Putting the Patient Back Together - Social Medicine, Network Medicine, and the Limits of Reductionism

DAMP-ing IBD: Extinguish the Fire and Prevent Smoldering

In inflammatory bowel diseases (IBD), the most promising therapies targeting cytokines or immune cell trafficking demonstrate around 40% efficacy. As IBD is a multifactorial inflammation of the intestinal tract, a single-target approach is unlikely to solve this problem, necessitating an alternative strategy that addresses its variability. One approach often overlooked by the pharmaceutically driven therapeutic options is to address the impact of environmental factors. This is somewhat surprising considering that IBD is increasingly viewed as a condition heavily influenced by such factors, including diet, stress, and environmental pollution-often referred to as the "Western lifestyle". In IBD, intestinal responses result from a complex interplay among the genetic background of the patient, molecules, cells, and the local inflammatory microenvironment where danger- and microbe-associated molecular patterns (D/MAMPs) provide an adjuvant-rich environment. Through activating DAMP receptors, this array of pro-inflammatory factors can stimulate, for example, the NLRP3 inflammasome-a major amplifier of the inflammatory response in IBD, and various immune cells via non-specific bystander activation of myeloid cells (e.g., macrophages) and lymphocytes (e.g., tissue-resident memory T cells). Current single-target biological treatment approaches can dampen the immune response, but without reducing exposure to environmental factors of IBD, e.g., by changing diet (reducing ultra-processed foods), the adjuvant-rich landscape is never resolved and continues to drive intestinal mucosal dysregulation. Thus, such treatment approaches are not enough to put out the inflammatory fire. The resultant smoldering, low-grade inflammation diminishes physiological resilience of the intestinal (micro)environment, perpetuating the state of chronic disease. Therefore, our hypothesis posits that successful interventions for IBD must address the complexity of the disease by simultaneously targeting all modifiable aspects: innate immunity cytokines and microbiota, adaptive immunity cells and cytokines, and factors that relate to the (micro)environment. Thus the disease can be comprehensively treated across the nano-, meso-, and microscales, rather than with a focus on single targets. A broader perspective on IBD treatment that also includes options to adapt the DAMPing (micro)environment is warranted.

Systems approach in planetary health education for medical students: a mixed methods study

BACKGROUND

Introducing students to the "planetary health lenses" perspective is crucial. Comprehensive strategies for teaching this perspective are lacking, especially in the domains of "interconnection within nature (IWN)" and "systems thinking/complexity." There is also a scarcity of studies assessing medical students' opinions on planetary health and evaluating teaching strategies.

OBJECTIVE

To understand Brazilian medical students' perceptions and knowledge of planetary health (PH) and evaluate the application of the educational material "Patient and Clinic through the Lens of Planetary Health," which addresses "IWN" and "complexity" through the sociological lens of Actor-Network Theory, in an integrative course at a medical school in Brazil.

METHODS

A mixed-methods, quasi-experimental design involving two medical student classes during 2022/2023. Participants completed a questionnaire on sociodemographic data; pre- and post-intervention closed-ended questions about perceptions related to PH, and an open-ended questionnaire on experience and learning. Each student group presented a portfolio under the planetary health lenses regarding a real patient, developing a network diagram that described the social network involving both human and non-human actors with which this person is interconnected. The cohorts participated in "IWN" activities: a contemplative trail or reflection on belonging to the planet.

RESULTS

Ninety-six students and 9 professors participated. The majority of students (66.7%) reported significant or extremely significant learning from the sessions. There was an increase in perception of the need for physicians to incorporate PH into their clinical practice (p = 0.002; r = 0.46) and an intensification of the sense of interconnection with the environment (p = 0.003; r = 0.46). There was a gain in knowledge about how many diseases were related to PH (p < 0.02 for all 13 listed diseases). The majority (83%) found the sessions relevant or highly relevant and commented on their impact, both professionally and personally.

CONCLUSIONS

Teaching PH in a medical school allowed students to learn from the patient's perspective, considering psychosocial and environmental determinants, about the intrinsic interdependence between population's health and PH. This strategy made a significant contribution by proposing pioneering didactics and offering valuable insights into the challenges and nuances of teaching PH.

Bioinformatic platforms for clinical stratification of natural history of atherosclerotic cardiovascular diseases

Although bioinformatic methods gained a lot of attention in the latest years, their use in real-world studies for primary and secondary prevention of atherosclerotic cardiovascular diseases (ASCVD) is still lacking. Bioinformatic resources have been applied to thousands of individuals from the Framingham Heart Study as well as health care-associated biobanks such as the UK Biobank, the Million Veteran Program, and the CARDIoGRAMplusC4D Consortium and randomized controlled trials (i.e. ODYSSEY, FOURIER, ASPREE, and PREDIMED). These studies contributed to the development of polygenic risk scores (PRS), which emerged as novel potent genetic-oriented tools, able to calculate the individual risk of ASCVD and to predict the individual response to therapies such as statins and proprotein convertase subtilisin/kexin type 9 inhibitor. ASCVD are the first cause of death around the world including coronary heart disease (CHD), peripheral artery disease, and stroke. To achieve the goal of precision medicine and personalized therapy, advanced bioinformatic platforms are set to link clinically useful indices to heterogeneous molecular data, mainly epigenomics, transcriptomics, metabolomics, and proteomics. The DIANA study found that differential methylation of ABCA1, TCF7, PDGFA, and PRKCZ significantly discriminated patients with acute coronary syndrome from healthy subjects and their expression levels positively associated with CK-MB serum concentrations. The ARIC Study revealed several plasma proteins, acting or not in lipid metabolism, with a potential role in determining the different pleiotropic effects of statins in each subject. The implementation of molecular high-throughput studies and bioinformatic techniques into traditional cardiovascular risk prediction scores is emerging as a more accurate practice to stratify patients earlier in life and to favour timely and tailored risk reduction strategies. Of note, radiogenomics aims to combine imaging features extracted for instance by coronary computed tomography angiography and molecular biomarkers to create CHD diagnostic algorithms useful to characterize atherosclerotic lesions and myocardial abnormalities. The current view is that such platforms could be of clinical value for prevention, risk stratification, and treatment of ASCVD.

Network pharmacology: towards the artificial intelligence-based precision traditional Chinese medicine

Network pharmacology (NP) provides a new methodological perspective for understanding traditional medicine from a holistic perspective, giving rise to frontiers such as traditional Chinese medicine network pharmacology (TCM-NP). With the development of artificial intelligence (AI) technology, it is key for NP to develop network-based AI methods to reveal the treatment mechanism of complex diseases from massive omics data. In this review, focusing on the TCM-NP, we summarize involved AI methods into three categories: network relationship mining, network target positioning and network target navigating, and present the typical application of TCM-NP in uncovering biological basis and clinical value of Cold/Hot syndromes. Collectively, our review provides researchers with an innovative overview of the methodological progress of NP and its application in TCM from the AI perspective.

Promoting whole person health: Exploring the role of traditional Chinese medicine in Polish healthcare

Poland has a unique history of traditional Chinese medicine (TCM) dating back to the 17th century when Polish missionary Michael (Michał) Boym was a pioneer in the field. In the 20th century, his successor, Professor Zbigniew Garnuszewski, reintroduced acupuncture to medical practice in Poland. However, other methods of TCM and its holistic approach to patient care have not found their place in modern medicine in Poland. At present, the legal status of TCM in Poland remains unregulated, with TCM included in the broad spectrum of complementary and alternative medicine (CAM) practices. Few reports are available on the use of TCM methods among the Polish population. Integrative medicine combines conventional medicine with evidence-based CAM interventions and considers all aspects of a patient's health, including physical, emotional, mental, social, and environmental factors. An integrative healthcare model that incorporates TCM modalities and lifestyle recommendations as well as a whole person approach may provide a more sustainable solution for the constantly underfinanced Polish healthcare system, which faces challenges of multimorbidity in an aging society and limited access to care. The coronavirus disease 2019 pandemic, war in Ukraine, and ongoing climate crisis have underscored the need to strengthen the resilience of the Polish healthcare system and search for new solutions. A model of care that blends the best of biomedicine and TCM healing approaches may be a better option for both patients and the healthcare system in Poland. Please cite this article as: Rybicka M, Zhao J, Piotrowicz K, Ptasnik S, Mitka K, Kocot-Kępska M, Hui KK. Promoting whole person health: Exploring the role of traditional Chinese medicine in Polish healthcare. J Integr Med. 2023; 21(6): 509-517.

The amyloid cascade hypothesis: an updated critical review

Results from recent clinical trials of antibodies that target amyloid-β (Aβ) for Alzheimer's disease have created excitement and have been heralded as corroboration of the amyloid cascade hypothesis. However, while Aβ may contribute to disease, genetic, clinical, imaging and biochemical data suggest a more complex aetiology. Here we review the history and weaknesses of the amyloid cascade hypothesis in view of the new evidence obtained from clinical trials of anti-amyloid antibodies. These trials indicate that the treatments have either no or uncertain clinical effect on cognition. Despite the importance of amyloid in the definition of Alzheimer's disease, we argue that the data point to Aβ playing a minor aetiological role. We also discuss data suggesting that the concerted activity of many pathogenic factors contribute to Alzheimer's disease and propose that evolving multi-factor disease models will better underpin the search for more effective strategies to treat the disease.

Health Equity through Clinical Research - Meeting the Challenge of Inclusion

Health Equity through Clinical Research Representativeness is perhaps one of the most important requirements in medical research, especially for health disparities research. Study findings should apply to all members of the population without selection bias. The authors detail the multiple approaches to ensuring representativeness that were developed in the HANDLS study.

Making sense of complexity: A qualitative 'Framework' analysis of naturopathic case management and clinical reasoning

BACKGROUND AND PURPOSE

The clinical encounter is inherently complex and uncertain. Naturopathic clinical practice is shaped by a traditional philosophy and practice guiding principles, with a therapeutic framework that incorporates a complex inter-systems approach. It is possible that this foundation may orient naturopathic practitioners to manage clinical complexity and uncertainty in a distinct manner. The aim of this study is to explore the perceptions of experienced naturopathic practitioners to the management of clinical complexity within naturopathic care.

MATERIALS AND METHODS

Twenty experienced Australian naturopathic practitioners participated across four focus groups, responding to semi-structured questions regarding their clinical reasoning strategies and case management processes. The data were analysed using a seven step Framework analysis method.

RESULTS

Three primary themes were identified: i) patient is encountered as a whole entity, ii) clinical reasoning is ampliative and explicative, and iii) treatment reflects systems thinking. Participants perceived a focus on the connections between various case elements, building a comprehensive internalised schematic of each case. Participants saw treatments as meeting various needs including prevention, symptom alleviation, causal mitigation, and support of innate healing processes.

CONCLUSION

Naturopathic practitioners perceive they clinically embody the traditional holistic philosophy of naturopathy as a systems orientation, incorporating traditional and contemporary bioscience knowledge. This appears to shape a distinct naturopathic case management approach, oriented to working with patients in a complexity-informed manner.

Reductionist methodology and the ambiguity of the categories of race and ethnicity in biomedical research: an exploratory study of recent evidence

In this article, we analyse how researchers use the categories of race and ethnicity with reference to genetics and genomics. We show that there is still considerable conceptual "messiness" (despite the wide-ranging and popular debate on the subject) when it comes to the use of ethnoracial categories in genetics and genomics that among other things makes it difficult to properly compare and interpret research using ethnoracial categories, as well as draw conclusions from them. Finally, we briefly reconstruct some of the biases of reductionism to which geneticists (as well as other researchers referring to genetic methods and explanations) are particularly exposed to, and we analyse the problem in the context of the biologization of ethnoracial categories. Our work constitutes a novel, in-depth contribution to the debate about reporting race and ethnicity in biomedical and health research. First, we reconstruct the theoretical background assumptions about racial ontology which researchers implicitly presume in their studies with the aid of a sample of recent papers published in medical journals about COVID-19. Secondly, we use the typology of the biases of reductionism to the problem of biologization of ethnoracial categories with reference to genetics and genomics.

Targeting stressor-induced dysfunctions in protein-protein interaction networks via epichaperomes

Diseases are manifestations of complex changes in protein-protein interaction (PPI) networks whereby stressors, genetic, environmental, and combinations thereof, alter molecular interactions and perturb the individual from the level of cells and tissues to the entire organism. Targeting stressor-induced dysfunctions in PPI networks has therefore become a promising but technically challenging frontier in therapeutics discovery. This opinion provides a new framework based upon disrupting epichaperomes - pathological entities that enable dysfunctional rewiring of PPI networks - as a mechanism to revert context-specific PPI network dysfunction to a normative state. We speculate on the implications of recent research in this area for a precision medicine approach to detecting and treating complex diseases, including cancer and neurodegenerative disorders.

Transcriptomics-based network medicine approach identifies metformin as a repurposable drug for atrial fibrillation

Effective drugs for atrial fibrillation (AF) are lacking, resulting in significant morbidity and mortality. This study demonstrates that network proximity analysis of differentially expressed genes from atrial tissue to drug targets can help prioritize repurposed drugs for AF. Using enrichment analysis of drug-gene signatures and functional testing in human inducible pluripotent stem cell (iPSC)-derived atrial-like cardiomyocytes, we identify metformin as a top repurposed drug candidate for AF. Using the active compactor, a new design analysis of large-scale longitudinal electronic health record (EHR) data, we determine that metformin use is significantly associated with a reduced risk of AF (odds ratio = 0.48, 95%, confidence interval [CI] 0.36-0.64, p < 0.001) compared with standard treatments for diabetes. This study utilizes network medicine methodologies to identify repurposed drugs for AF treatment and identifies metformin as a candidate drug.

Psychological impact of coronavirus disease 2019 (COVID-19) social distancing mandates on trauma survivors

Objective

Despite recognition that coronavirus disease 2019 (COVID-19) pandemic created an unprecedented impact on global mental health, information on the psychological health among trauma survivors during the COVID-19 pandemic is rare. We sought to examine psychological outcomes among individuals with preexisting traumatic experiences during COVID-19.

Methods

We sampled 1 242 adults in the mid-Atlantic region of the United States under a state-issued Phase 1 stay-at-home mandate to examine associations between pre-pandemic trauma exposure as measured by the Brief Trauma Questionnaire (BTQ) and anxiety and depression, as measured by the Patient Reported Outcome Scale Anxiety and Depression (PROMIS-A and PROMIS-D).

Results

Pre-pandemic trauma exposure among the sample was reported, with 281 (22.6%) participants identifying as experiencing one trauma, 209 (16.8%) reporting two, and 468 (37.7%) reporting three or more. As reported experiences of trauma increased, so did participant anxiety and depressive symptomatology. One-way Analysis of Variance indicated that reported trauma was significantly positively correlated with anxiety (P < 0.01) and depressive symptomatology (P < 0.01).

Conclusion

Findings highlight the immense psychological toll of the COVID-19 pandemic, specifically with individuals who were previously exposed to trauma. Public health officials can encourage physicians, employers, and universities to screen patients, employees, and students to assess previous trauma, psychological functioning, and risk factors. Collaboration between physicians and mental health providers including psychiatrists, psychologists, counselors, and social workers to provide evidence informed rapid coordination of care can better meet the global mental health crisis that is arising as a result of this unprecedented global trauma.

Coping With the Emotional Impact of Working in Cancer Care: The Importance of Team Working and Collective Processing

Hospitals provide the vast majority of cancer care. A necessary focus on survival has meant that they are less well-developed in terms of supporting patients with the emotional impact of cancer; and in supporting the frontline staff who contend with this. An integration of psychotherapeutic and neurobiological findings is used to develop an understanding of the patient-staff relationship and impact of high levels of distress within it. This includes reference to Transference and Countertransference, Mirror Neurons and Poly Vagal Theory. This paper considers how patients can unconsciously "transfer" emotional distress on to healthcare practitioners; and how this evokes an emotional response from the practitioner via the mirror neuron system (MNS). This can allow the practitioner to "feel into" the patient's experience and develop a more nuanced understanding. However, it may also activate emotions connected to the practitioner's life and can leave them feeling overwhelmed. The practitioner's capacity to regulate their own emotional arousal, via the vagus nerve, has a significant impact on their ability to support the patient and themselves within emotionally distressing interactions. This dynamic often unfolds without either party having significant awareness of it. A Systemic and Process-Oriented perspective is taken to understand this within the broader context of a hospital-based structure; and consider how practitioners on frontline teams may or may not support each other in working collectively with high levels of distress. A team's level of understanding and attunement to emotional experiences as well their primary relational and communication style has significant bearing on capacity for emotion-and-relationship focused coping. A failure to work with the emotional and relational interconnection between patients and staff can contribute to isolated patients, disconnected staff, conflict within teams and an overarching system lacking in compassion. However, due to the often unconscious nature of such processes and limited understanding or training on them, they are regularly left unaddressed. Over time, this can have an accumulated effect on everyone. Group-based collective processing is considered in terms of how it can be used in supporting practitioners to integrate an emotional and relational way of working with a problem-focused approach and integrated into regular daily working.

On the Interplay Between the Medicine of Hildegard of Bingen and Modern Medicine: The Role of Estrogen Receptor as an Example of Biodynamic Interface for Studying the Chronic Disease's Complexity

Introduction

Hildegard of Bingen (1098-1179) interpreted the origins of chronic disease highlighting and anticipating, although only in a limited fashion, the importance that complex interactions among numerous genetic, internal milieu and external environmental factors have in determining the disease phenotype. Today, we recognize those factors, capable of mediating the transmission of messages between human body and environment and vice versa, as biodynamic interfaces.

Aim

We analyzed, in the light of modern scientific evidence, Hildegard of Bingen's medical approach and her original humoral theory in order to identify possible insights included in her medicine that could be referred to in the context of modern evidence-based medicine. In particular, the abbess's humoral theory suggests the identification of biodynamic interfaces with sex hormones and their receptors.

Findings

We found that the Hildegardian holistic vision of the organism-environment relationship can actually represent a visionary approach to modern endocrinology and that sex hormones, in particular estrogens, could represent an example of a biodynamic interface. Estrogen receptors are found in regions of the brain involved in emotional and cognitive regulation, controlling the molecular mechanism of brain function. Estrogen receptors are involved in the regulation of the hypothalamic-pituitary-adrenal axis and in the epigenetic regulation of responses to physiological, social, and hormonal stimuli. Furthermore, estrogen affects gene methylation on its own and related receptor promoters in discrete regions of the developing brain. This scenario was strikingly perceived by the abbess in the XIIth century, and depicted as a complex interplay among different humors and flegmata that she recognized to be sex specific and environmentally regulated.

Viewpoint

Considering the function played by hormones, analyzed through the last scientific evidence, and scientific literature on biodynamic interfaces, we could suggest Hildegardian insights and theories as the first attempt to describe the modern holistic, sex-based medicine.

Conclusion

Hildegard anticipated a concept of pathogenesis that sees a central role for endocrinology in sex-specific disease. Furthermore, estrogens and estrogen receptors could represent a good example of molecular interfaces capable of modulating the interaction between the organism internal milieu and the environmental factors.

SperoPredictor: An Integrated Machine Learning and Molecular Docking-Based Drug Repurposing Framework With Use Case of COVID-19

The global spread of the SARS coronavirus 2 (SARS-CoV-2), its manifestation in human hosts as a contagious disease, and its variants have induced a pandemic resulting in the deaths of over 6,000,000 people. Extensive efforts have been devoted to drug research to cure and refrain the spread of COVID-19, but only one drug has received FDA approval yet. Traditional drug discovery is inefficient, costly, and unable to react to pandemic threats. Drug repurposing represents an effective strategy for drug discovery and reduces the time and cost compared to de novo drug discovery. In this study, a generic drug repurposing framework (SperoPredictor) has been developed which systematically integrates the various types of drugs and disease data and takes the advantage of machine learning (Random Forest, Tree Ensemble, and Gradient Boosted Trees) to repurpose potential drug candidates against any disease of interest. Drug and disease data for FDA-approved drugs (n = 2,865), containing four drug features and three disease features, were collected from chemical and biological databases and integrated with the form of drug-disease association tables. The resulting dataset was split into 70% for training, 15% for testing, and the remaining 15% for validation. The testing and validation accuracies of the models were 99.3% for Random Forest and 99.03% for Tree Ensemble. In practice, SperoPredictor identified 25 potential drug candidates against 6 human host-target proteomes identified from a systematic review of journals. Literature-based validation indicated 12 of 25 predicted drugs (48%) have been already used for COVID-19 followed by molecular docking and re-docking which indicated 4 of 13 drugs (30%) as potential candidates against COVID-19 to be pre-clinically and clinically validated. Finally, SperoPredictor results illustrated the ability of the platform to be rapidly deployed to repurpose the drugs as a rapid response to emergent situations (like COVID-19 and other pandemics).

Why Do We Care More About Disease than Health?

Modern Western biomedical research and clinical practice are primarily focused on disease. This disease-centric approach has yielded an impressive amount of knowledge around what goes wrong in illness. However, in comparison, researchers and physicians know little about health. What is health? How do we quantify it? And how do we improve it? We currently do not have good answers to these questions. Our lack of fundamental knowledge about health is partly driven by three main factors: (i) a lack of understanding of the dynamic processes that cause variations in health/disease states over time, (ii) an excessive focus on genes, and (iii) a pervasive psychological bias towards additive solutions. Here I briefly discuss potential reasons why scientists and funders have generally adopted a gene- and disease-centric framework, how medicine has ended up practicing "diseasecare" rather than healthcare, and present cursory evidence that points towards an alternative energetic view of health. Understanding the basis of human health with a similar degree of precision that has been deployed towards mapping disease processes could bring us to a point where we can actively support and promote human health across the lifespan, before disease shows up on a scan or in bloodwork.

Drugs repurposing for SARS-CoV-2: new insight of COVID-19 druggability

INTRODUCTION

The ongoing epidemic of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) creates a massive panic worldwide due to the absence of effective medicines. Developing a new drug or vaccine is time-consuming to pass safety and efficacy testing. Therefore, repurposing drugs have been introduced to treat COVID-19 until effective drugs are developed.

AREA COVERED

A detailed search of repurposing drugs against SARS-CoV-2 was carried out using the PubMed database, focusing on articles published 2020 years onward. A different class of drugs has been described in this article to target hosts and viruses. Based on the previous pandemic experience of SARS-CoV and MERS, several antiviral and antimalarial drugs are discussed here. This review covers the failure of some repurposed drugs that showed promising activity in the earlier CoV-pandemic but were found ineffective against SARS-CoV-2. All these discussions demand a successful drug development strategy for screening and identifying an effective drug for better management of COVID-19. The drug development strategies described here will serve a new scope of research for academicians and researchers.

EXPERT OPINION

Repurposed drugs have been used since COVID-19 to eradicate disease propagation. Drugs found effective for MERS and SARS may not be effective against SARS-CoV-2. Drug libraries and artificial intelligence are helpful tools to screen and identify different molecules targeting viruses or hosts.

Artificial Neural Network-Based Study Predicts GS-441524 as a Potential Inhibitor of SARS-CoV-2 Activator Protein Furin: a Polypharmacology Approach

Furin, a pro-protein convertase, plays a significant role as a biological scissor in bacterial, viral, and even mammalian substrates which in turn decides the fate of many viral and bacterial infections along with the numerous ailments caused by cancer, diabetes, inflammations, and neurological disorders. In the wake of the current pandemic caused by the virus SARS-CoV-2, furin has become the center of attraction for researchers as the spike protein contains a polybasic furin cleavage site. In the present work, we have searched for novel inhibitors against this interesting human target from FDA-approved antiviral. To enhance the selection of new inhibitors, we employed Kohonen's artificial neural network-based self-organizing maps for ligand-based virtual screening. Promising results were obtained which can help in drug repurposing and network pharmacology studies can address the errors generated due to promiscuity/polypharmacology. We found 15 existing FDA antiviral drugs having the potential to inhibit furin. Among these, six compounds have targets on important human proteins (LDLR, FCGR1A, PCK1, TLR7, DNA, and PNP). The role of these 15 drugs inhibiting furin can be established by studying further on patients infected with number of viruses including SARS-CoV-2. Here we propose two promising candidate FDA drugs GS-441524 and Grazoprevir (MK-5172) for repurposing as inhibitors of furin. The best results were observed with GS-441524.

Managing the Built Environment for Health Promotion and Disease Prevention With Maharishi Vastu Architecture: A Review

Background and objectives

The evolution of healthcare from 18th-century reductionism to 21st-century postgenomic holism has been described in terms of systems medicine, and the impact of the built environment on human health is the focus of investigation and development, leading to the new specialty of evidence-based, therapeutic architecture. The traditional system of Vāstu architecture-a design paradigm for buildings which is proposed to promote mental and physical health-has been applied and studied in the West in the last 20 years, and features elements absent from other approaches. This review critically evaluates the theory and research of a well-developed, standardized form of Vāstu-Maharishi Vastu® architecture (MVA). MVA's principles include development of the architect's consciousness, universal recommendations for building orientation, siting, and dimensions; placement of key functions; and occupants' head direction when sleeping or performing tasks. The effects of isolated Vāstu elements included in MVA are presented. However, the full value of MVA, documented as a systematic, globally applicable practice, is in the effect of its complete package, and thus this review of MVA includes evaluating the experience of living and working in MVA buildings.

Methods

The published medical and health-related literature was systematically surveyed for research on factors related to isolated principles applied in MVA as well as on the complete system.

Results

Published research suggests that incorporating MVA principles into buildings correlates with significant improvements in occupants' physical and mental health and quality of life: better sleep, greater happiness of children, and the experience of heightened sense of security and reduced stress. The frequency of burglaries, a social determinant of health, also correlates. Potential neurophysiological mechanisms are described.

Conclusions

Findings suggest that MVA offers an actionable approach for managing a key social determinant of health by using architectural design as preventive medicine and in public health.

Primary health care case management through the lens of complexity: an exploratory study of naturopathic practice using complexity science principles

Background

Advances in systems science creates an opportunity to bring a complexity perspective to health care practices and research. While medical knowledge has greatly progressed using a reductionist and mechanistic philosophy, this approach may be limited in its capacity to manage chronic and complex illness. With its holistic foundation, naturopathy is a primary health profession with a purported alignment with a complexity perspective. As such this pilot study aimed to investigate the application of complexity science principles, strategies, and tools to primary health care using naturopathy as a case study.

Methods

A network mapping and analysis of the naturopathic case management process was conducted. Mind maps were created by naturopathic practitioners to reflect their clinical conceptualisation of a common paper clinical case. These mind maps were inputed into Gephi, a network mapping, exploration, and analysis software. Various layouts of the data were produced, and these were analysed using exploratory data analysis and computational network analysis.

Results

Seven naturopathic practitioners participated in the study. In the combined network mapping, 133 unique elements and 399 links were identified. Obesity, the presenting issue in the case, was centrally located. Along with obesity, other keystone elements included: systemic inflammation, dysbiosis, diet, the liver, and mood. Each element was connected on average to 3.05 other elements, with a degree variation between one and 36. Six communities within the dataset were identified, comprising: the nervous system and mood, gastroinstetinal and dietary factors, systemic inflammation and obesity, the endocrine system and metabolism.

Conclusions

This pilot study demonstrates that it is feasible to apply a complexity science perspective to investigating primary health care case management. This supports a shift to viewing the human organism as a complex adaptive system within primary health care settings, with implications for health care practices that are more cognisant with the treatment of chronic and complex conditions and research opportunities to capture the complex clinical reasoning processes of practitioners.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12906-022-03585-2.

The Fractal Tapestry of Life: III Multifractals Entail the Fractional Calculus

This is the third essay advocating the use the (non-integer) fractional calculus (FC) to capture the dynamics of complex networks in the twilight of the Newtonian era. Herein, the focus is on drawing a distinction between networks described by monfractal time series extensively discussed in the prequels and how they differ in function from multifractal time series, using physiological phenomena as exemplars. In prequel II, the network effect was introduced to explain how the collective dynamics of a complex network can transform a many-body non-linear dynamical system modeled using the integer calculus (IC) into a single-body fractional stochastic rate equation. Note that these essays are about biomedical phenomena that have historically been improperly modeled using the IC and how fractional calculus (FC) models better explain experimental results. This essay presents the biomedical entailment of the FC, but it is not a mathematical discussion in the sense that we are not concerned with the formal infrastucture, which is cited, but we are concerned with what that infrastructure entails. For example, the health of a physiologic network is characterized by the width of the multifractal spectrum associated with its time series, and which becomes narrower with the onset of certain pathologies. Physiologic time series that have explicitly related pathology to a narrowing of multifractal time series include but are not limited to heart rate variability (HRV), stride rate variability (SRV) and breath rate variability (BRV). The efficiency of the transfer of information due to the interaction between two such complex networks is determined by their relative spectral width, with information being transferred from the network with the broader to that with the narrower width. A fractional-order differential equation, whose order is random, is shown to generate a multifractal time series, thereby providing a FC model of the information exchange between complex networks. This equivalence between random fractional derivatives and multifractality has not received the recognition in the bioapplications literature we believe it warrants.

[Epigenetics and oncology: Two faces of the personalization of medicine]

In this article, we outline the two dominant concepts of personalized medicine put forward by epigenetics in the field of oncology. First, knowledge on the molecular processes involved in tumor progression contributes to molecularize medicine, extending genomic medicine. Then, the identification of epigenetic mechanisms underlying the environmental causes of cancers brings scientific legitimacy to products and services whose advertising promotes the ability of each person to protect herself from cancer by adapting her lifestyle. Lastly, we argue that research in environmental epigenetics could open a new route for personalized medicine in oncology, where epigenetics contributes to an individualized assessment of patients' life paths.

Artificial intelligence framework identifies candidate targets for drug repurposing in Alzheimer's disease

BACKGROUND

Genome-wide association studies (GWAS) have identified numerous susceptibility loci for Alzheimer's disease (AD). However, utilizing GWAS and multi-omics data to identify high-confidence AD risk genes (ARGs) and druggable targets that can guide development of new therapeutics for patients suffering from AD has heretofore not been successful.

METHODS

To address this critical problem in the field, we have developed a network-based artificial intelligence framework that is capable of integrating multi-omics data along with human protein-protein interactome networks to accurately infer accurate drug targets impacted by GWAS-identified variants to identify new therapeutics. When applied to AD, this approach integrates GWAS findings, multi-omics data from brain samples of AD patients and AD transgenic animal models, drug-target networks, and the human protein-protein interactome, along with large-scale patient database validation and in vitro mechanistic observations in human microglia cells.

RESULTS

Through this approach, we identified 103 ARGs validated by various levels of pathobiological evidence in AD. Via network-based prediction and population-based validation, we then showed that three drugs (pioglitazone, febuxostat, and atenolol) are significantly associated with decreased risk of AD compared with matched control populations. Pioglitazone usage is significantly associated with decreased risk of AD (hazard ratio (HR) = 0.916, 95% confidence interval [CI] 0.861-0.974, P = 0.005) in a retrospective case-control validation. Pioglitazone is a peroxisome proliferator-activated receptor (PPAR) agonist used to treat type 2 diabetes, and propensity score matching cohort studies confirmed its association with reduced risk of AD in comparison to glipizide (HR = 0.921, 95% CI 0.862-0.984, P = 0.0159), an insulin secretagogue that is also used to treat type 2 diabetes. In vitro experiments showed that pioglitazone downregulated glycogen synthase kinase 3 beta (GSK3β) and cyclin-dependent kinase (CDK5) in human microglia cells, supporting a possible mechanism-of-action for its beneficial effect in AD.

CONCLUSIONS

In summary, we present an integrated, network-based artificial intelligence methodology to rapidly translate GWAS findings and multi-omics data to genotype-informed therapeutic discovery in AD.

Embracing the Complexity of Primary Health Care: System-Based Tools and Strategies for Researching the Case Management Process

The provision of health care is frequently a complex process, and favourable clinical outcomes are dependent on the effective management of this complexity. Contemporary medicine and health care practices that are biomedically aligned have been informed by a reductionist paradigm, potentially creating a misalignment between health care and the human organism as a complex adaptive system. Complexity science is increasingly gaining momentum within the academic literature and is being employed across a wide range of scientific disciplines, although this is less evident in medicine. Limited evidence was found within the literature of a complexity science framework being used to explore and inform individual health care practices; in this paper, this gap will be explored through consideration of the use of strategies and tools (specifically mind maps, computer-generated network mappings, exploratory data analysis, and computer-derived network analysis) which are congruent with a complexity science framework. This information may be useful to researchers investigating health care provision and to clinicians wishing to incorporate a complexity sensibility within their practice.

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A Machine Learning-Based Holistic Approach to Predict the Clinical Course of Patients within the Alzheimer's Disease Spectrum

BACKGROUND

Alzheimer's disease (AD) is a neurodegenerative condition driven by multifactorial etiology. Mild cognitive impairment (MCI) is a transitional condition between healthy aging and dementia. No reliable biomarkers are available to predict the conversion from MCI to AD.

OBJECTIVE

To evaluate the use of machine learning (ML) on a wealth of data offered by the Alzheimer's Disease Neuroimaging Initiative (ADNI) and Alzheimer's Disease Metabolomics Consortium (ADMC) database in the prediction of the MCI to AD conversion.

METHODS

We implemented an ML-based Random Forest (RF) algorithm to predict conversion from MCI to AD. Data related to the study population (587 MCI subjects) were analyzed by RF as separate or combined features and assessed for classification power. Four classes of variables were considered: neuropsychological test scores, AD-related cerebrospinal fluid (CSF) biomarkers, peripheral biomarkers, and structural magnetic resonance imaging (MRI) variables.

RESULTS

The ML-based algorithm exhibited 86% accuracy in predicting the AD conversion of MCI subjects. When assessing the features that helped the most, neuropsychological test scores, MRI data, and CSF biomarkers were the most relevant in the MCI to AD prediction. Peripheral parameters were effective when employed in association with neuropsychological test scores. Age and sex differences modulated the prediction accuracy. AD conversion was more effectively predicted in females and younger subjects.

CONCLUSION

Our findings support the notion that AD-related neurodegenerative processes result from the concerted activity of multiple pathological mechanisms and factors that act inside and outside the brain and are dynamically affected by age and sex.

Endophenotype-based in silico network medicine discovery combined with insurance record data mining identifies sildenafil as a candidate drug for Alzheimer's disease

We developed an endophenotype disease module-based methodology for Alzheimer's disease (AD) drug repurposing and identified sildenafil as a potential disease risk modifier. Based on retrospective case-control pharmacoepidemiologic analyses of insurance claims data for 7.23 million individuals, we found that sildenafil usage was significantly associated with a 69% reduced risk of AD (hazard ratio = 0.31, 95% confidence interval 0.25-0.39, P<1.0×10^-8). Propensity score stratified analyses confirmed that sildenafil is significantly associated with a decreased risk of AD across all four drug cohorts we tested (diltiazem, glimepiride, losartan and metformin) after adjusting age, sex, race, and disease comorbidities. We also found that sildenafil increases neurite growth and decreases phospho-tau expression in AD patient-induced pluripotent stem cells-derived neuron models, supporting mechanistically its potential beneficial effect in Alzheimer's disease. The association between sildenafil use and decreased incidence of AD does not establish causality or its direction, which requires a randomized clinical trial approach.

Reframing Palliative Care: An East-West Integrative Palliative Care Model

Convergence of principles of palliative care and integrative medicine has led to the introduction of the new practice of integrative palliative care in which integrative therapies (including mind-body modalities, traditional Chinese medicine, Ayurveda, and dietary supplements) are used to provide symptom management for patients who are dying or experiencing the sequelae of serious illness and its treatment. We propose an East-West Integrative palliative care model using non-drug therapies, such as acupuncture, diet, exercise, and stress management that shift the paradigm from suppressing the symptoms of illness to addressing both the root cause of the symptoms and the imbalance and declining homeostatic reserve that perpetuate these symptoms. This whole-person model expands the reach of palliative care, prolonging a better quality of life and allowing the patient to maintain as many activities as possible by preventing symptoms and improving function. Through this approach we reframe the dialogue such that patients are “living better” rather than “dying better” when faced with serious illness or death. In this article, we provide an overview of the principles of palliative care, integrative medicine, and the novel area of integrative palliative care, and propose an East-West integrative palliative care model that incorporates and broadens the scope of these existing approaches.

Modulome-Fangjiome Association Study (MoFAS) reveals differential target distribution among four similar fangjis (formulas)

ETHNOPHARMACOLOGICAL RELEVANCE

Precise target distribution is a key issue for further appropriate applications of fangjis (formulas) with similar efficacy and herbal constituents to maximize efficacy and minimize toxicity.

AIM OF THE STUDY

To develop an algorithm for investigating the differential target distributions and characteristic mechanisms of fangjis.

MAIN METHODS

In this study, we proposed a Modulome-Fangjiome Association Study (MoFAS) for comparing fangjis from qi-invigorating and xue-nourishing fangjiome (represented by four fangjis: FEJ, SDT, LYG and QOL). Firstly, the database-driven target network of these 4 fangjis was constructed as qi-xue network and decomposed into modules. Then, the modular map with functional landscape were constructed based on consistency score and enrichment analysis. Finally, we employed a targeting rate (TR) matrix to assess the contribution of this fangjiome to modulome (a set of modules) and compared characteristic effect of fangjis by principal component analysis (PCA).

RESULTS

A qi-xue network constituted by 579 proteins and 23 modules were constructed. In the functional landscape, 3 primary modules were mainly involved in the endocrine system and environmental adaptation. For the target distribution, SDT and QOL were more similar; the FEJ and LYG were located distant from other fangjis according to PCA. The common effects of FEJ, SDT, and QOL focused on stress response and organism development in environmental perturbation, but the FEJ was superior in regulating critical targets, primarily focusing on hormone and neurotransmitter processes. SDT and QOL were concentrated on the majority scale of the qi-xue network, especially for the mitotic cell cycle and development. LYG only targeted lymphocyte costimulation and icosanoid biosynthetic processes.

CONCLUSION

In this study, for the first time, we investigated the difference in the target distribution of qi-invigorating and xue-nourishing fangjiome and provided direct evidence for the characteristic therapeutic effect of these fangjis, which may promote the precise application of fangjis and support the identification of appropriate populations.

Potential of Marine Terpenoids against SARS-CoV-2: An In Silico Drug Development Approach

In an emergency, drug repurposing is the best alternative option against newly emerged severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infection. However, several bioactive natural products have shown potential against SARS-CoV-2 in recent studies. The present study selected sixty-eight broad-spectrum antiviral marine terpenoids and performed molecular docking against two novel SARS-CoV-2 enzymes (main protease or M^pro or 3CL^pro) and RNA-dependent RNA polymerase (RdRp). In addition, the present study analysed the physiochemical-toxicity-pharmacokinetic profile, structural activity relationship, and phylogenetic tree with various computational tools to select the 'lead' candidate. The genomic diversity study with multiple sequence analyses and phylogenetic tree confirmed that the newly emerged SARS-CoV-2 strain was up to 96% structurally similar to existing CoV-strains. Furthermore, the anti-SARS-CoV-2 potency based on a protein-ligand docking score (kcal/mol) exposed that the marine terpenoid brevione F (-8.4) and stachyflin (-8.4) exhibited similar activity with the reference antiviral drugs lopinavir (-8.4) and darunavir (-7.5) against the target SARS-CoV-M^pro. Similarly, marine terpenoids such as xiamycin (-9.3), thyrsiferol (-9.2), liouvilloside B (-8.9), liouvilloside A (-8.8), and stachyflin (-8.7) exhibited comparatively higher docking scores than the referral drug remdesivir (-7.4), and favipiravir (-5.7) against the target SARS-CoV-2-RdRp. The above in silico investigations concluded that stachyflin is the most 'lead' candidate with the most potential against SARS-CoV-2. Previously, stachyflin also exhibited potential activity against HSV-1 and CoV-A59 within IC₅₀, 0.16-0.82 µM. Therefore, some additional pharmacological studies are needed to develop 'stachyflin' as a drug against SARS-CoV-2.

Applying cooperative module pair analysis to uncover compatibility mechanism of Fangjis: An example of Wenxin Keli decoction

ETHNOPHARMACOLOGICAL RELEVANCE

Fangji is an ancient combinatorial formula. The compatibility mechanisms that how component herbs of Fangji work cooperatively to achieve the executive framework remain unexplored.

AIM OF THE STUDY

Toexplore compatibility mechanism and systematical effects of Fangjis by taking Wenxin Keli decoction (WXKL), a classical Fangji constituted by Codonopsis Radix, PolygonatiRhizoma, Notoginseng Radix Et Rhizoma, Ambrum, and Nardostachyos Radix Et Rhizoma., as example.

MAIN METHODS

Here, we employed bioinformatics approach, including cluster analysis, cooperative module pair analysis, primary module identification, and proximity examination among target profile of herbs, to investigate compatibility characterization and anti-arrhythmia mechanism of WXKL. Finally, core mechanisms of WXKL were validatedby in vivo experiments.

RESULTS

As a result, we identified 695 putative target proteins and 27 clusters (W-modules) inWXKL target network (W-network), in which W-module 1, 2, 4, 8, 10 were primary modules. The cooperative module pairs were W-module 2 and 4, W-module 2 and 8, and W-module 2 and 1, all of which existed in Codonopsis Radix- or Notoginseng Radix Et Rhizoma.-condition. And Nardostachyos Radix Et Rhizoma only yielded cooperation between W-module 1 and 2. The proximity of herbs' target profiles of Codonopsis Radix and Notoginseng Radix Et Rhizoma were similar, and Nardostachyos Radix Et Rhizoma and Ambrum were similar. For the compatibility framework, Codonopsis Radix general regulated 70.67% targets and majority W-modules (81.48%) as sovereign herb, contributing to primary therapeutic effect, mainly involving neurohormonal regulation, vasomotor, inflammation and oxidative stress. Other herbs assisted Codonopsis Radix to enhance major outcomes through common modules, and acted as complementary roles through unique process including mitotic cell cycle, biosynthetic and catabolic process, etc. Furthermore, WXKL regulated 66.67% hub proteins of arrhythmia-network, 68.18% and 47.37% proteins in primary arrhythmia-module 1 and 2, mainly involving ion channel activity, neurohormonal regulation, and stress response processes, to constitute regulatory network focusing on cardiovascular, renal, nervous system, to reverse the pathological process of arrhythmia. In vivo experiments demonstrated WXKL can attenuate adrenergic activation induced sympathetic atrial fibrillation by inhibiting calmodulin expression (CaM) and ryanodine receptor 2 (RYR2) phosphorylation to regulate neurohormonal action.

CONCLUSION

This strategy provided an overarching view of anti-arrhythmia mechanism of WXKL and its internal compatibility, and may facilitate the understanding of compatibility in Fangjis from the perspectives of modern biology.

Drug repurposing of dextromethorphan as a cellular target for the management of influenza

BACKGROUND

Influenza viruses are responsible for seasonal epidemics and sporadic pandemics of varying severity in humans, and additional treatment options are needed. High-throughput siRNA screens and a pre-clinical research model demonstrated that dextromethorphan (DM) has anti-viral activity as a cellular target for treatment of influenza. This study examined DM usage and hospitalization rates among patients with laboratory-confirmed influenza in a national cohort of United States veterans. We aimed to evaluate the potential drug repurposing of DM as a cellular target for the management of influenza utilizing a large, national claims and electronic health record database.

METHODS

This retrospective drug-disease association cohort study was conducted using data from the Veterans Affairs Informatics and Computing Infrastructure (VINCI). We used a cohort with laboratory-confirmed diagnosis of influenza and international classification of disease (ICD)-9/10 diagnosis codes of fever, cough, influenza, or acute upper respiratory infection in an outpatient setting. The study outcome is inpatient hospitalization (all-cause and respiratory) within 30 days of influenza diagnosis. We estimated the relative risk for all-cause and respiratory hospitalizations using Poisson generalized linear model (GLM) and a greedy nearest neighbor propensity score 1:1 matched sub-analysis for both hospitalization models.

FINDINGS

A total of 18,677 patients met the inclusion and exclusion criteria and were evaluated in our study. The cohorts consisted of 2801 patients dispensed DM and 15,876 untreated patients (no DM). The Poisson GLM adjusted for covariates demonstrated a relative risk reduction of 34% for all-cause hospitalizations (Relative Risk (RR) 0.66, 95% Confidence Interval (CI) 0.525-0.832) and 40% for respiratory hospitalizations (RR 0.597, 95% CI 0.423-0.843) in patients with influenza treated with DM.

CONCLUSION

Influenza viruses continue to emerge and cause infection (including pandemics) in humans, so there remains a critical need to advance the understanding of influenza treatment. Our results demonstrated reduced hospitalization rates for influenza patients treated with DM. Further research on cellular targets and/or DM is warranted for the treatment of influenza.

A Network of Psychopathological, Cognitive, and Motor Symptoms in Schizophrenia Spectrum Disorders

Schizophrenia spectrum disorders (SSDs) are complex syndromes involving psychopathological, cognitive, and also motor symptoms as core features. A better understanding of how these symptoms mutually impact each other could translate into diagnostic, prognostic, and, eventually, treatment advancements. The present study aimed to: (1) estimate a network model of psychopathological, cognitive, and motor symptoms in SSD; (2) detect communities and explore the connectivity and relative importance of variables within the network; and (3) explore differences in subsample networks according to remission status. A sample of 1007 patients from a multisite cohort study was included in the analysis. We estimated a network of 43 nodes, including all the items from the Positive and Negative Syndrome Scale, a cognitive assessment battery and clinical ratings of extrapyramidal symptoms. Methodologies specific to network analysis were employed to address the study's aims. The estimated network for the total sample was densely interconnected and organized into 7 communities. Nodes related to insight, abstraction capacity, attention, and suspiciousness were the main bridges between network communities. The estimated network for the subgroup of patients in remission showed a sparser density and a different structure compared to the network of nonremitted patients. In conclusion, the present study conveys a detailed characterization of the interrelations between a set of core clinical elements of SSD. These results provide potential novel clues for clinical assessment and intervention.

Artificial intelligence and machine learning-aided drug discovery in central nervous system diseases: State-of-the-arts and future directions

Neurological disorders significantly outnumber diseases in other therapeutic areas. However, developing drugs for central nervous system (CNS) disorders remains the most challenging area in drug discovery, accompanied with the long timelines and high attrition rates. With the rapid growth of biomedical data enabled by advanced experimental technologies, artificial intelligence (AI) and machine learning (ML) have emerged as an indispensable tool to draw meaningful insights and improve decision making in drug discovery. Thanks to the advancements in AI and ML algorithms, now the AI/ML-driven solutions have an unprecedented potential to accelerate the process of CNS drug discovery with better success rate. In this review, we comprehensively summarize AI/ML-powered pharmaceutical discovery efforts and their implementations in the CNS area. After introducing the AI/ML models as well as the conceptualization and data preparation, we outline the applications of AI/ML technologies to several key procedures in drug discovery, including target identification, compound screening, hit/lead generation and optimization, drug response and synergy prediction, de novo drug design, and drug repurposing. We review the current state-of-the-art of AI/ML-guided CNS drug discovery, focusing on blood-brain barrier permeability prediction and implementation into therapeutic discovery for neurological diseases. Finally, we discuss the major challenges and limitations of current approaches and possible future directions that may provide resolutions to these difficulties.

Emergent expansion of clinical epigenetics in patients with cardiovascular diseases

PURPOSE OF REVIEW

Cardiovascular diseases (CVDs) are typically caused by multifactorial events including mutations in a large number of genes. Epigenetic-derived modifications in the cells are normal but can be amended by aging, lifestyle, and exposure to toxic substances. Major epigenetic modifications are DNA methylation, histone modification, chromatin remodeling as well as the noncoding RNAs. These pivotal players are involved in the epigenetic-induced modifications observed during CVDs. Nevertheless, despite impressive efforts capitalized in epigenetic research in the last 50 years, clinical applications are still not satisfactory.

RECENT FINDINGS

Briefly, we present some of the recent steps forward in the epigenetic studies of CVDs. There is an increased appreciation for the contribution of epigenetic alterations in the development of CVDs. Now, we have novel epigenetic biomarkers and therapeutic trials with the use of statins, metformin, and some compounds affecting epigenetic pathways including a BET inhibitor apabetalone. The new knowledge of epigenetic regulation is also discussed in the light of precision medicine of CVDs.

SUMMARY

Epigenetic studies of CVDs have the promise to yield both mechanistic insights as well as adjunct treatments (repurposed drugs and apabetalone). The overall concept of precision medicine is not widely recognized in routine medical practice and the so-called reductionist approach remains the most used way to treat CVD patients.

The Role of Artificial Intelligence in Managing Multimorbidity and Cancer

Traditional healthcare paradigms rely on the disease-centered approach aiming at reducing human nature by discovering specific drivers and biomarkers that cause the advent and progression of diseases. This reductive approach is not always suitable to understand and manage complex conditions, such as multimorbidity and cancer. Multimorbidity requires considering heterogeneous data to tailor preventing and targeting interventions. Personalized Medicine represents an innovative approach to address the care needs of multimorbid patients considering relevant patient characteristics, such as lifestyle and individual preferences, in opposition to the more traditional “one-size-fits-all” strategy focused on interventions designed at the population level. Integration of omic (e.g., genomics) and non-strictly medical (e.g., lifestyle, the exposome) data is necessary to understand patients’ complexity. Artificial Intelligence can help integrate and manage heterogeneous data through advanced machine learning and bioinformatics algorithms to define the best treatment for each patient with multimorbidity and cancer. The experience of an Italian research hospital, leader in the field of oncology, may help to understand the multifaceted issue of managing multimorbidity and cancer in the framework of Personalized Medicine.

Personalized Clinical Phenotyping through Systems Medicine and Artificial Intelligence

Personalized Medicine (PM) has shifted the traditional top-down approach to medicine based on the identification of single etiological factors to explain diseases, which was not suitable for explaining complex conditions. The concept of PM assumes several interpretations in the literature, with particular regards to Genetic and Genomic Medicine. Despite the fact that some disease-modifying genes affect disease expression and progression, many complex conditions cannot be understood through only this lens, especially when other lifestyle factors can play a crucial role (such as the environment, emotions, nutrition, etc.). Personalizing clinical phenotyping becomes a challenge when different pathophysiological mechanisms underlie the same manifestation. Brain disorders, cardiovascular and gastroenterological diseases can be paradigmatic examples. Experiences on the field of Fondazione Policlinico Gemelli in Rome (a research hospital recognized by the Italian Ministry of Health as national leader in "Personalized Medicine" and "Innovative Biomedical Technologies") could help understanding which techniques and tools are the most performing to develop potential clinical phenotypes personalization. The connection between practical experiences and scientific literature highlights how this potential can be reached towards Systems Medicine using Artificial Intelligence tools.

Clinical epigenetics settings for cancer and cardiovascular diseases: real-life applications of network medicine at the bedside

Despite impressive efforts invested in epigenetic research in the last 50 years, clinical applications are still lacking. Only a few university hospital centers currently use epigenetic biomarkers at the bedside. Moreover, the overall concept of precision medicine is not widely recognized in routine medical practice and the reductionist approach remains predominant in treating patients affected by major diseases such as cancer and cardiovascular diseases. By its' very nature, epigenetics is integrative of genetic networks. The study of epigenetic biomarkers has led to the identification of numerous drugs with an increasingly significant role in clinical therapy especially of cancer patients. Here, we provide an overview of clinical epigenetics within the context of network analysis. We illustrate achievements to date and discuss how we can move from traditional medicine into the era of network medicine (NM), where pathway-informed molecular diagnostics will allow treatment selection following the paradigm of precision medicine.

Platelet Proteomes, Pathways, and Phenotypes as Informants of Vascular Wellness and Disease

Platelets rapidly undergo responsive transitions in form and function to repair vascular endothelium and mediate hemostasis. In contrast, heterogeneous platelet subpopulations with a range of primed or refractory phenotypes gradually arise in chronic inflammatory and other conditions in a manner that may indicate or support disease. Qualitatively distinguishable platelet phenotypes are increasingly associated with a variety of physiological and pathological circumstances; however, the origins and significance of platelet phenotypic variation remain unclear and conceptually vague. As changes in platelet function in disease exhibit many similarities to platelets following the activation of platelet agonist receptors, the intracellular responses of platelets common to hemostasis and inflammation may provide insights to the molecular basis of platelet phenotype. Here, we review concepts around how protein-level relations-from platelet receptors through intracellular signaling events-may help to define platelet phenotypes in inflammation, immune responses, aging, and other conditions. We further discuss how representing systems-wide platelet proteomics data profiles as circuit-like networks of causally related intracellular events, or, pathway maps, may inform molecular definitions of platelet phenotype. In addition to offering insights into platelets as druggable targets, maps of causally arranged intracellular relations underlying platelet function can also advance precision and interceptive medicine efforts by leveraging platelets as accessible, dynamic, endogenous, circulating biomarkers of vascular wellness and disease. Graphic Abstract: A graphic abstract is available for this article.

Racism and Kidney Health: Turning Equity Into a Reality

Kidney disease continues to manifest stark racial inequities in the United States, revealing the entrenchment of racism and bias within multiple facets of society, including in our institutions, practices, norms, and beliefs. In this perspective, we synthesize theory and evidence to describe why an understanding of race and racism is integral to kidney care, providing examples of how kidney health disparities manifest interpersonal and structural racism. We then describe racialized medicine and "colorblind" approaches as well as their pitfalls, offering in their place suggestions to embed antiracism and an "equity lens" into our practice. We propose examples of how we can enhance kidney health equity by enhancing our structural competency, using equity-focused race consciousness, and centering investigation and solutions around the needs of the most marginalized. To achieve equitable outcomes for all, our medical institutions must embed antiracism and equity into all aspects of advocacy, policy, patient/community engagement, educational efforts, and clinical care processes. Organizations engaged in kidney care should commit to promoting structural equity and eliminating potential sources of bias across referral practices, guidelines, research agendas, and clinical care. Kidney care providers should reaffirm our commitment to structurally competent patient care and educational endeavors in which empathy and continuous self-education about social drivers of health and inequity, racism, and bias are integral. We envision a future in which kidney health equity is a reality for all. Through bold collective and sustained investment, we can achieve this critical goal.

Complex Networks in Health and Disease

From protein interactions to signal transduction, from metabolism to the nervous system: Virtually all processes in health and disease rely on the careful orchestration of a large number of diverse individual components ranging from molecules to cells and entire organs. Networks provide a powerful framework for describing and understanding these complex systems in a wholistic fashion. They offer a unique combination of a highly intuitive, qualitative description, and a plethora of analytical, quantitative tools. Here we provide a brief introduction to the emerging field of network medicine. After an overview of the core concepts for connecting network characteristics to biological functions, we review commonly used networks, ranging from the molecular interaction networks that form the basis of all biological processes in the cell to the global transportation networks that govern the spread of global epidemics. Lastly, we highlight current conceptual and practical challenges.

Medicine and Phlebolymphology: Time to Change?

Biomedical science is undergoing a reappraisal of its scientific advancement process and of the related healthcare management. Progress in medicine should combine improvements of knowledge, efficacy, and safety of diagnostic/therapeutic procedures, with adequate cost-effectiveness profiles. This narrative review is aimed at assessing in medicine, more specifically in phlebology and lymphology: (a) scientific literature possible biases, (b) the level of evidence, comprehensiveness, and cost-effectiveness of the main therapeutic options, and (c) the possible contribution of integrative and translational medicine. Current medical research may have cognitive biases, or industry-tied influences, which impacts clinical practice. Some reductionism, with an increasing use of drugs and technology, often neglecting the understanding and care of the root causative pathways of the diseases, is affecting biomedical science as well. Aging brings a relevant burden of chronic degenerative diseases and disabilities, with relevant socio-economic repercussions; thus, a major attention to cost-effectiveness and appropriateness of healthcare is warranted. In this scenario, costly and innovative but relatively validated therapies may tend to be adopted in venous and lymphatic diseases, such as varicose veins, leg venous ulcer, post-thrombotic syndrome, pelvic congestion syndrome, and lymphedema. Conversely, a more comprehensive approach to the basic pathophysiology of chronic venous and lymphatic insufficiency and the inclusion of pharmacoeconomics analyses would benefit overall patients’ management. Erroneous lifestyle and nutrition, together with chronic stress-induced syndromes, significantly influence chronic degenerative phlebo-lymphatic diseases. The main active epigenetic socio-biologic factors are obesity, dysfunctions of musculo-respiratory-vascular pumps, pro-inflammatory nutrition, hyperactivation of stress axis, and sedentarism. An overall critical view of the scientific evidence and innovations in phebolymphology could be of help to improve efficacy, safety, and sustainability of current practice. Translational and integrative medicine may contribute to a patient-centered approach. Conversely, reductionism, eminence/reimbursement-based decisional processes, patients’ lack of education, industry-influenced science, and physician’s improvable awareness, may compromise efficacy, safety, appropriateness, and cost-effectiveness of future diagnostic and therapeutic patterns of phlebology and lymphology.

Acting Before; A Combined Strategy to Counteract the Onset and Progression of Dementia

Brain aging and aging-related neurodegenerative disorders are posing a significant challenge for health systems worldwide. To date, most of the therapeutic efforts aimed at counteracting dementiarelated behavioral and cognitive impairment have been focused on addressing putative determinants of the disease, such as β-amyloid or tau. In contrast, relatively little attention has been paid to pharmacological interventions aimed at restoring or promoting the synaptic plasticity of the aging brain. The review will explore and discuss the most recent molecular, structural/functional, and behavioral evidence that supports the use of non-pharmacological approaches as well as cognitive-enhancing drugs to counteract brain aging and early-stage dementia.

Artificial intelligence in COVID-19 drug repurposing

Drug repurposing or repositioning is a technique whereby existing drugs are used to treat emerging and challenging diseases, including COVID-19. Drug repurposing has become a promising approach because of the opportunity for reduced development timelines and overall costs. In the big data era, artificial intelligence (AI) and network medicine offer cutting-edge application of information science to defining disease, medicine, therapeutics, and identifying targets with the least error. In this Review, we introduce guidelines on how to use AI for accelerating drug repurposing or repositioning, for which AI approaches are not just formidable but are also necessary. We discuss how to use AI models in precision medicine, and as an example, how AI models can accelerate COVID-19 drug repurposing. Rapidly developing, powerful, and innovative AI and network medicine technologies can expedite therapeutic development. This Review provides a strong rationale for using AI-based assistive tools for drug repurposing medications for human disease, including during the COVID-19 pandemic.

Harnessing endophenotypes and network medicine for Alzheimer's drug repurposing

Following two decades of more than 400 clinical trials centered on the "one drug, one target, one disease" paradigm, there is still no effective disease-modifying therapy for Alzheimer's disease (AD). The inherent complexity of AD may challenge this reductionist strategy. Recent observations and advances in network medicine further indicate that AD likely shares common underlying mechanisms and intermediate pathophenotypes, or endophenotypes, with other diseases. In this review, we consider AD pathobiology, disease comorbidity, pleiotropy, and therapeutic development, and construct relevant endophenotype networks to guide future therapeutic development. Specifically, we discuss six main endophenotype hypotheses in AD: amyloidosis, tauopathy, neuroinflammation, mitochondrial dysfunction, vascular dysfunction, and lysosomal dysfunction. We further consider how this endophenotype network framework can provide advances in computational and experimental strategies for drug-repurposing and identification of new candidate therapeutic strategies for patients suffering from or at risk for AD. We highlight new opportunities for endophenotype-informed, drug discovery in AD, by exploiting multi-omics data. Integration of genomics, transcriptomics, radiomics, pharmacogenomics, and interactomics (protein-protein interactions) are essential for successful drug discovery. We describe experimental technologies for AD drug discovery including human induced pluripotent stem cells, transgenic mouse/rat models, and population-based retrospective case-control studies that may be integrated with multi-omics in a network medicine methodology. In summary, endophenotype-based network medicine methodologies will promote AD therapeutic development that will optimize the usefulness of available data and support deep phenotyping of the patient heterogeneity for personalized medicine in AD.

Importance of scientific collaboration in contemporary drug discovery and development: a detailed network analysis

BACKGROUND

Growing evidence shows that scientific collaboration plays a crucial role in transformative innovation in the life sciences. For example, contemporary drug discovery and development reflects the work of teams of individuals from academic centers, the pharmaceutical industry, the regulatory science community, health care providers, and patients. However, public understanding of how collaborations between academia and industry catalyze novel target identification and first-in-class drug discovery is limited.

RESULTS

We perform a comprehensive network analysis on a large scientific corpus of collaboration and citations (97,688 papers with 1,862,500 citations from 170 million scientific records) to quantify the success trajectory of innovative drug development. By focusing on four types of cardiovascular drugs, we demonstrate how knowledge flows between institutions to highlight the underlying contributions of many different institutions in the development of a new drug. We highlight how such network analysis could help to increase industrial and governmental support, and improve the efficiency or accelerate decision-making in drug discovery and development.

CONCLUSION

We demonstrate that network analysis of large public databases can identify and quantify investigator and institutional relationships in drug discovery and development. If broadly applied, this type of network analysis may help to enhance public understanding of and support for biomedical research, and could identify factors that facilitate decision-making in first-in-class drug discovery among academia, the pharmaceutical industry, and healthcare systems.

Rethinking Urban and Food Policies to Improve Citizens Safety After COVID-19 Pandemic

The ongoing pandemic caused by the coronavirus disease 2019 (COVID-19) is literally changing the world. From December 2019 to date, more than 22 million cases have been reported worldwide and global health institutions are acting to slow down the virus transmission and are looking for possible prevention strategies in case of a new outbreak. As in other endemic or pandemic phenomena, the issues mostly covered by scientific and media attention are related to the diagnostic and therapeutic approach of COVID-19. However, a still neglected issue regards the adoption of a more systemic approach considering the close connection among the infection, the environment, and human behaviors, including the role of diet and urban management. To shed light on this issue, we brought together a faculty group involving experts in environment and biodiversity, food safety, human nutrition, and behavior, bioprospecting, as well as medical doctors having a deep knowledge of the complex historical relationship between humanity and vector-borne infections. Two main aspects emerged from the integrative overview of the current COVID-19 pandemic: (i) the scientific community should start sharing social actions and policy advocacy based on the assumption that human health strongly depends upon a sustainable exploitation of natural resources in populated areas; (ii) the specific strategic role of the cities in developing sustainable food systems and promoting healthy dietary patterns. Definitely, some priority issues should be addressed to achieve these goals, such as global efforts to increase food safety and security, which would benefit from urban and peri-urban agriculture enhancement, smallholder food producers support, and ecosystem services and local biodiversity maintenance.

deepDR: a network-based deep learning approach to in silico drug repositioning

MOTIVATION

Traditional drug discovery and development are often time-consuming and high risk. Repurposing/repositioning of approved drugs offers a relatively low-cost and high-efficiency approach toward rapid development of efficacious treatments. The emergence of large-scale, heterogeneous biological networks has offered unprecedented opportunities for developing in silico drug repositioning approaches. However, capturing highly non-linear, heterogeneous network structures by most existing approaches for drug repositioning has been challenging.

RESULTS

In this study, we developed a network-based deep-learning approach, termed deepDR, for in silico drug repurposing by integrating 10 networks: one drug-disease, one drug-side-effect, one drug-target and seven drug-drug networks. Specifically, deepDR learns high-level features of drugs from the heterogeneous networks by a multi-modal deep autoencoder. Then the learned low-dimensional representation of drugs together with clinically reported drug-disease pairs are encoded and decoded collectively via a variational autoencoder to infer candidates for approved drugs for which they were not originally approved. We found that deepDR revealed high performance [the area under receiver operating characteristic curve (AUROC) = 0.908], outperforming conventional network-based or machine learning-based approaches. Importantly, deepDR-predicted drug-disease associations were validated by the ClinicalTrials.gov database (AUROC = 0.826) and we showcased several novel deepDR-predicted approved drugs for Alzheimer's disease (e.g. risperidone and aripiprazole) and Parkinson's disease (e.g. methylphenidate and pergolide).

AVAILABILITY AND IMPLEMENTATION

Source code and data can be downloaded from https://github.com/ChengF-Lab/deepDR.

SUPPLEMENTARY INFORMATION

Supplementary data are available online at Bioinformatics.

Toward heterogeneous information fusion: bipartite graph convolutional networks for in silico drug repurposing

MOTIVATION

Mining drug-disease association and related interactions are essential for developing in silico drug repurposing (DR) methods and understanding underlying biological mechanisms. Recently, large-scale biological databases are increasingly available for pharmaceutical research, allowing for deep characterization for molecular informatics and drug discovery. However, DR is challenging due to the molecular heterogeneity of disease and diverse drug-disease associations. Importantly, the complexity of molecular target interactions, such as protein-protein interaction (PPI), remains to be elucidated. DR thus requires deep exploration of a multimodal biological network in an integrative context.

RESULTS

In this study, we propose BiFusion, a bipartite graph convolution network model for DR through heterogeneous information fusion. Our approach combines insights of multiscale pharmaceutical information by constructing a multirelational graph of drug-protein, disease-protein and PPIs. Especially, our model introduces protein nodes as a bridge for message passing among diverse biological domains, which provides insights into utilizing PPI for improved DR assessment. Unlike conventional graph convolution networks always assuming the same node attributes in a global graph, our approach models interdomain information fusion with bipartite graph convolution operation. We offered an exploratory analysis for finding novel drug-disease associations. Extensive experiments showed that our approach achieved improved performance than multiple baselines for DR analysis.

AVAILABILITY AND IMPLEMENTATION

Source code and preprocessed datasets are at: https://github.com/zcwang0702/BiFusion.