National strategies for knowledge translation in health policy-making: A scoping review of grey literature.

BACKGROUND AND OBJECTIVES: Without strategic actions in its support, the translation of scientific research evidence into health policy is often absent or delayed. This review systematically maps and assesses national-level strategic documents in the field of knowledge translation (KT) for health policy, and develops a practical template that can support Evidence-informed Policy Network (EVIPNet) Europe countries in producing national strategies for evidence-informed policy-making. METHODS: Websites of organizations with strategic responsibilities in KT were electronically searched, on the basis of pre-defined criteria, in July-August 2017, and an updated search was carried out in April-June 2021. We included national strategies or elements of national strategies that dealt with KT activities, as well as similar strategies of individual institutions with a national policy focus. Two reviewers screened the strategies for inclusion. Data were analysed using qualitative content analysis. RESULTS: A total of 65 unique documents were identified, of which 17 were eligible and analysed for their structure and content. Of the 17, 1 document was a national health KT action plan and 6 documents were institution-level KT strategies. The remaining 10 strategies, which were also included were 2 national health strategies, 5 national health research strategies and 3 national KT strategies (not specific to the field of health alone). In all, 13 structural elements and 7 major themes of health policy KT strategies were identified from the included documents. CONCLUSION: KT in health policy, as emerged from the national strategies that our mapping identified, is based on the production and accessibility of policy-relevant research, its packaging for policy-making and the activities related to knowledge exchange. KT strategies may play different roles in the complex and context-specific process of policy-making. Our findings show that the main ideas of health-specific evidence-informed policy literature appear in these strategies, but their effectiveness depends on the way stakeholders use them. Specific knowledge-brokering institutions and organizational capacity, advocacy about the use of evidence, and close collaboration and co-decision-making with key stakeholders are essential in furthering the policy uptake of research results.

Advancing Translation of Clinical Research Into Practice and Population Health Impact Through Implementation Science.

Translational and implementation sciences aim to prioritize and guide efforts to create greater efficiency and speed of scientific innovation across the translational science continuum to improve patient and population health. Key principles and practices rooted in translational and implementation science may be incorporated into clinical trials research, particularly pragmatic trials, to improve the relevance and impact of scientific innovation. This thematic review intends to raise awareness on the value of translational and implementation science in clinical research and to encourage its use in designing and implementing clinical trials across the translational research continuum. Herein, we describe the gap in translating research findings into clinical practice, introduce translational and implementation science, and describe the principles and practices from implementation science that can be used in clinical trial research across the translational continuum to inform clinical practice, to improve population health impact, and to address health care inequities.

Advancing the utilization of real-world data and real-world evidence in clinical pharmacology and translational research-Proceedings from the ASCPT 2023 preconference workshop.

Real-world data (RWD) and real-world evidence (RWE) are now being routinely used in epidemiology, clinical practice, and post-approval regulatory decisions. Despite the increasing utility of the methodology and new regulatory guidelines in recent years, there remains a lack of awareness of how this approach can be applied in clinical pharmacology and translational research settings. Therefore, the American Society of Clinical Pharmacology & Therapeutics (ASCPT) held a workshop on March 21st, 2023 entitled "Advancing the Utilization of Real-World Data (RWD) and Real-World Evidence (RWE) in Clinical Pharmacology and Translational Research." The work described herein is a summary of the workshop proceedings.

Translational Research and Health Equity: Gene Therapies for Sickle Cell Disease as a Case Study.

In August of 2023, the National Academies of Science, Engineering, and Medicine published a timely report titled "Toward Equitable Innovation in Health and Medicine: A Framework." Here, we review some of the key contributions of the report, focusing on two dimensions of equity: input equity and deployment equity. We then use the example of new gene therapies to treat sickle cell disease (SCD) as a case study of input and deployment equity in translational research. The SCD case study illustrates the need for a kind of translational bioethics with deep understanding of lived experiences and clinical realities as well as a high degree of economic and policy sophistication.

Applications of Flow Cytometry in Drug Discovery and Translational Research.

Flow cytometry is a mainstay technique in cell biology research, where it is used for phenotypic analysis of mixed cell populations. Quantitative approaches have unlocked a deeper value of flow cytometry in drug discovery research. As the number of drug modalities and druggable mechanisms increases, there is an increasing drive to identify meaningful biomarkers, evaluate the relationship between pharmacokinetics and pharmacodynamics (PK/PD), and translate these insights into the evaluation of patients enrolled in early clinical trials. In this review, we discuss emerging roles for flow cytometry in the translational setting that supports the transition and evaluation of novel compounds in the clinic.

Spinocerebellar Ataxia Type 3 Pathophysiology-Implications for Translational Research and Clinical Studies.

The spinocerebellar ataxias (SCA) comprise a group of inherited neurodegenerative diseases. Machado-Joseph Disease (MJD) or spinocerebellar ataxia 3 (SCA3) is the most common autosomal dominant form, caused by the expansion of CAG repeats within the ataxin-3 (ATXN3) gene. This mutation results in the expression of an abnormal protein containing long polyglutamine (polyQ) stretches that confers a toxic gain of function and leads to misfolding and aggregation of ATXN3 in neurons. As a result of the neurodegenerative process, SCA3 patients are severely disabled and die prematurely. Several screening approaches, e.g., druggable genome-wide and drug library screenings have been performed, focussing on the reduction in stably overexpressed ATXN3(polyQ) protein and improvement in the resultant toxicity. Transgenic overexpression models of toxic ATXN3, however, missed potential modulators of endogenous ATXN3 regulation. In another approach to identify modifiers of endogenous ATXN3 expression using a CRISPR/Cas9-modified SK-N-SH wild-type cell line with a GFP-T2A-luciferase (LUC) cassette under the control of the endogenous ATXN3 promotor, four statins were identified as potential activators of expression. We here provide an overview of the high throughput screening approaches yet performed to find compounds or genomic modifiers of ATXN3(polyQ) toxicity in different SCA3 model organisms and cell lines to ameliorate and halt SCA3 progression in patients. Furthermore, the putative role of cholesterol in neurodegenerative diseases (NDDs) in general and SCA3 in particular is discussed.

An open source knowledge graph ecosystem for the life sciences.

Translational research requires data at multiple scales of biological organization. Advancements in sequencing and multi-omics technologies have increased the availability of these data, but researchers face significant integration challenges. Knowledge graphs (KGs) are used to model complex phenomena, and methods exist to construct them automatically. However, tackling complex biomedical integration problems requires flexibility in the way knowledge is modeled. Moreover, existing KG construction methods provide robust tooling at the cost of fixed or limited choices among knowledge representation models. PheKnowLator (Phenotype Knowledge Translator) is a semantic ecosystem for automating the FAIR (Findable, Accessible, Interoperable, and Reusable) construction of ontologically grounded KGs with fully customizable knowledge representation. The ecosystem includes KG construction resources (e.g., data preparation APIs), analysis tools (e.g., SPARQL endpoint resources and abstraction algorithms), and benchmarks (e.g., prebuilt KGs). We evaluated the ecosystem by systematically comparing it to existing open-source KG construction methods and by analyzing its computational performance when used to construct 12 different large-scale KGs. With flexible knowledge representation, PheKnowLator enables fully customizable KGs without compromising performance or usability.

Systematic synthesis of intersectional best practices: knowledge translation for circumpolar indigenous disability.

Numerous theories, models, and frameworks (TMFs) currently exist for knowledge translation (KT), with scholarship that is increasingly inclusive of populations experiencing health inequalities. This study proposes two objectives: 1) exploring a nine-step method for synthesising best practices, acknowledging existing syntheses in the form of tailored-databases and review-style publications; and 2) collating best practices to inform KT that is inclusive to indigenous individuals living with disabilities in circumpolar regions. The resulting synthesis emphasises 10 best practices: explicitly connect the accountability of stakeholders to the wellbeing of the people they serve; recognise entanglement with existing neoliberal systems; assess impacts of KT on indigenous treatment providers; employ personal outreach visits; rectify longstanding delegitimization; avoid assuming the target group to be homogeneous, critically examine inequitable distribution of benefits and risks; consider how emphasis on a KT initiative can distract from historical and systemic inequalities; target inequitable, systemic social and economic forces; consider how KT can also be mobilised to gain power and control; assess what is selected for KT, and how it intersects with power position of external stakeholders and internal champions; and, allow people access-to-knowledge which changes inequitable systems.

Translational research of boron neutron capture therapy for spinal cord gliomas using rat model.

Boron neutron capture therapy (BNCT) is a type of targeted particle radiation therapy with potential applications at the cellular level. Spinal cord gliomas (SCGs) present a substantial challenge owing to their poor prognosis and the lack of effective postoperative treatments. This study evaluated the efficacy of BNCT in a rat SCGs model employing the Basso, Beattie, and Bresnahan (BBB) scale to assess postoperative locomotor activity. We confirmed the presence of adequate in vitro boron concentrations in F98 rat glioma and 9L rat gliosarcoma cells exposed to boronophenylalanine (BPA) and in vivo tumor boron concentration 2.5 h after intravenous BPA administration. In vivo neutron irradiation significantly enhanced survival in the BNCT group when compared with that in the untreated group, with a minimal BBB scale reduction in all sham-operated groups. These findings highlight the potential of BNCT as a promising treatment option for SCGs.

[NOVEL TECHNOLOGIES AND FUTURE TRENDS IN TRANSLATIONAL RESEARCH IN ONCOLOGY].

INTRODUCTION: Translational research in medicine has undergone significant changes in the last decade, primarily due to the remarkable technological advancements made during this period. Oncology research is at the forefront of translational research in medicine and is heavily influenced by these changes. In this article, we briefly review the technologies that form the basis for the "next generation of translational research" in oncology in the coming decades, as well as the emerging trends in translational research in oncology through the implementation of these technologies.

[TRANSLATIONAL MEDICINE - THE FRONTLINE OF MEDICAL RESEARCH].

INTRODUCTION: In this journal we collected original papers and review papers in translational medicine. Translational medicine is a bio-medical field of research that translates findings discovered in the laboratory to clinical life, for example: diagnostic methods, new cutting-edge medications, high-tech tools, and novel public health policy. This research combines different fields in science using new advanced technologies to improve medical care and wellbeing. The most significant impact was accomplished by the National Institutes of Health (NIH) twenty years ago, when a National Center for Translational Research was built. One of the achievements was the opening of a genetic research center that focused on RNA, circular RNAs and interference RNA. This research discovered the importance of these RNA particles in neurological and metabolic diseases, which were found to be applicable for treatment of these diseases. In this journal, original papers and reviews will express the diversity and the capabilities of this research field that has inspired so many physicians and researchers.

The impact of a regionally based translational cancer research collaborative in Australia using the FAIT methodology.

BACKGROUND: Translating research, achieving impact, and assessing impact are important aspirations for all research collaboratives but can prove challenging. The Hunter Cancer Research Alliance (HCRA) was funded from 2014 to 2021 to enhance capacity and productivity in cancer research in a regional centre in Australia. This study aimed to assess the impact and benefit of the HCRA to help inform future research investments of this type. METHOD: The Framework to Assess the Impact from Translational health research (FAIT) was selected as the preferred methodology. FAIT incorporates three validated methodologies for assessing impact: 1) Modified Payback; 2) Economic Analysis; and 3) Narrative overview and case studies. All three FAIT methods are underpinned by a Program Logic Model. Data were collected from HCRA and the University of Newcastle administrative records, directly from HCRA members, and website searches. RESULTS: In addition to advancing knowledge and providing capacity building support to members via grants, fellowships, scholarships, training, events and targeted translation support, key impacts of HCRA-member research teams included: (i) the establishment of a regional biobank that has distributed over 13,600 samples and became largely self-sustaining; (ii) conservatively leveraging $43.8 M (s.a.$20.5 M - $160.5 M) in funding and support from the initial $9.7 M investment; (iii) contributing to clinical practice guidelines and securing a patent for identification of stem cells for endometrial cell regeneration; (iv) shifting the treatment paradigm for all tumour types that rely on nerve cell innervation, (v) development and implementation of the world's first real-time patient treatment verification system (Watchdog); (vi) inventing the effective 'EAT' psychological intervention to improve nutrition and outcomes in people experiencing radiotherapy for head and neck cancer; (vi) developing effective interventions to reduce smoking rates among priority groups, currently being rolled out to disadvantaged populations in NSW; and (vii) establishing a Consumer Advisory Panel and Consumer Engagement Committee to increase consumer involvement in research. CONCLUSION: Using FAIT methodology, we have demonstrated the significant impact and downstream benefits that can be achieved by the provision of infrastructure-type funding to regional and rural research collaboratives to help address inequities in research activity and health outcomes and demonstrates a positive return on investment.

ImageJ in Computational Fractal-Based Neuroscience: Pattern Extraction and Translational Research.

To explore questions asked in neuroscience, neuroscientists rely heavily on the tools available. One such toolset is ImageJ, open-source, free, biological digital image analysis software. Open-source software has matured alongside of fractal analysis in neuroscience, and today ImageJ is not a niche but a foundation relied on by a substantial number of neuroscientists for work in diverse fields including fractal analysis. This is largely owing to two features of open-source software leveraged in ImageJ and vital to vigorous neuroscience: customizability and collaboration. With those notions in mind, this chapter's aim is threefold: (1) it introduces ImageJ, (2) it outlines ways this software tool has influenced fractal analysis in neuroscience and shaped the questions researchers devote time to, and (3) it reviews a few examples of ways investigators have developed and used ImageJ for pattern extraction in fractal analysis. Throughout this chapter, the focus is on fostering a collaborative and creative mindset for translating knowledge of the fractal geometry of the brain into clinical reality.