Online Media Use and COVID-19 Vaccination in Real-World Personal Networks: Quantitative Study.

BACKGROUND: Most studies assessing the impact of online media and social media use on COVID-19 vaccine hesitancy predominantly rely on survey data, which often fail to capture the clustering of health opinions and behaviors within real-world networks. In contrast, research using social network analysis aims to uncover the diverse communities and discourse themes related to vaccine support and hesitancy within social media platforms. Despite these advancements, there is a gap in the literature on how a person's social circle affects vaccine acceptance, wherein an important part of social influence stems from offline interactions. OBJECTIVE: We aimed to examine how online media consumption influences vaccination decisions within real-world social networks by analyzing unique quantitative network data collected from Romania, an Eastern European state and member of the European Union. METHODS: We conducted 83 face-to-face interviews with participants from a living lab in Lerești, a small rural community in Romania, using a personal network analysis framework. This approach involved gathering data on both the respondents and individuals within their social circles (referred to as alters). After excluding cases with missing data, our analysis proceeded with 73% (61/83) of the complete personal networks. To examine the hierarchical structure of alters nested within ego networks, we used a mixed multilevel logistic regression model with random intercepts. The model aimed to predict vaccination status among alters, with the focal independent variable being the respondents' preferred source of health and prevention information. This variable was categorized into 3 types: traditional media, online media (including social media), and a combination of both, with traditional media as the reference category. RESULTS: In this study, we analyzed 61 personal networks, encompassing between 15 and 25 alters each, totaling 1280 alters with valid data across all variables of interest. Our primary findings indicate that alters within personal networks, whose respondents rely solely on online media for health information, exhibit lower vaccination rates (odds ratio [OR] 0.37, 95% CI 0.15-0.92; P=.03). Conversely, the transition from exclusive traditional media use to a combination of both traditional and online media does not significantly impact vaccination rate odds (OR 0.75, 95% CI 0.32-1.78; P=.52). In addition, our analysis revealed that alters in personal networks of respondents who received the vaccine are more likely to have received the vaccine themselves (OR 3.75, 95% CI 1.79-7.85; P<.001). CONCLUSIONS: Real-world networks combine diverse human interactions and attributes along with consequences on health opinions and behaviors. As individuals' vaccination status is influenced by how their social alters use online media and vaccination behavior, further insights are needed to create tailored communication campaigns and interventions regarding vaccination in areas with low levels of digital health literacy and vaccination rates, as Romania exposes.

European Groundshot-addressing Europe's cancer research challenges: a Lancet Oncology Commission.

Cancer research is a crucial pillar for countries to deliver more affordable, higher quality, and more equitable cancer care. Patients treated in research-active hospitals have better outcomes than patients who are not treated in these settings. However, cancer in Europe is at a crossroads. Cancer was already a leading cause of premature death before the COVID-19 pandemic, and the disastrous effects of the pandemic on early diagnosis and treatment will probably set back cancer outcomes in Europe by almost a decade. Recognising the pivotal importance of research not just to mitigate the pandemic today, but to build better European cancer services and systems for patients tomorrow, the Lancet Oncology European Groundshot Commission on cancer research brings together a wide range of experts, together with detailed new data on cancer research activity across Europe during the past 12 years. We have deployed this knowledge to help inform Europe's Beating Cancer Plan and the EU Cancer Mission, and to set out an evidence-driven, patient-centred cancer research roadmap for Europe. The high-resolution cancer research data we have generated show current activities, captured through different metrics, including by region, disease burden, research domain, and effect on outcomes. We have also included granular data on research collaboration, gender of researchers, and research funding. The inclusion of granular data has facilitated the identification of areas that are perhaps overemphasised in current cancer research in Europe, while also highlighting domains that are underserved. Our detailed data emphasise the need for more information-driven and data-driven cancer research strategies and planning going forward. A particular focus must be on central and eastern Europe, because our findings emphasise the widening gap in cancer research activity, and capacity and outcomes, compared with the rest of Europe. Citizens and patients, no matter where they are, must benefit from advances in cancer research. This Commission also highlights that the narrow focus on discovery science and biopharmaceutical research in Europe needs to be widened to include such areas as prevention and early diagnosis; treatment modalities such as radiotherapy and surgery; and a larger concentration on developing a research and innovation strategy for the 20 million Europeans living beyond a cancer diagnosis. Our data highlight the important role of comprehensive cancer centres in driving the European cancer research agenda. Crucial to a functioning cancer research strategy and its translation into patient benefit is the need for a greater emphasis on health policy and systems research, including implementation science, so that the innovative technological outputs from cancer research have a clear pathway to delivery. This European cancer research Commission has identified 12 key recommendations within a call to action to reimagine cancer research and its implementation in Europe. We hope this call to action will help to achieve our ambitious 70:35 target: 70% average 10-year survival for all European cancer patients by 2035.

Mapping of clinical research on artificial intelligence in the treatment of cancer and the challenges and opportunities underpinning its integration in the European Union health sector.

BACKGROUND: Although current efforts are made to diminish the incidence and burden of disease, cancer is still widely identified late at stage. This study aims to conduct a systematic review mapping the existent and emerging clinical research on artificial intelligence (AI) in the treatment of cancer and to underpin its integration challenges and opportunities in the European Union (EU) health sector. METHODS: A systematic literature review (SLR) evaluating global clinical trials (CTs; published between 2010 and 2020 or forthcoming) was concluded. Additionally, a horizon scanning (HS) exercise focusing on emerging trends (published between 2017 and 2020) was conducted. RESULTS: Forty-four CTs were identified and analyzed. Selected CTs were divided into three research areas: (i) potential of AI combined with imaging techniques, (ii) AI's applicability in robotic surgery interventions and (iii) AI's potential in clinical decision making. Twenty-one studies presented an interventional nature, nine papers were observational and 14 articles did not explicitly mention the type of study performed. The papers presented an increased heterogeneity in sample size, type of tumour, type of study and reporting of results. In addition, a shift in research is observed and only a small fraction of studies were completed in the EU. These findings could be further linked to the current socio-economic, political, scientific, technological and environmental state of the EU in regard to AI innovation. CONCLUSION: To overcome the challenges threatening the EU's integration of such technology in the healthcare field, new strategies taking into account the EU's socio-economic and political environment are deemed necessary.

A framework for validating AI in precision medicine: considerations from the European ITFoC consortium.

BACKGROUND: Artificial intelligence (AI) has the potential to transform our healthcare systems significantly. New AI technologies based on machine learning approaches should play a key role in clinical decision-making in the future. However, their implementation in health care settings remains limited, mostly due to a lack of robust validation procedures. There is a need to develop reliable assessment frameworks for the clinical validation of AI. We present here an approach for assessing AI for predicting treatment response in triple-negative breast cancer (TNBC), using real-world data and molecular -omics data from clinical data warehouses and biobanks. METHODS: The European "ITFoC (Information Technology for the Future Of Cancer)" consortium designed a framework for the clinical validation of AI technologies for predicting treatment response in oncology. RESULTS: This framework is based on seven key steps specifying: (1) the intended use of AI, (2) the target population, (3) the timing of AI evaluation, (4) the datasets used for evaluation, (5) the procedures used for ensuring data safety (including data quality, privacy and security), (6) the metrics used for measuring performance, and (7) the procedures used to ensure that the AI is explainable. This framework forms the basis of a validation platform that we are building for the "ITFoC Challenge". This community-wide competition will make it possible to assess and compare AI algorithms for predicting the response to TNBC treatments with external real-world datasets. CONCLUSIONS: The predictive performance and safety of AI technologies must be assessed in a robust, unbiased and transparent manner before their implementation in healthcare settings. We believe that the consideration of the ITFoC consortium will contribute to the safe transfer and implementation of AI in clinical settings, in the context of precision oncology and personalized care.

Fast Screening of Whole Blood and Tumor Tissue for Bladder Cancer Biomarkers Using Stochastic Needle Sensors.

Bladder cancer is one of the most common urologic malignancies, which is more frequent in men than in women. The early diagnosis for this type of cancer still remains a challenge, therefore, the development of a fast screening test for whole blood and tumor tissue samples may save lives. Four biomarkers, p53, E-cadherin, bladder tumor antigen (BTA), and hyaluronic acid were considered for the screening tests using stochastic needle sensors. Three stochastic needle sensors, based on graphite powder and modified with three types of chitosan, were designed and characterized for the screening test. The proposed sensors showed low limits of quantification, and high sensitivity and selectivity levels. The recoveries of p53, E-cadherin, BTA, and hyaluronic acid in whole blood samples and tissue samples were higher than 95.00% with a relative standard deviation lower than 1.00%.

Romania's Pioneering Law: Establishing the Right to Personalized Medicine.

This analysis and commentary discuss Romania's landmark law, the first globally, acknowledging the right of citizens and patients to personalized medicine. Initiated following the EU Council's 2015 policy on personalized medicine, the law is a result of intersectoral collaborative efforts led by the Centre for Innovation in Medicine in Romania using a quadruple (later evolved to penta) helix model involving academia, public, private, and civil society sectors. Promulgated on May 24, 2023, the law legally entitles patients to personalized health care and in ways informed by individual genetic and phenotypic consideration. The law mandates informed consent for medical interventions and ensures data protection in accordance with the General Data Protection Regulation. We suggest that this pioneering legislation paves the way for integrating personalized medicine into Romania's health care system, shaping clinical practice, research, and health policy. In all, it marks a significant step in redefining health care delivery, emphasizing individualized treatment and the political determinants of personalized medicine, and setting a precedent for future health care innovations worldwide.

Assortative mixing of opinions about COVID-19 vaccination in personal networks.

Many countries worldwide had difficulties reaching a sufficiently high vaccination uptake during the COVID-19 pandemic. Given this context, we collected data from a panel of 30,000 individuals, which were representative of the population of Romania (a country in Eastern Europe with a low 42.6% vaccination rate) to determine whether people are more likely to be connected to peers displaying similar opinions about COVID-19 vaccination. We extracted 443 personal networks, amounting to 4430 alters. We estimated multilevel logistic regression models with random-ego-level intercepts to predict individual opinions about COVID-19 vaccination. Our evidence indicates positive opinions about the COVID-19 vaccination cluster. Namely, the likelihood of having a positive opinion about COVID-19 vaccination increases when peers have, on average, a more positive attitude than the rest of the nodes in the network (OR 1.31, p < 0.001). We also found that individuals with higher education and age are more likely to hold a positive opinion about COVID-19 vaccination. With the given empirical data, our study cannot reveal whether this assortative mixing of opinions is due to social influence or social selection. However, it may nevertheless have implications for public health interventions, especially in countries that strive to reach higher uptake rates. Understanding opinions about vaccination can act as an early warning system for potential outbreaks, inform predictions about vaccination uptake, or help supply chain management for vaccine distribution.

The Emerging Role of Large Language Models in Improving Prostate Cancer Literacy.

This study assesses the effectiveness of chatbots powered by Large Language Models (LLMs)-ChatGPT 3.5, CoPilot, and Gemini-in delivering prostate cancer information, compared to the official Patient's Guide. Using 25 expert-validated questions, we conducted a comparative analysis to evaluate accuracy, timeliness, completeness, and understandability through a Likert scale. Statistical analyses were used to quantify the performance of each model. Results indicate that ChatGPT 3.5 consistently outperformed the other models, establishing itself as a robust and reliable source of information. CoPilot also performed effectively, albeit slightly less so than ChatGPT 3.5. Despite the strengths of the Patient's Guide, the advanced capabilities of LLMs like ChatGPT significantly enhance educational tools in healthcare. The findings underscore the need for ongoing innovation and improvement in AI applications within health sectors, especially considering the ethical implications underscored by the forthcoming EU AI Act. Future research should focus on investigating potential biases in AI-generated responses and their impact on patient outcomes.

The Potential Impact of Large Language Models on Doctor-Patient Communication: A Case Study in Prostate Cancer.

BACKGROUND: In recent years, the integration of large language models (LLMs) into healthcare has emerged as a revolutionary approach to enhancing doctor-patient communication, particularly in the management of diseases such as prostate cancer. METHODS: Our paper evaluated the effectiveness of three prominent LLMs-ChatGPT (3.5), Gemini (Pro), and Co-Pilot (the free version)-against the official Romanian Patient's Guide on prostate cancer. Employing a randomized and blinded method, our study engaged eight medical professionals to assess the responses of these models based on accuracy, timeliness, comprehensiveness, and user-friendliness. RESULTS: The primary objective was to explore whether LLMs, when operating in Romanian, offer comparable or superior performance to the Patient's Guide, considering their potential to personalize communication and enhance the informational accessibility for patients. Results indicated that LLMs, particularly ChatGPT, generally provided more accurate and user-friendly information compared to the Guide. CONCLUSIONS: The findings suggest a significant potential for LLMs to enhance healthcare communication by providing accurate and accessible information. However, variability in performance across different models underscores the need for tailored implementation strategies. We highlight the importance of integrating LLMs with a nuanced understanding of their capabilities and limitations to optimize their use in clinical settings.

Horizon Scanning: Rise of Planetary Health Genomics and Digital Twins for Pandemic Preparedness.

The Covid-19 pandemic accelerated research and development not only in infectious diseases but also in digital technologies to improve monitoring, forecasting, and intervening on planetary and ecological risks. In the European Commission, the Destination Earth (DestinE) is a current major initiative to develop a digital model of the Earth (a "digital twin") with high precision. Moreover, omics systems science is undergoing digital transformation impacting nearly all dimensions of the field, including real-time phenotype capture to data analytics using machine learning and artificial intelligence, to name but a few emerging frontiers. We discuss the ways in which the current ongoing digital transformation in omics offers synergies with digital twins/DestinE. Importantly, we note here the rise of a new field of scholarship, planetary health genomics. We conclude that digital transformation in public and private sectors, digital twins/DestinE, and their convergence with omics systems science are poised to build robust capacities for pandemic preparedness and resilient societies in the 21st century.

Clinical Applications of Artificial Intelligence-An Updated Overview.

Artificial intelligence has the potential to revolutionize modern society in all its aspects. Encouraged by the variety and vast amount of data that can be gathered from patients (e.g., medical images, text, and electronic health records), researchers have recently increased their interest in developing AI solutions for clinical care. Moreover, a diverse repertoire of methods can be chosen towards creating performant models for use in medical applications, ranging from disease prediction, diagnosis, and prognosis to opting for the most appropriate treatment for an individual patient. In this respect, the present paper aims to review the advancements reported at the convergence of AI and clinical care. Thus, this work presents AI clinical applications in a comprehensive manner, discussing the recent literature studies classified according to medical specialties. In addition, the challenges and limitations hindering AI integration in the clinical setting are further pointed out.

Meeting the Need for a Discussion of Unmet Medical Need.

As Europe and the world continue to battle against COVID, the customary complacency of society over future threats is clearly on display. Just 30 months ago, such a massive disruption to global lives, livelihoods and quality of life seemed unimaginable. Some remedial European Union action is now emerging, and more is proposed, including in relation to tackling "unmet medical need" (UMN). This initiative-directing attention to the future of treating disease and contemplating incentives to stimulate research and development-is welcome in principle. But the current approach being considered by EU officials merits further discussion, because it may prove counter-productive, impeding rather than promoting innovation. This paper aims to feed into these ongoing policy discussions, and rather than presenting research in the classical sense, it discusses the key elements from a multistakeholder perspective. Its central concern is over the risk that the envisaged support will fail to generate valuable new treatments if the legislation is phrased in a rigidly linear manner that does not reflect the serpentine realities of the innovation process, or if the definition placed on unmet medical need is too restrictive. It cautions that such an approach presumes that "unmet need" can be precisely and comprehensively defined in advance on the basis of the past. It cautions that such an approach can reinforce the comfortable delusion that the future is totally predictable-the delusion that left the world as easy prey to COVID. Instead, the paper urges reflection on how the legislation that will shortly enter the pipeline can be phrased so as to allow for the flourishing of a culture capable of rapid adaptation to the unexpected.

Paediatric familial hypercholesterolaemia screening in Europe: public policy background and recommendations.

Familial hypercholesterolaemia (FH) is under-recognized and under-treated in Europe leading to significantly higher risk for premature heart disease in those affected. As treatment beginning early in life is highly effective in preventing heart disease and cost-effective in these patients, screening for FH is crucial. It has therefore now been recognized by the European Commission Public Health Best Practice Portal as an effective strategy. Model programmes exist in Europe to identify young individuals with FH, which are based on cascade screening of first-degree relatives of affected individuals, universal screening for high cholesterol, opportunistic screening of high-risk individuals, or a combination of the above approaches. Recommendations presented herein to improve identification of FH emphasize that every country should have an FH screening programme. These programmes should be adapted from existing strategies to best fit the individual country's healthcare system, governments should provide financial support for these programmes and related care, and further research to optimize care and implementations should be conducted.

The time is now: Achieving FH paediatric screening across Europe - The Prague Declaration.

Familial hypercholesterolaemia (FH) is the most common inherited metabolic disorder characterized by high cholesterol and if left untreated leads to premature cardiovascular disease, such as heart attacks. Treatment that begins early in life, particularly in childhood, is highly efficacious in preventing cardiovascular disease and cost-effective, thus early detection of FH is crucial. However, in Europe, less than 10% of people living with FH are diagnosed and even less receive life-saving treatment. The Prague Declaration is a call to action for national and European Union policymakers and decision-makers and a result of the Czech EU Presidency meeting on FH Paediatric Screening (early detection of inherited high cholesterol) at the Czech Senate in Prague on 6th September 2022. It builds on a considerable body of evidence which was discussed at the Technical Meeting under the auspices of the Slovenian EU Presidency in October 2021. The Prague meeting addressed the outstanding barriers to the systematic implementation of FH paediatric screening across Europe. In this article, we present the key points from the Prague meeting and concrete actions needed to move forward.

Thanatechnology and the Living Dead: New Concepts in Digital Transformation and Human-Computer Interaction.

In a digital society, shall we be the authors of our own experience, not only during our lifetime but also after we die? We ask this question because dying and bereavement have become even harder, and much less private, in the digital age. New big data-driven digital industries and technologies are on the rise, with promises of interactive 3D avatars and storage of digital memories of the deceased, so they can continue to exist online as the "living dead" in a digital afterlife. Famous rock and roll icons like Roy Orbison, Frank Zappa, Ronnie James Dio, and Amy Winehouse have famously been turned into holograms that can once again give "live" performances on the touring circuit, often pulling in large audiences. Death studies, dying, and grief have become virtual in the 21st century. We live in truly unprecedented times for human-computer interactions. Thanatology is the scientific study of death, dying, loss, and grief. In contrast to the biological study of biological aging (cellular senescence) and programmed cell death (apoptosis), thanatology employs multiple professional lenses, medical, psychological, physical, spiritual, ethical, descriptive, and normative. In 1997, Carla Sofka introduced the term thanatechnology as "technological mechanisms such as interactive videodiscs and computer programs that are used to access information or aid in learning about thanatology topics." Onward to 2021, the advent of social media, the Internet of Things, and sensors that digitize and archive nearly every human movement and experience are taking thanatechnology, and by extension, digital transformation, to new heights. For example, what happens to digital remains of persons once they cease to exist physically? This article offers a critical study and snapshot of this nascent field, and the "un-disciplinary" sociotechnical issues digital thanatechnologies raise in relation to big data. We also discuss how best to critically govern this new frontier in systems science and the digital society. We suggest that new policy narratives such as (1) the right to nonparticipation in relation to information and communication technologies and (2) the planetary public goods deserve further attention to democratize thanatechnology and big data. To the extent that systems science often depends on data from online platforms, for example, in times of pandemics and ecological crises, "critical thanatechnology studies," introduced in this article, is a timely and essential field of scholarship with broad importance for systems science and planetary health.

Death by SARS-CoV 2: a Romanian COVID-19 multi-centre comorbidity study.

Evidence regarding the relation between SARS-CoV-2 mortality and the underlying medical condition is scarce. We conducted an observational, retrospective study based on Romanian official data about location, age, gender and comorbidities for COVID-19 fatalities. Our findings indicate that males, hypertension, diabetes, obesity and chronic kidney disease were most frequent in the COVID-19 fatalities, that the burden of disease was low, and that the prognosis for 1-year survival probability was high in the sample. Evidence shows that age-dependent pairs of comorbidities could be a negative prognosis factor for the severity of disease for the SARS-CoV 2 infection.

The FDA Changed Everything.

On May 23, 2017, the FDA approved the first cancer treatment (pembrolizumab) for any solid tumor with a specific genetic biomarker: microsatellite instability-high (MSI-H) or mismatch repair deficient (dMMR). For the first time in history, a solid cancer treatment was approved based on the genetic makeup of tumor not on the location in the body where the cancer originated, for example lung or breast cancer (TNM staging). This indication covers patients with solid tumors that have progressed following prior treatment and who have no satisfactory alternative treatment options and patients with colorectal cancer that has progressed following treatment with certain chemotherapeutics. All cancer drug approvals in the last 30 years were grounded on TNM staging independent of the therapy type (chemotherapy, monoclonal antibodies, TKI inhibitors, immune therapies or targeted therapies) and despite the huge and fast advances in understanding tumor biology. In fact, the FDA previously approved pembrolizumab taking into consideration the TNM staging, for the treatment of certain patients with metastatic melanoma, metastatic non-small cell lung cancer, recurrent or metastatic head and neck cancer, refractory classical Hodgkin lymphoma, and urothelial carcinoma. The archaic TNM staging will probably be changed under the disruptive wave of molecular biology. The recent FDA approval could be considered the certificate of birth for a truly new dimension of personalized medicine in cancer. We recommend European Union to follow the FDA approach of tissue-agnostic cancer drugs in order to speed up the development of next-generation oncologic therapies and to increase the access of patients to truly personalized medicine.

The Three-Way Pendulum of Healthcare Innovation.

We are, understandably, forever hearing about the high cost of bringing innovative new drugs and treatments to the healthcare market, especially medicines for smaller subgroups, and the fact that member state health systems often baulk at the prices. This article will argue that such a bypassing and blocking of innovative medicines and treatments is not only counterproductive when it comes to the health of Europe's patients, but actually fails to take into account the economic arguments. The article seeks to show that the long-term benefit to patients and the economy (health means wealth) will outweigh initial costs down the line. Couple this with a smarter use of information technologies and other resources and it will be possible to get much closer to building sustainable healthcare systems in a Europe struggling under the burden of an ageing population.