Impact Of Clinical Research Research Articles

Extracellular vesicles regulate metastable phenotypes of lymphangioleiomyomatosis cells via shuttling ATP synthesis to pseudopodia and activation of integrin adhesion complexes.

Pulmonary lymphangioleiomyomatosis (LAM) is metastatic sarcoma but mechanisms regulating LAM metastasis are unknown. Extracellular vesicle (EV) regulate cancer metastasis but their roles in LAM have not yet been investigated. Here, we report that EV biogenesis is increased in LAM and LAM EV cargo is enriched with lung tropic integrins, metalloproteinases, and cancer stem cell markers. LAM-EV increase LAM cell migration and invasion via the ITGα6/β1-c-Src-FAK-AKT axis. Metastable (hybrid) phenotypes of LAM metastasizing cells, pivotal for metastasis, are regulated by EV from primary tumor or metastasizing LAM cells via shuttling ATP synthesis to cell pseudopodia or activation of integrin adhesion complex, respectively. In mouse models of LAM, LAM-EV increase lung metastatic burden through mechanisms involving lung extracellular matrix remodeling. Collectively, these data provide evidence for the role of EV in promoting LAM lung metastasis and identify novel EV-dependent mechanisms regulating metastable phenotypes of tumor cells. Clinical impact of research is that it establishes LAM pathway as novel target for LAM therapy.

Implementability and impact in clinical research and the role of clinical trial networks.

Implementability and impact in clinical research and the role of clinical trial networks.

Identification errors in medical research: Privacy at all costs?

In laboratory medicine, a misidentified patient sample can lead to an incorrect tissue diagnosis, a potentially fatal blood transfusion error or other serious adverse events. Although well characterised in routine patient care, the overall impacts of misidentification errors in the clinical research setting are less conspicuous but potentially greater, with downstream effects that may extend beyond care at an individual level. When data discrepancies or queries arise in clinical trial data then a data clarification form (DCF) is issued to the researcher by the overseeing trial coordinator or sponsor. Higher rates of DCF's are sometimes used as a crude surrogate marker of poorer trial quality. However, data is scarce on misidentification rates in clinical trials. In five clinical trials involving 822 histology or blood specimens analysed by our pathology department, DCF's were issued for 21% (174) of specimens. Amongst these 67% (117 / 174) were related to sample identification. Although these errors were recognised before data was compromised or an adverse event occurred, they highlight an alarming lack of stringency of use of patient identifiers in the research setting. We therefore propose the use of an appropriate number of de-identified data points and a formalised specimen accession process as employed in routine care to mitigate misidentification errors and their impact in clinical research. Increased recognition in the research community of the likely effect of truncating or reducing the number of patient identifiers is needed to minimise misidentification errors in the research setting.

Harnessing the nursing and midwifery workforce to boost Australia's clinical research impact.

Harnessing the nursing and midwifery workforce to boost Australia's clinical research impact.

Impact of clinical research on public health policy of neonatal screening for congenital heart disease in China

Impact of clinical research on public health policy of neonatal screening for congenital heart disease in China

Clinical Impact of Research: Introduction to the Forum

Purpose:In communication sciences and disorders (CSD), clinical impact is paramount. Without clinical impact of research, evidence-based practice is not possible. Unfortunately, clinical impact of research is often overlooked or underappreciated. The purpose of this forum is to provide guidance to current and future CSD researchers about how to maximize the clinical impact of their research. A secondary purpose is to increase researchers’ and their institutions’ awareness of the importance of clinical impact and the need to document it for tenure and promotion. The prologue introduces four articles that cover topics related to clinical practice research, knowledge brokering, disseminating via social media, and documenting impact for promotion.

How to Document Scientific and Clinical Impact of Research: Six Steps to Success

Purpose: The purpose of this article was to provide a brief tutorial about impact metrics and how to use these metrics to document scientific impact. In addition, examples are provided that describe possible ways to document clinical impact of research. Method: We briefly introduce traditional bibliometrics for journals (e.g., impact factor), articles (e.g., citation counts), and authors (e.g., h index). We describe alternative metrics (i.e., altmetrics) that focus on other types of dissemination metrics such as usage (e.g., downloads, reads, and views), engagement (e.g., comments, shares, and replies), and attention (e.g., Altmetric Attention Score). We also discuss how these metrics are used by others to make decisions about employment, tenure and promotion, funding, and the like. We detail six steps to documenting the scientific and clinical impact of your research. Steps include establishing an ORCID (Open Researcher and Contributor ID) account, creating research profiles on academic platforms, engaging in broad dissemination activities, harvesting bibliometric and altmetric data, adding bibliometric and altmetric information to your curriculum vitae (CV), and submitting your promotion and tenure portfolio with confidence. Examples for ways in which scientific impact and clinical impact of research can be documented on your CV are provided. Results: Readers will have an introductory understanding of bibliometrics and altmetrics and how these data may be used in the evaluation of their research impact and reach. Some strategies are offered as means to increase scientific and clinical impact of research using research profiles on academic platforms. Others are suggestions for documenting scholarly activity aimed to increase clinical impact of research. Conclusions: Creating profiles with organizations that document scientific impact and engaging in the research community using less traditional methods may allow researchers to achieve a broader reach and greater clinical impact for their research. Documenting such research outputs should facilitate a researcher's career advancement.

Creating Clinical Research Impact Through Social Media: Five Easy Steps to Get Started

Purpose: The purpose of this tutorial is to provide a brief tutorial on how to get started with creating clinical impact of research through the use of varied social media channels. Method: We summarize the literature and provide examples for creating social media content for clinical research impact. Results: We describe a five-step process for creating clinical impact: (a) identify your goal or purpose of creating impact for a specific project, (b) identify your target audience and match it to a social media platform, (c) study benchmark posts made by active users and influencers on your social media outlet of interest, (d) create novel and engaging content, and (e) share content and track responses. Conclusions: Creating clinical impact of research through social media does not have to be overwhelming or time consuming and is an important part of disseminating research evidence to help bridge the research-to-practice gap. Readers will be able to share their work on social media to begin creating clinical impact because of reading this tutorial.

Recent advances of HCI in decision-making tasks for optimized clinical workflows and precision medicine.

The ever-increasing amount of biomedical data is enabling new large-scale studies, even though ad hoc computational solutions are required. The most recent Machine Learning (ML) and Artificial Intelligence (AI) techniques have been achieving outstanding performance and an important impact in clinical research, aiming at precision medicine, as well as improving healthcare workflows. However, the inherent heterogeneity and uncertainty in the healthcare information sources pose new compelling challenges for clinicians in their decision-making tasks. Only the proper combination of AI and human intelligence capabilities, by explicitly taking into account effective and safe interaction paradigms, can permit the delivery of care that outperforms what either can do separately. Therefore, Human-Computer Interaction (HCI) plays a crucial role in the design of software oriented to decision-making in medicine. In this work, we systematically review and discuss several research fields strictly linked to HCI and clinical decision-making, by subdividing the articles into six themes, namely: Interfaces, Visualization, Electronic Health Records, Devices, Usability, and Clinical Decision Support Systems. However, these articles typically present overlaps among the themes, revealing that HCI inter-connects multiple topics. With the goal of focusing on HCI and design aspects, the articles under consideration were grouped into four clusters. The advances in AI can effectively support the physicians' cognitive processes, which certainly play a central role in decision-making tasks because the human mental behavior cannot be completely emulated and captured; the human mind might solve a complex problem even without a statistically significant amount of data by relying upon domain knowledge. For this reason, technology must focus on interactive solutions for supporting the physicians effectively in their daily activities, by exploiting their unique knowledge and evidence-based reasoning, as well as improving the various aspects highlighted in this review.

The impact of clinical research and evidence-based practice in occupational therapy

The impact of clinical research and evidence-based practice in occupational therapy

Trauma training: Competencies, initiatives, and resources.

Traumatic stress is currently not a required component of the standard curricula in graduate-level education in clinical and counseling psychology. However, due to the high prevalence of trauma and its potentially deleterious physical and mental health effects in the general and clinical populations, it is imperative that psychology graduate students and practitioners understand the relevance of trauma in their clients' lives and its impact in clinical research. A comprehensive model of trauma-focused empirically informed competencies (knowledge, skills, and attitudes) was developed at a national consensus conference in 2013 and approved by the American Psychological Association in 2015 as part of that organization's education and training policy. These trauma competencies predated the American Psychological Association's Posttraumatic Stress Disorder Guidelines, and provided consensus about the scientific, theoretical, ethical, and professional foundational knowledge, skills, and attitudes for all trauma-informed professional practice, not solely treatment. The two endeavors are related and potentially synergistic, but separate. Intended to guide training programs' curriculum development and psychologists' self-monitoring, the trauma competencies serve as aspirational goals for psychologists. Training issues in these and other trauma competencies are discussed. Perhaps, most importantly, the scientific literature on trauma is constantly evolving, and thus embracing an ever-evolving curriculum and lifelong-learning approach is essential. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

Direct impact of clinical research in metastatic renal cell carcinoma (mRCC): A cost-effectiveness analysis of patient care outcomes and cost savings in a real-life scenario of a large public university hospital in Spain.

637 Background: Public health sustainability is a major concern worldwide. Clinical research is considered to leverage patient care outcome but also can lead costs savings and, subsequently, to maintain public health system. Thus, we analyzed the direct impact of clinical research in terms of improvement in clinical outcomes and costs related to research in patients with mRCC in real-life setting. Methods: We retrospectively collected data related to overall survival (OS) and direct health care costs from all mRCC patients who were treated with oral anti-tumour medication and followed at the Medical Oncology Department of Ramón y Cajal University Hospital in Madrid, between January 2010 and February 2017. A statistical analysis comparing the outcomes of patients included in clinical trials versus those not included was conducted. Results: In the study period, 65 patients were newly diagnosed with mRCC and received treatment. Those patients included in clinical trials showed higher median OS (91 vs. 29 months. HR 0.389; 95% CI: 0.150: 1.000; p=0.04). Median direct cost per mRCC patient in ‘real-life’ was €67,376. Median cost for a patient enrolled in at least one clinical trial was €53,673 vs. €63,834 for those who were never recruited for a trial. Participation in clinical trials contributed to a decrease in total health expenditure by 9.02% (€362,367), mainly due to reduction in the cost of medications and diagnostic tests (91.46% vs. 8.54%, respectively). Furthermore, clinical trial participants have required less number of hospitalizations (0.08 vs. 1.75) and emergency visits (0.39 vs. 3.2) per patient. Conclusions: Under the public health system perspective, participation in clinical trials is related to an improvement in overall survival as well as direct and indirect cost savings in mRCC patients.

Measuring regional impact: The case for bigger data

Measuring regional impact: The case for bigger data

Lipid fingerprint image accurately conveys human colon cell pathophysiologic state: A solid candidate as biomarker

Membrane lipids are gaining increasing attention in the clinical biomarker field, as they are associated with different pathologic processes such as cancer or neurodegenerative diseases. Analyzing human colonoscopic sections by matrix assisted laser/desorption ionization (MALDI) mass spectrometry imaging techniques, we identified a defined number of lipid species changing concomitant to the colonocyte differentiation and according to a quite simple mathematical expression. These species felt into two lipid families tightly associated in signaling: phosphatidylinositols and arachidonic acid-containing lipids. On the other hand, an opposed pattern was observed in lamina propria for AA-containing lipids, coinciding with the physiological distribution of the immunological response cells in this tissue. Importantly, the lipid gradient was accompanied by a gradient in expression of enzymes involved in lipid mobilization. Finally, both lipid and protein gradients were lost in adenomatous polyps. The latter allowed us to assess how different a single lipid species is handled in a pathological context depending on the cell type. The strict patterns of distribution in lipid species and lipid enzymes described here unveil the existence of fine regulatory mechanisms orchestrating the lipidome according to the physiological state of the cell. In addition, these results provide solid evidence that the cell lipid fingerprint image can be used to predict precisely the physiological and pathological status of a cell, reinforcing its translational impact in clinical research.

Quo vadis computational analysis of PPI data or why the future isn't here yet.

Proteins have been proven to be among the most significant cellular molecules as they participate in most cellular functionalities. Researchers have deployed a variety of experimental methods for the identification of Protein–Protein Interactions (PPIs). The emergence of high-throughput experimental techniques for the prediction of PPIs, revealed a wide range of PPIs in many organisms. This information alongside with information from small scale experimental techniques has been stored in public available databases and repositories. It is well-known that experimental data include many false positive predictions and provide only low coverage on the full interactomes. This fact has led to the design and development of many computational methods for the prediction of PPIs (Theofilatos et al., 2011). The experimental PPI data have been extensively used in many studies during the last decades and their availability gave a significant boost in training new algorithmic models for the prediction of PPIs and in the overall analysis of PPI data. Despite the promising results of algorithmic solutions for PPIs' analysis which fostered molecular biology research, in our opinion the research on computational methods for analyzing PPI data has been recently stagnated. Using the online tool MLtrends introduced by Palidwor and Andrade-Navarro (2010) in a preliminary investigation we have observed that the publications related to the search term “protein–protein interactions” AND analysis (abstract and title were searched for this term) present a constant increase in absolute numbers. However, when applying normalization by dividing with the total number of annual publications, we observe a relatively stable percentage of publications related PPI analysis in the last decade. In contrast, systems biology publications present a big positive slope in the last decade even when normalized by the total number of annual publications. This diversification shows that even if the actual total number of publications related to PPIs analysis is increasing as the total number of scientific journals is increasing in the last decade, their total impact on the systems biology domain is decreasing. Additionally, only a few PPI based research works have been published lately with significant impact in clinical research and translational bioinformatics. In this paper, first we summarize the developments on computational analysis of PPI data and second, we present our belief about the future of PPI data analysis emphasizing in presenting the constraints that have delayed the transition from the current methodologies to a holistic bioinformatics approach, for linking biological and clinical data. Specific solutions are also proposed for all these constraints in order to achieve the optimal exploitation of PPI bioinformatics' approaches.

Autoantibodies to cardiac troponin in patients after renal transplant: A new target for statins?

o o d n i s h r a t There have been few studies on the immune response to myocardial damage and their findings are of little practical value. Even in myocardial infarction (MI), the classic example of myocardial injury not due to inflammation or infection, the clinical impact of research is limited. An example of what we would hope for from such research, with potential application to clinical practice, is the recent publication of an interesting study by Savukoski et al., who have developed a new immunoassay for troponin measurement that is free from interference from circulating anti-troponin autoantibodies.1 In another study by the same group, the prevalence of troponin-specific autoantibodies in a cohort of 510 patients with suspected MI was 9.2%, but they found no correlation with 12-month outcome.2 The practical relevance of this observation is that the presence of these antibodies can lead to false negative readings for serum troponin in patients with MI and thus delay diagnosis. The presence of these antibodies has also been reported in apparently healthy individuals. Adamczyk et al.3 reported

Impact on Rehabilitation Effect of Post-Stroke Abnormal Movement Pattern Prevented and Treated with Multi-Needle Puncture of Scalp-Points

The Journal of Alternative and Complementary MedicineVol. 20, No. 5 Clinical ResearchImpact on Rehabilitation Effect of Post-Stroke Abnormal Movement Pattern Prevented and Treated with Multi-Needle Puncture of Scalp-PointsHuimin ZhangHuimin ZhangSearch for more papers by this authorPublished Online:7 May 2014https://doi.org/10.1089/acm.2014.5124.abstractAboutSectionsView articleView Full TextPDF/EPUB Permissions & CitationsPermissionsDownload CitationsTrack CitationsAdd to favorites Back To Publication ShareShare onFacebookTwitterLinked InRedditEmail View article"Impact on Rehabilitation Effect of Post-Stroke Abnormal Movement Pattern Prevented and Treated with Multi-Needle Puncture of Scalp-Points." The Journal of Alternative and Complementary Medicine, 20(5), pp. A47–A48FiguresReferencesRelatedDetailsCited byAcupuncture for stroke rehabilitation26 August 2016 | Cochrane Database of Systematic Reviews, Vol. 2016, No. 8 Volume 20Issue 5May 2014 InformationCopyright 2014, Mary Ann Liebert, Inc.To cite this article:Huimin Zhang.Impact on Rehabilitation Effect of Post-Stroke Abnormal Movement Pattern Prevented and Treated with Multi-Needle Puncture of Scalp-Points.The Journal of Alternative and Complementary Medicine.May 2014.A47-A48.http://doi.org/10.1089/acm.2014.5124.abstractPublished in Volume: 20 Issue 5: May 7, 2014PDF download

Ductal Carcinoma In Situ of the Breast: Can Biomarkers Improve Current Management?

Screening for invasive cancer has led to a marked increase in the detection of ductal carcinoma in situ (DCIS). DCIS is, if appropriately managed, a low-risk disease which has a small chance of impacting on patient life expectancy. However, despite significant advances in prognostic marker development in invasive breast cancer, there are no validated diagnostic assays to inform treatment choice for women with DCIS. Therefore we are unable to target effective treatment strategies to women at high risk and avoid over-treatment of women at low risk of progression to invasive breast cancer. Paradoxically, one effect of this uncertainty is undertreatment of some women. We review current practice and research in the field to identify key challenges in the management of DCIS. The impact of clinical research, particularly on the over and undertreatment of women with DCIS is assessed. We note slow progress toward development of diagnostic biomarkers and highlight key opportunities to accelerate advances in this area. DCIS is a low-risk disease, its incidence is increasing, and current treatment is effective. However, many women are either over- or undertreated. Despite repeated calls for development of diagnostic biomarkers, progress in this area has been slow, reflecting a relative lack of investment of research effort and funding. Given the low event rate in treated patients and the lateness of recurrences, many previous studies have only limited power to identify independent prognostic and predictive biomarkers. However, the potential for such biomarkers to personalize treatment for DCIS is extremely high.

Improving the impact of clinical research: A systematic analysis of kidney cancer trials.

6051 Background: Clinical trials are essential to advancing cancer care, but the means to evaluate and improve the portfolio have been lacking. This study analyzes the renal cell carcinoma (RCC) trial portfolio using the database for the Aggregate Analysis of ClinicalTrials.gov. As RCC is an area of promise, what insights can an evaluation of the portfolio provide? Methods: 40,970 clinical studies registered with ClinicalTrials.gov between October 2007 and September 2010 were aggregated by specialty using MeSH terms and submitted conditions. 8,942 oncology trials were identified and categorized by cancer type. 108 trials opening in October 2007 or later were identified as evaluating treatments for RCC. Descriptive statistics were used to characterize trial design, study agent(s), accrual and sponsorship. Results: 52 trials (48%) assessed agents already recommended as 1st or 2nd-line treatments in the National Comprehensive Cancer Network (NCCN) RCC Guidelines at the time of study initiation and 19 (18%) studied other FDA-approved treatments. 37 trials (34%) included a novel (non-FDA approved) agent or vaccine. Total anticipated or actual accrual was 12,753, with 50% accrued/accruing to trials of only NCCN agents, 34% to novel agents and 16% to other FDA-approved agents. Industry was identified as a sponsor or collaborator in 48% of trials assessing only NCCN agents, 73% of novel agent trials and 53% of other FDA-approved agent trials. As shown in the table, a minority of trials were randomized, blinded or late-phase (Phase III or IV), regardless of approval status of study agent. Conclusions: The majority of new studies and accrual in RCC assess questions of treatment sequence and setting for established therapies, many of which lack rigorous design. Across the portfolio, studies are predominantly industry sponsored. Optimizing clinical research includes increasing studies of novel therapeutics and improving the comparative effectiveness research portfolio by increasing utilization of pragmatic designs, registries and late-phase programs. [Table: see text]

Revisiting the Benefits Debate: Does Qualitative Social Work Research Produce Salubrious Effects?

Social work discussion about the intersection of therapy and research has been heated. There is ongoing theoretical debate about the fit of qualitative research and social work practice, as well as the proper goals and potential impact of clinical research. In this article, two qualitative researchers report empirical findings and discuss the implications of their research participants' unsolicited identification of the benefits of engaging in intensive interviews. These benefits include therapeutic benefits such as telling a story to an interested, empathic researcher and experiencing social validation in having one's story accepted; a sense that participants' experiences may be joined with others through the research process to create or enhance voices of stigmatized or marginalized populations; and the possibility that the research, through publication and dissemination, will influence individuals and society to become aware of the social justice and political implications of research participants' experiences.