EPMA position paper in cancer:current overview and future perspectives

At present, a radical shift in cancer treatment is occurring in terms of predictive, preventive, and personalized medicine (PPPM). Individual patients will participate in more aspects of their healthcare. During the development of PPPM, many rapid, specific, and sensitive new methods for earlier detection of cancer will result in more efficient management of the patient and hence a better quality of life. Coordination of the various activities among different healthcare professionals in primary, secondary, and tertiary care requires well-defined competencies, implementation of training and educational programs, sharing of data, and harmonized guidelines. In this position paper, the current knowledge to understand cancer predisposition and risk factors, the cellular biology of cancer, predictive markers and treatment outcome, the improvement in technologies in screening and diagnosis, and provision of better drug development solutions are discussed in the context of a better implementation of personalized medicine. Recognition of the major risk factors for cancer initiation is the key for preventive strategies (EPMA J. 4(1):6, 2013). Of interest, cancer predisposing syndromes in particular the monogenic subtypes that lead to cancer progression are well defined and one should focus on implementation strategies to identify individuals at risk to allow preventive measures and early screening/diagnosis. Implementation of such measures is disturbed by improper use of the data, with breach of data protection as one of the risks to be heavily controlled. Population screening requires in depth cost-benefit analysis to justify healthcare costs, and the parameters screened should provide information that allow an actionable and deliverable solution, for better healthcare provision

Prospect patents, data markets, and the commons in data-driven medicine : openness and the political economy of intellectual property rights

Scholars who point to political influences and the regulatory function of patent courts in the USA have long questioned the courts’ subjective interpretation of what ‘things’ can be claimed as inventions. The present article sheds light on a different but related facet: the role of the courts in regulating knowledge production. I argue that the recent cases decided by the US Supreme Court and the Federal Circuit, which made diagnostics and software very difficult to patent and which attracted criticism for a wealth of different reasons, are fine case studies of the current debate over the proper role of the state in regulating the marketplace and knowledge production in the emerging information economy. The article explains that these patents are prospect patents that may be used by a monopolist to collect data that everybody else needs in order to compete effectively. As such, they raise familiar concerns about failure of coordination emerging as a result of a monopolist controlling a resource such as datasets that others need and cannot replicate. In effect, the courts regulated the market, primarily focusing on ensuring the free flow of data in the emerging marketplace very much in the spirit of the ‘free the data’ language in various policy initiatives, yet at the same time with an eye to boost downstream innovation. In doing so, these decisions essentially endorse practices of personal information processing which constitute a new type of public domain: a source of raw materials which are there for the taking and which have become most important inputs to commercial activity. From this vantage point of view, the legal interpretation of the private and the shared legitimizes a model of data extraction from individuals, the raw material of information capitalism, that will fuel the next generation of data-intensive therapeutics in the field of data-driven medicine

Enhancing mental health with Artificial Intelligence: Current trends and future prospects

Artificial Intelligence (AI) has emerged as a transformative force in various fields, and its application in mental healthcare is no exception. Hence, this review explores the integration of AI into mental healthcare, elucidating current trends, ethical considerations, and future directions in this dynamic field. This review encompassed recent studies, examples of AI applications, and ethical considerations shaping the field. Additionally, regulatory frameworks and trends in research and development were analyzed. We comprehensively searched four databases (PubMed, IEEE Xplore, PsycINFO, and Google Scholar). The inclusion criteria were papers published in peer-reviewed journals, conference proceedings, or reputable online databases, papers that specifically focus on the application of AI in the field of mental healthcare, and review papers that offer a comprehensive overview, analysis, or integration of existing literature published in the English language. Current trends reveal AI's transformative potential, with applications such as the early detection of mental health disorders, personalized treatment plans, and AI-driven virtual therapists. However, these advancements are accompanied by ethical challenges concerning privacy, bias mitigation, and the preservation of the human element in therapy. Future directions emphasize the need for clear regulatory frameworks, transparent validation of AI models, and continuous research and development efforts. Integrating AI into mental healthcare and mental health therapy represents a promising frontier in healthcare. While AI holds the potential to revolutionize mental healthcare, responsible and ethical implementation is essential. By addressing current challenges and shaping future directions thoughtfully, we may effectively utilize the potential of AI to enhance the accessibility, efficacy, and ethicality of mental healthcare, thereby helping both individuals and communities

Enhancing Mental Health with Artificial Intelligence: Current Trends and Future Prospects

Artificial Intelligence (AI) has emerged as a transformative force in various fields, and its application in mental healthcare is no exception. Hence, this review explores the integration of AI into mental healthcare, elucidating current trends, ethical considerations, and future directions in this dynamic field. This review encompassed recent studies, examples of AI applications, and ethical considerations shaping the field. Additionally, regulatory frameworks and trends in research and development were analyzed. We comprehensively searched four databases (PubMed, IEEE Xplore, PsycINFO, and Google Scholar). The inclusion criteria were papers published in peer-reviewed journals, conference proceedings, or reputable online databases, papers that specifically focus on the application of AI in the field of mental healthcare, and review papers that offer a comprehensive overview, analysis, or integration of existing literature published in the English language. Current trends reveal AI's transformative potential, with applications such as the early detection of mental health disorders, personalized treatment plans, and AI-driven virtual therapists. However, these advancements are accompanied by ethical challenges concerning privacy, bias mitigation, and the preservation of the human element in therapy. Future directions emphasize the need for clear regulatory frameworks, transparent validation of AI models, and continuous research and development efforts. Integrating AI into mental healthcare and mental health therapy represents a promising frontier in healthcare. While AI holds the potential to revolutionize mental healthcare, responsible and ethical implementation is essential. By addressing current challenges and shaping future directions thoughtfully, we may effectively utilize the potential of AI to enhance the accessibility, efficacy, and ethicality of mental healthcare, thereby helping both individuals and communities

Targeting the tumor microenvironment in colorectal peritoneal metastases

Peritoneal metastasis (PM) occurs in approximately one in four colorectal cancer (CRC) patients. The pathophysiology of colorectal PM remains poorly characterized. Also, the efficacy of current treatment modalities, including surgery and intraperitoneal (IP) delivery of chemotherapy, is limited. Increasingly, therefore, efforts are being developed to unravel the PM cascade and at understanding the PM-associated tumor microenvironment (TME) and peritoneal ecosystem as potential therapeutic targets. Here, we review recent insights in the structure and components of the TME in colorectal PM, and discuss how these may translate into novel therapeutic approaches aimed at re-engineering the metastasis-promoting activity of the stroma

Portinari: A Data Exploration Tool to Personalize Cervical Cancer Screening

Socio-technical systems play an important role in public health screening programs to prevent cancer. Cervical cancer incidence has significantly decreased in countries that developed systems for organized screening engaging medical practitioners, laboratories and patients. The system automatically identifies individuals at risk of developing the disease and invites them for a screening exam or a follow-up exam conducted by medical professionals. A triage algorithm in the system aims to reduce unnecessary screening exams for individuals at low-risk while detecting and treating individuals at high-risk. Despite the general success of screening, the triage algorithm is a one-size-fits all approach that is not personalized to a patient. This can easily be observed in historical data from screening exams. Often patients rely on personal factors to determine that they are either at high risk or not at risk at all and take action at their own discretion. Can exploring patient trajectories help hypothesize personal factors leading to their decisions? We present Portinari, a data exploration tool to query and visualize future trajectories of patients who have undergone a specific sequence of screening exams. The web-based tool contains (a) a visual query interface (b) a backend graph database of events in patients' lives (c) trajectory visualization using sankey diagrams. We use Portinari to explore diverse trajectories of patients following the Norwegian triage algorithm. The trajectories demonstrated variable degrees of adherence to the triage algorithm and allowed epidemiologists to hypothesize about the possible causes.Comment: Conference paper published at ICSE 2017 Buenos Aires, at the Software Engineering in Society Track. 10 pages, 5 figure

Wearable feedback systems for rehabilitation

In this paper we describe LiveNet, a flexible wearable platform intended for long-term ambulatory health monitoring with real-time data streaming and context classification. Based on the MIT Wearable Computing Group's distributed mobile system architecture, LiveNet is a stable, accessible system that combines inexpensive, commodity hardware; a flexible sensor/peripheral interconnection bus; and a powerful, light-weight distributed sensing, classification, and inter-process communications software architecture to facilitate the development of distributed real-time multi-modal and context-aware applications. LiveNet is able to continuously monitor a wide range of physiological signals together with the user's activity and context, to develop a personalized, data-rich health profile of a user over time. We demonstrate the power and functionality of this platform by describing a number of health monitoring applications using the LiveNet system in a variety of clinical studies that are underway. Initial evaluations of these pilot experiments demonstrate the potential of using the LiveNet system for real-world applications in rehabilitation medicine

Getting old through the blood. Circulating molecules in aging and senescence of cardiovascular regenerative cells

Global aging is a hallmark of our century. The natural multifactorial process resulting in aging involves structural and functional changes, affecting molecules, cells, and tissues. As the western population is getting older, we are witnessing an increase in the burden of cardiovascular events, some of which are known to be directly linked to cellular senescence and dysfunction. In this review, we will focus on the description of a few circulating molecules, which have been correlated to life span, aging, and cardiovascular homeostasis. We will review the current literature concerning the circulating levels and related signaling pathways of selected proteins (insulin-like growth factor 1, growth and differentiation factor-11, and PAI-1) and microRNAs of interest (miR-34a, miR-146a, miR-21), whose bloodstream levels have been associated to aging in different organisms. In particular, we will also discuss their potential role in the biology and senescence of cardiovascular regenerative cell types, such as endothelial progenitor cells, mesenchymal stromal cells, and cardiac progenitor cells

The Impact of Uncertainty Regarding Patent Eligible Subject Matter for Investment in U.S. Medical Diagnostic Technologies

Historically, 35 U.S.C. § 101, the statute governing patent eligible subject matter, has been construed broadly—with its legislative history indicating that it should cover “anything under the sun that is made by man.” The Supreme Court crafted three exceptions to § 101: (1) abstract ideas, (2) laws of nature, and (3) natural phenomena. In recent years, the Supreme Court’s eligibility jurisprudence has further narrowed § 101 to effectively exclude meritorious medical diagnostic methods. Indeed, since the Court’s decision in Mayo Collaborative Services v. Prometheus Laboratories, Inc., the Federal Circuit has held every single diagnostic method claim brought before it patent ineligible. This Note begins by discussing the merits of medical diagnostic tests and their relevance to modern precision medicine. It then dissects the Supreme Court’s decisions in Bilski v. Kappos and Mayo, highlighting the uncertainty regarding patent eligibility of medical diagnostic methods following these cases and the Federal Circuit’s application of the Court’s Alice/Mayo test. This uncertainty has rippled through the medical diagnostic and venture capital industries, sparking concerns about under-investment in diagnostic R&D. The heart of this Note is an empirical study of venture capital investment in disease diagnostic technologies before and after Bilski and Mayo. Using a difference-in-difference methodology to analyze venture capital investment data from the PriceWater Clearinghouse Moneytree Tool, this Note examines whether current § 101 jurisprudence has caused these selective investors to decrease investment in companies developing medical diagnostics technologies that—in light of Mayo and its progeny—appear to be patent ineligible. Ultimately, the study indicates that in the four years following Mayo, investment in disease diagnostic technologies was nearly $9.3 billion dollars lower than it would have been absent Mayo. This Note presents five key implications related to its central finding. First, the data supports the recent calls to Congress for reform of § 101. Second, it complements other key research regarding investment behavior following Mayo and Alice. Third, the data raises the question whether remaining innovation in the diagnostics space will be enough to support the precision medicine movement. Fourth, underinvestment in diagnostics and the discovery of disease biomarkers may lead to underinvestment in treatments. Lastly, this Note’s findings suggest that at least some venture capital firms employ greater caution when determining whether to invest in a company developing (or aiming to develop) diagnostics, which may spur hesitancy to form such companies in the first place