Scientific Data Research Articles

Update on Neoadjuvant and Adjuvant BRAF Inhibitors in Papillary Craniopharyngioma: A Systematic Review

Background/Objectives: The recent discovery of BRAF mutation in papillary craniopharyngiomas opened new avenues for targeted therapies to control tumour growth, decreasing the need for invasive treatments and relative complications. The aim of this systematic review was to summarize the recent scientific data dealing with the use of targeted therapies in papillary craniopharyngiomas, as adjuvant and neoadjuvant treatments. Methods: The PRISMA guidelines were followed with searches performed in Scopus, MEDLINE, and Embase, following a dedicated PICO approach. Results: We included 21 pertinent studies encompassing 53 patients: 26 patients received BRAF inhibitors (BRAFi) as adjuvant treatment, while 25 received them as neoadjuvant treatment. In the adjuvant setting, BRAFi were used to treat recurrent tumours after surgery or adjuvant radiation therapy. The most common regimen combined dabrafenib (BRAFi) with trametinib (MEK1 and 2 inhibitor) in 81% of cases. The mean treatment length was 8.8 months (range 1.6 to 28 months) and 32% were continuing BRAFi. A reduction of tumour volume variable from 24% to 100% was observed at cerebral MRI during treatment and volumetric reduction ≥80% was described in 64% of cases. Once the treatment was stopped, adjuvant treatments were performed to stabilize patients in remission in 11 cases (65%) or when a progression was detected in three cases (12%). In four cases no further therapies were administered (16%). Mean follow-up after the end of targeted therapy was 17.1 months. As neoadjuvant regimen, 36% of patients were treated with dabrafenib and trametinib with a near complete radiological response in all the cases with a mean treatment of 5.7 months. The neoadjuvant use of verumafenib (BRAFi) and cometinib (MEK1 inhibitor) induced a near complete response in 15 patients (94%), with a median volumetric reduction between 85% and 91%. Ten patients did not receive further treatments. Side effects varied among studies. The optimal timing, sequencing, and duration of treatment of these new therapies should be established. Moreover, questions remain about the choice of specific BRAF/MEK inhibitors, the optimal protocol of treatment, and the strategies for managing adverse events. Conclusions: Treatment is shifting to a wider multidisciplinary management, where a key role is played by targeted therapies, to improve outcomes and quality of life for patients with BRAF-mutated craniopharyngiomas. Future, larger comparative trials will optimize their protocol of use and integration into multimodal strategies of treatment.

Reproductive physiology of Indian pangolin (Manis crassicaudata) in Pothwar Plateau, Pakistan

Abstract The Indian pangolin (Manis crassicaudata) is an “endangered” mammal species that is restricted to four range countries of Asia. The ecology and breeding behavior of Indian pangolins is poorly understood due to a lack of scientific data. Due to heavy poaching and illegal killing, the species population has declined drastically in the last decade. Considering the fact that Indian pangolin has low reproductive output, it needs special conservation measures and authentic information about its breeding ecology. We estimated concentrations of three reproductive hormones from scats using ELISA kits in male and female pangolins inhabiting the Pothwar Plateau, Pakistan, to confirm its breeding pattern in its range. Testosterone levels were found elevated from May (0.59 ng/ml) until October (1.28 ng/ml). Oestradiol levels were raised from May (0.59 pg/ml) until December (2.64 pg/ml). Progesterone concentrations were relatively low from January until March but increased from April (4.85 pg/ml) through December (14.0 pg/ml) with highest concentrations (20.13 pg/ml) in September and October. Mean hormone (testosterone, oestradiol and progesterone) concentrations were elevated in winter compared to summer (p < 0.001). The current study is the first one to quantify hormonal levels in this species, and our findings establish the breeding pattern of Indian pangolin.

The Study on Sentence Difficulty of the Chinese Components

In recent years, reading comprehension has gradually become a proficiency indicator of interest in lexical and grammar. As sentences are the basic units of discourse structure, sentence difficulty is often applied to the study of text difficulty. Although there have been a number of studies on sentence difficulty, the lack of consistency in the indicators chosen or the discussion of specific grammatical issues have limited the research on sentence difficulty. Therefore, this study adopts a corpus-based approach, using a corpus as an objective and scientific data source. The study utilizes the Digital Platform for Chinese Grammar and the 8000 Chinese Words as important reference sources. Additionally, the CRIE 3.0 is employed to validate the texts and establish sentence difficulty indicators. However, due to the incomplete development of certain indicators in the "Chinese Grammar Digital Platform", the study refers to the Chinese Proficiency Grading Standards for International Chinese Language Education and Hanyu Shuiping Kaoshi to establish comprehensive sentence structure and sentence component difficulty indicators. Subsequently, the established difficulty indicators are validated by conducting comparative analyses using corpora as the basis. Native speaker corpora are used as benchmarks, while Mandarin learner corpora are used for comparison, and then validate objectively through the machine learning model. These validation aims to examine the validity and reliability of the selected indicators and establish a calculation method involving " level of grammar * point distribution ratio of grammar " to determine the difficulty indicators for Chinese sentences, Additionally, expert reliability is accessed to ensure the credibility of indicators.

Best Practice for Publishing Environmental DNA (eDNA) Data According to FAIR Principles

Reversing global biodiversity loss will require transformational human actions and robust measurements of their effectiveness. Diversity assessment using environmental DNA (eDNA) has emerged as a cutting-edge technique with the potential to address the challenges of measuring biodiversity. Vast amounts of eDNA sequences and eDNA-based species detections are generated in scientific studies. These datasets are typically stored in a variety of different repositories in multiple formats, hindering their reuse (Berry et al. 2020). Ensuring the publication of eDNA data following the FAIR (Findable, Accessible, Interoperable, Reusable) principles (Wilkinson et al. 2016) would revolutionise environmental assessment, including monitoring of biodiversity, individual species, and interactions across extensive spatial and temporal scales, and generate critical knowledge for evidence-based management. Archiving FAIR eDNA data requires standardising data formats and vocabularies, cyberinfrastructures, guidelines, data sharing policy, and collaboration among scientists and institutions. Some of these requirements are addressed by existing data standards and infrastructures, including Darwin Core (DwC) (Wieczorek et al. 2012), Minimum Information about any (x) Sequence (MIxS) (Yilmaz et al. 2011), the Global Biodiversity Information Facility (GBIF) network, and International Nucleotide Sequence Database Collaboration (INSDC) partners (Arita et al. 2020). However, multiple challenges remain and FAIR data practices have yet to be established among the eDNA community. This is partly because critical attributes unique to eDNA data are not adequately accommodated by existing standards. For example, monitoring contamination and excluding non-target taxa, and the parameters used for quality filtering and species detection vary greatly between studies, depending on the study scopes and the associated financial and ecological costs of incorrectly inferring presence or absence. Making such information FAIR is needed for future studies reusing data and requiring high confidence levels in species detection and taxonomic assignment. Furthermore, the procedures of targeted-taxon detection approach (e.g., interpretations of quantitative polymerase chain reaction (qPCR) results in detecting the presence of DNA from individual taxa) have not yet been fully captured by existing standards. Increasing efforts have been made to establish minimum reporting requirements to validate eDNA study methods and data (Klymus et al. 2020, Thalinger et al. 2021). These requirements need further development to be translated into data standards and formats to enhance machine readability and reusability, and to support and guide the eDNA community, so that they are effectively utilised. In this talk, we share our best practice guide for formatting and publishing eDNA data, developed by an international multidisciplinary working-group comprising eDNA researchers, journal editors, and biodiversity and omics data scientists. We identified required data types, formats and metadata checklists through reviewing and integrating existing data standards, devising subject-specific vocabularies, and introducing additional terms to accommodate the distinctive properties of eDNA data. Implementing the FAIR eDNA data best practice guide, offers a pivotal step towards standardising and enhancing the publication and re-use of eDNA data.

Have Your Cake and Eat It Too

As datasets have become a more significant aspect of Open Science, attention has turned to the data transformations that drive their creation. Li and Ludäscher have pointed out the importance of identifying data cleaning workflows as a series of modular transformations that can be extracted for reuse. This modular approach aids reproducibility and allows for transparency in data provenance. However, the constantly evolving nature of data science technology means that even once these modules have been identified and implemented, their functionality must be ported to new platforms as old ones become less applicable or less common in a field of study. When these transformations take place, it is important to consider not only practicality and functionality, but also transparency within a data processing team. Clarity of communication within a team is the first step towards providing clear and transparent documentation to the end user. This case study of an updated workflow process for a long-running longitudinal health and well-being study provides practical examples of these principles.

An Algorithm for Clustering with Confidence-Based Must-Link and Cannot-Link Constraints

We study here the semisupervised k-clustering problem where information is available on whether pairs of objects are in the same or different clusters. This information is available either with certainty or with a limited level of confidence. We introduce the pair-wise confidence constraints clustering (PCCC) algorithm, which iteratively assigns objects to clusters while accounting for the information provided on the pairs of objects. Our algorithm uses integer programming for the assignment of objects, which allows us to include relationships as hard constraints that are guaranteed to be satisfied or as soft constraints that can be violated subject to a penalty. This flexibility distinguishes our algorithm from the state of the art, in which all pair-wise constraints are considered hard or all are considered soft. We developed an enhanced multistart approach and a model-size reduction technique for the integer program that contribute to the effectiveness and efficiency of the algorithm. Unlike existing algorithms, our algorithm scales to large-scale instances with up to 60,000 objects, 100 clusters, and millions of cannot-link constraints (which are the most challenging constraints to incorporate). We compare the PCCC algorithm with state-of-the-art approaches in an extensive computational study. Even though the PCCC algorithm is more general than the state-of-the-art approaches in its applicability, it outperforms the state-of-the-art approaches on instances with all hard or all soft constraints in terms of both run time and various metrics of solution quality. The code of the PCCC algorithm is publicly available on GitHub. History: Accepted by Ram Ramesh, Area Editor for Data Science and Machine Learning. Funding: The research of D. S. Hochbaum was supported by the AI Institute NSF Award [Grant 2112533]. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information ( https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2023.0419 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2023.0419 ). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/ .

Algorithm XXXX: The Delaunay Density Diagnostic

Accurate approximation of a real-valued function depends on two aspects of the available data: the density of inputs within the domain of interest and the variation of the outputs over that domain. There are few methods for assessing whether the density of inputs is sufficient to identify the relevant variations in outputs—i.e., the “geometric scale” of the function—despite the fact that sampling density is closely tied to the success or failure of an approximation method. In this paper, we introduce a general purpose, computational approach to detecting the geometric scale of real-valued functions over a fixed domain using a deterministic interpolation technique from computational geometry. The algorithm is intended to work on scalar data in moderate dimensions (2-10). Our algorithm is based on the observation that a sequence of piecewise linear interpolants will converge to a continuous function at a quadratic rate (in \(L^{2}\) norm) if and only if the data are sampled densely enough to distinguish the feature from noise (assuming sufficiently regular sampling). We present numerical experiments demonstrating how our method can identify feature scale, estimate uncertainty in feature scale, and assess the sampling density for fixed (i.e. static) datasets of input–output pairs. We include analytical results in support of our numerical findings and have released lightweight code that can be adapted for use in a variety of data science settings.

AI Applications for Nutrition and Food Security Research

Data analytics (DA) and artificial intelligence (AI) play increasingly vital roles in food, nutrition, environment, and public health research and practice. With the continued successful development, professionals in these fields could rely on data analytics and AI for tasks such as data collection, decision-making, and policy development. Proficiency in research methods, statistical analysis, and ethical considerations is crucial. As AI applications grow in complexity, it is essential for professionals and the public to embrace and regulate them effectively. This review outlines AI’s diverse applications in nutrition research, including data analysis, prediction, personalized recommendations, and food safety monitoring. Select illustrative examples demonstrate its potential across various domains and highlight common challenges. The narrative underscores the importance of integrating data science and AI competencies into graduate education to equip the modern workforce.

Synergizing AI, Data Science, and Data Integration: Transforming Insights into Intelligent Actions

While Artificial Intelligence (AI), Data Science and Data Integration have dramatically changed the technological landscape in their own right, their impact is even more dramatic when combined. This article looks into the commonalities in these fields of practice and shows them to be extracts from a framework that incorporates integrated data management, advanced analytics capabilities and intelligent algorithms which collectively have had profound impacts on how we continue to innovate across industries. The article then explores the nature of these technologies at present, the associated challenges and their future potential to yield insights into strategic approaches toward the alignment of AI, Data Science and Data Integration with the common goal of converting data into actionable intelligence.

TRATAMENTO ESTÉTICO COM RADIOFREQUÊNCIA NO REJUVENESCIMENTO FACIAL: UMA REVISÃO INTEGRATIVA

Introduction: Aging is a natural, continuous, gradual phenomenon or process that manifests itself in a multifactorial and degenerative way in accordance with intrinsic and extrinsic factors, and these affect the entire human body, especially its largest organ, the skin. Aging in the facial region is due to the loss of elastin and collagen proteins, which support the physiological tissue and muscle functions of the skin. There are several aesthetic treatments for facial rejuvenation, among which Radiofrequency can be highlighted to treat sagging skin. Objective: To check the impacts of Radiofrequency as an aesthetic resource for the treatment of facial skin sagging with an emphasis on the rejuvenation process. Methods: it will be used based on the techniques and methods of bibliographic research, which consisted of collecting research sources using scientific platforms: Scientific Electronic Library Online (Scielo), National Library of Medicine (PubMed), Virtual Libraries (BVS). Results: After detecting scientific data using the inclusion and exclusion criteria, reading titles and abstracts, 14 articles were requested, then the pre-selected publications were read in full, paying attention to the proposed theme, 11 were counted. articles for analysis and construction of the review. Conclusion: The studies carried out show that radiofrequency is established as an effective and safe technique for facial rejuvenation, providing significant beneficial results and showed that radiofrequency is a viable and promising alternative for treating signs of facial aging, contributing to the good -being and self-esteem of individuals.

Analysing the Socio-Economic Adjustment of Syrians under Temporary Protection: The Case of Ankara

The socio-economic adjustment of Syrians residing in Ulubey, Alemdağ, and Önder neighbourhoods in the Altındağ district of Ankara is the subject of this study. The study aims to examine the factors affecting the socio-economic adjustment of Syrians under temporary protection and the impact of the economic adjustment process on the social adjustment process within the framework of a mixed research method. The findings reveal that Syrian participants' legal and economic problems in the economic adjustment process affect their social adjustment process. The study is thought to shed light on the solution to these problems by providing scientific data based on the field research findings on Syrians' socio-economic adjustment process and adjustment problems.

NEW PERSPECTIVES ON URINALYSIS: RECOMMENDATIONS FROM THE EUROPEAN FEDERATION OF CLINICAL CHEMISTRY AND LABORATORY MEDICINE (EFLM)

This article highlights the significance of the updated recommendations from the European Federation of Clinical Chemistry and Laboratory Medicine (EFLM) – Urinalysis Guideline 2023. The guidelines emphasize the need for standardizing laboratory procedures to enhance research quality and reduce diagnostic errors. Based on current scientific data and international standards, these recommendations aim to ensure high reliability and accuracy of laboratory results. The implementation of urinalysis guideline has the potential to significantly improve diagnostic effectiveness, positively impacting the quality of patient care. Attention is also given to new technologies considered in the recommendations, which can greatly facilitate advancements in laboratory medicine. EFLM guidelines are essential for enhancing laboratory practices and maintaining high standards in medical diagnostics.

Artemisia afra and COVID-19, the Media Storm Versus the Current State-of-the-Art

AbstractDuring the COVID-19 pandemic, the traditional herbal medicine Artemisia afra Jacq. ex Willd., Asteraceae, was widely used as a remedy, and a true media storm erupted in (South) Africa regarding this matter. Throughout the pandemic, A. afra was not only extensively used, but the herb was also exceedingly difficult to find, which is reminiscent of its historical popularity during the influenza pandemic of 1918. This popularity was further fueled by the launch of Covid-Organics in Madagascar, containing Artemisia annua L., which led to international media attention regarding Artemisia as a treatment for COVID-19. In 2020, millions in research funding were made available in South Africa to study promising traditional medicines, including A. afra, and for the development of an effective remedy which was lacking at the time. Due to its popularity and widespread use, this updated overview of A. afra aimed to collate all scientific data on its chemistry, general in vitro and in vivo biological bioactivity, and its antiviral and Sars-Cov-2 activity reported since 2019. Several studies reported good in vitro antiparasitic activity and in vivo antispasmodic activity. However, many studies reported on biological activities that were many orders of magnitude less active than their positive controls. Surprisingly, not a single in vivo study, patent, or (registered) clinical trial could be found regarding A. afra and COVID-19. Only one study, conducted by a German-Danish research group, reported on the in vitro activity of A. afra extracts against Sars-Cov-2. The results indicated that there was relatively weak activity of 0.09 mg/ml ± 0.03 (selectivity index of 26.22), but the context cannot be established due to the absence of a positive control, which did not exist at the time. Artemisia afra is still widely used as a general antiviral remedy, and in-depth research in this field must be conducted. Several promising results regarding antispasmodic and antischistosomal activity should also be further explored. Graphical Abstract

The Effects of Spatial Structure and Development Intensity of the Urban Landscape on Bird Biodiversity in Anhui Province

Exploring the factors that drive changes in biodiversity is a hot and critically important topic in landscape ecology and biogeography. In this study, semi-structured citizen science data and bird distribution maps were employed to gather data from 2015 to 2020 for the calculation of bird species richness, the Shannon index, and the Pielou index in Anhui Province. These metrics were utilized to assess avian biodiversity and to elucidate the spatial patterns of biodiversity distribution across the region. In this research, a structural equation model (SEM) was utilized to investigate the relationships between the three dimensions of landscape spatial structure, urban development intensity, and environmental factors on bird biodiversity, and a conceptual framework was established to identify the key driving factors. The validity, reliability, and fit of the hypothesized model were substantiated through rigorous testing, demonstrating its reasonableness. The results indicate the following: (1) In landscape spatial structure, landscape composition and configuration play crucial roles in influencing bird diversity. An increased proportion of cultivated land negatively impacts bird diversity, whereas the expansion of forested areas promotes it. At the configuration level, the Largest Patch Index (LPI) significantly enhances bird diversity, serving as the primary driving force. Landscape spatial structure affects bird diversity both directly, through its composition, and indirectly, through its configuration. (2) The dimension of urban development intensity generally shows significant negative impacts; among these, GDP has the greatest comprehensive impact and shows a significant negative impact. (3) Topography has the greatest overall impact on bird diversity among the environmental factors, with a predominantly direct positive effect. (4) Overall, urban landscape spatial structure and urban development intensity are the main driving forces of bird diversity in Anhui Province, the greatest of which is the direct effect of the urban development intensity. These results provide an important scientific basis for landscape planning and ecological protection and provide inspiration for assessing the driving factors of animal and plant diversity in other regions.

Health data science course for clinicians: Time to bridge the skills gap?

Data science skills are highly relevant for clinicians working in an era of big data in healthcare. However, these skills are not routinely taught, representing a growing unmet educational need. This education report presents a structured short course that was run to teach clinicians data science and the lessons learnt. A 1-day introductory course was conducted within a tertiary hospital in London. It consisted of lectures followed by facilitated pair programming exercises in R, an object-oriented programming language. Feedback was collated and participant responses were graded using a Likert scale. The course was attended by 20 participants. The majority of participants (69%) were in higher speciality cardiology training. While more than half of the participants (56%) received prior training in statistics either through formal taught programmes (e.g., a Master's degree) or online courses, the participants reported several barriers to expanding their skills in data science due to limited programming skills, lack of dedicated time, training opportunities and awareness. After the short course, there was a significant increase in participants' self-rated confidence in using R for data analysis (mean response; before the course: 1.69 ± 1.0, after the course: 3.2 ± 0.9, p = .0005) and awareness of the capabilities of R (mean response; before the course: 2.1 ± 0.9, after the course: 3.6 ± 0.7, p = .0001, on a 5-point Likert scale). This proof-of-concept study demonstrates that a structured short course can effectively introduce data science skills to clinicians and supports future educational initiatives to integrate data science teaching into medical education.

Planetary health in action: developing a heatwave vulnerability tool for primary care.

Heatwaves are becoming longer and more frequent. Despite the availability of open environmental data, little is operable and formatted for primary care use. Create a user-friendly online mapping tool to assess the vulnerability of communities to heatwaves for use by primary care practitioners. This study questioned what knowledge needed to be deployed, who needed to participate and how the knowledge should be shared. A participatory action-research project based on knowledge mobilization in France as part of the Green Data for Health Challenge. Knowledge was summarized on the factors most affecting heatwave vulnerability in a collaborative process, enabling a consensus on data variables and mobilised content for the online tool. Purposive sampling included primary care stakeholders with Regional Health Agencies (ARS), Public Health France, and data scientists. Nineteen participants participated in ten co-construction workshops, a brainstorming carousel strategy and five weekly co-design meetings between December 2022 and June 2023. The heatwave vulnerability variable was constructed using surface temperature, social deprivation, vegetation coverage, and presence of air conditioning equipment. Identified experts mobilized data on the national composite indicator at the communal level for heatwave morbidity. There is no standard platform for sharing environmental data in France. This co-creation study offers a new approach to incorporating environmental data on heatwaves into primary care consultations. We demonstrate the importance of knowledge mobilisation in primary care to bridge the research-practice gap. Integrating primary care records with environmental data may promote broader applications for planetary health research.

Knot data analysis using multiscale Gauss link integral

In the past decade, topological data analysis has emerged as a powerful algebraic topology approach in data science. Although knot theory and related subjects are a focus of study in mathematics, their success in practical applications is quite limited due to the lack of localization and quantization. We address these challenges by introducing knot data analysis (KDA), a paradigm that incorporates curve segmentation and multiscale analysis into the Gauss link integral. The resulting multiscale Gauss link integral (mGLI) recovers the global topological properties of knots and links at an appropriate scale and offers a multiscale geometric topology approach to capture the local structures and connectivities in data. By integration with machine learning or deep learning, the proposed mGLI significantly outperforms other state-of-the-art methods across various benchmark problems in 13 intricately complex biological datasets, including protein flexibility analysis, protein-ligand interactions, human Ether-à-go-go-Related Gene potassium channel blockade screening, and quantitative toxicity assessment. Our KDA opens a research area-knot deep learning-in data science.

Machine Learning Approaches in Multi-Cancer Early Detection

Cancer is a prominent global cause of mortality, primarily due to delayed detection leading to limited treatment options. Current screening methods are mostly invasive and involve complex lengthy processes with high costs. Moreover, each screening typically focuses on a single type of cancer. This imposes a growing need for innovative, precise, and minimally invasive methods for early cancer detection. With the current advances in assay technologies and data science, multi-cancer early detection (MCED) tests are gaining increased interest in the research community as they offer potential for earlier diagnosis and improved patient outcomes. Different approaches are followed for MCED, and multiple machine learning methods are considered. In this paper, we systematically explore various MCED studies and their applied machine learning (ML) models for different types of biomarker data. We discuss the strengths and limitations of different study designs and compare their performance. Future directions are proposed, emphasizing the importance of integrating multi-omics data, enhancing model transparency, and fostering collaborative efforts to develop robust, cost effective and clinically applicable MCED tools.

Analysis of Antioxidants from Three Parts of Polygonum Chinense Based on Online Extraction High Performance Liquid Chromatography Antioxidant Analysis System.

In the current study, a rapid online extraction combined with high-performance liquid chromatography (HPLC) antioxidant analysis approach was developed to explore the antioxidant ingredients in three different parts of Polygonum Chinense. A total of 22 chromatographic peaks were found, among which 8 components were found for the first time. And among these 22 peaks, 19 of them were demonstrated as antioxidants. Furthermo, all three parts from Polygonum Chinense exhibited antioxidant activity (13.19, 3.89, and 19.85 mg CE/g extract, for the leaf, stem, and flower, respectively) and the antioxidant activity values of the leaf and flower were more than 3 times that of the stem. The leaf, flower, and stem of Polygonum Chinense had 17, 13, and 15 antioxidant components, respectively. Among them, neochlorogenic acid (2) and quercetin-3-O-malonylglucoside (18) were considered as the main antioxidants in the leaf of Polygonum Chinense. In terms of flower, geraniin (10) and quercetin-3-O-malonylglucoside (18) were proved as the major antioxidants. This is the first report on the antioxidant components from different parts of Polygonum Chinense. It provides foundational scientific data for the continued exploration of chemical and pharmacological study of Polygonum Chinense, beneficial for the future product development and quality evaluation improvement of Polygonum Chinense.

Effect of Complexation with Different Molecular Weights of Oat β-Glucan and Sea Buckthorn Flavonoid on the Digestion of Rice Flour.

The complex of oat β-glucan (OBG) and flavonoids hampered the digestion of starch-based food and retarded the blood glucose response; however, its effect on gastric emptying and its relative mechanism have not been thoroughly investigated. By using Fourier transform infrared (FT-IR), X-ray diffraction (XRD), scanning electron microscopy (SEM), antioxidant ability, and enzymic inhibitory tests for the characterization and in vitro semi-dynamic digestion of complexes of OBG (high and low molecular weights) and sea buckthorn flavonoids, we found that the higher molecular weight complex (FU) exhibited stronger ABTS and DPPH radical scavenging abilities and higher α-glucosidase and α-amylase inhibition rates. Mice fed with rice flour with FU addition exhibited the slowest gastric emptying and intestinal propulsion rates and blood glucose rise and had the lowest activity of digestive enzymes and levels of insulin, ghrelin, motilin (MTL), and relevant gene (ghrelin and GHSR mRNA) expression than those in the control and low-molecular-weight groups. This study provided scientific data for the development of foods with delayed gastric rate and hypoglycemic index for specific populations.