combining traditional surveillance with more novel data sources like mobility data, social media, and wastewater monitoring. Significant advancements were noted in the development of predictive models, with examples from various countries showcasing the use of machine learning and artificial intelligence in detecting and monitoring disease trends. The role of open collaboration between various stakeholders in modelling was stressed, advocating for the continuation of such partnerships beyond the pandemic. A major gap identified was the absence of a common international framework for data sharing, which is crucial for global pandemic preparedness. Overall, the workshop underscored the need for robust, adaptable modelling frameworks and the integration of different data sources and collaboration across sectors, as key elements in enhancing future pandemic response and preparedness., Competing Interests: MCN reports grants from the Bill & Melinda Gates Foundation, European & Developing Countries Clinical Trials Partnership, Pfizer, AstraZeneca, and Sanofi; and consultation fees outside the work reported here from Sanofi. TL received funding from the Government of the Saarland for the maintenance and development of the COVID Simulator. SMM reports advisory roles for Janssen Canada and Sanofi for cost-effectiveness of their vaccine products, and received consultation fees outside the work reported here. JW acknowledges support from NSERC-Sanofi Industrial Research Chair program and the NSERC Alliance program. PC reports consulting fees from Sanofi, Pfizer, and Seqirus. Fraunhofer-Institute declares various national and international public and private grants which are in line with its status as a non-for-profit research organization., (© 2024 The Authors.)

FOXG1 is a critical transcription factor in human brain where loss-of-function mutations cause a severe neurodevelopmental disorder, while increased FOXG1 expression is frequently observed in glioblastoma. FOXG1 is an inhibitor of cell patterning and an activator of cell proliferation in chordate model organisms but different mechanisms have been proposed as to how this occurs. To identify genomic targets of FOXG1 in human neural progenitor cells (NPCs), we engineered a cleavable reporter construct in endogenous FOXG1 and performed chromatin immunoprecipitation (ChIP) sequencing. We also performed deep RNA sequencing of NPCs from two females with loss-of-function mutations in FOXG1 and their healthy biological mothers. Integrative analyses of RNA and ChIP sequencing data showed that cell cycle regulation and Bone Morphogenic Protein (BMP) repression gene ontology categories were over-represented as FOXG1 targets. Using engineered brain cell lines, we show that FOXG1 specifically activates SMAD7 and represses CDKN1B. Activation of SMAD7 which inhibits BMP signaling may be one way that FOXG1 patterns the forebrain, while repression of cell cycle regulators such as CDKN1B may be one way that FOXG1 expands the NPC pool to ensure proper brain size. Our data reveal novel mechanisms on how FOXG1 may control forebrain patterning and cell proliferation in human brain development., (© The Author(s) 2023. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.)

Background: Despite the increasing vaccination rates against SARS-CoV‑2, there is a risk of a renewed wave of infections in autumn 2021 due to the high seasonality of the pathogen, with the associated renewed possible heavy burden on intensive care. In the following manuscript we simulated different scenarios using defined mathematical models to estimate the burden of intensive care treatment by COVID-19 patients within certain limits during the coming autumn., Methods: The simulation of the scenarios uses a stationary model supplemented by the effect of vaccinations. The age group-specific risk profile for intensive care unit (ICU)-associated disease progression is calculated using third wave ICU admission data from sentinel hospitals, local DIVI registry occupancy data and the corresponding local incidence rates by linear regression with time lag. We simulated vaccination rates of 15% for the over 18-year-old cohort, 70% for the 15-34 year cohort, 75%/80%/85% for the 35-59 year cohort and 85%/90%/95% for the over 60-year-old cohort. The simulations take into account that vaccination provides 100% protection against disease progression requiring intensive care. Regarding protection against infection in vaccinated persons the simulations are depicted for the scenario of 70% protection against infection in vaccinated persons and for the scenario of 85% protection against infection in vaccinated persons., Results: The incidence is proportional to ICU bed occupancy. The proportionality factor is higher than in the second and third waves, so that comparable ICU bed occupancy is only achieved at a higher incidence. A 10% increase in vaccination rates of the over 35-year-olds to 85% and of the over 60-year-olds to 95% leads to a significant reduction in ICU bed occupancy., Discussion: There will continue to be a close and linear relationship between SARS-CoV‑2 incidence and ICU bed occupancy in the coming months. Even above incidences of 200/100,000 a considerable burden of ICUs with more than 3000 COVID-19 patients can be expected again, unless the vaccination rate is significantly increased. A few percentage points in the vaccination rate have a significant impact on potential ICU occupancy in the autumn, so efforts to increase vaccination acceptance should be a priority in the coming weeks. For intensive care medicine, the vaccination rate of those over 35 years of age is crucial., (© 2021. The Author(s).)

Artificial intelligence (AI) can predict the presence of molecular alterations directly from routine histopathology slides. However, training robust AI systems requires large datasets for which data collection faces practical, ethical and legal obstacles. These obstacles could be overcome with swarm learning (SL), in which partners jointly train AI models while avoiding data transfer and monopolistic data governance. Here, we demonstrate the successful use of SL in large, multicentric datasets of gigapixel histopathology images from over 5,000 patients. We show that AI models trained using SL can predict BRAF mutational status and microsatellite instability directly from hematoxylin and eosin (H&E)-stained pathology slides of colorectal cancer. We trained AI models on three patient cohorts from Northern Ireland, Germany and the United States, and validated the prediction performance in two independent datasets from the United Kingdom. Our data show that SL-trained AI models outperform most locally trained models, and perform on par with models that are trained on the merged datasets. In addition, we show that SL-based AI models are data efficient. In the future, SL can be used to train distributed AI models for any histopathology image analysis task, eliminating the need for data transfer., (© 2022. The Author(s).)

How genetic haploinsufficiency contributes to the clonal dominance of hematopoietic stem cells (HSCs) in del(5q) myelodysplastic syndrome (MDS) remains unresolved. Using a genetic barcoding strategy, we performed a systematic comparison on genes implicated in the pathogenesis of del(5q) MDS in direct competition with each other and wild-type (WT) cells with single-clone resolution. Csnk1a1 haploinsufficient HSCs expanded (oligo)clonally and outcompeted all other tested genes and combinations. Csnk1a1-/+ multipotent progenitors showed a proproliferative gene signature and HSCs showed a downregulation of inflammatory signaling/immune response. In validation experiments, Csnk1a1-/+ HSCs outperformed their WT counterparts under a chronic inflammation stimulus, also known to be caused by neighboring genes on chromosome 5. We therefore propose a crucial role for Csnk1a1 haploinsufficiency in the selective advantage of 5q-HSCs, implemented by creation of a unique competitive advantage through increased HSC self-renewal and proliferation capacity, as well as increased fitness under inflammatory stress., (© 2022 by The American Society of Hematology. Licensed under Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0), permitting only noncommercial, nonderivative use with attribution. All other rights reserved.)

Heterozygous loss-of-function mutations in Forkhead box G1 (FOXG1), a uniquely brain-expressed gene, cause microcephaly, seizures, and severe intellectual disability, whereas increased FOXG1 expression is frequently observed in glioblastoma. To investigate the role of FOXG1 in forebrain cell proliferation, we modeled FOXG1 syndrome using cells from three clinically diagnosed cases with two sex-matched healthy parents and one unrelated sex-matched control. Cells with heterozygous FOXG1 loss showed significant reduction in cell proliferation, increased ratio of cells in G0/G1 stage of the cell cycle, and increased frequency of primary cilia. Engineered loss of FOXG1 recapitulated this effect, while isogenic repair of a patient mutation reverted output markers to wild type. An engineered inducible FOXG1 cell line derived from a FOXG1 syndrome case demonstrated that FOXG1 dose-dependently affects all cell proliferation outputs measured. These findings provide strong support for the critical importance of FOXG1 levels in controlling human brain cell growth in health and disease., (Crown Copyright © 2022. Published by Elsevier Inc. All rights reserved.)

The COVID-19 pandemic is a global health emergency of historic dimension. In this situation, researchers worldwide wanted to help manage the pandemic by using artificial intelligence (AI). This narrative review aims to describe the usage of AI in the combat against COVID-19. The addressed aspects encompass AI algorithms for analysis of thoracic X-rays or CTs, prediction models for severity and outcome of the disease, AI applications in development of new drugs and vaccines as well as forecasting models for spread of the virus. The review shows, which approaches were pursued, and which were successful., Competing Interests: Erklärung zu finanziellen Interessen Forschungsförderung erhalten: nein; Honorar/geldwerten Vorteil für Referententätigkeit erhalten: nein; Bezahlter Berater/interner Schulungsreferent/Gehaltsempfänger: nein; Patent/Geschäftsanteile/Aktien (Autor/Partner, Ehepartner, Kinder) an im Bereich der Medizin aktiven Firma: nein; Patent/Geschäftsanteile/Aktien (Autor/Partner, Ehepartner, Kinder) an zu Sponsoren dieser Fortbildung bzw. durch die Fortbildung in ihren Geschäftsinteressen berührten Firma: nein Erklärung zu nichtfinanziellen Interessen Die Autorinnen/Autoren geben an, dass kein Interessenkonflikt besteht., (Thieme. All rights reserved.)

Myeloproliferative neoplasms (MPN), comprising essential thrombocythemia (ET), polycythemia vera (PV), and primary myelofibrosis (PMF), are hematological disorders of the myeloid lineage characterized by hyperproliferation of mature blood cells. The prediction of the clinical course and progression remains difficult and new therapeutic modalities are required. We conducted a CD34 + gene expression study to identify signatures and potential biomarkers in the different MPN subtypes with the aim to improve treatment and prevent the transformation from the rather benign chronic state to a more malignant aggressive state. We report here on a systematic gene expression analysis (GEA) of CD34 + peripheral blood or bone marrow cells derived from 30 patients with MPN including all subtypes (ET (n = 6), PV (n = 11), PMF (n = 9), secondary MF (SMF; post-ET-/post-PV-MF; n = 4)) and six healthy donors. GEA revealed a variety of differentially regulated genes in the different MPN subtypes vs. controls, with a higher number in PMF/SMF (200/272 genes) than in ET/PV (132/121). PROGENγ analysis revealed significant induction of TNFα/NF-κB signaling (particularly in SMF) and reduction of estrogen signaling (PMF and SMF). Consistently, inflammatory GO terms were enriched in PMF/SMF, whereas RNA splicing-associated biological processes were downregulated in PMF. Differentially regulated genes that might be utilized as diagnostic/prognostic markers were identified, such as AREG, CYBB, DNTT, TIMD4, VCAM1, and S100 family members (S100A4/8/9/10/12). Additionally, 98 genes (including CLEC1B, CMTM5, CXCL8, DACH1, and RADX) were deregulated solely in SMF and may be used to predict progression from early to late stage MPN., (© 2021. The Author(s).)

A third SARS-CoV-2 infection wave has affected Germany from March 2021 until April 24th, until the ´Bundesnotbremse´ introduced nationwide shutdown measures. The ´Bundesnotbremse´ is the technical term which was used by the German government to describe nationwide shutdown measures to control the rising infection numbers. These measures included mainly contact restrictions on several level. This study investigates which effects locally dispersed pre- and post-´Bundesnotbremse´ measures had on the infection dynamics. We analyzed the variability and strength of the rates of the changes of weekly case numbers considering different regions, age groups, and contact restrictions. Regionally diverse measures slowed the rate of weekly increase by about 50% and about 75% in regions with stronger contact restrictions. The 'Bundesnotbremse' induced a coherent reduction of infection numbers across all German federal states and age groups throughout May 2021. The coherence of the infection dynamics after the 'Bundesnotbremse' indicates that these stronger measures induced the decrease of infection numbers. The regionally diverse non-pharmaceutical interventions before could only decelerate further spreading, but not prevent it alone., (© 2021. The Author(s).)

Mutations in HPRT1, a gene encoding a rate-limiting enzyme for purine salvage, cause Lesch-Nyhan disease which is characterized by self-injury and motor impairments. We leveraged stem cell and genetic engineering technologies to model the disease in isogenic and patient-derived forebrain and midbrain cell types. Dopaminergic progenitor cells deficient in HPRT showed decreased intensity of all developmental cell-fate markers measured. Metabolic analyses revealed significant loss of all purine derivatives, except hypoxanthine, and impaired glycolysis and oxidative phosphorylation. real-time glucose tracing demonstrated increased shunting to the pentose phosphate pathway for de novo purine synthesis at the expense of ATP production. Purine depletion in dopaminergic progenitor cells resulted in loss of RHEB, impairing mTORC1 activation. These data demonstrate dopaminergic-specific effects of purine salvage deficiency and unexpectedly reveal that dopaminergic progenitor cells are programmed to a high-energy state prior to higher energy demands of terminally differentiated cells., (Copyright © 2021 The Authors. Published by Elsevier Inc. All rights reserved.)

Background: The second wave of the COVID-19 pandemic led to substantial differences in incidence rates across Germany., Methods: Assumption-free k-nearest neighbour clustering from the principal component analysis of weekly incidence rates of German counties groups similar spreading behaviour. Different spreading dynamics was analysed by the derivative plots of the temporal evolution of tuples [x(t),x'(t)] of weekly incidence rates and their derivatives. The effectiveness of the different shutdown measures in Germany during the second wave is assessed by the difference of weekly incidences before and after the respective time periods., Findings: The implementation of non-pharmaceutical interventions of different extents resulted in four distinct time periods of complex, spatially diverse, and age-related spreading patterns during the second wave of the COVID-19 pandemic in Germany. Clustering gave three regions of coincident spreading characteristics. October 2020 showed a nationwide exponential growth of weekly incidence rates with a doubling time of 10 days. A partial shutdown during November 2020 decreased the overall infection rates by 20-40% with a plateau-like behaviour in northern and southwestern Germany. The eastern parts exhibited a further near-linear growth by 30-80%. Allover the incidence rates among people above 60 years still increased by 15-35% during partial shutdown measures. Only an extended shutdown led to a substantial decrease in incidence rates. These measures decreased the numbers among all age groups and in all regions by 15-45%. This decline until January 2021 was about -1•25 times the October 2020 growth rates with a strong correlation of -0•96., Interpretation: Three regional groups with different dynamics and different degrees of effectiveness of the applied measures were identified. The partial shutdown was moderately effective and at most stopped the exponential growth, but the spread remained partly plateau-like and regionally continued to grow in a nearly linear fashion. Only the extended shutdown reversed the linear growth., Funding: Institutional support and physical resources were provided by the University Witten/ Herdecke and Kliniken der Stadt Köln, German ministry of education and research 'Netzwerk Universitätsmedizin' (NUM), egePan Unimed (01KX2021)., Competing Interests: Dr. Karagiannidis reports personal fees from Maquet, personal fees from Xenios, personal fees from Bayer, non-financial support from Speaker of the German register of ICUs, grants from German Ministry of Research and Education, during the conduct of the study. Dr. Schuppert reports grants from Bayer AG, outside the submitted work. Dr. Jens Karschau has nothing to disclose. Dr. Polotzek has nothing to disclose. Dr. Schmitt reports personal fees from Sanofi, Lilly, ALK, Novartis, grants from Sanofi, Pfizer, ALK, Novartis, outside the submitted work. Dr. Busse reports grants from Berlin University Alliance, non-financial support from German Federal Ministry of Health, during the conduct of the study., (© 2021 The Author(s).)

Background: The increasing development of artificial intelligence (AI) systems in medicine driven by researchers and entrepreneurs goes along with enormous expectations for medical care advancement. AI might change the clinical practice of physicians from almost all medical disciplines and in most areas of health care. While expectations for AI in medicine are high, practical implementations of AI for clinical practice are still scarce in Germany. Moreover, physicians' requirements and expectations of AI in medicine and their opinion on the usage of anonymized patient data for clinical and biomedical research have not been investigated widely in German university hospitals., Objective: This study aimed to evaluate physicians' requirements and expectations of AI in medicine and their opinion on the secondary usage of patient data for (bio)medical research (eg, for the development of machine learning algorithms) in university hospitals in Germany., Methods: A web-based survey was conducted addressing physicians of all medical disciplines in 8 German university hospitals. Answers were given using Likert scales and general demographic responses. Physicians were asked to participate locally via email in the respective hospitals., Results: The online survey was completed by 303 physicians (female: 121/303, 39.9%; male: 173/303, 57.1%; no response: 9/303, 3.0%) from a wide range of medical disciplines and work experience levels. Most respondents either had a positive (130/303, 42.9%) or a very positive attitude (82/303, 27.1%) towards AI in medicine. There was a significant association between the personal rating of AI in medicine and the self-reported technical affinity level (H 4 =48.3, P<.001). A vast majority of physicians expected the future of medicine to be a mix of human and artificial intelligence (273/303, 90.1%) but also requested a scientific evaluation before the routine implementation of AI-based systems (276/303, 91.1%). Physicians were most optimistic that AI applications would identify drug interactions (280/303, 92.4%) to improve patient care substantially but were quite reserved regarding AI-supported diagnosis of psychiatric diseases (62/303, 20.5%). Of the respondents, 82.5% (250/303) agreed that there should be open access to anonymized patient databases for medical and biomedical research., Conclusions: Physicians in stationary patient care in German university hospitals show a generally positive attitude towards using most AI applications in medicine. Along with this optimism comes several expectations and hopes that AI will assist physicians in clinical decision making. Especially in fields of medicine where huge amounts of data are processed (eg, imaging procedures in radiology and pathology) or data are collected continuously (eg, cardiology and intensive care medicine), physicians' expectations of AI to substantially improve future patient care are high. In the study, the greatest potential was seen in the application of AI for the identification of drug interactions, assumedly due to the rising complexity of drug administration to polymorbid, polypharmacy patients. However, for the practical usage of AI in health care, regulatory and organizational challenges still have to be mastered., (©Oliver Maassen, Sebastian Fritsch, Julia Palm, Saskia Deffge, Julian Kunze, Gernot Marx, Morris Riedel, Andreas Schuppert, Johannes Bickenbach. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 05.03.2021.)

The aim of this work was to develop and evaluate the reinforcement learning algorithm VentAI, which is able to suggest a dynamically optimized mechanical ventilation regime for critically-ill patients. We built, validated and tested its performance on 11,943 events of volume-controlled mechanical ventilation derived from 61,532 distinct ICU admissions and tested it on an independent, secondary dataset (200,859 ICU stays; 25,086 mechanical ventilation events). A patient "data fingerprint" of 44 features was extracted as multidimensional time series in 4-hour time steps. We used a Markov decision process, including a reward system and a Q-learning approach, to find the optimized settings for positive end-expiratory pressure (PEEP), fraction of inspired oxygen (FiO 2 ) and ideal body weight-adjusted tidal volume (Vt). The observed outcome was in-hospital or 90-day mortality. VentAI reached a significantly increased estimated performance return of 83.3 (primary dataset) and 84.1 (secondary dataset) compared to physicians' standard clinical care (51.1). The number of recommended action changes per mechanically ventilated patient constantly exceeded those of the clinicians. VentAI chose 202.9% more frequently ventilation regimes with lower Vt (5-7.5 mL/kg), but 50.8% less for regimes with higher Vt (7.5-10 mL/kg). VentAI recommended 29.3% more frequently PEEP levels of 5-7 cm H 2 O and 53.6% more frequently PEEP levels of 7-9 cmH 2 O. VentAI avoided high (>55%) FiO 2 values (59.8% decrease), while preferring the range of 50-55% (140.3% increase). In conclusion, VentAI provides reproducible high performance by dynamically choosing an optimized, individualized ventilation strategy and thus might be of benefit for critically ill patients.

Classical Philadelphia chromosome-negative myeloproliferative neoplasms (MPN) are a heterogeneous group of hematopoietic malignancies including polycythemia vera (PV), essential thrombocythemia (ET), and primary myelofibrosis (PMF). The JAK2V617F mutation plays a central role in these disorders and can be found in 90% of PV and ~50-60% of ET and PMF. Hypoxia-inducible factor 1 (HIF-1) is a master transcriptional regulator of the response to decreased oxygen levels. We demonstrate the impact of pharmacological inhibition and shRNA-mediated knockdown (KD) of HIF-1α in JAK2V617F-positive cells. Inhibition of HIF-1 binding to hypoxia response elements (HREs) with echinomycin, verified by ChIP, impaired growth and survival by inducing apoptosis and cell cycle arrest in Jak2V617F-positive 32D cells, but not Jak2WT controls. Echinomycin selectively abrogated clonogenic growth of JAK2V617F cells and decreased growth, survival, and colony formation of bone marrow and peripheral blood mononuclear cells and iPS cell-derived progenitor cells from JAK2V617F-positive patients, while cells from healthy donors were unaffected. We identified HIF-1 target genes involved in the Warburg effect as a possible underlying mechanism, with increased expression of Pdk1, Glut1, and others. That was underlined by transcriptome analysis of primary patient samples. Collectively, our data show that HIF-1 is a new potential therapeutic target in JAK2V617F-positive MPN.

The development of new drug therapies requires substantial and ever increasing investments from the pharmaceutical company. Ten years ago, the average time from early target identification and optimization until initial market authorization of a new drug compound took more than 10 years and involved costs in the order of one billion US dollars. Recent studies indicate even a significant growth of costs in the meanwhile, mainly driven by the increasing complexity of diseases addressed by pharmaceutical research.Modeling and simulation are proven approaches to handle highly complex systems; hence, systems medicine is expected to control the spiral of complexity of diseases and increasing costs. Today, the main focus of systems medicine applications in industry is on mechanistic modeling. Biological mechanisms are represented by explicit equations enabling insight into the cooperation of all relevant mechanisms. Mechanistic modeling is widely accepted in pharmacokinetics, but prediction from cell behavior to patients is rarely possible due to lacks in our understanding of the controlling mechanisms. Data-driven modeling aims to compensate these lacks by the use of advanced statistical and machine learning methods. Future progress in pharmaceutical research and development will require integrated hybrid modeling technologies allowing realization of the benefits of both mechanistic and data-driven modeling. In this chapter, we sketch typical industrial application areas for both modeling techniques and derive the requirements for future technology development.