Step In Analysis Research Articles

Quantitative Methodological Approach in Measuring Entrepreneurial Capabilities

Purpose: The paper seeks to develop and validate a comprehensive framework for measuring entrepreneurial capabilities across business contexts and organisational scales. Design/Methodology/Approach: Cross-sectional research involved 254 owners of SMEs from Sukuma and Chagga ethnic groups. The snowball sampling procedure was first adopted to obtain the list of the Sukuma and Chagga owners of SMEs. Then, proportionate stratified sampling was adopted to select a final subject. Data were gathered using a questionnaire, where factor analysis was performed to reduce the number of predictors. A logit model was thereafter performed to establish the influence of predictors on entrepreneurial capabilities. Further, using the Whitney U test, a comparison of ways to preserve drivers of entrepreneurial capabilities between the Sukuma and Chagga owners of SMEs was performed. Findings: Factor analysis reduced 52 predictors into six variables, which were further subjected to a logit model. The findings indicated that social factors, beliefs, norms, attitudes, and values were statistically significant while perception was insignificant. The difference in ways to preserve socio-cultural determinants between the Sukuma and Chagga was statistically significant, and the effect size was very high. Research Limitation: The research was limited by the use of the logit model and the Man Whitney U test. Other econometric models, such as multiple linear regression, Tobit, Probit, etc., should be considered in the future. Practical implications: The approach will help researchers and academicians to quantify socio-cultural studies and perform factor analysis step by step to reduce variables for further analysis using econometric models. Social Implication: This approach will have social implications. Social scientists will be able to forecast the effect of social variables on certain variables under study, thus enabling the setting of priorities during intervention. Originality: This research fills a significant gap in entrepreneurship literature by providing a validated, comprehensive framework for measuring entrepreneurial capabilities across different contexts and scales.

Protocol for the three-dimensional analysis of rodent skeletal muscle.

Confocal imaging is a powerful tool capable of analyzing cellular spatial data within a given tissue. Here, we present a protocol for preparing optically cleared extensor digitorum longus (EDL) skeletal muscle samples suitable for confocal imaging/computational analysis. We describe steps for sample preparation (including perfusion fixation and tissue clearing of muscle samples), image acquisition, and computational analysis, with sample segmentation/3D rendering outlined. This protocol can be applied to characterize various cell types, including muscle satellite cells (muscle stem cells) and capillary endothelial cells within rodent skeletal muscle. For complete details on the use and execution of this protocol, please refer to Verma etal.,1 Verma etal.,2 Karthikeyan etal.,3 and Karthikeyan et al.4.

Microextraction-Single Particle-Inductively Coupled Plasma-Mass Spectrometry for the Direct Analysis of Nanoparticles on Surfaces.

A novel employment of single particle-inductively coupled plasma-mass spectrometry (SP-ICP-MS) was developed, where a microextraction (ME) probe is used to sample nanoparticles from a surface and analyze them in a single analytical step. The effects of several parameters on the performance of ME-SP-ICP-MS were investigated, including the flow rate, choice of carrier solution, particle size, and the design of the microextraction probe head itself. The optimized ME-SP-ICP-MS technique was used to compare the extraction efficiency (EE, defined as the ratio of particles measured to particles deposited on the surface) of the commercial probe head to a newly designed SP polyether ether ketone (PEEK) probe head. The SP PEEK probe head was found to have increased EE compared to the commercial probe head (8.5 ± 3% vs 3.9 ± 3%, respectively). Increasing the carrier solution flow rate was found to decrease the total analysis time at the cost of decreasing EE. Extraction efficiencies for ME-SP-ICP-MS were typically 4-10%, which is similar to transport efficiencies (1-10%) for conventional SP-ICP-MS. Lastly, ME-SP-ICP-MS was employed for the analysis of nano- and microparticles. The sizes of gold nanoparticles, 30 ± 3 and 51 ± 1.9 nm (certified sizes), and iron-based microparticles, 1000 ± 50 nm (certified size), were accurately determined to be 32.2 ± 2.5, 50.8 ± 3.4, and 1030 ± 57 nm, respectively, by ME-SP-ICP-MS. This work demonstrates the potential of ME-SP-ICP-MS for the direct analysis of particles on common collection surfaces (GSR tabs, carbon planchettes, etc.) while retaining spatial information on particle distribution across the surface.

Recent advances in centrifugal microfluidics for point-of-care testing.

Point-of-care testing (POCT) holds significant importance in the field of infectious disease prevention and control, as well as personalized precision medicine. The emerging microfluidics, capable of minimal reagent consumption, integration, and a high degree of automation, play a pivotal role in POCT. Centrifugal microfluidics, also termed lab-on-a-disc (LOAD), is a significant subfield of microfluidics that integrates crucial analytical steps onto a single chip, thereby optimizing the process and enabling high-throughput, automated analysis. By utilizing rotational mechanics to precisely control fluid dynamics without external pressure sources, centrifugal microfluidics facilitates swift operations ideal for urgent medical and field settings. This review provides a comprehensive overview of the latest advancements in centrifugal microfluidics for POCT, covering both theoretical principles and practical applications. We begin by introducing the fundamental operational principles, fluidic control mechanisms, and signal output detection methods. Subsequently, we delve into the typical applications of centrifugal microfluidic platforms in immunoassays, nucleic acid testing, antimicrobial susceptibility testing, and other tests. We also discuss the strengths and potential limitations of centrifugal microfluidic platforms, underscoring their transformative impact on traditional conventional procedures and their significant role in diagnostic practices.

Impact of sample heterogeneity on the evaluation of uncertainty from sampling and analytical steps in pharmaceutical analysis.

Measurement uncertainty is a critical factor in the reliability of pharmaceutical analyses, since it directly affects batch acceptance and regulatory compliance. While analytical uncertainty has been extensively studied, uncertainty arising from sampling remains less explored. This study aims to address this gap by evaluating the contributions of sampling and analytical uncertainties to the overall uncertainty for acetaminophen tablets and oral solution. The duplicate method and analysis of variance (ANOVA) were used to assess the uncertainty from sampling and analytical steps. Ten different batches of acetaminophen tablets and oral solution were analyzed, and the combined uncertainties were calculated. For tablets, uncertainty from sampling accounted for 89 % of the overall uncertainty, highlighting the critical role of sample heterogeneity. In contrast, for oral solutions, analytical uncertainty dominated (90 % of the total), reflecting the homogeneous nature of the solution. Considering the uncertainty from sampling can be important to ensure a reduced risk of false acceptance or false rejection, particularly for heterogeneous dosage forms. In conclusion, incorporating sampling uncertainty into uncertainty budgets can significantly improve decision-making in pharmaceutical quality control.

A Multi-Level Task Framework for Event Sequence Analysis.

Despite the development of numerous visual analytics tools for event sequence data across various domains, including but not limited to healthcare, digital marketing, and user behavior analysis, comparing these domain-specific investigations and transferring the results to new datasets and problem areas remain challenging. Task abstractions can help us go beyond domain-specific details, but existing visualization task abstractions are insufficient for event sequence visual analytics because they primarily focus on multivariate datasets and often overlook automated analytical techniques. To address this gap, we propose a domain-agnostic multi-level task framework for event sequence analytics, derived from an analysis of 58 papers that present event sequence visualization systems. Our framework consists of four levels: objective, intent, strategy, and technique. Overall objectives identify the main goals of analysis. Intents comprises five high-level approaches adopted at each analysis step: augment data, simplify data, configure data, configure visualization, and manage provenance. Each intent is accomplished through a number of strategies, for instance, data simplification can be achieved through aggregation, summarization, or segmentation. Finally, each strategy can be implemented by a set of techniques depending on the input and output components. We further show that each technique can be expressed through a quartet of action-input-output-criteria. We demonstrate the framework's descriptive power through case studies and discuss its similarities and differences with previous event sequence task taxonomies.

Apache Spark based distributed clustering for big data analytic with application to 3D road network

The vast amount of data stored nowadays has turned big data analytics into a very promising research field. Clustering is an essential step in data analysis, widely used for classification, collecting statistics, and acquiring insights in specific domains of knowledge. However, the most of existing algorithms based on Lloyd-Forgy’s method, have an enormously huge average-case complexity while clustering data sets with a large number of features, which may be superpolynomial time (NP-hard) and are severely constrained in terms of speed, productivity, and adaptability. Aiming to improve Lloyd-Forgy’s clustering performance, K-means++ algorithms, a variety of algorithm-level optimizations which is not been well-studied, is discussed along with very promising gaussian mixture model (GMM) and soft clustering based Fuzzy C-means (FCM). Further, for fast and distributed data processing and to leverage the benefits of big data platforms, such as Apache Spark, Spark-based clustering methods are applied on three-dimensional (3D) road network data set which is collected from UCI repository. However, Spark-based clustering research is still in infancy. The distributed computation tests are conducted by allocating two core processors and one databricks unit (DBU) with 15 GB memory and measuring execution times, as well as root mean square error (RMSE), mean absolute error (MAE), clustering accuracy, and silhouette values. The results are promising and provide new research directions in the field of spark-based clustering on big data.

Using HBmito Crimson to Observe Mitochondrial Cristae Through STED Microscopy.

Mitochondrial cristae, formed by folding the mitochondrial inner membrane (IM), are essential for cellular energy supply. However, the observation of the IM is challenging due to the limitations in spatiotemporal resolution offered by conventional microscopy and the absence of suitable in vitro probes specifically targeting the IM. Here, we describe a detailed imaging protocol for the mitochondrial inner membrane using the Si-rhodamine dye HBmito Crimson, which has excellent photophysical properties, to label live cells for imaging via stimulated emission depletion (STED) microscopy. This allows for STED imaging over more than 500 frames (approximately one hour), with a spatial resolution of 40 nm, enabling the observation of cristae dynamics during various mitochondrial processes. The protocol includes detailed steps for cell staining, image acquisition, image processing, and resolution analysis. Utilizing the superior resolution of STED microscopy, the structure and complex dynamic changes of cristae can be visualized. Key features • The protocol is designed to visualize mitochondrial cristae in living cells using STED microscopy. • The protocol enables nanoscale observation of dynamic mitochondrial cristae. • Real-time observation of mitochondrial morphological changes, fusion, and fission events.

Magnetic molecularly imprinted polymers coupled with UPLC-MS/MS for simultaneous detection of 19 steroid hormones in human plasma.

Steroid hormones constitute a group of hormones with molecular weights ranging from 200 to 400 daltons, characterized by their highly similar chemical structures. Each hormone within this group holds significant value for the diagnosis of various diseases. Accurate clinical measurement of the levels of each hormone is crucial for the diagnosis in clinical settings. Due to the wide variety and different properties of steroid hormones in organisms, sample pretreatment is the rate-limiting step for analysis and detection. In this paper, magnetic molecule-imprinting polymers (MMIPs) were prepared by surface imprinting on silicon coated magnetic spheres, and a detection method of MMIPs-combined with liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) for 19 steroid hormones in plasma was developed and verified. Fourier transform infrared spectroscopy, field emission scanning electron microscopy and transmission electron microscopy were used to confirm the formation of MMIPs materials, and the conditions of material synthesis and pretreatment of steroid hormones from plasma were optimized. The detection method was evaluated by using linearity, LOD and LOQ, precision, accuracy and matrix effect. The linearity of 19 steroid hormones was good, and the linear correlation coefficient was greater than 0.995. The coefficients of variation were 2.1-9.8 % and 1.1-9.7 % for intra-day and inter-day, respectively, and the recoveries were 83.6 %-118.2 % at low concentration, and the recoveries of medium and high concentrations were 94.6 %-113.8 % and 89.5 %-113.6 %, respectively. The coefficient of variation of Relative matrix effect of 19 steroid hormones measured in different batches of plasma was 2.1-5.4 % after isotope internal standard correction, which eliminated the influence of matrix effect.

A Protocol for GC-MS Profiling of Chiral Secondary Amino Acids.

A simple analytical workflow is described for gas chromatographic-mass spectrometric (GC-MS)-based chiral profiling of secondary amino acids (AAs) in biological matrices. The sample preparation is carried out directly in aqueous biological sample extracts and involves in situ heptafluorobutyl chloroformate (HFBCF) derivatization-liquid-liquid microextraction of nonpolar products into hexane phase followed by subsequent formation of the corresponding methylamides from the HFB esters by direct treatment with methylamine reagent solution. The (O, N) HFB-butoxycarbonyl-methylamide AA products (HFBOC-MA) are separated on a Chirasil-L-Val capillary column and quantitatively measured by GC-MS operated in selected ion monitoring (SIM) mode. The protocol includes 12 simple pipetting steps and covers the quantitative analysis of 8L, D pairs of secondary amino acids, including proline, isomeric 3-, 4-hydroxyprolines, pipecolic acid, nipecotic acid, azetidine-2-carboxylic acid, and cis- and trans-5-hydroxy-L-pipecolic acid using 13C5 -L-proline as an internal standard. The individual analytical steps are commented on and explained, with emphasis on the chiral GC-MS analysis of secondary amino acids in human urine, serum, and peptide hydrolysate samples.

Views and Experiences of Nurses Receiving Care as Inpatients: An Empirical Qualitative Study From Ankara.

To determine the views and experiences of inpatient nurses receiving care. Switching from the care provider role to being a patient allows nurses to realise patients' real care expectations and to test their colleagues' care practices. Nurses' experience as inpatients is essential for understanding patients' care-related expectations and improving care quality by reflecting on their practices. This qualitative study used a descriptive phenomenological design. Data were collected from nine volunteer nurses between July 2018 and June 2020 in Ankara, Türkiye, through face-to-face interviews using semi-structured questionnaires and audio recordings. The research data were analysed by following the descriptive phenomenological data analysis steps. The Consolidated Criteria for Reporting Qualitative Research [COREQ] checklist was used to report study findings. Three themes emerged from the views and experiences of nurses receiving inpatient care: care-receiving experiences, components of good nursing care and change in perception of care. It is important to reveal the opinions and experiences of hospitalised nurses regarding care to understand the expectations of the care recipients and to increase the quality of the care provided. The study revealed that the experiences of nurses receiving inpatient care improved their professional sensitivity and this was reflected in the quality of the nursing care they provided. Nurses can provide higher quality and humanistic care by combining their professional knowledge and skills with the increased awareness of the inpatient experience of nurses. No patient or public contribution.

Integration of Educational Content in Social Media Platforms: Content Creators’ Experiences

Introduction: This study addresses the problem of understanding educational content creators' lived experiences and challenges in developing impactful social media content. Methods: The study employed a qualitative phenomenological research design involving 10 content creators. The inclusion criteria specified that participants must be educators who were content creators actively producing educational materials for social media platforms, with a minimum of one (1) year of experience in content creation and social media channels with 300,000 or more subscribers. It used the thematic analysis steps. Results: Through a phenomenological approach, the research identified several themes, including (1) designing user-centered content, (2) balancing educational values and engagement, (3) applying principles of accuracy and relevance, (4) considering ethics and fact-checking, (5) fostering community building and collaboration, (6) managing challenges on time management and limited resources, and (7) measuring effectiveness through engagement metrics. The findings reveal that creators prioritize audience needs and strive to balance entertaining and informative content while maintaining ethical standards. Conclusion: Despite facing significant challenges such as resource limitations and time management, creators were committed to producing meaningful educational content that enhanced student engagement and learning outcomes

A Network-Based Clustering Method to Ensure Homogeneity in Regional Frequency Analysis of Extreme Rainfall

The social impacts of extreme rainfall events are expected to intensify with climate change, making reliable statistical analyses essential. High quantile estimation requires substantial data; however, available records are sometimes limited. Additionally, finite data and variability across statistical models introduce uncertainties in the final estimates. This study addresses the uncertainty that arises when selecting parameters in Regional Frequency Analysis (RFA) by proposing a method to objectively identify statistically homogeneous regions. Station coordinates, elevation, annual mean rainfall, maximum annual rainfall, and l-skewness from 55 meteorological stations are selected to study annual maximum daily rainfall. These covariates are employed to investigate the interdependency of the covariates in Principal Component Analysis (PCA) as a preprocessing step in cluster analysis. Network theory, implemented through an iterative clustering process, is used in network creation where stations are linked based on the frequency of their co-occurrence in clusters. Communities are formed by maximizing the modularity index after creating a network of stations. RFA is performed in the final communities using L-moment theory to estimate regional and InSite quantiles. Quantile uncertainty is calculated through parametric bootstrapping. The application of PCA has a negligible effect on network creation in the study area. The results show that the iterative clustering approach with network theory ensures statistically created homogeneous regions, as demonstrated in Thessaly’s complex terrain for regionalisation of extreme rainfall.

The Effects of Artificial Intelligence on the Fashion Industry—Opportunities and Challenges for Sustainable Transformation

ABSTRACTArtificial intelligence (AI) is crucial in the fashion industry nowadays. It facilitates innovation in design, production, e‐commerce, personalisation and supply chain management, improving efficient operations and providing opportunities to support sustainability. The research aimed to identify the current use of artificial intelligence in the fashion industry and its arrangement and determine to what extent AI is used to support fashion sustainability. Data collection and selection were according to PRISMA guidelines and were retrieved from Scopus and WoS (January 2017–October 2024; n = 82 (from 234 identified items)). Data analysis was performed using six steps of thematic analysis, including topic modelling (in the MAXQDA 24 programme). The identified themes revealed the current effects of AI use in the fashion industry (RQ1): data‐centric design, forecasting using big data, and experience‐oriented services. AI technologies are predominantly utilised in fabric production and B2B distribution. Experience‐focused services are enhanced through precise image searches and chatbot support. Platforms like SaaS, generative fashion, and Science4Fashion enable the creation of new designs. Applications such as Style. Me serve as personal stylists, facilitating customised outfit selection and streamlining the purchasing process. The last theme (RQ2) allowed to establish that sustainable development requires innovation based on AI technology. Despite optimistic forecasts, available solutions are only used to a limited extent. The main barrier is that companies put economic goals above sustainable development goals. Models that combine commercial and environmental perspectives are essential in developing beneficial change strategies. Therefore, it is important to monitor progress in this area.

Research Trend of Science Process Skills in Prospective Physics Teachers: An Analytical Review

It is imperative to research the science process skills of prospective physics teachers in higher education to improve the quality of physics education, support curriculum development, and enhance student understanding. This study examines the research trends regarding the science process skills of prospective physics teachers in physics education at higher education institutions. The review of journal publications from 2019 to 2023 was conducted using content analysis, a qualitative method. A total of 24 journal articles focusing on the science process skills of prospective physics teachers in higher education were identified through Publish or Perish, using the Google Scholar and Scopus databases between 2019 and 2023. The analysis steps included framing review questions, identifying relevant research, assessing study quality, summarizing evidence, and conducting data analysis. The results showed that research on the science process skills of prospective physics teachers has been prevalent since 2019, with quantitative research dominating the field. Basic physics practicum courses are the primary focus for exploring students' science process skills, with tests being the most commonly used instruments. Several interventions, such as Project-Based Learning (PjBL), STEAM, Discovery, and Inquiry, have been implemented to enhance the science process skills of physics teacher candidates. Communication skills, a component of the 21st-century 4C skills, are particularly pertinent to the future challenges that educators will encounter, as evidenced by the science process skills examined in previous research. These skills are critical in fostering collaboration, problem-solving, and effective teaching. In conclusion, this study emphasizes the importance of developing physics teacher candidates' science process skills, particularly communication skills, in higher education. Future research is expected to deepen the understanding and develop more effective learning strategies to improve these essential skills in physics teacher candidates.

Unveiling the power of high-dimensional cytometry data with cyCONDOR

High-dimensional cytometry (HDC) is a powerful technology for studying single-cell phenotypes in complex biological systems. Although technological developments and affordability have made HDC broadly available in recent years, technological advances were not coupled with an adequate development of analytical methods that can take full advantage of the complex data generated. While several analytical platforms and bioinformatics tools have become available for the analysis of HDC data, these are either web-hosted with limited scalability or designed for expert computational biologists, making their use unapproachable for wet lab scientists. Additionally, end-to-end HDC data analysis is further hampered due to missing unified analytical ecosystems, requiring researchers to navigate multiple platforms and software packages to complete the analysis. To bridge this data analysis gap in HDC we develop cyCONDOR, an easy-to-use computational framework covering not only all essential steps of cytometry data analysis but also including an array of downstream functions and tools to expand the biological interpretation of the data. The comprehensive suite of features of cyCONDOR, including guided pre-processing, clustering, dimensionality reduction, and machine learning algorithms, facilitates the seamless integration of cyCONDOR into clinically relevant settings, where scalability and disease classification are paramount for the widespread adoption of HDC in clinical practice. Additionally, the advanced analytical features of cyCONDOR, such as pseudotime analysis and batch integration, provide researchers with the tools to extract deeper insights from their data. We use cyCONDOR on a variety of data from different tissues and technologies demonstrating its versatility to assist the analysis of high-dimensional data from preprocessing to biological interpretation.

Deep Depthwise Residual Network for Knee Meniscus Segmentation From Magnetic Resonance Imaging

ABSTRACTThe menisci within the knee are essential for various anatomical functions, including load‐bearing, joint stability, cartilage protection, shock absorption, and lubrication. Magnetic resonance imaging (MRI) provides highly detailed images of internal organs and soft tissues, which are indispensable for physicians and radiologists assessing the meniscus. Given the multitude of images in each MRI sequence and diverse MRI data, the segmentation of the meniscus presents considerable challenges through image processing methods. The region‐specific characteristics of the meniscus can vary from one image to another within the sequence. Consequently, achieving automatic and accurate segmentation of meniscus in knee MRI images is a crucial step in meniscus analysis. This paper introduces the “UNet with depthwise residual network” (DR‐UNet), a depthwise convolutional neural network, designed specifically for meniscus segmentation in MRI images. The proposed architecture significantly improves the accuracy of meniscus segmentation compared to different segmentation networks. The training and testing phases utilized fat suppression turbo‐spin‐echo (FS TSE) MRI sequences collected from 100 distinct knee joints using a Siemens 3 Tesla MRI machine. Additionally, we employed data augmentation techniques to expand the dataset strategically, addressing the challenge of a substantial training dataset requirement. The DR‐UNet model demonstrated impressive meniscus segmentation performance, achieving a Dice similarity coefficient range of 0.743–0.9646 and a Jaccard index range of 0.653–0.869, thereby showcasing its advanced segmentation capabilities.

Predicting the General Self-Efficacy of the People with Diabetes in Bandargaz- 2023: The Role of Rumination and Alexithymia

Objective: This study aimed to investigate the impact of rumination and alexithymia on the general self- efficacy of individuals with diabetes. Materials and Methods: This correlational study targeted diabetic individuals aged 30–50 years residing in Bandargaz in 2023. A total of 217 participants were selected through convenience sampling. Data collection instruments included the General Self-Efficacy Scale (GSES), the Ruminative Response Scale (RRS), and the Toronto Alexithymia Scale (TAS). Data were analyzed using SPSS version 24, employing Pearson's correlation coefficient and stepwise regression analysis. Results: The results revealed a significant negative relationship between alexithymia and general self-efficacy. In the first step of the regression analysis, the beta coefficient was -0.446, indicating that a one standard deviation increase in alexithymia was associated with a 0.446 standard deviation decrease in general self- efficacy. In the second step, the beta coefficient for rumination was -0.152, suggesting that a one standard deviation increase in rumination was associated with a 0.152 standard deviation decrease in general self- efficacy. Conclusion: The findings demonstrate that higher levels of alexithymia and rumination negatively affect general self-efficacy in individuals with diabetes. Educational interventions and workshops focused on improving emotional regulation and cognitive coping strategies could enhance self-efficacy, enabling individuals to achieve personal goals and improve their overall well-being.

Conceptual framework to provide culturally congruent care to epilepsy patients in selected rural communities in South Africa

Background: Epilepsy is a neurological disorder affecting millions of people in Africa. Among other reported findings, many people living with epilepsy (PLWE) believe that the condition is caused by spiritual factors. Previous studies have revealed that majority of PLWE are not receiving adequate care and treatment because of diverse cultural beliefs associated with epilepsy. Consequently, they consult either faith-based healers or traditional healers. Others, however, acknowledge the medical causes of epilepsy and seek developed medical treatment.Aim: To develop a culturally congruent care conceptual framework to provide comprehensive and holistic epilepsy care.Setting: This study was conducted in the selected rural communities of Limpopo and Mpumalanga provinces.Methods: A qualitative multi-method research approach was employed and sub-divided into three phases. Phase 1: All 15 articles were reviewed and appraised using the Critical Appraisal Skills Programme (CASP) rating tool. Phase 2 involved two stages of empirical study in which an exploratory-descriptive study design was used. The study population comprised of 20 traditional healers, 15 faith-based healers, 20 professional nurses and 22 PLWE. Data were collected using in-depth individual interviews and analysed through eight Tesch’s steps of data analysis. Following data analysis, Phase 3 entailed synthesising the systematic and empirical findings into a conceptual framework.Results: Three themes that emerged from the findings were cultural epilepsy interventions, faith-based epilepsy intervention and medically developed epilepsy interventions.Conclusion: Incorporation of cultural beliefs, values and practices into the medically developed management of epilepsy is crucial for providing culturally congruent care that is preferred by majority of PLWE.Contribution: Healthcare providers may tailor care interventions that are culturally appropriate and acceptable hence promoting early diagnosis and treatment adherence leading to improved quality of life.

Aquaphotomics study of fresh cannabis inflorescence: near infrared spectral analysis of water matrix structures.

Aquaphotomics is an approach that describes the water-light interactions in aqueous solutions or biological systems and retrieves information about the nature of the underlying water-related interactions. We evaluated the water spectral pattern (WASP) and water matrix structure of freshly harvested cannabis inflorescence from seven different chemovars using near-infrared (NIR) spectral data coupled with chemometric models. Six activated water bands-1342, 1364, 1384, 1412, 1440, and 1462nm, occurred consistently in all of the spectrum exploration steps as well as in the partial least squares-discriminant analysis (PLS-DA) steps. However, according to major class and chemovar aquagram values, the largest spectral variation was associated with the following bands: 1412, 1364, 1374, 1384, 1488, and 1512nm. A strong positive correlation between 1364, 1374, and 1384nm aquagram values and a strong negative correlation between 1412 and 1512nm aquagram values were observed through all aquagram analysis steps. These water activated bands were found to serve as good discriminators and classifiers according to either major class or chemovar. Furthermore, significant differences in the water matrix structure of different cannabis chemovars were observed, with the highest variations associated with the presence of free water molecules, small molecule solvation shells, extent of strongly bound water, and the number of hydrogen bonds per water molecule. Minor cannabinoids and terpenes such as cannabigerolic acid and (-)-guaiol displayed relatively high correlations with these bands. The results of this study suggest that the most accurate way to explore the cannabis inflorescence water matrix spectral pattern is by chemovars and not by major classes.