Use Of Robust Methodology Research Articles

Bridging the "Know-Do" Gaps in Five Non-Communicable Diseases Using a Common Framework Driven by Implementation Science.

According to the United Nations High-Level Meeting 2018, five non-communicable diseases (NCDs) including cardiovascular diseases, chronic respiratory diseases, diabetes mellitus, cancer, and mental health conditions accounted for two-thirds of global deaths. These five NCDs share five common risk factors including tobacco use, unhealthy diets, physical inactivity, alcohol use, and air pollution. Low- and middle-income countries (LMICs) face larger burden of NCDs than high-income countries (HICs), due to differences in ecological, technological, socioeconomic and health system development. Based on high-level evidence albeit mainly from HICs, the burden caused by NCDs can be reduced by affordable medicines and best practices. However, "know-do" gaps, ie, gaps between what we know in science and what we do in practice, has limited the impact of these strategies, especially in LMICs. Implementation science advocates the use of robust methodologies to evaluate sustainable solutions in health, education and social care aimed at informing practice and policies. In this article, physician researchers with expertise in NCDs reviewed the common challenges shared by these five NCDs with different clinical courses. They explained the principles of implementation science and advocated the use of an evidence-based framework to implement solutions focusing on early detection, prevention and empowerment, supplemented by best practices in HICs and LMICs. These successful stories can be used to motivate policymakers, payors, providers, patients and public to co-design frameworks and implement context-relevant, multi-component, evidence-based practices. In pursuit of this goal, we propose partnership, leadership, and access to continuing care as the three pillars in developing roadmaps for addressing the multiple needs during the journey of a person with or at risk of these five NCDs. By transforming the ecosystem, raising awareness and aligning context-relevant practices and policies with ongoing evaluation, it is possible to make healthcare accessible, affordable and sustainable to reduce the burden of these five NCDs.

Data Classification Methodology for Electronic Noses Using Uniform Manifold Approximation and Projection and Extreme Learning Machine

The classification and use of robust methodologies in sensor array applications of electronic noses (ENs) remain an open problem. Among the several steps used in the developed methodologies, data preprocessing improves the classification accuracy of this type of sensor. Data preprocessing methods, such as data transformation and data reduction, enable the treatment of data with anomalies, such as outliers and features, that do not provide quality information; in addition, they reduce the dimensionality of the data, thereby facilitating the tasks of a machine learning classifier. To help solve this problem, in this study, a machine learning methodology is introduced to improve signal processing and develop methodologies for classification when an EN is used. The proposed methodology involves a normalization stage to scale the data from the sensors, using both the well-known min−max approach and the more recent mean-centered unitary group scaling (MCUGS). Next, a manifold learning algorithm for data reduction is applied using uniform manifold approximation and projection (UMAP). The dimensionality of the data at the input of the classification machine is reduced, and an extreme learning machine (ELM) is used as a machine learning classifier algorithm. To validate the EN classification methodology, three datasets of ENs were used. The first dataset was composed of 3600 measurements of 6 volatile organic compounds performed by employing 16 metal-oxide gas sensors. The second dataset was composed of 235 measurements of 3 different qualities of wine, namely, high, average, and low, as evaluated by using an EN sensor array composed of 6 different sensors. The third dataset was composed of 309 measurements of 3 different gases obtained by using an EN sensor array of 2 sensors. A 5-fold cross-validation approach was used to evaluate the proposed methodology. A test set consisting of 25% of the data was used to validate the methodology with unseen data. The results showed a fully correct average classification accuracy of 1 when the MCUGS, UMAP, and ELM methods were used. Finally, the effect of changing the number of target dimensions on the reduction of the number of data was determined based on the highest average classification accuracy.

Tuning the Magnetically Segregated Nanolayering in Mn–Ni–As Intermetallics

2D materials exhibiting alternating ferro- and non-ferromagnetic layers are highly sought due to their potential for applications. In this work, we demonstrate, through the use of robust methodology, the existence of single-phase, Mn1–xNixAs (0.25 ≤ x ≤ 0.50), with a unique intrinsic nanostructuring where the thickness of MnAs and NiAs nanolayers can be tuned via the composition, x. We describe a periodic variation of nanolayers of edge-sharing MnAs6 and NiAs6 octahedra layers with thicknesses ranging from 1 to 6 octahedra, 2–16 Å. These crystalline structures are modulated and fully described based on 3D electron diffraction and neutron diffraction. Moreover, by X-ray magnetic circular dichroism, an elemental and orbital-specific technique, we probe the 3d magnetic moments of Mn and Ni. We unveil that the nanosegregated MnAs and NiAs layers behave very differently, with the magnetic moment of manganese being one order of magnitude greater than that of nickel. This study opens the path to a completely new class of materials with intrinsic layers of high and low magnetization with potential applications within spintronics.

Clinician certainty with the evidence: In practice, it's complicated

Clinician certainty with the evidence: In practice, it's complicated

Why health policies should be transnational: A case for East Asia Pacific countries

This paper argues that health policies should transcend national boundaries yet should not reach the supranational level. Along with multinational global health efforts, such cross-national health policies are essential to leverage joint efforts by countries learning from their peers that experience similar health system challenges. In our analysis, we used World Bank Health, Nutrition, and Population (HNP) data from 1995 to 2014 for East Asia Pacific (EAP) countries to explore health system comparability across member nations. We applied a hierarchical cluster analysis using Ward's method and a squared Euclidean distance approach to classify 24 EAP countries into four relatively stable clusters based on their (dis)similarities over nine selected health expenditure and health system performance related indicators. One-way analysis of variance (ANOVA) was used to assess the discreteness of the formed clusters. Each cluster had unique characteristics based on the included indicators and health system performance of the member countries. We present transnational health policy recommendations for the EAP region based on both our use of robust methodology and the resulting comparative clusters.

Seeing through Musculoskeletal Tissues: Improving In Situ Imaging of Bone and the Lacunar Canalicular System through Optical Clearing.

In situ, cells of the musculoskeletal system reside within complex and often interconnected 3-D environments. Key to better understanding how 3-D tissue and cellular environments regulate musculoskeletal physiology, homeostasis, and health is the use of robust methodologies for directly visualizing cell-cell and cell-matrix architecture in situ. However, the use of standard optical imaging techniques is often of limited utility in deep imaging of intact musculoskeletal tissues due to the highly scattering nature of biological tissues. Drawing inspiration from recent developments in the deep-tissue imaging field, we describe the application of immersion based optical clearing techniques, which utilize the principle of refractive index (RI) matching between the clearing/mounting media and tissue under observation, to improve the deep, in situ imaging of musculoskeletal tissues. To date, few optical clearing techniques have been applied specifically to musculoskeletal tissues, and a systematic comparison of the clearing ability of optical clearing agents in musculoskeletal tissues has yet to be fully demonstrated. In this study we tested the ability of eight different aqueous and non-aqueous clearing agents, with RIs ranging from 1.45 to 1.56, to optically clear murine knee joints and cortical bone. We demonstrated and quantified the ability of these optical clearing agents to clear musculoskeletal tissues and improve both macro- and micro-scale imaging of musculoskeletal tissue across several imaging modalities (stereomicroscopy, spectroscopy, and one-, and two-photon confocal microscopy) and investigational techniques (dynamic bone labeling and en bloc tissue staining). Based upon these findings we believe that optical clearing, in combination with advanced imaging techniques, has the potential to complement classical musculoskeletal analysis techniques; opening the door for improved in situ investigation and quantification of musculoskeletal tissues.

The trials methodological research agenda: results from a priority setting exercise

BackgroundResearch into the methods used in the design, conduct, analysis, and reporting of clinical trials is essential to ensure that effective methods are available and that clinical decisions made using results from trials are based on the best available evidence, which is reliable and robust.MethodsAn on-line Delphi survey of 48 UK Clinical Research Collaboration registered Clinical Trials Units (CTUs) was undertaken. During round one, CTU Directors were asked to identify important topics that require methodological research. During round two, their opinion about the level of importance of each topic was recorded, and during round three, they were asked to review the group’s average opinion and revise their previous opinion if appropriate. Direct reminders were sent to maximise the number of responses at each round. Results are summarised using descriptive methods.ResultsForty one (85%) CTU Directors responded to at least one round of the Delphi process: 25 (52%) responded in round one, 32 (67%) responded in round two, 24 (50%) responded in round three. There were only 12 (25%) who responded to all three rounds and 18 (38%) who responded to both rounds two and three. Consensus was achieved amongst CTU Directors that the top three priorities for trials methodological research were ‘Research into methods to boost recruitment in trials’ (considered the highest priority), ‘Methods to minimise attrition’ and ‘Choosing appropriate outcomes to measure’. Fifty other topics were included in the list of priorities and consensus was reached that two topics, ‘Radiotherapy study designs’ and ‘Low carbon trials’, were not priorities.ConclusionsThis priority setting exercise has identified the research topics felt to be most important to the key stakeholder group of Directors of UKCRC registered CTUs. The use of robust methodology to identify these priorities will help ensure that this work informs the trials methodological research agenda, with a focus on topics that will have most impact and relevance.