Project description:At least 10% of adults and nearly all children who receive renal-replacement therapy have an inherited kidney disease. These patients rarely die when their disease progresses and can remain alive for many years because of advances in organ-replacement therapy. However, these disorders substantially decrease their quality of life and have a large effect on health-care systems. Since the kidneys regulate essential homoeostatic processes, inherited kidney disorders have multisystem complications, which add to the usual challenges for rare disorders. In this review, we discuss the nature of rare inherited kidney diseases, the challenges they pose, and opportunities from technological advances, which are well suited to target the kidney. Mechanistic insights from rare disorders are relevant for common disorders such as hypertension, kidney stones, cardiovascular disease, and progression of chronic kidney disease.
Project description:Incidence of hepatocellular carcinoma (HCC) is on the rise due to the prevalence of chronic hepatitis and cirrhosis. Although there are surgical and chemotherapy treatment avenues the mortality rate of HCC remains high. Immunotherapy is currently the new frontier of cancer treatment and the immunobiology of HCC is emerging as an area for further exploration. The tumor microenvironment coexists and interacts with various immune cells to sustain the growth of HCC. Thus, immunosuppressive cells play an important role in the anti-tumor immune response. This review will discuss the current concepts of immunosuppressive cells, including tumor-associated macrophages, marrow-derived suppressor cells, tumor-associated neutrophils, cancer-associated fibroblasts, and regulatory T cell interactions to actively promote tumorigenesis. It further elaborates on current treatment modalities and future areas of exploration.
Project description:Conservation physiology represents a recently emerging arm of conservation science that applies physiological tools and techniques to understand and solve conservation issues. While a multi-disciplinary toolbox can only help to address the global biodiversity crisis, any field can face challenges while becoming established, particularly highly applied disciplines that require multi-stakeholder involvement. Gaining first-hand knowledge of the challenges that conservation physiologists are facing can help characterize the current state of the field and build a better foundation for determining how it can grow. Through an online survey of 468 scientists working at the intersection of physiology and conservation, we aimed to identify characteristics of those engaging in conservation physiology research (e.g. demographics, primary taxa of study), gauge conservation physiology's role in contributing to on-the-ground conservation action, identify the perceived barriers to achieving success and determine how difficult any identified barriers are to overcome. Despite all participants having experience combining physiology and conservation, only one-third considered themselves to be 'conservation physiologists'. Moreover, there was a general perception that conservation physiology does not yet regularly lead to tangible conservation success. Respondents identified the recent conceptualization of the field and the broader issue of adequately translating science into management action as the primary reasons for these deficits. Other significant barriers that respondents have faced when integrating physiology and conservation science included a lack of funding, logistical constraints (e.g. sample sizes, obtaining permits) and a lack of physiological baseline data (i.e. reference ranges of a physiological metric's 'normal' or pre-environmental change levels). We identified 12 actions based on suggestions of survey participants that we anticipate will help deconstruct the barriers and continue to develop a narrative of physiology that is relevant to conservation science, policy and practice.
Project description:Climate-smart sustainable management of agricultural soil is critical to improve soil health, enhance food and water security, contribute to climate change mitigation and adaptation, biodiversity preservation, and improve human health and wellbeing. The European Joint Programme for Soil (EJP SOIL) started in 2020 with the aim to significantly improve soil management knowledge and create a sustainable and integrated European soil research system. EJP SOIL involves more than 350 scientists across 24 Countries and has been addressing multiple aspects associated with soil management across different European agroecosystems. This study summarizes the key findings of stakeholder consultations conducted at the national level across 20 countries with the aim to identify important barriers and challenges currently affecting soil knowledge but also assess opportunities to overcome these obstacles. Our findings demonstrate that there is significant room for improvement in terms of knowledge production, dissemination and adoption. Among the most important barriers identified by consulted stakeholders are technical, political, social and economic obstacles, which strongly limit the development and full exploitation of the outcomes of soil research. The main soil challenge across consulted member states remains to improve soil organic matter and peat soil conservation while soil water storage capacity is a key challenge in Southern Europe. Findings from this study clearly suggest that going forward climate-smart sustainable soil management will benefit from (1) increases in research funding, (2) the maintenance and valorisation of long-term (field) experiments, (3) the creation of knowledge sharing networks and interlinked national and European infrastructures, and (4) the development of regionally-tailored soil management strategies. All the above-mentioned interventions can contribute to the creation of healthy, resilient and sustainable soil ecosystems across Europe.
Project description:This study explores Bangladesh's mental health services from an individual- and system-level perspective and provides insights and recommendations for strengthening it's mental health system. We conducted 13 in-depth interviews and 2 focus group discussions. Thirty-one participants were recruited using a combination of purposive and snowball sampling methods. All interviews and group discussions were audio-recorded and transcribed, and key findings were translated from Bengali to English. Data were coded manually and analysed using a thematic and narrative analysis approach. Stakeholders perceived scarcity of service availability at the peripheral level, shortage of professionals, weak referral systems, lack of policy implementation and regulatory mechanisms were significant challenges to the mental health system in Bangladesh. At the population level, low levels of mental health literacy, high societal stigma, and treatment costs were barriers to accessing mental healthcare. Key recommendations included increasing the number of mental health workers and capacity building, strengthening regulatory mechanisms to enhance the quality of care within the health systems, and raising awareness about mental health. Introducing measures that relate to tackling stigma, mental health literacy as well as building the capacity of the health workforce and governance systems will help ensure universal mental health coverage.
Project description:BackgroundThe ability of machine learning (ML) to process and learn from large quantities of heterogeneous patient data is gaining attention in the precision oncology community. Some remarkable developments have taken place in the domain of image classification tasks in areas such as digital pathology and diagnostic radiology. The application of ML approaches to the analysis of DNA data, including tumor-derived genomic profiles, microRNAs, and cancer epigenetic signatures, while relatively more recent, has demonstrated some utility in identifying driver variants and molecular signatures with possible prognostic and therapeutic applications.MethodsWe conducted semi-structured interviews with academic and clinical experts to capture the status quo, challenges, opportunities, ethical implications, and future directions.ResultsOur participants agreed that machine learning in precision oncology is in infant stages, with clinical integration still rare. Overall, participants equated ongoing developments with better clinical workflows and improved treatment decisions for more cancer patients. They underscored the ability of machine learning to tackle the dynamic nature of cancer, break down the complexity of molecular data, and support decision-making. Our participants emphasized obstacles related to molecular data access, clinical utility, and guidelines. The availability of reliable and well-curated data to train and validate machine learning algorithms and integrate multiple data sources were described as constraints yet necessary for future clinical implementation. Frequently mentioned ethical challenges included privacy risks, equity, explainability, trust, and incidental findings, with privacy being the most polarizing. While participants recognized the issue of hype surrounding machine learning in precision oncology, they agreed that, in an assistive role, it represents the future of precision oncology.ConclusionsGiven the unique nature of medical AI, our findings highlight the field's potential and remaining challenges. ML will continue to advance cancer research and provide opportunities for patient-centric, personalized, and efficient precision oncology. Yet, the field must move beyond hype and toward concrete efforts to overcome key obstacles, such as ensuring access to molecular data, establishing clinical utility, developing guidelines and regulations, and meaningfully addressing ethical challenges.
Project description:BackgroundInteroperability-the exchange and integration of data across the health care system-remains a challenge despite ongoing policy efforts aimed at promoting interoperability.ObjectiveThis study aimed to identify current challenges and opportunities to advancing interoperability across stakeholders.MethodsPrimary data were collected through qualitative, semistructured interviews with stakeholders (n=24) in Ohio from July to October 2021. Interviewees were sampled using a stratified purposive sample of key informants from 4 representative groups as follows: acute care and children's hospital leaders, primary care providers, behavioral health providers, and regional health information exchange networks. Interviews focused on key informant perspectives on electronic health record implementation, the alignment of public policy with organizational strategy, interoperability implementation challenges, and opportunities for health information technology. The interviews were transcribed verbatim followed by rigorous qualitative analysis using directed content analysis.ResultsThe findings illuminate themes related to challenges and opportunities for interoperability that align with technological (ie, implementation challenges, mismatches in interoperability capabilities across stakeholders, and opportunities to leverage new technology and integrate social determinants of health data), organizational (ie, facilitators of interoperability and strategic alignment of participation in value-based payment programs with interoperability), and environmental (ie, policy) domains.ConclusionsInteroperability, although technically feasible for most providers, remains challenging for technological, organizational, and environmental reasons. Our findings suggest that the incorporation of end user considerations into health information technology development, implementation, policy, and standard deployment may support interoperability advancement.
Project description:BackgroundInterest in the oligometastatic prostate cancer (OMPC) is increasing, and various clinical studies have reported the benefits of metastasis-directed radiation therapy (MDRT) in OMPC. However, the recognition regarding the adopted definitions, methodologies of assessment, and therapeutic approaches is diverse among radiation oncologists. This study aims to evaluate the level of agreement for issues in OMPC among radiation oncologists.MethodsWe generated 15 key questions (KQs) for OMPC relevant to definition, diagnosis, local therapies, and endpoints. Additionally, three clinical scenarios representing synchronous metastatic prostate cancer (mPC) (case 1), metachronous mPC with visceral metastasis (case 2), and metachronous mPC with castration-resistance and history of polymetastasis (case 3) were developed. The 15 KQs were adapted according to each scenario and transformed into 23 questions with 6-9 per scenario. The survey was distributed to 80 radiation oncologists throughout the Republic of Korea. Answer options with 0.0-29.9%, 30-49.9%, 50-69.9%, 70-79.9%, 80-89.9%, and 90-100% agreements were considered as no, minimal, weak, moderate, strong, and near perfect agreement, respectively.ResultsForty-five candidates voluntarily participated in this study. Among 23 questions, near perfect (n = 4), strong (n = 3), or moderate (n = 2) agreements were shown in nine. For the case recognized as OMPC with agreements of 93% (case 1), near perfect agreements on the application of definitive radiation therapy (RT) for whole metastatic lesions were achieved. While ≥70% agreements regarding optimal dose-fractionation for metastasis-directed RT (MDRT) has not been achieved, stereotactic body RT (SBRT) is favored by clinicians with higher clinical volume.ConclusionFor the case recognized as OMPC, near perfect agreement for the application of definitive RT for whole metastatic lesions was reached. SBRT was more favored as a MDRT by clinicians with a higher clinical volume.
Project description:Using social media is one important strategy to communicate research and public health guidelines to the scientific community and general public. Empirical evidence about which communication strategies are effective around breastfeeding messaging is scarce. To fill this gap, we aimed to identify influencers in the largest available Twitter database using social network analysis (n = 10,694 users), inductively analyze tweets, and explore communication strategies, motivations, and challenges via semi-structured interviews. Influencers had diverse backgrounds within and beyond the scientific health community (SHC; 42.7%): 54.7% were from the general public and 3% were companies. SHC contributed to most of the tweets (n = 798 tweets), disseminating guidelines and research findings more frequently than others (p < 0.001). Influencers from the general community mostly tweeted opinions regarding the current state of breastfeeding research and advocacy. Interviewees provided practical strategies (e.g., preferred visuals, tone, and writing style) to achieve personal and societal goals including career opportunities, community support, and improved breastfeeding practices. Complex challenges that need to be addressed were identified. Ideological differences regarding infant feeding may be hampering constructive communication, including differences in influencers' interpretation of the WHO International Code of Marketing of Breast-milk Substitutes and in perspectives regarding which social media interactions encompass conflict of interest.
Project description:Monitoring drug safety is a central concern throughout the drug life cycle. Information about toxicity and adverse events is generated at every stage of this life cycle, and stakeholders have a strong interest in applying text mining and artificial intelligence (AI) methods to manage the ever-increasing volume of this information. Recognizing the importance of these applications and the role of challenge evaluations to drive progress in text mining, the organizers of BioCreative VII (Critical Assessment of Information Extraction in Biology) convened a panel of experts to explore 'Challenges in Mining Drug Adverse Reactions'. This article is an outgrowth of the panel; each panelist has highlighted specific text mining application(s), based on their research and their experiences in organizing text mining challenge evaluations. While these highlighted applications only sample the complexity of this problem space, they reveal both opportunities and challenges for text mining to aid in the complex process of drug discovery, testing, marketing and post-market surveillance. Stakeholders are eager to embrace natural language processing and AI tools to help in this process, provided that these tools can be demonstrated to add value to stakeholder workflows. This creates an opportunity for the BioCreative community to work in partnership with regulatory agencies, pharma and the text mining community to identify next steps for future challenge evaluations.