Lesson plan analysis protocol (LPAP): A useful tool for researchers and educational evaluators.
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ABSTRACT:
SUBMITTER: Ndihokubwayo K
PROVIDER: S-EPMC8760448 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BackgroundThe Publons platform provides integrated information on researchers, peer reviewers, publications and certain author metrics. Central Asia is a potentially growing region in terms of young researchers.MethodsUsing the inbuilt Publons search, the top institutes of nine countries of Central Asia and neighbours were identified and data on their reviewers, number of publications, number of peer reviews completed were extracted. These were compared with demographics of the countries such as population, gross domestic product, number of physicians and proportion of population enrolled for higher education.ResultsAmongst the top 15 institutes in Central Asia, China has claim to 12 while Kazakhstan has two and Iran has one. The number of top peer reviewers, number of verified reviews and Web of Science indexed publications from these top institutes varied directly with the number of researchers each had. Afghanistan, Tajikistan and Turkmenistan are not performing well on most of these while China seems to be an outlier on the upper edge of the graphs. There is good correlation between the number of researchers in the top institutes per country and both number of publications and number of completed reviews. The number of total publications per top ten institutes of each country has high correlation with various demographic parameters like total population (Spearman rho, ρ = 0.85), gross domestic product (ρ = 0.82), total number of physicians (ρ = 0.72), and number enrolled for higher education (ρ = 0.93).ConclusionThere appears to be much disparity among the rankings, number of researchers, reviewers and published manuscripts across various countries in Central Asia. The gross heterogeneity of Central Asia needs to be minimized by nurturing and mentoring potentially upcoming researchers in publication, peer reviewing as well as in ethics involved.
| S-EPMC8167409 | biostudies-literature
Project description:The regulatory review and approval process is a significant part of the workflow associated with initiating clinical and translational research projects. Ambiguity concerning submission requirements and expected times associated with the review process can create additional work for research teams and ultimately delay important scientific projects. In an effort to provide assistance to investigators, we have developed an online interactive interface which elicits basic study characteristics for a single project and subsequently produces a list of required administrative applications needed for approval along with clear instructions concerning expectations from the research team. This system, the Vanderbilt Customized Action Plan (V-CAP), was launched in October 2006 and has been well received by researchers. In this article, the authors present (1) the V-CAP project design, (2) a reference workflow associated with Vanderbilt policies and regulations, (3) the V-CAP metrics of use by Vanderbilt research teams, and (4) a list of recommendations for other academic centers considering a similar systems-based approach for helping researchers efficiently navigate processes related to regulatory approval. The informatics-systems-based approach of the V-CAP is scalable to other academic medical centers and may serve as a useful model to help researchers navigate the complexity of the regulatory approvals process.
| S-EPMC3767144 | biostudies-literature
Project description:BackgroundSince the start of the COVID-19 pandemic, efforts have been made to develop early warning risk scores to help clinicians decide which patient is likely to deteriorate and require hospitalization. The RECAP (Remote COVID-19 Assessment in Primary Care) study investigates the predictive risk of hospitalization, deterioration, and death of patients with confirmed COVID-19, based on a set of parameters chosen through a Delphi process performed by clinicians. We aim to use rich data collected remotely through the use of electronic data templates integrated in the electronic health systems of several general practices across the United Kingdom to construct accurate predictive models. The models will be based on preexisting conditions and monitoring data of a patient's clinical parameters (eg, blood oxygen saturation) to make reliable predictions as to the patient's risk of hospital admission, deterioration, and death.ObjectiveThis statistical analysis plan outlines the statistical methods to build the prediction model to be used in the prioritization of patients in the primary care setting. The statistical analysis plan for the RECAP study includes the development and validation of the RECAP-V1 prediction model as a primary outcome. This prediction model will be adapted as a three-category risk score split into red (high risk), amber (medium risk), and green (low risk) for any patient with suspected COVID-19. The model will predict the risk of deterioration and hospitalization.MethodsAfter the data have been collected, we will assess the degree of missingness and use a combination of traditional data imputation using multiple imputation by chained equations, as well as more novel machine-learning approaches to impute the missing data for the final analysis. For predictive model development, we will use multiple logistic regression analyses to construct the model. We aim to recruit a minimum of 1317 patients for model development and validation. We will then externally validate the model on an independent dataset of 1400 patients. The model will also be applied for multiple different datasets to assess both its performance in different patient groups and its applicability for different methods of data collection.ResultsAs of May 10, 2021, we have recruited 3732 patients. A further 2088 patients have been recruited through the National Health Service Clinical Assessment Service, and approximately 5000 patients have been recruited through the DoctalyHealth platform.ConclusionsThe methodology for the development of the RECAP-V1 prediction model as well as the risk score will provide clinicians with a statistically robust tool to help prioritize COVID-19 patients.Trial registrationClinicalTrials.gov NCT04435041; https://clinicaltrials.gov/ct2/show/NCT04435041.International registered report identifier (irrid)DERR1-10.2196/30083.
| S-EPMC8494068 | biostudies-literature
Project description:BackgroundTimely detection and management of gestational diabetes mellitus (GDM) have been identified as a high priority for policymakers in low- and middle-income countries (LMICs). The GUIDES trial will evaluate a package of three interconnected film-based interventions aimed at improving the timely detection and management of GDM. The protocol for this trial has previously been published; this publication outlines the statistical analysis plan for the trial.Methods and designThe GUIDES study is a multi-country cluster-randomised controlled trial consisting of one trial conducted in Uganda and one in India (30 clusters in each country). Mixed effects models will be used to compare the primary study outcomes of the proportion of women who are tested for GDM between 24 and 32 weeks of pregnancy and the mean fasting blood sugar of women with GDM at 34-week follow-up while accounting for clustering. Secondary analyses will compare the proportion of women with self-reported GDM diagnosis at 32 weeks of pregnancy and the proportion of women with adverse perinatal outcomes related to GDM up to 4 weeks after birth in each trial arm.Trial status and discussionFollow-up is expected to end in March 2023 in Uganda and in May 2023 in India. Analyses will be carried out following this statistical analysis plan in the month following trial completion.Trial registrationClinicalTrials.gov NCT03937050. Registered on 3 May 2019. Clinical Trials Registry India CTRI/2020/02/023605. Registered on 26 February 2020.
| S-EPMC10422739 | biostudies-literature
Project description:Thirty years ago, the type 1 ribosome-inactivating protein (RIP) saporin-S6 (also known as saporin) was isolated from Saponaria officinalis L. seeds. Since then, the properties and mechanisms of action of saporin-S6 have been well characterized, and it has been widely employed in the construction of conjugates and immunotoxins for different purposes. These immunotoxins have shown many interesting results when used in cancer therapy, particularly in hematological tumors. The high enzymatic activity, stability and resistance to conjugation procedures and blood proteases make saporin-S6 a very useful tool in cancer therapy. High efficacy has been reported in clinical trials with saporin-S6-containing immunotoxins, at dosages that induced only mild and transient side effects, which were mainly fever, myalgias, hepatotoxicity, thrombocytopenia and vascular leak syndrome. Moreover, saporin-S6 triggers multiple cell death pathways, rendering impossible the selection of RIP-resistant mutants. In this review, some aspects of saporin-S6, such as the chemico-physical characteristics, the structural properties, its endocytosis, its intracellular routing and the pathogenetic mechanisms of the cell damage, are reported. In addition, the recent progress and developments of saporin-S6-containing immunotoxins in cancer immunotherapy are summarized, including in vitro and in vivo pre-clinical studies and clinical trials.
| S-EPMC3813907 | biostudies-literature
Project description:Medical research has a heavy and continuing demand for rodent models across a range of disciplines. Behavioural assessment of pain in such models is highly time consuming, thus limiting the number of models and analgesics that can be studied. Facial expressions are widely used to assess pain in human infants. Recently the mouse grimace scale (MGS) has been developed and shown to be accurate and reliable, requiring only a short amount of training for the observer. This system therefore has the potential to become a highly useful tool both in pain research and clinical assessment of mouse pain. To date, the MGS has only been used as a research tool, however there is increasing interest in its use in cage-side clinical assessment. It is often wrongly assumed that MGS scores of animals not in pain (i.e. at baseline) are zero. Here, we aimed to assess the variability in baseline MGS scores between cohorts, sexes and strains of mice. Establishing the presence of a consistent baseline MGS score could lead to a valuable clinical pain assessment tool for mice when baseline information from the individual mouse may not be available as a comparator. Results demonstrated a significant difference in baseline MGS scores between both sexes (males > females) and strains of mice. The method used to score the facial action units (Live vs. retrospectively from still images) demonstrated significant differences in scores with live scores being significantly lower than retrospective scoring from images. The level of variation shown demonstrates the need for further research to be undertaken with regard to establishing baseline MGS scores for specific strains and sexes of mice, taking into account the method of scoring, prior to considering clinical implementation of this method in pain assessment.
| S-EPMC4583436 | biostudies-literature
Project description:BackgroundInadequate sustainability of implementation of evidence-based interventions has led to calls for research on how sustainability can be optimized. To advance our understanding of intervention sustainability, we explored how implementation researchers conceptualized and planned for the sustainability of their implemented interventions with studies funded by the United States (US) National Institutes of Health (NIH).MethodsWe used sequential, mixed methods to explore how researchers conceptualized and planned for the sustainability of the health interventions using (1) a document review of all active and completed US NIH R01 Grants and Equivalents reviewed within the Dissemination and Implementation Research in Health (DIRH) Study Section between 2004 and 2016 and (2) a qualitative content analysis of semi-structured interviews with NIH R01 DIRH grant recipients.ResultsWe found 277 R01 profiles within the DIRH study section listed on the US NIH RePORTER website including 84 that were eligible for screening. Of the 84 unique projects, 76 (90.5%) had primary implementation outcomes. Of the 76 implementation project profiles, 51 (67.1%) made references to sustainability and none referred to sustainability planning. In both profiles and interviews, researchers conceptualized sustainability primarily as the continued delivery of interventions, programs, or implementation strategies. Few researchers referenced frameworks with sustainability constructs and offered limited information on how they operationalized frameworks. Researchers described broad categories of approaches and strategies to promote sustainability and key factors that may influence researchers to plan for sustainability, such as personal beliefs, self-efficacy, perception of their role, and the challenges of the grant funding system.ConclusionsWe explored how US NIH R01 DIRH grant recipients conceptualized and planned for the sustainability of their interventions. Our results identified the need to test, consolidate, and provide guidance on how to operationalize sustainability frameworks, and to develop strategies on how funders and researchers can advance sustainability research.
| S-EPMC6506963 | biostudies-literature
Project description:Recent advancements in contact map-based protein three-dimensional (3D) structure prediction have been driven by the evolution of deep learning algorithms. However, the gap in accessible software tools for novices in this domain remains a significant challenge. This study introduces GoFold, a novel, standalone graphical user interface (GUI) designed for beginners to perform contact map overlap (CMO) problems for better template selection. Unlike existing tools that cater more to research needs or assume foundational knowledge, GoFold offers an intuitive, user-friendly platform with comprehensive tutorials. It stands out in its ability to visually represent the CMO problem, allowing users to input proteins in various formats and explore the CMO problem. The educational value of GoFold is demonstrated through benchmarking against the state-of-the-art contact map overlap method, map_align, using two datasets: PSICOV and CAMEO. GoFold exhibits superior performance in terms of TM-score and Z-score metrics across diverse qualities of contact maps and target difficulties. Notably, GoFold runs efficiently on personal computers without any third-party dependencies, thereby making it accessible to the general public for promoting citizen science. The tool is freely available for download for macOS, Linux, and Windows.
| S-EPMC10982686 | biostudies-literature
Project description:BackgroundEpidemiologic and medical studies often rely on evaluators to obtain measurements of exposures or outcomes for study participants, and valid estimates of associations depends on the quality of data. Even though statistical methods have been proposed to adjust for measurement errors, they often rely on unverifiable assumptions and could lead to biased estimates if those assumptions are violated. Therefore, methods for detecting potential 'outlier' evaluators are needed to improve data quality during data collection stage.MethodsIn this paper, we propose a two-stage algorithm to detect 'outlier' evaluators whose evaluation results tend to be higher or lower than their counterparts. In the first stage, evaluators' effects are obtained by fitting a regression model. In the second stage, hypothesis tests are performed to detect 'outlier' evaluators, where we consider both the power of each hypothesis test and the false discovery rate (FDR) among all tests. We conduct an extensive simulation study to evaluate the proposed method, and illustrate the method by detecting potential 'outlier' audiologists in the data collection stage for the Audiology Assessment Arm of the Conservation of Hearing Study, an epidemiologic study for examining risk factors of hearing loss in the Nurses' Health Study II.ResultsOur simulation study shows that our method not only can detect true 'outlier' evaluators, but also is less likely to falsely reject true 'normal' evaluators.ConclusionsOur two-stage 'outlier' detection algorithm is a flexible approach that can effectively detect 'outlier' evaluators, and thus data quality can be improved during data collection stage.
| S-EPMC10391872 | biostudies-literature
Project description:The ability to plan for future events is one of the defining features of human intelligence. Whether non-human animals can plan for specific future situations remains contentious: despite a sustained research effort over the last two decades, there is still no consensus on this question. Here, we show that New Caledonian crows can use tools to plan for specific future events. Crows learned a temporal sequence where they were (a) shown a baited apparatus, (b) 5 min later given a choice of five objects and (c) 10 min later given access to the apparatus. At test, these crows were presented with one of two tool-apparatus combinations. For each combination, the crows chose the right tool for the right future task, while ignoring previously useful tools and a low-value food item. This study establishes that planning for specific future tool use can evolve via convergent evolution, given that corvids and humans shared a common ancestor over 300 million years ago, and offers a route to mapping the planning capacities of animals.
| S-EPMC7735258 | biostudies-literature