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Background: Recent literature suggests that ferroptosis (FPT) may be a key player in polycystic ovary syndrome (PCOS) pathogenesis, but the underlying mechanism(s) remain(s) unclear. Aim: Therefore, herein, we made an effort to reproduce the molecular signature of the syndrome by including FPT and exploring novel drug targets for PCOS. Methods: (a) Our previously constructed PCOS interactions molecular network was extended with the addition of FPT–associated genes (interaction score above 0.7) and (b) gene set enrichment analysis was performed so as to detect over-represented KEGG pathways. Results: The updated interactome includes 140 molecules, 20 of which are predicted/novel, with an interaction score of 7.3, and 12 major hubs. Moreover, we identified 16 over-represented KEGG pathways, with FPT being the most overexpressed pathway. The FPT subnetwork is connected with the PCOS network through KDM1A. Conclusions: FPT cell death is involved in PCOS development, as its major hub TP53 was shown to be the most important hub in the whole PCOS interactome, hence representing a prioritized drug target. [ABSTRACT FROM AUTHOR]

The categorization of human diseases is mainly based on the affected organ system and phenotypic characteristics. This is limiting the view to the pathological manifestations, while it neglects mechanistic relationships that are crucial to develop therapeutic strategies. This work aims to advance the understanding of diseases and their relatedness beyond traditional phenotypic views. Hence, the similarity among 502 diseases is mapped using six different data dimensions encompassing molecular, clinical, and pharmacological information retrieved from public sources. Multiple distance measures and multi‐view clustering are used to assess the patterns of disease relatedness. The integration of all six dimensions into a consensus map of disease relationships reveals a divergent disease view from the International Classification of Diseases (ICD), emphasizing novel insights offered by a multi‐view disease map. Disease features such as genes, pathways, and chemicals that are enriched in distinct disease groups are identified. Finally, an evaluation of the top similar diseases of three candidate diseases common in the Western population shows concordance with known epidemiological associations and reveals rare features shared between Type 2 diabetes (T2D) and Alzheimer's disease. A revision of disease relationships holds promise for facilitating the reconstruction of comorbidity patterns, repurposing drugs, and advancing drug discovery in the future. [ABSTRACT FROM AUTHOR]

This paper defines a revolution as an orthogonal change in direction, a 90-degree perpendicular turn from the status quo ways of thinking, being and doing, so as to create a complete break, an abolitionist rupture with current and past ways of producing knowledge. David Bowie was a relatable example of a revolutionary and orthogonal innovator who completely and courageously broke with the past and the present and opened up new vistas in music and performing arts. The late anthropologist and public intellectual David Graeber also argued that a revolution fundamentally changes the assumptions in a given field of inquiry. Changing the entrenched assumptions that are long ossified, outdated or uncritically internalized by a knowledge community and profession can have multiplying revolutionary effects on downstream knowledge production. Thinking orthogonally to change the prevailing assumptions is indeed a revolutionary act. Orthogonal innovation as described in this paper is not a repackaging of an innovation in a different field. An orthogonal innovation is proposed as coalescence of ideas drawn from orthogonal domains, e.g., epistemologically speaking as in medicine and political theory, with an eye to pave the way for unprecedented social change and innovation. Grounding systems medicine in political determinants of planetary health, to link two fields of inquiry that have remained isolated and orthogonal since the 17th century, is nothing short of a revolution and orthogonal innovation in the making. For systems medicine to be a truly revolutionary field, it ought to acknowledge that there is no single-issue health nor single-issue politics. [ABSTRACT FROM AUTHOR]

Abstract Background The inflammatory response is fundamental to the maintenance of an organism’s physiological homeostasis. Inflammation is controlled by a series of biological events driven by specific inflammatory molecules. When inflammation is within the homeostatic range, it is considered physiological; however, it becomes pathological when it exceeds the immune system’s homeostatic control. Main text Nowadays, the treatment of chronic pathological inflammation is a challenge for pharmacology, as current anti-inflammatory drugs are intended to control acute inflammation. The aim of this narrative review was to provide an overview of the role of molecular pharmacognosy and to demonstrate how current transcriptomics techniques can make an important contribution to the study of the biological functions of natural products in the context of multicomponent/multitarget medication. From our findings, although very few studies have been identified, encouraging results for low-grade chronic inflammations (LGCIs) of various causes emerged in recent transcriptomic studies on multicomponent medicinal products composed of plant and organ extracts at the level of the skin and the musculoskeletal system (Traumeel: Tr14), the liver (Lycopodium compositum: HC-24), and the joints (Zeel-T: Ze-14). Conclusion For adequate control of LGCI, molecular pharmacognosy may be an effective approach to exploring potentially useful herbal agents that are consistent with both physiotherapeutic tradition and modern pharmacology.

The emergence of personalized medicine, facilitated by the progress in ‐omics technologies, has initiated a new era in medical diagnostics and treatment. This review examines the potential of ‐omics approaches in heart failure, a condition that has not yet fully capitalized on personalized strategies compared to other medical fields like cancer therapy. Here, we argue that integrating multi‐omics technology with systems medicine approaches could fundamentally transform heart failure management, moving away from the traditional paradigm of 'one size fits all'. Our review examines how omics can enhance understanding of heart failure's molecular foundations and contribute to a more comprehensive disease classification. We draw attention to the current state of medical practice that only relies on clinical evidence and a number of standard laboratory tests. At the same time, we propose a shift towards a universal approach that uses quantitative data from multi‐omics to unravel complex molecular interactions. The discussion centres around the potential of the transition as a means to enhance individual risk assessment and emphasizes management within clinical settings. While the use of omics in cardiovascular research is not recent, many past studies have focused only on a single omics approach. In order to achieve a better understanding of disease mechanisms, we explore more holistic approaches using genomics, transcriptomics, epigenomics, and proteomics. This review concludes with a call to action to adopt multi‐omics in clinical trials and practice to pave the way for more personalized disease management and more effective heart failure interventions. [ABSTRACT FROM AUTHOR]

A quiet quadruple revolution has been in the making in systems science with convergence of (1) artificial intelligence, machine learning, and other digital technologies; (2) multiomics big data integration; (3) growing interest in the "variability science" of precision/personalized medicine that aims to account for patient-to-patient and between-population differences in disease susceptibilities and responses to health interventions such as drugs, nutrition, vaccines, and radiation; and (4) planetary health scholarship that both scales up and integrates biological, clinical, and ecological contexts of health and disease. Against this overarching background, this article presents and highlights some of the salient challenges and prospects of multiomics research, emphasizing the attendant pivotal role of systems medicine and systems biology. In addition, we emphasize the rapidly growing importance of planetary health research for systems medicine, particularly amid climate emergency, ecological degradation, and loss of planetary biodiversity. Looking ahead, we anticipate that the integration and utilization of multiomics big data and artificial intelligence will drive further progress in systems medicine and systems biology, heralding a promising future for both human and planetary health. [ABSTRACT FROM AUTHOR]

Background: The inflammatory response is fundamental to the maintenance of an organism's physiological homeostasis. Inflammation is controlled by a series of biological events driven by specific inflammatory molecules. When inflammation is within the homeostatic range, it is considered physiological; however, it becomes pathological when it exceeds the immune system's homeostatic control. Main text: Nowadays, the treatment of chronic pathological inflammation is a challenge for pharmacology, as current anti-inflammatory drugs are intended to control acute inflammation. The aim of this narrative review was to provide an overview of the role of molecular pharmacognosy and to demonstrate how current transcriptomics techniques can make an important contribution to the study of the biological functions of natural products in the context of multicomponent/multitarget medication. From our findings, although very few studies have been identified, encouraging results for low-grade chronic inflammations (LGCIs) of various causes emerged in recent transcriptomic studies on multicomponent medicinal products composed of plant and organ extracts at the level of the skin and the musculoskeletal system (Traumeel: Tr14), the liver (Lycopodium compositum: HC-24), and the joints (Zeel-T: Ze-14). Conclusion: For adequate control of LGCI, molecular pharmacognosy may be an effective approach to exploring potentially useful herbal agents that are consistent with both physiotherapeutic tradition and modern pharmacology. [ABSTRACT FROM AUTHOR]

This article explores the integration of Digital Twins in Systems and Predictive Medicine, focusing on eye diagnosis. By utilizing the Digital Twin models, the proposed framework can support early diagnosis and predict evolution after treatment by providing customized simulation scenarios. Furthermore, a structured architectural framework comprising five levels has been proposed, integrating Digital Twin, Systems Medicine, and Predictive Medicine for managing eye diseases. Based on demographic parameters, statistics were performed to identify potential correlations that may contribute to predispositions to glaucoma. With the aid of a dataset, a neural network was trained with the goal of identifying glaucoma. This comprehensive approach, based on statistical analysis and Machine Learning, is a promising method to enhance diagnostic accuracy and provide personalized treatment approaches. [ABSTRACT FROM AUTHOR]

Currently, vaccines have shown efficacy against new SARS-CoV-2 variants. This study aimed to develop a systems medicine framework that can predict and validate drug combinations that can be repurposed for treating COVID-19 and its comorbidities, specifically type 2 diabetes and hypertension. This study found that gut microbes could potentially influence the action of drugs, innate immune response, intestinal dysfunction, and susceptibility to the virus in individuals with these comorbidities. It was also discovered that the spike protein of the virus interacts with 57 human genes, many of which are linked to food-borne bacteria. An analysis of disease enrichment showed that arthritis and hypertension were frequently observed as comorbidities in patients infected with SARS-CoV-2. Several drugs, including Fluvoxamine, Donepezil, and Ifenprodil, have been identified as potentially repurposable drugs for treating COVID-19 in individuals with hypertension. Moreover, nitazoxanide and tocilizumab (antivirals), bacitracin (antibacterial), and gliclazide (antidiabetics) were also identified as potential repurposable drugs. Tocilizumab and gliclazide are effective drug combinations for treating COVID-19 in individuals with type 2 diabetes. A combination of tocilizumab and lidocaine has also been suggested for treating COVID-19 in individuals infected with food-borne bacteria.

Gestational diabetes mellitus (GDM) is a common metabolic disorder affecting approximately 16.5% of pregnancies worldwide and causing significant health concerns. GDM is a serious pregnancy complication caused by chronic insulin resistance in the mother and has been associated with the development of neurodevelopmental disorders in offspring. Emerging data support the notion that GDM affects both the maternal and fetal microbiome, altering the composition and function of the gut microbiota, resulting in dysbiosis. The observed dysregulation of microbial presence in GDM pregnancies has been connected to fetal neurodevelopmental problems. Several reviews have focused on the intricate development of maternal dysbiosis affecting the fetal microbiome. Omics data have been instrumental in deciphering the underlying relationship among GDM, gut dysbiosis, and fetal neurodevelopment, paving the way for precision medicine. Microbiome-associated omics analyses help elucidate how dysbiosis contributes to metabolic disturbances and inflammation, linking microbial changes to adverse pregnancy outcomes such as those seen in GDM. Integrating omics data across these different layers—genomics, transcriptomics, proteomics, metabolomics, and microbiomics—offers a comprehensive view of the molecular landscape underlying GDM. This review outlines the affected pathways and proposes future developments and possible personalized therapeutic interventions by integrating omics data on the maternal microbiome, genetics, lifestyle factors, and other relevant biomarkers aimed at identifying women at high risk of developing GDM. For example, machine learning tools have emerged with powerful capabilities to extract meaningful insights from large datasets.

We introduce multiple-organ objective segmentation (MOOSE) software that generates subject-specific, multiorgan segmentation using data-centric artificial intelligence principles to facilitate high-throughput systemic investigations of the human body via whole-body PET imaging. Methods: Image data from 2 PET/CT systems were used in training MOOSE. For noncerebral structures, 50 whole-body CT images were used, 30 of which were acquired from healthy controls (14 men and 16 women), and 20 datasets were acquired from oncology patients (14 men and 6 women). Noncerebral tissues consisted of 13 abdominal organs, 20 bone segments, subcutaneous fat, visceral fat, psoas muscle, and skeletal muscle. An expert panel manually segmented all noncerebral structures except for subcutaneous fat, visceral fat, and skeletal muscle, which were semiautomatically segmented using thresholding. A majority-voting algorithm was used to generate a reference-standard segmentation. From the 50 CT datasets, 40 were used for training and 10 for testing. For cerebral structures, 34 18F-FDG PET/MRI brain image volumes were used from 10 healthy controls (5 men and 5 women imaged twice) and 14 nonlesional epilepsy patients (7 men and 7 women). Only 18F-FDG PET images were considered for training: 24 and 10 of 34 volumes were used for training and testing, respectively. The Dice score coefficient (DSC) was used as the primary metric, and the average symmetric surface distance as a secondary metric, to evaluate the automated segmentation performance. Results: An excellent overlap between the reference labels and MOOSE-derived organ segmentations was observed: 92% of noncerebral tissues showed DSCs of more than 0.90, whereas a few organs exhibited lower DSCs (e.g., adrenal glands [0.72], pancreas [0.85], and bladder [0.86]). The median DSCs of brain subregions derived from PET images were lower. Only 29% of the brain segments had a median DSC of more than 0.90, whereas segmentation of 60% of regions yielded a median DSC of 0.80-0.89. The results of the average symmetric surface distance analysis demonstrated that the average distance between the reference standard and the automatically segmented tissue surfaces (organs, bones, and brain regions) lies within the size of image voxels (2 mm). Conclusion: The proposed segmentation pipeline allows automatic segmentation of 120 unique tissues from whole-body 18F-FDG PET/CT images with high accuracy.

Abstract Background Retinitis pigmentosa is the prevailing genetic cause of blindness in developed nations with no effective treatments. In the pursuit of unraveling the intricate dynamics underlying this complex disease, mechanistic models emerge as a tool of proven efficiency rooted in systems biology, to elucidate the interplay between RP genes and their mechanisms. The integration of mechanistic models and drug-target interactions under the umbrella of machine learning methodologies provides a multifaceted approach that can boost the discovery of novel therapeutic targets, facilitating further drug repurposing in RP. Methods By mapping Retinitis Pigmentosa-related genes (obtained from Orphanet, OMIM and HPO databases) onto KEGG signaling pathways, a collection of signaling functional circuits encompassing Retinitis Pigmentosa molecular mechanisms was defined. Next, a mechanistic model of the so-defined disease map, where the effects of interventions can be simulated, was built. Then, an explainable multi-output random forest regressor was trained using normal tissue transcriptomic data to learn causal connections between targets of approved drugs from DrugBank and the functional circuits of the mechanistic disease map. Selected target genes involvement were validated on rd10 mice, a murine model of Retinitis Pigmentosa. Results A mechanistic functional map of Retinitis Pigmentosa was constructed resulting in 226 functional circuits belonging to 40 KEGG signaling pathways. The method predicted 109 targets of approved drugs in use with a potential effect over circuits corresponding to nine hallmarks identified. Five of those targets were selected and experimentally validated in rd10 mice: Gabre, Gabra1 (GABARα1 protein), Slc12a5 (KCC2 protein), Grin1 (NR1 protein) and Glr2a. As a result, we provide a resource to evaluate the potential impact of drug target genes in Retinitis Pigmentosa. Conclusions The possibility of building actionable disease models in combination with machine learning algorithms to learn causal drug-disease interactions opens new avenues for boosting drug discovery. Such mechanistically-based hypotheses can guide and accelerate the experimental validations prioritizing drug target candidates. In this work, a mechanistic model describing the functional disease map of Retinitis Pigmentosa was developed, identifying five promising therapeutic candidates targeted by approved drug. Further experimental validation will demonstrate the efficiency of this approach for a systematic application to other rare diseases.

Background: Recent literature suggests that ferroptosis (FPT) may be a key player in polycystic ovary syndrome (PCOS) pathogenesis, but the underlying mechanism(s) remain(s) unclear. Aim: Therefore, herein, we made an effort to reproduce the molecular signature of the syndrome by including FPT and exploring novel drug targets for PCOS. Methods: (a) Our previously constructed PCOS interactions molecular network was extended with the addition of FPT–associated genes (interaction score above 0.7) and (b) gene set enrichment analysis was performed so as to detect over-represented KEGG pathways. Results: The updated interactome includes 140 molecules, 20 of which are predicted/novel, with an interaction score of 7.3, and 12 major hubs. Moreover, we identified 16 over-represented KEGG pathways, with FPT being the most overexpressed pathway. The FPT subnetwork is connected with the PCOS network through KDM1A. Conclusions: FPT cell death is involved in PCOS development, as its major hub TP53 was shown to be the most important hub in the whole PCOS interactome, hence representing a prioritized drug target.

In this white paper, we describe the founding of a new ELIXIR Community - the Systems Biology Community - and its proposed future contributions to both ELIXIR and the broader community of systems biologists in Europe and worldwide. The Community believes that the infrastructure aspects of systems biology - databases, (modelling) tools and standards development, as well as training and access to cloud infrastructure - are not only appropriate components of the ELIXIR infrastructure, but will prove key components of ELIXIR’s future support of advanced biological applications and personalised medicine. By way of a series of meetings, the Community identified seven key areas for its future activities, reflecting both future needs and previous and current activities within ELIXIR Platforms and Communities. These are: overcoming barriers to the wider uptake of systems biology; linking new and existing data to systems biology models; interoperability of systems biology resources; further development and embedding of systems medicine; provisioning of modelling as a service; building and coordinating capacity building and training resources; and supporting industrial embedding of systems biology. A set of objectives for the Community has been identified under four main headline areas: Standardisation and Interoperability, Technology, Capacity Building and Training, and Industrial Embedding. These are grouped into short-term (3-year), mid-term (6-year) and long-term (10-year) objectives.

Introduction: The COVID-19 Disease Map project is a large-scale community effort uniting 277 scientists from 130 Institutions around the globe. We use high-quality, mechanistic content describing SARS-CoV-2-host interactions and develop interoperable bioinformatic pipelines for novel target identification and drug repurposing. Methods: Extensive community work allowed an impressive step forward in building interfaces between Systems Biology tools and platforms. Our framework can link biomolecules from omics data analysis and computational modelling to dysregulated pathways in a cell-, tissue- or patient-specific manner. Drug repurposing using text mining and AI-assisted analysis identified potential drugs, chemicals and microRNAs that could target the identified key factors. Results: Results revealed drugs already tested for anti-COVID-19 efficacy, providing a mechanistic context for their mode of action, and drugs already in clinical trials for treating other diseases, never tested against COVID-19. Discussion: The key advance is that the proposed framework is versatile and expandable, offering a significant upgrade in the arsenal for virus-host interactions and other complex pathologies. [ABSTRACT FROM AUTHOR]

Climate emergency is a planetary health and systems science challenge because human health, nonhuman animal health, and the health of the planetary ecosystems are coproduced and interdependent. Yet, we live in a time when climate emergency is tackled by platitudes and weak reforms instead of structural and systems changes, and with tools of the very same systems and metanarratives, for example, infinite growth at all costs, that are causing climate change in the first place. Seeking solutions to problems from within the knowledge frames and metanarratives that are causing the problems reproduces the same problems across time and geographies. This article examines and underlines the importance of an epistemological gaze on knowledge economy, an epistemological X-ray, as another solution in the toolbox of decolonial and other social justice struggles in an era of climate emergency. Epistemology questions and excavates the metanarratives embedded in knowledge forms that are popular, dominant, and hegemonic as well as knowledges that are silent, omitted, or erased. In this sense, epistemology does not take the "archives" of data and knowledge for granted but asks questions such as who, when, how, and with what and whose funding the archive was built, and what is included and left out? Epistemological choices made by innovators, funders, and knowledge actors often remain opaque in knowledge economies. Epistemology research is crucial for science and innovations to be responsive to planetary society and climate emergency and mindful of the social, political, neocolonial, and historical contexts of science and technology in the 21st century. [ABSTRACT FROM AUTHOR]

In this white paper, we describe the founding of a new ELIXIR Community - the Systems Biology Community - and its proposed future contributions to both ELIXIR and the broader community of systems biologists in Europe and worldwide. The Community believes that the infrastructure aspects of systems biology - databases, (modelling) tools and standards development, as well as training and access to cloud infrastructure - are not only appropriate components of the ELIXIR infrastructure, but will prove key components of ELIXIR’s future support of advanced biological applications and personalised medicine. By way of a series of meetings, the Community identified seven key areas for its future activities, reflecting both future needs and previous and current activities within ELIXIR Platforms and Communities. These are: overcoming barriers to the wider uptake of systems biology; linking new and existing data to systems biology models; interoperability of systems biology resources; further development and embedding of systems medicine; provisioning of modelling as a service; building and coordinating capacity building and training resources; and supporting industrial embedding of systems biology. A set of objectives for the Community has been identified under four main headline areas: Standardisation and Interoperability, Technology, Capacity Building and Training, and Industrial Embedding. These are grouped into short-term (3-year), mid-term (6-year) and long-term (10-year) objectives.

Abstract Background For decades, the basic treatment strategies of necrotizing soft tissue infections (NSTI) have remained unchanged, primarily relying on aggressive surgical removal of infected tissue, broad-spectrum antibiotics, and supportive intensive care. One treatment strategy that has been proposed as an adjunctive measure to improve patient outcomes is hyperbaric oxygen (HBO2) treatment. HBO2 treatment has been linked to several immune modulatory effects; however, investigating these effects is complicated due to the disease's acute life-threatening nature, metabolic and cell homeostasis dependent variability in treatment effects, and heterogeneity with respect to both patient characteristics and involved pathogens. To embrace this complexity, we aimed to explore the underlying biological mechanisms of HBO2 treatment in patients with NSTI on the gene expression level. Methods We conducted an observational cohort study on prospective collected data, including 85 patients admitted to the intensive care unit (ICU) for NSTI. All patients were treated with one or two HBO2 treatments and had one blood sample taken before and after the intervention. Total RNAs from blood samples were extracted and mRNA purified with rRNA depletion, followed by whole-transcriptome RNA sequencing with a targeted sequencing depth of 20 million reads. A model for differentially expressed genes (DEGs) was fitted, and the functional aspects of the obtained set of genes was predicted with GO (Gene Ontology) and KEGG (Kyoto Encyclopedia of genes and Genomes) enrichment analyses. All analyses were corrected for multiple testing with FDR. Results After sequential steps of quality control, a final of 160 biological replicates were included in the present study. We found 394 protein coding genes that were significantly DEGs between the two conditions with FDR

IntroductionThe COVID-19 Disease Map project is a large-scale community effort uniting 277 scientists from 130 Institutions around the globe. We use high-quality, mechanistic content describing SARS-CoV-2-host interactions and develop interoperable bioinformatic pipelines for novel target identification and drug repurposing. MethodsExtensive community work allowed an impressive step forward in building interfaces between Systems Biology tools and platforms. Our framework can link biomolecules from omics data analysis and computational modelling to dysregulated pathways in a cell-, tissue- or patient-specific manner. Drug repurposing using text mining and AI-assisted analysis identified potential drugs, chemicals and microRNAs that could target the identified key factors.ResultsResults revealed drugs already tested for anti-COVID-19 efficacy, providing a mechanistic context for their mode of action, and drugs already in clinical trials for treating other diseases, never tested against COVID-19. DiscussionThe key advance is that the proposed framework is versatile and expandable, offering a significant upgrade in the arsenal for virus-host interactions and other complex pathologies.

The traits of Alzheimer's disease (AD) include amyloid plaques made of Aβ1-40 and Aβ1-42, and neurofibrillary tangles by the hyperphosphorylation of tau protein. AD is a complex disorder that is heterogenous in genetical, neuropathological, and clinical contexts. Current available therapeutics are unable to cure AD. Systems medicine is a strategy by viewing the body as a whole system, taking into account each individual's unique health profile, provide treatment and associated nursing care clinically for the patient, aiming for precision. Since the onset of AD can lead towards cognitive impairment, it is vital to intervene and diagnose early and prevent further progressive loss of neurons. Moreover, as the individual's brain functions are impaired due to neurodegeneration in AD, it is essential to reconstruct the neurons or brain cells to enable normal brain functions. Although there are different subtypes of AD due to varied pathological lesions, in the majority cases of AD, neurodegeneration and severe brain atrophy develop at the chronic stage. Novel approaches including RNA based gene therapy, stem cell based technology, bioprinting technology, synthetic biology for brain tissue reconstruction are researched in recent decades in the hope to decrease neuroinflammation and restore normal brain function in individuals of AD. Systems medicine include the prevention of disease, diagnosis and treatment by viewing the individual's body as a whole system, along with systems medicine based nursing as a strategy against AD that should be researched further. [ABSTRACT FROM AUTHOR]

Rationale: Liver cirrhosis is known to affect drug pharmacokinetics, but the functional assessment of the underlying pathophysiological alterations in drug metabolism is difficult. Methods: Cirrhosis in mice was induced by repeated treatment with carbon tetrachloride for 12 months. A cocktail of six drugs was administered, and parent compounds as well as phase I and II metabolites were quantified in blood, bile, and urine in a time-dependent manner. Pharmacokinetics were modeled in relation to the altered expression of metabolizing enzymes. In discrepancy with computational predictions, a strong increase of glucuronides in blood was observed in cirrhotic mice compared to vehicle controls. Results: The deviation between experimental findings and computational simulations observed by analyzing different hypotheses could be explained by increased sinusoidal export and corresponded to increased expression of export carriers (Abcc3 and Abcc4). Formation of phase I metabolites and clearance of the parent compounds were surprisingly robust in cirrhosis, although the phase I enzymes critical for the metabolism of the administered drugs in healthy mice, Cyp1a2 and Cyp2c29, were downregulated in cirrhotic livers. RNA-sequencing revealed the upregulation of numerous other phase I metabolizing enzymes which may compensate for the lost CYP isoenzymes. Comparison of genome-wide data of cirrhotic mouse and human liver tissue revealed similar features of expression changes, including increased sinusoidal export and reduced uptake carriers. Conclusion: Liver cirrhosis leads to increased blood concentrations of glucuronides because of increased export from hepatocytes into the sinusoidal blood. Although individual metabolic pathways are massively altered in cirrhosis, the overall clearance of the parent compounds was relatively robust due to compensatory mechanisms. [ABSTRACT FROM AUTHOR]

Background: For decades, the basic treatment strategies of necrotizing soft tissue infections (NSTI) have remained unchanged, primarily relying on aggressive surgical removal of infected tissue, broad-spectrum antibiotics, and supportive intensive care. One treatment strategy that has been proposed as an adjunctive measure to improve patient outcomes is hyperbaric oxygen (HBO2) treatment. HBO2 treatment has been linked to several immune modulatory effects; however, investigating these effects is complicated due to the disease's acute life-threatening nature, metabolic and cell homeostasis dependent variability in treatment effects, and heterogeneity with respect to both patient characteristics and involved pathogens. To embrace this complexity, we aimed to explore the underlying biological mechanisms of HBO2 treatment in patients with NSTI on the gene expression level. Methods: We conducted an observational cohort study on prospective collected data, including 85 patients admitted to the intensive care unit (ICU) for NSTI. All patients were treated with one or two HBO2 treatments and had one blood sample taken before and after the intervention. Total RNAs from blood samples were extracted and mRNA purified with rRNA depletion, followed by whole-transcriptome RNA sequencing with a targeted sequencing depth of 20 million reads. A model for differentially expressed genes (DEGs) was fitted, and the functional aspects of the obtained set of genes was predicted with GO (Gene Ontology) and KEGG (Kyoto Encyclopedia of genes and Genomes) enrichment analyses. All analyses were corrected for multiple testing with FDR. Results: After sequential steps of quality control, a final of 160 biological replicates were included in the present study. We found 394 protein coding genes that were significantly DEGs between the two conditions with FDR < 0.01, of which 205 were upregulated and 189 were downregulated. The enrichment analysis of these DEGs revealed 20 GO terms in biological processes and 12 KEGG pathways that were significantly overrepresented in the upregulated DEGs, of which the term; "adaptive immune response" (GO:0002250) (FDR = 9.88E-13) and "T cell receptor signaling pathway" (hsa04660) (FDR = 1.20E-07) were the most significant. Among the downregulated DEGs two biological processes were significantly enriched, of which the GO term "apoptotic process" (GO:0006915) was the most significant (FDR = 0.001), followed by "Positive regulation of T helper 1 cell cytokine production" (GO:2000556), and "NF-kappa B signaling pathway" (hsa04064) was the only KEGG pathway that was significantly overrepresented (FDR = 0.001). Conclusions: When one or two sessions of HBO2 treatment were administered to patients with a dysregulated immune response and systemic inflammation due to NSTI, the important genes that were regulated during the intervention were involved in activation of T helper cells and downregulation of the disease-induced highly inflammatory pathway NF-κB, which was associated with a decrease in the mRNA level of pro-inflammatory factors. Trial registration: Biological material was collected during the INFECT study, registered at ClinicalTrials.gov (NCT01790698). [ABSTRACT FROM AUTHOR]

Gene regulatory networks (GRNs) and gene co-expression networks (GCNs) allow genome-wide exploration of molecular regulation patterns in health and disease. The standard approach for obtaining GRNs and GCNs is to infer them from gene expression data, using computational network inference methods. However, since network inference methods are usually applied on aggregate data, distortion of the networks by demographic confounders might remain undetected, especially because gene expression patterns are known to vary between different demographic groups. In this paper, we present a computational framework to systematically evaluate the influence of demographic confounders on network inference from gene expression data. Our framework compares similarities between networks inferred for different demographic groups with similarity distributions obtained for random splits of the expression data. Moreover, it allows to quantify to which extent demographic groups are represented by networks inferred from the aggregate data in a confounder-agnostic way. We apply our framework to test four widely used GRN and GCN inference methods as to their robustness w. r. t. confounding by age, ethnicity and sex in cancer. Our findings based on more than |$ {44000}$| inferred networks indicate that age and sex confounders play an important role in network inference for certain cancer types, emphasizing the importance of incorporating an assessment of the effect of demographic confounders into network inference workflows. Our framework is available as a Python package on GitHub: https://github.com/bionetslab/grn-confounders. [ABSTRACT FROM AUTHOR]

Alzheimer's disease (AD) and other forms of dementia are together a leading cause of disability and death in the aging global population, imposing a high personal, societal, and economic burden. They are also among the most prominent examples of failed drug developments. Indeed, after more than 40 AD trials of anti-amyloid interventions, reduction of amyloid-β (Aβ) has never translated into clinically relevant benefits, and in several cases yielded harm. The fundamental problem is the century-old, brain-centric phenotype-based definitions of diseases that ignore causal mechanisms and comorbidities. In this hypothesis article, we discuss how such current outdated nosology of dementia is a key roadblock to precision medicine and articulate how Network Medicine enables the substitution of clinicopathologic phenotypes with molecular endotypes and propose a new framework to achieve precision and curative medicine for patients with neurodegenerative disorders. [ABSTRACT FROM AUTHOR]

Bayesian philosophy and Bayesian statistics have diverged in recent years, because Bayesian philosophers have become more interested in philosophical problems other than the foundations of statistics and Bayesian statisticians have become less concerned with philosophical foundations. One way in which this divergence manifests itself is through the use of direct inference principles: Bayesian philosophers routinely advocate principles that require calibration of degrees of belief to available non-epistemic probabilities, while Bayesian statisticians rarely invoke such principles. As I explain, however, the standard Bayesian framework cannot coherently employ direct inference principles. Direct inference requires a shift towards a non-standard Bayesian framework, which further increases the gap between Bayesian philosophy and Bayesian statistics. This divergence does not preclude the application of Bayesian philosophical methods to real-world problems. Data consolidation is a key challenge for present-day systems medicine and other systems sciences. I show that data consolidation requires direct inference and that the non-standard Bayesian methods outlined here are well suited to this task. [ABSTRACT FROM AUTHOR]

Glioblastoma (GBM) is a malignant cancer that is fatal even after standard therapy and the effects of current available therapeutics are not promising due its complex and evolving epigenetic and genetic profile. The mysteries that lead to GBM intratumoral heterogeneity and subtype transitions are not entirely clear. Systems medicine is an approach to view the patient in a whole picture integrating systems biology and synthetic biology along with computational techniques. Since the GBM oncogenesis involves genetic mutations, various therapies including gene therapeutics based on CRISPR-Cas technique, MicroRNAs, and implanted synthetic cells endowed with synthetic circuits against GBM with neural stem cells and mesenchymal stem cells acting as potential vehicles carrying therapeutics via the intranasal route, avoiding the risks of invasive methods in order to reach the GBM cells in the brain are discussed and proposed in this review. Systems medicine approach is a rather novel strategy, and since the GBM of a patient is complex and unique, thus to devise an individualized treatment strategy to tailor personalized multimodal treatments for the individual patient taking into account the phenotype of the GBM, the unique body health profile of the patient and individual responses according to the systems medicine concept might show potential to achieve optimum effects. [ABSTRACT FROM AUTHOR]

This article explores the integration of Digital Twins in Systems and Predictive Medicine, focusing on eye diagnosis. By utilizing the Digital Twin models, the proposed framework can support early diagnosis and predict evolution after treatment by providing customized simulation scenarios. Furthermore, a structured architectural framework comprising five levels has been proposed, integrating Digital Twin, Systems Medicine, and Predictive Medicine for managing eye diseases. Based on demographic parameters, statistics were performed to identify potential correlations that may contribute to predispositions to glaucoma. With the aid of a dataset, a neural network was trained with the goal of identifying glaucoma. This comprehensive approach, based on statistical analysis and Machine Learning, is a promising method to enhance diagnostic accuracy and provide personalized treatment approaches.

Largely unknown just a few decades ago, computational systems biology is now a central methodology for biological and medical research. This amazing ascent raises the question of what the community should do next. The article outlines our personal vision for the future of computational systems biology, suggesting the need to address both mindsets and methodologies. We present this vision by focusing on current and anticipated research goals, the development of strong computational tools, likely prominent applications, education of the nextgeneration of scientists, and outreach to the public. In our opinion, two classes of broad research goals have emerged in recent years and will guide future efforts. The first goal targets computational models of increasing size and complexity, aimed at solving emerging health-related challenges, such as realistic whole-cell and organ models, disease simulators and digital twins, in silico clinical trials, and clinically translational applications in the context of therapeutic drug development. Such large models will also lead us toward solutions to pressing issues in agriculture and environmental sustainability, including sufficient food availability and life in changing habitats. The second goal is a deep understanding of the essence of system designs and strategies with which nature solves problems. This understanding will help us explain observed biological structures and guide forays into synthetic biological systems. Regarding effective methodologies, we suggest efforts toward automated data pipelines from raw biomedical data all the way to spatiotemporal mechanistic model. These will be supported by dynamic methods of statistics, machine learning, artificial intelligence and streamlined strategies of dynamic model design, striking a fine balance between modeling realistic complexity and abstracted simplicity. Finally, we suggest the need for a concerted, community-wide emphasis on effective education in systems biology, implemented as a combination of formal instruction and hands-on mentoring. The educational efforts should furthermore be extended toward the public through books, blogs, social media, and interactive networking opportunities, with the ultimate goal of training in state-of-the-art technology while recapturing the lost art of synthesis. [ABSTRACT FROM AUTHOR]

Precision medicine relies on the identification of robust disease and risk factor signatures from omics data. However, current knowledge-driven approaches may overlook novel or unexpected phenomena due to the inherent biases in biological knowledge. In this study, we present a data-driven signature discovery workflow for DNA methylation analysis utilizing network-coherent autoencoders (NCAEs) with biologically relevant latent embeddings. First, we explored the architecture space of autoencoders trained on a large-scale pan-tissue compendium (n  = 75 272) of human epigenome-wide association studies. We observed the emergence of co-localized patterns in the deep autoencoder latent space representations that corresponded to biological network modules. We determined the NCAE configuration with the strongest co-localization and centrality signals in the human protein interactome. Leveraging the NCAE embeddings, we then trained interpretable deep neural networks for risk factor (aging, smoking) and disease (systemic lupus erythematosus) prediction and classification tasks. Remarkably, our NCAE embedding-based models outperformed existing predictors, revealing novel DNA methylation signatures enriched in gene sets and pathways associated with the studied condition in each case. Our data-driven biomarker discovery workflow provides a generally applicable pipeline to capture relevant risk factor and disease information. By surpassing the limitations of knowledge-driven methods, our approach enhances the understanding of complex epigenetic processes, facilitating the development of more effective diagnostic and therapeutic strategies. [ABSTRACT FROM AUTHOR]

NAD+ and glutathione precursors are currently used as metabolic modulators for improving the metabolic conditions associated with various human diseases, including non-alcoholic fatty liver disease, neurodegenerative diseases, mitochondrial myopathy, and age-induced diabetes. Here, we performed a one-day double blinded, placebo-controlled human clinical study to assess the safety and acute effects of six different Combined Metabolic Activators (CMAs) with 1 g of different NAD+ precursors based on global metabolomics analysis. Our integrative analysis showed that the NAD+ salvage pathway is the main source for boosting the NAD+ levels with the administration of CMAs without NAD+ precursors. We observed that incorporation of nicotinamide (Nam) in the CMAs can boost the NAD+ products, followed by niacin (NA), nicotinamide riboside (NR) and nicotinamide mononucleotide (NMN), but not flush free niacin (FFN). In addition, the NA administration led to a flushing reaction, accompanied by decreased phospholipids and increased bilirubin and bilirubin derivatives, which could be potentially risky. In conclusion, this study provided a plasma metabolomic landscape of different CMA formulations, and proposed that CMAs with Nam, NMN as well as NR can be administered for boosting NAD+ levels to improve altered metabolic conditions. [Display omitted] • The administration of serine, NAC and LCAT with NAD precursor is safe and well tolerated. • NAD salvage pathway is the main source of NAD when orally taking serine, NAC and LCAT. • Nam in the CMAs showed the best ability to boost plasma NAD+ products. • NA in the CMAs led to flushing reaction, decreased phospholipids and increased bilirubin. • Nam, NMN as well as NR are good candidates for boosting NAD level. [ABSTRACT FROM AUTHOR]

In the SMART-CARE project- a systems medicine approach to stratification of cancer recurrence in Heidelberg, Germany - a streamlined mass-spectrometry (MS) workflow for identification of cancer relapse was developed. This project has multiple partners from clinics, laboratories and computational teams. For optimal collaboration, consistent documentation and centralized storage, the linked data repository was designed. Clinical, laboratory and computational group members interact with this platform and store meta- and raw-data. The specific architectural choices, such as pseudonymization service, uploading process and other technical specifications as well as lessons learned are presented in this work. Altogether, relevant information in order to provide other research groups with a head-start for tackling MS data management in the context of systems medicine research projects is described. [ABSTRACT FROM AUTHOR]

Liver cirrhosis is the end stage of all chronic liver diseases and contributes significantly to overall mortality of 2% globally. The age-standardized mortality from liver cirrhosis in Europe is between 10 and 20% and can be explained by not only the development of liver cancer but also the acute deterioration in the patient's overall condition. The development of complications including accumulation of fluid in the abdomen (ascites), bleeding in the gastrointestinal tract (variceal bleeding), bacterial infections, or a decrease in brain function (hepatic encephalopathy) define an acute decompensation that requires therapy and often leads to acute-on-chronic liver failure (ACLF) by different precipitating events. However, due to its complexity and organ-spanning nature, the pathogenesis of ACLF is poorly understood, and the common underlying mechanisms leading to the development of organ dysfunction or failure in ACLF are still elusive. Apart from general intensive care interventions, there are no specific therapy options for ACLF. Liver transplantation is often not possible in these patients due to contraindications and a lack of prioritization. In this review, we describe the framework of the ACLF-I project consortium funded by the Hessian Ministry of Higher Education, Research and the Arts (HMWK) based on existing findings and will provide answers to these open questions. [ABSTRACT FROM AUTHOR]

Systems precision medicine is an interdisciplinary approach that recognises the complexity of diseases and emphasises the integration of clinical knowledge, multi-omics data, analytical models, and the expertise of physicians and data analysts to personalise the care pathway in complex diseases, such as cancer or diabetes. The aim is to gain a comprehensive understanding of diseases by analysing individual components and identifying relevant aspects for therapy and diagnosis. Key components, their interactions and emerging patterns can be studied using statistical, mathematical and computational tools. The combination of data analysis and clinical evaluation is crucial to effective decision-making, emphasising the need for an integrative approach rather than relying on data alone. Therefore, the crucial point discussed in this paper is that the "computational" part and the "artistic" part (i.e., the physician's intuition) cannot be separated, and therefore, systems precision medicine can be configured as a collective work of art, involving not only different medical professionals but also, and above all, professional data analysts. The work is "artistic" because data and mathematics alone, without medical knowledge of the context, are not enough. But the work is also "collective" in the sense that it must be the place of cultural integration between the professional intuition of the physician, which cannot be translated into mathematical formulas, and the ability to extract information from multi-omics data of the data analysts, who instead use formal and computational mathematical methods. However, to drive the medical revolution and reassemble a patient's parts, data analysts need to be involved in the hospital context, and precision medicine physicians should embrace data analytical perspectives. This will require ongoing dialogue, new languages of communication, and education that promotes continuous learning and collaboration between professions, fostering a new level of interdisciplinary collaboration for personalised care. [ABSTRACT FROM AUTHOR]

Cancer is a heterogeneous disease mainly driven by abnormal gene perturbations in regulatory networks. Therefore, it is appealing to identify the common and specific perturbed genes from multiple cancer networks. We developed an integrative network medicine approach to identify novel biomarkers and investigate drug repurposing across cancer types. We used a network-based method to prioritize genes in cancer-specific networks reconstructed using human transcriptome and interactome data. The prioritized genes show extensive perturbation and strong regulatory interaction with other highly perturbed genes, suggesting their vital contribution to tumorigenesis and tumor progression, and are therefore regarded as cancer genes. The cancer genes detected show remarkable performances in discriminating tumors from normal tissues and predicting survival times of cancer patients. Finally, we developed a network proximity approach to systematically screen drugs and identified dozens of candidates with repurposable potential in several cancer types. Taken together, we demonstrated the power of the network medicine approach to identify novel biomarkers and repurposable drugs in multiple cancer types. We have also made the data and code freely accessible to ensure reproducibility and reusability of the developed computational workflow.

Cancer progression is linked to gene-environment interactions that alter cellular homeostasis. The use of biomarkers as early indicators of disease manifestation and progression can substantially improve diagnosis and treatment. Large omics datasets generated by high-throughput profiling technologies, such as microarrays, RNA sequencing, whole-genome shotgun sequencing, nuclear magnetic resonance, and mass spectrometry, have enabled data-driven biomarker discoveries. The identification of differentially expressed traits as molecular markers has traditionally relied on statistical techniques that are often limited to linear parametric modeling. The heterogeneity, epigenetic changes, and high degree of polymorphism observed in oncogenes demand biomarker-assisted personalized medication schemes. Deep learning (DL), a major subunit of machine learning (ML), has been increasingly utilized in recent years to investigate various diseases. The combination of ML/DL approaches for performance optimization across multi-omics datasets produces robust ensemble-learning prediction models, which are becoming useful in precision medicine. This review focuses on the recent development of ML/DL methods to provide integrative solutions in discovering cancer-related biomarkers, and their utilization in precision medicine.

Abstract Background Alzheimer’s disease (AD) is associated with metabolic abnormalities linked to critical elements of neurodegeneration. We recently administered combined metabolic activators (CMA) to the AD rat model and observed that CMA improves the AD-associated histological parameters in the animals. CMA promotes mitochondrial fatty acid uptake from the cytosol, facilitates fatty acid oxidation in the mitochondria, and alleviates oxidative stress. Methods Here, we designed a randomised, double-blinded, placebo-controlled phase-II clinical trial and studied the effect of CMA administration on the global metabolism of AD patients. One-dose CMA included 12.35 g L-serine (61.75%), 1 g nicotinamide riboside (5%), 2.55 g N-acetyl-L-cysteine (12.75%), and 3.73 g L-carnitine tartrate (18.65%). AD patients received one dose of CMA or placebo daily during the first 28 days and twice daily between day 28 and day 84. The primary endpoint was the difference in the cognitive function and daily living activity scores between the placebo and the treatment arms. The secondary aim of this study was to evaluate the safety and tolerability of CMA. A comprehensive plasma metabolome and proteome analysis was also performed to evaluate the efficacy of the CMA in AD patients. Results We showed a significant decrease of AD Assessment Scale-cognitive subscale (ADAS-Cog) score on day 84 vs day 0 (P = 0.00001, 29% improvement) in the CMA group. Moreover, there was a significant decline (P = 0.0073) in ADAS-Cog scores (improvement of cognitive functions) in the CMA compared to the placebo group in patients with higher ADAS-Cog scores. Improved cognitive functions in AD patients were supported by the relevant alterations in the hippocampal volumes and cortical thickness based on imaging analysis. Moreover, the plasma levels of proteins and metabolites associated with NAD + and glutathione metabolism were significantly improved after CMA treatment. Conclusion Our results indicate that treatment of AD patients with CMA can lead to enhanced cognitive functions and improved clinical parameters associated with phenomics, metabolomics, proteomics and imaging analysis. Trial registration ClinicalTrials.gov NCT04044131 Registered 17 July 2019, https://clinicaltrials.gov/ct2/show/NCT04044131

Molecular interaction networks lay the foundation for studying how biological functions are controlled by the complex interplay of genes and proteins. Investigating perturbed processes using biological networks has been instrumental in uncovering mechanisms that underlie complex disease phenotypes. Rapid advances in omics technologies have prompted the generation of high-throughput datasets, enabling large-scale, network-based analyses. Consequently, various modeling techniques, including network enrichment, differential network extraction, and network inference, have proven to be useful for gaining new mechanistic insights. We provide an overview of recent network-based methods and their core ideas to facilitate the discovery of disease modules or candidate mechanisms. Knowledge generated from these computational efforts will benefit biomedical research, especially drug development and precision medicine. We further discuss current challenges and provide perspectives in the field, highlighting the need for more integrative and dynamic network approaches to model disease development and progression.

Rationale, Aims, and Objectives: The disciplinary profile and the quality of production of knowledge on Corona pandemic is studied. This scientific field is called 'Medical Corona Science'. Methods: Criteria of analytical philosophy of science and science studies are systematically applied. Results: It is shown that mainly auxiliary medical disciplines such as virology and epidemiology but not clinical disciplines provide Corona knowledge. We see a laboratory‐centered, technology‐ and data‐driven science, largely ignoring clinical issues. Therefore we call these approaches "Medical Corona Science" (MCS). We see the need to adapt to features of a 'post‐normal science', a 'mode 2 science' and of 'Integration and Implementation Science', especially as clinical knowledge must be integrated. There is also a severe lack of theoretical considerations that could help to frame the pandemic as a complex dynamic system. Conclusions: We suggest a deeper meta‐scientific discussion of the epistemic value of MCS and propose the application of tools from systems science. [ABSTRACT FROM AUTHOR]