Mechanotransduction is the process that enables the conversion of mechanical cues into biochemical signaling. While all our cells are well known to be sensitive to such stimuli, the details of the systemic interaction between mechanical input and inflammation are not well integrated. Often, indeed, they are considered and studied in relatively compartmentalized areas, and we therefore argue here that to understand the relationship of mechanical stimuli with inflammation – with a high translational potential - it is crucial to offer and analyze a unified view of mechanotransduction. We therefore present here pathway representation, recollected with the standard systems biology markup language (SBML) and explored with network biology approaches, offering RAC1 as an exemplar and emerging molecule with potential for medical translation.

Electrical stimulation (ES) is widely employed in both clinical therapies and research settings where it has shown promise in promoting tissue regeneration, wound healing, and inflammation control. Research has also highlighted ES as a regulator of DNA demethylation, which plays a critical role in nerve regeneration and cellular repair mechanisms. While the impact of ES on epigenetic processes is recognized, its broader effects on cellular functions, particularly in inflammation and wound healing, are less understood. We recently showed how ES impacts inflammatory states by modulating transcriptomic and metabolomic profiles in a 3Din vitromodel where human fibroblasts and keratinocytes are included in a collagen matrix, i.e., even in the absence of the nervous system. Here, we propose to deepen our exploration on the differential effects on DNA methylation, including an investigation of the correlation with age acceleration using a mitotic clock. These results confirm and caution on the differential effect of DC on inflamed and non-inflamed samples and suggest an involvement of direct current stimuli at 1 V ((Formula presented.)) in the control of senescent processes associated with mitosis and inflammation; the mechanistic details of these will have to be clarified with additional experiments.

Background and Objective: Hepatoblastoma is the most common pediatric liver cancer and represents a serious clinical challenge as no effective therapies have yet been found for advanced states and relapses of the disease. Methods: In this work, we use a well-established agent-based model of the immune response now equipped with anti-cancer therapy response to study the evolution of the disease and the role of the immune system in its containment. Results: We simulate the course of hepatoblastoma over three years in a population of virtual patients, successfully mimicking clinical mortality and symptom onset rates, as well as observations on the main tumor transcriptomic subtypes. Conclusions: The capacity of the introduced framework to reproduce clinical data and the heterogeneity of hepatoblastoma, combined with the possibility of observing the dynamics of cellular entities at the microscopic scale and the key chemical signals involved in disease progression, makes the model a promising resource for future research on in silico trials.

The medical discourse entails the analysis of the modalities, which are far from unbiased, by which hypotheses and results are laid out in the dissemination of findings in scientific publications. This gives different emphases on the background, relevance, robustness, and assumptions that the audience takes for granted. This concept is extensively studied in socio-anthropology. However, it remains generally overlooked within the scientific community conducting the research. Yet, analyzing the discourse is crucial for several reasons: to frame policies that take into account an appropriately large screen of medical opportunities; to avoid overseeing promising but less walked paths; to grasp different types of representations of diseases, therapies, patients, and other stakeholders; to understand how these terms are conditioned by time and culture. While socio-anthropologists traditionally use manual curation methods–limited by the lengthy process–machine learning and AI may offer complementary tools to explore the vastness of an ever-growing body of medical literature. In this work, we propose a pipeline for the analysis of the medical discourse on the therapeutic approaches to rheumatoid arthritis using topic modeling and transformer-based emotion and sentiment analysis, overall offering complementary insights to previous curation.

Addressing complexity in the study of life sciences through Systems Biology and Systems Medicine has been transformative, making Systems Pharmacology the next logical step. In this review, we focus on physical stimuli, whose potential in pharmacology has been neglected, despite demonstrated therapeutic properties. To address this overlooked aspect of pharmacology, we aim to (i), highlight how physical stimuli (mechanical, optical, magnetic, electrical) influence inflammation; (ii) identify known overlaps among transduction mechanisms of physical stimuli and highlight the need for deeper understanding of these mechanisms; (iii) promote advanced network approaches as tools to understand this complexity and enhance the potential of anti-inflammatory physical therapies; and (iv), integrate physical stimuli into the mindset of pharmacologists. The overall purpose of this review is to spark questions rather than provide answers, and to drive research in this critically underexplored area.

Personalized medicine strategies are gaining momentum nowadays, enabling the introduction of targeted treatments based on individual differences that can lead to greater therapeutic efficacy by reducing adverse effects. Despite its crucial role, studying the contribution of the immune system (IS) in this context is difficult because of the intricate interplay between host, pathogen, therapy, and other external stimuli. To address this problem, a multidisciplinary approach involving in silico models can be of great help. In this perspective, we will discuss the use of a well-established agent-based model of the immune response, C-ImmSim, to study the relationship between long-lasting diseases and the combined effects of IS, drug therapies and exogenous factors such as physical activity and dietary habits.

Summary: We present tidysbml, an R package able to perform compartments, species, and reactions data extraction from Systems Biology Markup Language (SBML) documents (up to Level 3) in tabular data structures (i.e. R dataframes) to easily access and handle the richness of the biological information. Thanks to its output format, the package facilitates data manipulation, enabling manageable construction, and therefore analysis, of custom networks, as well as data retrieval, by means of R packages such as igraph, RCy3, and biomaRt. Exemplar data (i.e. SBML files) are extracted from Reactome.

Motivation: methyLImp, a method we recently introduced for the missing value estimation of DNA methylation data, has demonstrated competitive performance in data imputation compared to the existing, general-purpose, approaches. However, imputation running time was considerably long and unfeasible in case of large datasets with numerous missing values. Results: methyLImp2 made possible computations that were previously unfeasible. We achieved this by introducing two important modifications that have significantly reduced the original running time without sacrificing prediction performance. First, we implemented a chromosome-wise parallel version of methyLImp. This parallelization reduced the runtime by several 10-fold in our experiments. Then, to handle large datasets, we also introduced a mini-batch approach that uses only a subset of the samples for the imputation. Thus, it further reduces the running time from days to hours or even minutes in large datasets.

Predictive power of epigenetic age – opportunities and cautions

The medical discourse, entails the analysis of the modalities, far from unbiased, by which hypotheses and results are laid out in the dissemination of findings in scientific publications, giving different emphases on the background, relevance, robustness, and assumptions that the audience should take for granted. While this concept is extensively studied in socio-anthropology, it remains generally overlooked within the scientific community conducting the research. Yet, analyzing the discourse is crucial for several reasons: to frame policies that take into account an appropriately large screen of medical opportunities, to avoid overseeing promising but less walked paths, to grasp different types of representations of diseases, therapies, patients, and other stakeholders, understanding and being aware of how these very terms are conditioned by time, culture and so on. While socio-anthropologists traditionally use manual curation methods, automated approaches like topic modeling offer a complementary way to explore the vast and ever-growing body of medical literature. In this work, we propose a complementary analysis of the medical discourse regarding the therapies offered for rheumatoid arthritis using topic modeling and large language model-based emotion and sentiment analysis.

Wound healing (WH) is a complex phenomenon recollecting the ability of the body to preserve homeostasis. Its description ranges from the very minute details on the progression of the molecular and cellular events (bench) occurring locally to a wound, to the very general, and almost colloquial description of how injuries recover more or less quickly and smoothly in an individual or a patient (bed).The connection between the two representations of WH is far from clear and rarely discussed from an overarching and theoretical perspective in biological, computational and medical terms that represent three fundamental components of modern PPPM. Importantly, understanding WH and its eliciting/modulating factors (including physical stimuli) as a continuum between molecular (local) and clinical (systemic) events is of particular relevance for advancing in the management of chronic inflammation, a hallmark of non-communicable diseases (NCDs). The ambition of this chapter is to make the necessity for such continuum explicit. This evidence will be supported with the first integrated overview on the scattered basic knowledge existing about WH's neglected eliciting factors: physical stimuli. Further, an exemplar translational process using rheumatoid arthritis (RA), proceeding from experimental data in animal models to a pilot clinical study, will cover from bench to bedside the relevance of WH as a systemic anti-inflammatory phenomenon, and will be discussed in the frame of PPPM for its therapeutic potential.

Electrostimulation is the object of the study of a variety of clinical approaches, ranging from bioelectronic medicine where the aim is to elicit the activity of the autonomic nervous system (ANS), to electroacupuncture with the general objective to restore homeostasis, to transcutaneous electrical nerve stimulation (TENS) to control pain and degeneration, to name a few. Among the numerous obstacles preventing from a clear adoption or rejection of these approaches in mainstream clinical practice, is the difficulty in standardizing experimental systems for testing and validation. Consequently, indications on the appropriate magnitude of an effective stimulus (duration, frequency, intensity) remain unclear. To approach this issue we present preliminary results on the differential molecular activity elicited in a 3D bioprinted construct containing fibroblasts and keratinocytes in a collagen matrix, by two diverse types of electrical stimulation (direct and alternate current). Two conditions, physiology and inflammation induced by TNF? perfusion were tested with anelectrobiomedical device. The system mimics a simplified model of skin, the largest and most accessible of our organs, in inflamed or physiological states, treated by electrostimulation. The bioprinted sample is constructed to yield an appropriate number of cell enabling high-throughput screens. We report here our preliminary results on RNA-seq differential expression comparing direct and alternate current stimuli, with a focus on wound healing and inflammation as part of the greater inflammatory pathway. Our construct offer reproducibility of the experience, and direct comparison among potentially numerous conditions and types of stimulation. Our preliminary results shows that electrostimulation offers differential elicitation of biological functions. In particular, direct and alternate current provoke differential activation of proliferation and development associated functions.

In recent years there has been a widespread interest in researching biomarkers of aging that could predict physiological vulnerability better than chronological age. Aging, in fact, is one of the most relevant risk factors for a wide range of maladies, and molecular surrogates of this phenotype could enable better patients stratification. Among the most promising of such biomarkers is DNA methylation-based biological age. Given the potential and variety of computational implementations (epigenetic clocks), we here present a systematic review of such clocks. Furthermore, we provide a large-scale performance comparison across different tissues and diseases in terms of age prediction accuracy and age acceleration, a measure of deviance from physiology. Our analysis offers both a state-of-the-art overview of the computational techniques developed so far and a heterogeneous picture of performances, which can be helpful in orienting future research.

Inflammaging is a low-grade inflammatory state generated by the aging process that can contribute to frailty and age-related diseases in the elderly. However, it can have distinct effects in the elderly living in endemic areas for infectious diseases. An increased inflammatory response may confer protection against infectious agents in these areas, although this advantage can cause accelerating epigenetic aging. In this study, we evaluated the inflammatory profile and the epigenetic age of infected and noninfected individuals from an endemic area in Brazil. The profile of cytokines, chemokines and growth factors analyzed in the sera of the two groups of individuals showed similarities, although infected individuals had a higher concentration of these mediators. A significant increase in IL-1ra, CXCL8, CCL2, CCL3 and CCL4 production was associated with leprosy infection. Notably, elderly individuals displayed distinct immune responses associated with their infection status when compared to adults suggesting an adaptive remodelling of their immune responses. Epigenetic analysis also showed that there was no difference in epigenetic age between the two groups of individuals. However, individuals from the endemic area had a significant accelerated aging when compared to individuals from São Paulo, a non-endemic area in Brazil. Moreover, the latter cohort was also epigenetically aged in relation to an Italian cohort. Our data shows that living in endemic areas for chronic infectious diseases results in remodelling of inflammaging and acceleration of epigenetic aging in individuals regardless of their infectious status. It also highlights that geographical, genetic and environmental factors influence aging and immunosenescence in their pace and profile.

Parkinson's disease (PD) is the neurological disorder showing the greatest rise in prevalence from 1990 to 2016. Despite clinical definition criteria and a tremendous effort to develop objective biomarkers, precise diagnosis of PD is still unavailable at early stage. In recent years, an increasing number of studies have used omic methods to unveil the molecular basis of PD, providing a detailed characterization of potentially pathological alterations in various biological specimens. Metabolomics could provide useful insights to deepen our knowledge of PD aetiopathogenesis, to identify signatures that distinguish groups of patients and uncover responsive biomarkers of PD that may be significant in early detection and in tracking the disease progression and drug treatment efficacy. The present work is the first large metabolomic study based on nuclear magnetic resonance (NMR) with an independent validation cohort aiming at the serum characterization of de novo drug-naive PD patients. Here, NMR is applied to sera from large training and independent validation cohorts of German subjects. Multivariate and univariate approaches are used to infer metabolic differences that characterize the metabolite and the lipoprotein profiles of newly diagnosed de novo drug-naive PD patients also in relation to the biological sex of the subjects in the study, evidencing a more pronounced fingerprint of the pathology in male patients. The presence of a validation cohort allowed us to confirm altered levels of acetone and cholesterol in male PD patients. By comparing the metabolites and lipoproteins levels among de novo drug-naive PD patients, age- and sex-matched healthy controls, and a group of advanced PD patients, we detected several descriptors of stronger oxidative stress.

Background The debilitating effects of noncommunicable diseases (NCDs) and the accompanying chronic inflammation represent a significant obstacle for the sustainability of our development, with efforts spreading worldwide to counteract the diffusion of NCDs, as per the United Nations Sustainable Development Goals (SDG 3). In fact, despite efforts of varied intensity in numerous directions (from innovations in biotechnology to lifestyle modifications), the incidence of NCDs remains pandemic. The present work wants to contribute to addressing this major concern, with a specific focus on the fragmentation of medical approaches, via an interdisciplinary analysis of the medical discourse, i.e. the heterogenous reporting that biomedical scientific literature uses to describe the anti-inflammatory therapeutic landscape in NCDs. The aim is to better capture the roots of this compartmentalization and the power relations existing among three segregated pharmacological, experimental and unstandardized biomedical approaches to ultimately empower collaboration beyond medical specialties and possibly tap into a more ample and effective reservoir of integrated therapeutic opportunities.

Drug repurposing is a highly active research area, aiming at finding novel uses for drugs that have been previously developed for other therapeutic purposes. Despite the flourishing of methodologies, success is still partial, and different approaches offer, each, peculiar advantages. In this composite landscape, we present a novel methodology focusing on an efficient mathematical procedure based on gene similarity scores and biased random walks which rely on robust drug-gene-disease association data sets. The recommendation mechanism is further unveiled by means of the Markov chain underlying the random walk process, hence providing explainability about how findings are suggested. Performances evaluation and the analysis of a case study on rheumatoid arthritis show that our approach is accurate in providing useful recommendations and is computationally efficient, compared to the state of the art of drug repurposing approaches.

Parkinson's disease (PD) is the neurological disorder showing the greatest rise in prevalence from 1990 to 2016. Despite clinical definition criteria and a tremendous effort to develop objective biomarkers, precise diagnosis of PD is still unavailable at early stage. In recent years, an increasing number of studies have used omic methods to unveil the molecular basis of PD, providing a detailed characterization of potentially pathological alterations in various biological specimens. Metabolomics could provide useful insights to deepen our knowledge of PD aetiopathogenesis, to identify signatures that distinguish groups of patients and uncover responsive biomarkers of PD that may be significant in early detection and in tracking the disease progression and drug treatment efficacy. The present work is the first large metabolomic study based on nuclear magnetic resonance (NMR) with an independent validation cohort aiming at the serum characterization of de novo drug-naive PD patients. Here, NMR is applied to sera from large training and independent validation cohorts of German subjects. Multivariate and univariate approaches are used to infer metabolic differences that characterize the metabolite and the lipoprotein profiles of newly diagnosed de novo drug-naive PD patients also in relation to the biological sex of the subjects in the study, evidencing a more pronounced fingerprint of the pathology in male patients. The presence of a validation cohort allowed us to confirm altered levels of acetone and cholesterol in male PD patients. By comparing the metabolites and lipoproteins levels among de novo drug-naive PD patients, age- and sex-matched healthy controls, and a group of advanced PD patients, we detected several descriptors of stronger oxidative stress.

Advanced age represents one of the major risk factors for Parkinson's Disease. Recent biomedical studies posit a role for microRNAs, also known to be remodelled during ageing. However, the relationship between microRNA remodelling and ageing in Parkinson's Disease, has not been fully elucidated. Therefore, the aim of the present study is to unravel the relevance of microRNAs as biomarkers of Parkinson's Disease within the ageing framework. We employed Next Generation Sequencing to profile serum microRNAs from samples informative for Parkinson's Disease (recently diagnosed, drug-naïve) and healthy ageing (centenarians) plus healthy controls, age-matched with Parkinson's Disease patients. Potential microRNA candidates markers, emerging from the combination of differential expression and network analyses, were further validated in an independent cohort including both drug-naïve and advanced Parkinson's Disease patients, and healthy siblings of Parkinson's Disease patients at higher genetic risk for developing the disease. While we did not find evidences of microRNAs co-regulated in Parkinson's Disease and ageing, we report that hsa-miR-144-3p is consistently down-regulated in early Parkinson's Disease patients. Moreover, interestingly, functional analysis revealed that hsa-miR-144-3p is involved in the regulation of coagulation, a process known to be altered in Parkinson's Disease. Our results consistently show the down-regulation of hsa-mir144-3p in early Parkinson's Disease, robustly confirmed across a variety of analytical and experimental analyses. These promising results ask for further research to unveil the functional details of the involvement of hsa-mir144-3p in Parkinson's Disease.

Inflammaging is a low-grade inflammatory state generated by the aging process that can contribute to frailty and age-related diseases in the elderly. However, it can have distinct effects in the elderly living in endemic areas for infectious diseases. An increased inflammatory response may confer protection against infectious agents in these areas, although this advantage can cause accelerating epigenetic aging. In this study, we evaluated the inflammatory profile and the epigenetic age of infected and noninfected individuals from an endemic area in Brazil. The profile of cytokines, chemokines and growth factors analyzed in the sera of the two groups of individuals showed similarities, although infected individuals had a higher concentration of these mediators. A significant increase in IL-1ra, CXCL8, CCL2, CCL3 and CCL4 production was associated with leprosy infection. Notably, elderly individuals displayed distinct immune responses associated with their infection status when compared to adults suggesting an adaptive remodelling of their immune responses. Epigenetic analysis also showed that there was no difference in epigenetic age between the two groups of individuals. However, individuals from the endemic area had a significant accelerated aging when compared to individuals from São Paulo, a non-endemic area in Brazil. Moreover, the latter cohort was also epigenetically aged in relation to an Italian cohort. Our data shows that living in endemic areas for chronic infectious diseases results in remodelling of inflammaging and acceleration of epigenetic aging in individuals regardless of their infectious status. It also highlights that geographical, genetic and environmental factors influence aging and immunosenescence in their pace and profile.