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The authors propose a blockchain platform for managing clinical trial data to enhance data validity, integrity, trust and transparency in the pharmaceutical research process. The authors also provide an extensive review of how blockchain technology supports the business processes of clinical trials.

A systematic literature review was conducted to identify the existing applications of blockchain in pharmaceutical process management. A conceptual design for a blockchain infrastructure to address clinical trial challenges is developed by outlining the entire clinical trial value chain and identifying the coordination and communication among its stakeholders. A stakeholder analysis is conducted to ensure that the clinical trial processes satisfy the requirements and preferences of each stakeholder.

The proposed blockchain platform offers a promising solution for enhancing integrity, trust and transparency in the clinical trial process. Additionally, blockchain can help streamline communication and collaboration between stakeholders by enabling multiple parties to access and share data in real time, lowering the possibility of delays or errors in data analysis and reporting.

The proposed blockchain platform can benefit patients by empowering them to have better-controlled access to their data and by allowing researchers to maintain adherence to reporting requirements. Additionally, the platform can benefit granting agencies, researchers and decision-makers by ensuring the integrity of clinical trial data and streamlining communication and collaboration between stakeholders.

This study builds on existing blockchain applications in pharmaceutical process management by developing a blockchain framework that can address clinical trial concerns from an integrated perspective.

The study aims to investigate the utilisation of open research data repositories (RDRs) for storing and sharing research data in higher learning institutions (HLIs) in Tanzania.

A survey research design was employed to collect data from postgraduate students at the Nelson Mandela African Institution of Science and Technology (NM-AIST) in Arusha, Tanzania. The data were collected and analysed quantitatively and qualitatively. A census sampling technique was employed to select the sample size for this study. The quantitative data were analysed using the Statistical Package for the Social Sciences (SPSS), whilst the qualitative data were analysed thematically.

Less than half of the respondents were aware of and were using open RDRs, including Zenodo, DataVerse, Dryad, OMERO, GitHub and Mendeley data repositories. More than half of the respondents were not willing to share research data and cited a lack of ownership after storing their research data in most of the open RDRs and data security. HILs need to conduct training on using trusted repositories and motivate postgraduate students to utilise open repositories (ORs). The challenges for underutilisation of open RDRs were a lack of policies governing the storage and sharing of research data and grant constraints.

Research data storage and sharing are of great interest to researchers in HILs to inform them to implement open RDRs to support these researchers. Open RDRs increase visibility within HILs and reduce research data loss, and research works will be cited and used publicly. This paper identifies the potential for additional studies focussed on this area.

This research aims to explore the research data ethics governance framework and collaborative network to optimize research data ethics governance practices, to balance the relationship between data development and utilization, open sharing, data security and to reduce the ethical risks that may arise from data sharing and utilization.

This study explores the framework and collaborative network of research data ethics policies by using the UK as an example. 78 policies from the UK government, university, research institution, funding agency, publisher, database, library and third-party organization are obtained. Adopting grounded theory (GT) and social network analysis (SNA), Nvivo12 is used to analyze these samples and summarize the research data ethics governance framework. Ucinet and Netdraw are used to reveal collaborative networks in policy.

Results indicate that the framework covers governance context, subject and measure. The content of governance context contains context description and data ethics issues analysis. Governance subject consists of defining subjects and facilitating their collaboration. Governance measure includes governance guidance and ethics governance initiatives in the data lifecycle. The collaborative network indicates that research institution plays a central role in ethics governance. The core of the governance content are ethics governance initiatives, governance guidance and governance context description.

This research provides new insights for policy analysis by combining GT and SNA methods. Research data ethics and its governance are conceptualized to complete data governance and research ethics theory.

A research data ethics governance framework and collaborative network are revealed, and actionable guidance for addressing essential aspects of research data ethics and multiple subjects to confer their functions in collaborative governance is provided.

This study analyzes policy text using qualitative and quantitative methods, ensuring fine-grained content profiling and improving policy research. A typical research data ethics governance framework is revealed. Various stakeholders' roles and priorities in collaborative governance are explored. These contribute to improving governance policies and governance levels in both theory and practice.

The authors explore the under-researched area of post-decision evaluation in PPPs (public–private partnerships), focusing upon how and whether Post-decision Project Evaluation (PdPE) is considered and provided for in United Kingdom (UK) public infrastructure projects.

The authors’ research design sought insights from overviewing UK PPP planning and more focused exploration of PPP operational practice. The authors combine the extensive analysis of planning documents for operational UK PPP projects with interviews of different stakeholders in PPP projects in one city. Mobilising an open critical perspective, documents were analysed using ethnographic content analysis (ECA) and interviews were analysed using thematic analysis consistent therewith. The authors theorise the absence and ambiguities of PdPE drawing on the sociology of ignorance.

The authors find a long-standing absence and lack of PdPE in PPP projects throughout planning and operational practice, reflecting a dynamic, multi-faceted ignorance. Concerning planning practice, the authors’ documentary analysis evidences a trend in PdPE from its absence in the early years (which may indicate some natural or genuine ignorance) to different levels or forms of weak inclusion later. Regarding this inclusion, the authors find strategic ignorance played a substantive role, involving “deliberate engineering” by both public sector and private partners. Interview findings indicate lack of clarity over PdPE and its under-development in PPP practice, deficiencies again suggestive of natural and strategic ignorance.

The authors draw from the sociology of ignorance vis-à-vis accounting's absence and ambiguity in the context of PPP, contributing to an under-researched area.

This study aims to evaluate and summarize the effectiveness of cognitive behavioral therapy (CBT) and internet-based CBT (ICBT) interventions on relapse prevention and severity of symptoms among individuals with major depressive disorder (MDD). CBT is one of the most used and suggested interventions to manage MDD, whereas ICBT is a novel effective proposed approach.

The review was conducted following the preferred reporting items for systematic review and meta-analysis protocol. A comprehensive and extensive search was performed to identify and evaluate the relevant studies about the effectiveness of CBT and ICBT on relapse prevention and severity of symptoms among patients with MDD.

A total of eight research studies met the inclusion criteria and were included in this systematic review. RCT studies were conducted to assess and evaluate the effectiveness of CBT and ICBT on relapse prevention and severity of symptoms among patients with MDD. It has been found that CBT is a well-supported and evidently based effective psychotherapy for managing depressive symptoms and reducing the relapse and readmission rate among patients diagnosed with MDD. The ICBT demonstrated greater improvements in depressive symptoms during major depressive episodes among patients with MDDS. The ICBT program had good acceptability and satisfaction among participants in different countries.

Despite the significant findings from this systematic review, certain limitations should be acknowledged. First, it is important to note that all the studies included in this review were exclusively conducted in the English language, potentially limiting the generalizability of the findings to non-English speaking populations. Second, the number of research studies incorporated in this systematic review was relatively limited, which may have resulted in a narrower scope of analysis. Finally, a few studies within the selected research had small sample sizes, which could potentially impact the precision and reliability of the overall conclusions drawn from this review. The authors recommend that nurses working in psychiatric units should use CBT interventions with patients with MDD.

This paper, a review of the literature gives an overview of CBT and ICBT interventions to reduce the severity of depressive symptoms and prevent patients’ relapse and rehospitalization and shows that CBT interventions are effective on relapse prevention among patients with MDD. In addition, there is still no standardized protocol to apply the CBT intervention in the scope of reducing the severity of depressive symptoms and preventing depression relapse among patients with major depressive disorder. Further research is needed to confirm the findings of this review. Future research is also needed to find out the most effective form and contents of CBT and ICBT interventions for MDD.

CBT is a psychological intervention that has been recommended by the literature for the treatment of major depressive disorder (MDD). It is a widely recognized and accepted approach that combines cognitive and behavioral techniques to assist individuals overcome their depressive symptoms and improve their overall mental well-being. This would speculate that effectiveness associated with several aspects and combinations of different approaches in CBT interventions and the impact of different delivery models are essential for clinical practice and appropriate selection of the interventional combinations.

This systematic review focuses on the various studies that explore the effectiveness of face-to-face CBT and ICBT in reducing depressive symptoms among patients with major depressive disorder. These studies were conducted in different countries such as Iran, Australia, Pennsylvania and the USA.

Health profession education has evolved to incorporate didactic instruction and experiential learning opportunities over time. Constructivism and interprofessional education are essential theoretical concepts that have shaped modern health profession education. However, transformative active learning is an approach that is particularly well suited to the needs of healthcare professionals. By integrating theoretical knowledge with practical skills, transformative active learning helps prepare students for the complex challenges they will face in their future careers and encourages them to become agents of change committed to improving healthcare practice. Saudi National Institute of Health (Saudi NIH) is one of the Ministry of Health's initiatives in the National Transformation Program 2020 to achieve the Kingdom's Vision 2030, as it supports biomedical research in the health sector in Saudi Arabia. One of the mandates of Saudi NIH is to build the research capacity through well-designed educational and training programs through the directory of education and research skills adopting active learning strategies. This chapter aims to communicate the methodological framework of the Education and Research Skills Directory of the SNIH for integrating active learning in the various training programs and initiatives aiming to promote the core learning capabilities with excellence, diversity, diversity, uniqueness, competency, and efficiency values.

This paper aims to delve into the complexities of terminology mapping and annotation, particularly within the context of the COVID-19 pandemic. It underscores the criticality of harmonizing clinical knowledge organization systems (KOS) through a cohesive clinical knowledge representation approach. Central to the study is the pursuit of a novel method for integrating emerging COVID-19-specific vocabularies with existing systems, focusing on simplicity, adaptability and minimal human intervention.

A design science research (DSR) methodology is used to guide the development of a terminology mapping and annotation workflow. The KNIME data analytics platform is used to implement and test the mapping and annotation techniques, leveraging its powerful data processing and analytics capabilities. The study incorporates specific ontologies relevant to COVID-19, evaluates mapping accuracy and tests performance against a gold standard.

The study demonstrates the potential of the developed solution to map and annotate specific KOS efficiently. This method effectively addresses the limitations of previous approaches by providing a user-friendly interface and streamlined process that minimizes the need for human intervention. Additionally, the paper proposes a reusable workflow tool that can streamline the mapping process. It offers insights into semantic interoperability issues in health care as well as recommendations for work in this space.

The originality of this study lies in its use of the KNIME data analytics platform to address the unique challenges posed by the COVID-19 pandemic in terminology mapping and annotation. The novel workflow developed in this study addresses known challenges by combining mapping and annotation processes specifically for COVID-19-related vocabularies. The use of DSR methodology and relevant ontologies with the KNIME tool further contribute to the study’s originality, setting it apart from previous research in the terminology mapping and annotation field.

Because of the importance of having enough sleep in life and health, this study aims to determine the effect of vitamin D supplementation on sleep quality and pregnancy symptoms (primary outcomes) and side effects (secondary outcome).

In this triple-blind randomized controlled clinical trial, 88 pregnant women with gestational age of 8–10 weeks and serum vitamin D concentration less than 30 ng/ml were allocated into vitamin D (n = 44) and control (n = 44) groups by blocked randomization method. The vitamin D group received a 4,000 IU vitamin D pill, and the control group received a placebo pill daily for 18 weeks. Independent t-, Mann–Whitney U and ANCOVA tests were used to analyze the data.

The post-intervention mean (SD: standard deviation) of total sleep quality score in the vitamin D and placebo group were 1.94 (2.1) and 4.62 (1.71), respectively. According to the Mann–Whitney U test, this difference between the two groups was statistically significant (p < 0.001). The mean (SD) of pregnancy symptoms in the vitamin D and placebo groups was 23.95 (16.07) and 26.62 (13.84), respectively, and there was no significant difference between the two groups based on ANCOVA test (p = 0.56). Considerable side effects were not observed in any groups.

This study was conducted due to the contradictory results of the effect of vitamin D on sleep quality and the high prevalence of sleep disorders and pregnancy symptoms.

No study has investigated the effects of different parameters on publication bias in meta-analyses using a machine learning approach. Therefore, this study aims to evaluate the impact of various factors on publication bias in meta-analyses.

An electronic questionnaire was created according to some factors extracted from the Cochrane Handbook and AMSTAR-2 tool to identify factors affecting publication bias. Twelve experts were consulted to determine their opinion on the importance of each factor. Each component was evaluated based on its content validity ratio (CVR). In total, 616 meta-analyses comprising 1893 outcomes from PubMed that assessed the presence of publication bias in their reported outcomes were randomly selected to extract their data. The multilayer perceptron (MLP) technique was used in IBM SPSS Modeler 18.0 to construct a prediction model. 70, 15 and 15% of the data were used for the model's training, testing and validation partitions.

There was a publication bias in 968 (51.14%) outcomes. The established model had an accuracy rate of 86.1%, and all pre-selected nine variables were included in the model. The results showed that the number of databases searched was the most important predictive variable (0.26), followed by the number of searches in the grey literature (0.24), search in Medline (0.17) and advanced search with numerous operators (0.13).

The results of this study can help clinical researchers minimize publication bias in their studies, leading to improved evidence-based medicine.

To the best of the author’s knowledge, this is the first study to model publication bias using machine learning.

The purpose of this study is to collect and assess the evidence available on the effect of non nutritive sweeteners on appetite, weight and glycemic regulation. As a replacement for sugars, non-nutritive sweeteners (NNSs) are widely being used in different food products with the assumption that these would lower calorie intake and help to manage weight and blood sugar levels better. However, studies using animal models have reported that chronic exposure to NNSs leads to increased food consumption, weight gain and insulin resistance.

Evidence was acquired from systematic reviews or meta-analyses (2016–2021) of relevant clinical studies, especially randomized control trials using Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines.

The review showed NNSs exposure did not conclusively induce increased food intake or change in subjective appetite ratings. Appetite biomarkers like ghrelin, gastric inhibitory peptide, C-peptide levels and Peptide YY remained mostly unaffected by NNSs. Meta-analyses of human randomized control studies showed a reduced energy intake and body weight. No significant change was seen in blood glucose levels, post-prandial glycemic or insulin response after consumption of NNSs. Adequate evidence is not available to conclusively say that NNSs influence gut health at doses relevant to human use.

Most studies which are prospective cohort, observational and cross-sectional studies suggest that use of NNSs may promote obesity and metabolic syndrome in adults. Such studies are plagued by confounding variables and reverse causation. Mechanistic evidence is mostly based on in-vitro and in-vivo studies. The same causal pathways may not be operative or relevant in humans.

This review of available literature concludes that to achieve specific public health and clinical goals, the safe use of NNSs for the reduction of intakes of free sugars and energy should be explored. This would be possible by educating the consumer about energy compensation and understanding the nutritional content of artificially sweetened products in terms of calories coming from fat and complex carbohydrates used in the product.

This study was, thus, designed with the objective of examining the usefulness of NNSs in human population, especially with respect to insulin regulation, glycemic control and weight management. Well-designed randomized control trials which control for confounding variables are needed to generate high quality evidence.