Periodontitis is a common inflammatory disease of infectious origins that often evolves into a chronic condition. Aside from its importance as a stomatologic ailment, chronic periodontitis has gained relevance since it has been shown that it can develop into a systemic condition characterized by unresolved hyper-inflammation, disruption of the innate and adaptive immune system, dysbiosis of the oral, gut and other location's microbiota and other system-wide alterations that may cause, coexist or aggravate other health issues associated to elevated morbi-mortality. The relationships between the infectious, immune, inflammatory, and systemic features of periodontitis and its many related diseases are far from being fully understood and are indeed still debated. However, to date, a large body of evidence on the different biological, clinical, and policy-enabling sources of information, is available. The aim of the present work is to summarize many of these sources of information and contextualize them under a systemic inflammation framework that may set the basis to an integral vision, useful for basic, clinical, and therapeutic goals.

Cardiovascular diseases are the leading cause of human mortality worldwide. Among the many factors associated with the etiology, incidence, and evolution of such diseases; social and environmental issues constitute an important and often overlooked component. Understanding to a greater extent the scope to which such social determinants of cardiovascular diseases (SDCVD) occur as well as the connections among them would be useful for public health policy making. Here, we will explore the historical trends and associations among the main SDCVD in the published literature. Our aim will be finding meaningful relations among those that will help us to have an integrated view on this complex phenomenon by providing historical context and a relational framework. To uncover such relations, we used a data mining approach to the current literature, followed by network analysis of the interrelationships discovered. To this end, we systematically mined the PubMed/MEDLINE database for references of published studies on the subject, as outlined by the World Health Organization’s framework on social determinants of health. The analyzed structured corpus consisted in circa 1190 articles categorized by means of the Medical Subheadings (MeSH) content-descriptor. The use of data analytics techniques allowed us to find a number of non-trivial connections among SDCVDs. Such relations may be relevant to get a deeper understanding of the social and environmental issues associated with cardiovascular disease and are often overlooked by traditional literature survey approaches, such as systematic reviews and meta-analyses.

Background: Around the world, there is a significant difference in the proportion of women with access to leadership in healthcare with respect to men. This article studies gender imbalance and wage gap in managerial, executive, and directive job positions at the Mexican National Institutes of Health. Methods: Cohort data were described using a visual circular representation and modeled using a generalized linear model. Analysis of variance was used to assess model significance, and posterior Fisher's least significant differences were analyzed when appropriate. Results: This study demonstrated that there is a gender imbalance distribution among the hierarchical position at the Mexican National Health Institutes and also exposed that the wage gap exists mainly in the (highest or lowest) ranks in hierarchical order. Conclusions: Since the majority of the healthcare workforce is female, Mexican women are still underrepresented in executive and directive management positions at national healthcare organizations.

Background: Cardiovascular diseases are the leading causes of mortality worldwide. One reason behind this lethality lies in the fact that often cardiovascular illnesses develop into systemic failure due to the multiple connections to organismal metabolism. This in turn is associated with co-morbidities and multimorbidity. The prevalence of coexisting diseases and the relationship between the molecular origins adds to the complexity of the management of cardiovascular diseases and thus requires a profound knowledge of the genetic interaction of diseases. Objective: In order to develop a deeper understanding of this phenomenon, we examined the patterns of comorbidity as well as their genetic interaction of the diseases (or the lack of evidence of it) in a large set of cases diagnosed with cardiovascular conditions at the national reference hospital for cardiovascular diseases in Mexico. Methods: We performed a cross-sectional study of the National Institute of Cardiology. Socioeconomic information, principal diagnosis that led to the hospitalization and other conditions identified by an ICD-10 code were obtained for 34,099 discharged cases. With this information a cardiovascular comorbidity networks were built both for the full database and for ten 10-years age brackets. The associated cardiovascular comorbidities modules were found. Data mining was performed in the comprehensive ClinVar database with the disease names (as extracted from ICD-10 codes) to establish (when possible) connections between the genetic associations of the genetic interaction of diseases. The rationale is that some comorbidities may have a stronger genetic origin, whereas for others, the environment and other factors may be stronger. Results: We found that comorbidity networks are highly centralized in prevalent diseases, such as cardiac arrhythmias, heart failure, chronic kidney disease, hypertension, and ischemic diseases. Said comorbidity networks are actually modular on their connectivity. Modules recapitulate physiopathological commonalities, e.g., ischemic diseases clustering together. This is also the case of chronic systemic diseases, of congenital malformations and others. The genetic and environmental commonalities behind some of the relations in these modules were also found by resorting to clinical genetics databases and functional pathway enrichment studies. Conclusions: This methodology, hence may allow the clinician to look up for non-evident comorbidities whose knowledge will lead to improve therapeutically designs. By continued and consistent analysis of these types of patterns, we envisaged that it may be possible to acquire, strong clinical and basic insights that may further our advance toward a better understanding of cardiovascular diseases as a whole. Hopefully these may in turn lead to further development of better, integrated therapeutic strategies.

Health systems are paradigmatic examples of human organizations that blend a multitude of different professional and disciplinary features within a critically performance environment. Communication failure and defective processes in health systems have a tremendous impact in society, both in the financial and human aspects. Traditionally, health systems have been regarded as linear hierarchic structures. However, recent developments in the sciences of complexity point out to health systems as complex entities governed by non-linear interaction laws, self-organization and emergent phenomena. In this work we review some aspects of complexity behind health systems and how they can be applied to improve the performance of healthcare organizations.

Cancer is a set of complex pathologies that has been recognized as a major public health problem worldwide for decades. A myriad of therapeutic strategies is indeed available. However, the wide variability in tumor physiology, response to therapy, added to multi-drug resistance poses enormous challenges in clinical oncology. The last years have witnessed a fast-paced development of novel experimental and translational approaches to therapeutics, that supplemented with computational and theoretical advances are opening promising avenues to cope with cancer defiances. At the core of these advances, there is a strong conceptual shift from gene-centric emphasis on driver mutations in specific oncogenes and tumor suppressors—let us call that the silver bullet approach to cancer therapeutics—to a systemic, semi-mechanistic approach based on pathway perturbations and global molecular and physiological regulatory patterns—we will call this the shrapnel approach. The silver bullet approach is still the best one to follow when clonal mutations in driver genes are present in the patient, and when there are targeted therapies to tackle those. Unfortunately, due to the heterogeneous nature of tumors this is not the common case. The wide molecular variability in the mutational level often is reduced to a much smaller set of pathway-based dysfunctions as evidenced by the well-known hallmarks of cancer. In such cases “shrapnel gunshots” may become more effective than “silver bullets”. Here, we will briefly present both approaches and will abound on the discussion on the state of the art of pathway-based therapeutic designs from a translational bioinformatics and computational oncology perspective. Further development of these approaches depends on building collaborative, multidisciplinary teams to resort to the expertise of clinical oncologists, oncological surgeons, and molecular oncologists, but also of cancer cell biologists and pharmacologists, as well as bioinformaticians, computational biologists and data scientists. These teams will be capable of engaging on a cycle of analyzing high-throughput experiments, mining databases, researching on clinical data, validating the findings, and improving clinical outcomes for the benefits of the oncological patients.

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BackgroundCurrently, thanks to the growing number of public database resources, most evidence on planning and management, healthcare institutions, policies and practices is becoming available to everyone. However, one of the limitations for the advancement of data and literature-driven research has been the lack of flexibility of the methodological resources used in qualitative research. There is a need to incorporate friendly, cheaper and faster tools for the systematic, unbiased analysis of large data corpora, in particular regarding the qualitative aspects of the information (often overlooked).MethodsThis article proposes a series of novel techniques, exemplified by the case of the role of Institutional Committees of Bioethics to (1) massively identify the documents relevant to a given issue, (2) extract the fundamental content, focusing on qualitative analysis, (3) synthesize the findings in the published literature, (4) categorize and visualize the evidence, and (5) analyse and report the results.ResultsA critical study of the institutional role of public health policies and practices in Institutional Committees of Bioethics was used as an example application of the method. Interactive strategies were helpful to define and conceptualise variables, propose research questions and refine research interpretation. These methods are additional aids to systematic reviews, pre-coding schemes and construction of a priori diagrams to survey and analyse social science literature.ConclusionsThese novel methods have proven to facilitate the formulation and testing of hypotheses on the subjects to be studied. Such tools may allow important advances going from descriptive approaches to decision-making and even institutional assessment and policy redesign, by pragmatic reason of time and costs.Electronic supplementary materialThe online version of this article (10.1186/s12961-018-0404-z) contains supplementary material, which is available to authorized users.