Efficacy of a Diabetes Specific Nutrition Supplement on Glycemic, Anthropometric, Dietary and Gut Health Markers in Adults with Type 2 Diabetes: An RCT

With increasing incidence of diabetes, use of diabetes specific nutrition supplements (DSNS) is common for better management of the disease. To study effect of 12-week DSNS supplementation on glycemic markers, anthropometry, lipid profile, SCFAs, and gut microbiome in individuals with diabetes. Markers studied were glycemic [Fasting Blood Glucose (FBG), Post Prandial Glucose (PPG), HbA1c, Incremental Area under curve (iAUC), Mean Amplitude of Glycemic Excursions (MAGE), Time in/above Range (TIR/TAR)], anthropometry [weight, Body Mass Index (BMI), waist circumference (WC)], lipid profile, diet and gut health [plasma short chain fatty acids (SCFAs)]. N = 210 adults were randomized to receive either DSNS with standard care (DSNS + SC;n = 105) or standard care alone (SC alone;n = 105). After 12 weeks, significant differences between DSNS + SC versus SC alone was observed in FBG [−3 ± 6 vs 14 ± 6 mg/dl;p = 0.03], PPG [−35 ± 9 vs −3 ± 9 mg/dl;p = 0.01], weight [−0.6 ± 0.1 vs 0.2 ± 0.1 kg;p = 0.0001], BMI [−0.3 ± 0.1 vs 0.1 ± 0.1 kg/m2;p = 0.0001] and WC [−0.3 ± 0.2 vs 0.2 ± 0.2 cm;p = 0.01]. HbA1C and low-density lipoprotein (LDL) were significantly reduced in DSNS + SC [−0.2 ± 0.9;p = 0.04 and −5 mg/dl;p = 0.03] respectively with no change in control. Continuous Glucose Monitoring (CGM) reported significant differences between DSNS + SC versus SC alone for mean glucose [−12 ± 65 vs 28 ± 93 mg/dl;p < 0.01], TAR 180 [−9 ± 42 vs 7 ± 45 mg/dl;p = 0.04], TAR 250 [−3 ± 27 vs 9 ± 38 mg/dl;p = 0.05], iAUC [−192 (1.1) vs −48 (1.1) mg/dl;p = 0.03]. MAGE was significantly reduced for both DSNS + SC (−19 ± 67;p < 0.001) and SC alone (−8 ± 70;p = 0.04), with reduction being more pronounced for DSNS + SC. DSNS + SC reported a decrease in carbohydrate energy % [−9.4 (−11.3, −7.6) %;p < 0.0001] and amount [−47.4 (−67.1, −27.7) g;p < 0.0001], increased dietary fiber [9.5 (7.2, 11.8) g;p < 0.0001] and protein energy % [0.9 (0.5, 1.3) %;p < 0.0001] versus SC alone. DSNS + SC reported significant increases versus SC alone in total (0.3 ng/ml;p = 0.03) and individual plasma SCFAs. The consumption of DSNS significantly improves the glycemic, anthropometric, dietary, and gut health markers in diabetes.

Efficacy of a Diabetes Specific Nutritional Supplement (DSNS) on Glycemic Response in Prediabetic Adults: A Two-Armed, Open-Labelled Randomized Controlled Study

It is well known that Diabetes Specific Nutritional Supplements (DSNSs) are linked to improved glycemic control in individuals with diabetes. However, data on efficacy of DSNSs in prediabetics is limited. This was a two-armed, open-labelled, randomized controlled six-week study on 199 prediabetics [30 - 65 years;Glycosylated Hemoglobin (HbA1c) 5.7% - 6.4% and/or Fasting Blood Glucose (FBG) 100-125 mg/dl]. Two parallel phases were conducted: Acute Blood Glucose Response (ABGR) and Intervention phase. Prediabetic participants were randomized into test (n = 100) and control (n = 99). The primary objective was to assess the ABGR of DSNS versus an isocaloric snack, measured by incremental Area under the Curve (iAUC). Test and control received 60 g of DSNS and 56 g of isocaloric snack (cornflakes) respectively, both in 250 ml double-toned milk on visit days 1, 15, 29 and 43. Postprandial Blood Glucose (PPG) was estimated at 30, 60, 90, 120, 150 and 180 minutes. During the 4 weeks intervention phase, the test group received DSNS with lifestyle counselling (DSNS + LC) and was compared with the control receiving lifestyle counselling alone (LC alone). Impact was studied on FBG, HbA1C, anthropometry, body composition, blood pressure, nutrient intake, and physical activity. The impact of DSNS was also studied using CGM between two 14-day phases: CGM1 baseline (days 1 - 14) and CGM2 endline (days 28 - 42). DSNS showed significantly lower PPG versus isocaloric snack at 30 (p 12, and chromium were reported by DSNS + LC versus LC alone. No other significant changes were reported between groups. It may be concluded that DSNS may be considered as a snack for prediabetic or hyperglycemic individuals requiring nutritional support for improved glycemic control.

Extensive prediction of drug response in mutation-subtype-specific LUAD with machine learning approach

Lung cancer is the most prevalent cancer diagnosis and the leading cause of cancer death worldwide.Therapeutic failure in lung cancer(LUAD)is heavily influenced by drug resistance.This challenge stems from the diverse cell populations within the tumor,each having unique genetic,epigenetic,and phenotypic profiles.Such variations lead to varied therapeutic responses,thereby contributing to tumor relapse and disease progression.Methods:The Genomics of Drug Sensitivity in Cancer(GDSC)database was used in this investigation to obtain the mRNA expression dataset,genomic mutation profile,and drug sensitivity information of NSCLS.Machine Learning(ML)methods,including Random Forest(RF),Artificial Neurol Network(ANN),and Support Vector Machine(SVM),were used to predict the response status of each compound based on the mRNA and mutation characteristics determined using statistical methods.The most suitable method for each drug was proposed by comparing the prediction accuracy of different ML methods,and the selected mRNA and mutation characteristics were identified as molecular features for the drug-responsive cancer subtype.Finally,the prognostic influence of molecular features on the mutational subtype of LUAD in publicly available datasets.Results:Our analyses yielded 1,564 gene features and 45 mutational features for 46 drugs.Applying the ML approach to predict the drug response for each medication revealed an upstanding performance for SVM in predicting Afuresertib drug response(area under the curve[AUC]0.875)using CIT,GAS2L3,STAG3L3,ATP2B4-mut,and IL15RA-mut as molecular features.Furthermore,the ANN algorithm using 9 mRNA characteristics demonstrated the highest prediction performance(AUC 0.780)in Gefitinib with CCL23-mut.Conclusion:This work extensively investigated the mRNA and mutation signatures associated with drug response in LUAD using a machine-learning approach and proposed a priority algorithm to predict drug response for different drugs.

Design of Fully Automatic Specification Selection System for Resistance Welding Equipment

A system for fully automatic selection of welding specifications in resistance welding equipment has been developed to address the problem of workers frequently choosing the wrong specifications during manual welding of multiple parts on a single machine in automobile factories. The system incorporates an automatic recognition system for different workpiece materials using the added machine fixture,visual detection system for nuts and bolts,and secondary graphical confirmation to ensure the correctness of specification calling. This system achieves reliable,fully automatic selection of welding specifications in resistance welding equipment and has shown significant effects in improving welding quality for massproduced workpieces,while solving the problem of specification calling errors that can occur with traditional methods involving process charts and code adjustments. This system is particularly suitable for promoting applications in manual welding of multiple parts on a single machine in automobile factories,ensuring correct specification calling and welding quality.

Enhancing Relational Triple Extraction in Specific Domains:Semantic Enhancement and Synergy of Large Language Models and Small Pre-Trained Language Models

In the process of constructing domain-specific knowledge graphs,the task of relational triple extraction plays a critical role in transforming unstructured text into structured information.Existing relational triple extraction models facemultiple challenges when processing domain-specific data,including insufficient utilization of semantic interaction information between entities and relations,difficulties in handling challenging samples,and the scarcity of domain-specific datasets.To address these issues,our study introduces three innovative components:Relation semantic enhancement,data augmentation,and a voting strategy,all designed to significantly improve the model’s performance in tackling domain-specific relational triple extraction tasks.We first propose an innovative attention interaction module.This method significantly enhances the semantic interaction capabilities between entities and relations by integrating semantic information fromrelation labels.Second,we propose a voting strategy that effectively combines the strengths of large languagemodels(LLMs)and fine-tuned small pre-trained language models(SLMs)to reevaluate challenging samples,thereby improving the model’s adaptability in specific domains.Additionally,we explore the use of LLMs for data augmentation,aiming to generate domain-specific datasets to alleviate the scarcity of domain data.Experiments conducted on three domain-specific datasets demonstrate that our model outperforms existing comparative models in several aspects,with F1 scores exceeding the State of the Art models by 2%,1.6%,and 0.6%,respectively,validating the effectiveness and generalizability of our approach.

Basic regulatory science behind drug substance and drug product specifications of monoclonal antibodies and other protein therapeutics

In this review,we focus on providing basics and examples for each component of the protein therapeutic specifications to interested pharmacists and biopharmaceutical scientists with a goal to strengthen understanding in regulatory science and compliance.Pharmaceutical specifications comprise a list of important quality attributes for testing,references to use for test procedures,and appropriate acceptance criteria for the tests,and they are set up to ensure that when a drug product is administered to a patient,its intended therapeutic benefits and safety can be rendered appropriately.Conformance of drug substance or drug product to the specifications is achieved by testing an article according to the listed tests and analytical methods and obtaining test results that meet the acceptance criteria.Quality attributes are chosen to be tested based on their quality risk,and consideration should be given to the merit of the analytical methods which are associated with the acceptance criteria of the specifications.Acceptance criteria are set forth primarily based on efficacy and safety profiles,with an increasing attention noted for patient-centric specifications.Discussed in this work are related guidelines that support the biopharmaceutical specification setting,how to set the acceptance criteria,and examples of the quality attributes and the analytical methods from 60 articles and 23 pharmacopeial monographs.Outlooks are also explored on process analytical technologies and other orthogonal tools which are on-trend in biopharmaceutical characterization and quality control.

Comparative Analysis of Statistical Thickness Models for the Determination of the External Specific Surface and the Surface of the Micropores of Materials: The Case of a Clay Concrete Stabilized Using Sugar Cane Molasses

In this work, four empirical models of statistical thickness, namely the models of Harkins and Jura, Hasley, Carbon Black and Jaroniec, were compared in order to determine the textural properties (external surface and surface of micropores) of a clay concrete without molasses and clay concretes stabilized with 8%, 12% and 16% molasses. The results obtained show that Hasley’s model can be used to obtain the external surfaces. However, it does not allow the surface of the micropores to be obtained, and is not suitable for the case of simple clay concrete (without molasses) and for clay concretes stabilized with molasses. The Carbon Black, Jaroniec and Harkins and Jura models can be used for clay concrete and stabilized clay concrete. However, the Carbon Black model is the most relevant for clay concrete and the Harkins and Jura model is for molasses-stabilized clay concrete. These last two models augur well for future research.

Development of donor specific antibodies after SARS-CoV-2 vaccination:What do we know so far?

Vaccination against Coronavirus disease-19(COVID-19)was pivotal to limit spread,morbidity and mortality.Our aim is to find out whether vaccines against COVID-19 lead to an immunological response stimulating the production of de novo donor specific antibodies(DSAs)or increase in mean fluorescence intensity(MFI)of pre-existing DSAs in kidney transplant recipients(KTRs).This study involved a detailed literature search through December 2nd,2023 using PubMed as the primary database.The search strategy incorporated a combination of relevant Medical Subject Headings terms and keywords:"COVID-19","SARS-CoV-2 Vaccination","Kidney,Renal Transplant",and"Donor specific antibodies".The results from related studies were collated and analyzed.A total of 6 studies were identified,encompassing 460 KTRs vaccinated against COVID-19.Immunological responses were detected in 8 KTRs of which 5 had increased MFIs,1 had de novo DSA,and 2 were categorized as either having de novo DSA or increased MFI.There were 48 KTRs with pre-existing DSAs prior to vaccination,but one study(Massa et al)did not report whether pre-existing DSAs were associated with post vaccination outcomes.Of the remaining 5 studies,35 KTRs with pre-existing DSAs were identified of which 7 KTRs(20%)developed de novo DSAs or increased MFIs.Overall,no immunological response was detected in 452(98.3%)KTRs.Our study affirms prior reports that COVID-19 vaccination is safe for KTRs,especially if there are no pre-existing DSAs.However,if KTRs have pre-existing DSAs,then an increased immunological risk may be present.These findings need to be taken cautiously as they are based on a limited number of patients so further studies are still needed for confirmation.

RFFsNet-SEI:a multidimensional balanced-RFFs deep neural network framework for specific emitter identification

Existing specific emitter identification(SEI)methods based on hand-crafted features have drawbacks of losing feature information and involving multiple processing stages,which reduce the identification accuracy of emitters and complicate the procedures of identification.In this paper,we propose a deep SEI approach via multidimensional feature extraction for radio frequency fingerprints(RFFs),namely,RFFsNet-SEI.Particularly,we extract multidimensional physical RFFs from the received signal by virtue of variational mode decomposition(VMD)and Hilbert transform(HT).The physical RFFs and I-Q data are formed into the balanced-RFFs,which are then used to train RFFsNet-SEI.As introducing model-aided RFFs into neural network,the hybrid-driven scheme including physical features and I-Q data is constructed.It improves physical interpretability of RFFsNet-SEI.Meanwhile,since RFFsNet-SEI identifies individual of emitters from received raw data in end-to-end,it accelerates SEI implementation and simplifies procedures of identification.Moreover,as the temporal features and spectral features of the received signal are both extracted by RFFsNet-SEI,identification accuracy is improved.Finally,we compare RFFsNet-SEI with the counterparts in terms of identification accuracy,computational complexity,and prediction speed.Experimental results illustrate that the proposed method outperforms the counterparts on the basis of simulation dataset and real dataset collected in the anechoic chamber.

Genetic regulation of cell type-specific chromatin accessibility shapes brain disease etiology

Nucleotide variants in cell type-specific gene regulatory elements in the human brain are risk factors for human disease.We measured chromatin accessibility in 1932 aliquots of sorted neurons and non-neurons from 616 human postmortem brains and identified 34,539 open chromatin regions with chromatin accessibility quantitative trait loci(caQTLs).Only 10.4%of caQTLs are shared between neurons and non-neurons,which supports cell type-specific genetic regulation of the brain regulome.Incorporating allele-specific chromatin accessibility improves statistical fine-mapping and refines molecular mechanisms that underlie disease risk.Using massively parallel reporter assays in induced excitatory neurons,we screened 19,893 brain QTLs and identified the functional impact of 476 regulatory variants.Combined,this comprehensive resource captures variation in the human brain regulome and provides insights into disease etiology.

Identity Study of English for Specific Purposes Teachers in the Context of Shifts in Public English Teaching Positioning

This study investigates the identity of English for Specific Purposes(ESP)teachers within the dynamic landscape of shifts in public English teaching positioning.Adopting a mixed-methods approach,qualitative and quantitative analyses were employed to explore the complexities of ESP teacher identity construction and adaptation.Qualitative findings revealed key themes including strong professional identity grounded in specialized expertise,the impact of changing educational policies and curriculum reforms,and the importance of cultural competence and intercultural communication.Quantitative analysis of survey data indicated high levels of job satisfaction among ESP teachers,with significant correlations between variables such as professional development participation,perceived efficacy in technology integration,and self-perceptions of identity as ESP educators.

Comparative Analysis of Machine Learning Models for Customer Churn Prediction in the U.S. Banking and Financial Services: Economic Impact and Industry-Specific Insights

Customer churn poses a significant challenge for the banking and finance industry in the United States, directly affecting profitability and market share. This study conducts a comprehensive comparative analysis of machine learning models for customer churn prediction, focusing on the U.S. context. The research evaluates the performance of logistic regression, random forest, and neural networks using industry-specific datasets, considering the economic impact and practical implications of the findings. The exploratory data analysis reveals unique patterns and trends in the U.S. banking and finance industry, such as the age distribution of customers and the prevalence of dormant accounts. The study incorporates macroeconomic factors to capture the potential influence of external conditions on customer churn behavior. The findings highlight the importance of leveraging advanced machine learning techniques and comprehensive customer data to develop effective churn prevention strategies in the U.S. context. By accurately predicting customer churn, financial institutions can proactively identify at-risk customers, implement targeted retention strategies, and optimize resource allocation. The study discusses the limitations and potential future improvements, serving as a roadmap for researchers and practitioners to further advance the field of customer churn prediction in the evolving landscape of the U.S. banking and finance industry.

多主体协同开发社区教育课程的PERSONAL模式探索——基于“我爱我家”的个案研究

通过对“我爱我家”这一社区自发课程的跟踪调查,发现社区自发课程开发实质上是一个组织学习过程,经过个体直觉、知识解释、知识整合等环节实现组织的制度化学习,在推动学习型无边界组织生成的同时,生成社区教育课程。在这一过程中,政策(policies)支持、专家(expert)指导、区域需求和区域特征(regional)、社区系统(system)、组织化(organization)等多要素叠加发挥作用,通过不断拓展新鲜(new)领域的积极行动(action)整合所有资源,并推动跨越边界的学习(learning)贯穿始终。因此,多主体协同的社区自发课程可以不完全地概括为PERSONAL模式。要以PERSONAL模式发展社区自发课程,需要以学习者为中心,注重发展人际互动,营造有利于跨组织交互学习的教育生态系统。

SbMYB3 transcription factor promotes root-specific f lavone biosynthesis in Scutellaria baicalensis

Scutellaria baicalensis Georgi produces abundant root-specific f lavones(RSFs),which provide various benefits to human health.We have elucidated the complete biosynthetic pathways of baicalein and wogonin.However,the transcriptional regulation of f lavone biosynthesis in S.baicalensis remains unclear.We show that the SbMYB3 transcription factor functions as a transcriptional activator involved in the biosynthesis of RSFs in S.baicalensis.Yeast one-hybrid and transcriptional activation assays showed that SbMYB3 binds to the promoter of flavone synthase II-2(SbFNSII-2)and enhances its transcription.In S.baicalensis hairy roots,RNAi of SbMYB3 reduced the accumulation of baicalin and wogonoside,and SbMYB3 knockout decreased the biosynthesis of baicalein,baicalin,wogonin,and wogonoside,whereas SbMYB3 overexpression enhanced the contents of baicalein,baicalin,wogonin,and wogonoside.Transcript profiling by qRT–PCR demonstrated that SbMYB3 activates SbFNSII-2 expression directly,thus leading to more abundant accumulation of RSFs.This study provides a potential target for metabolic engineering of RSFs.

Product Specification Analysis for Modular Product Design Using Big Sales Data

Big data on product sales are an emerging resource for supporting modular product design to meet diversified customers’requirements of product specification combinations.To better facilitate decision-making of modular product design,correlations among specifications and components originated from customers’conscious and subconscious preferences can be investigated by using big data on product sales.This study proposes a framework and the associated methods for supporting modular product design decisions based on correlation analysis of product specifications and components using big sales data.The correlations of the product specifications are determined by analyzing the collected product sales data.By building the relations between the product components and specifications,a matrix for measuring the correlation among product components is formed for component clustering.Six rules for supporting the decision making of modular product design are proposed based on the frequency analysis of the specification values per component cluster.A case study of electric vehicles illustrates the application of the proposed method.

Performances of a Stinger PDC cutter breaking granite: Cutting force and mechanical specific energy in single cutter tests

The Stinger PDC cutter has high rock-breaking efficiency and excellent impact and wear resistance, which can significantly increase the rate of penetration (ROP) and extend PDC bit life for drilling hard and abrasive formation. The knowledge of force response and mechanical specific energy (MSE) for the Stinger PDC cutter is of great importance for improving the cutter's performance and optimizing the hybrid PDC bit design. In this paper, 87 single cutter tests were conducted on the granite. A new method for precisely obtaining the rock broken volume was proposed. The influences of cutting depth, cutting angle, and cutting speed on cutting force and MSE were analyzed. Besides, a phenomenological cutting force model of the Stinger PDC cutter was established by regression of experimental data. Moreover, the surface topography and fracture morphology of the cutting groove and large size cuttings were measured by a 3D profilometer and a scanning electron microscope (SEM). Finally, the rock-breaking mechanism of the Stinger PDC cutter was illustrated. The results indicated that the cutting depth has the greatest influence on the cutting force and MSE, while the cutting speed has no obvious effects, especially at low cutting speeds. As the increase of cutting depth, the cutting force increases linearly, and MSE reduces with a quadratic polynomial relationship. When the cutting angle raises from 10° to 30°, the cutting force increases linearly, and the MSE firstly decreases and then increases. The optimal cutting angle for breaking rock is approximately 20°. The Stinger PDC cutter breaks granite mainly by high concentrated point loading and tensile failure, which can observably improve the rock breaking efficiency. The key findings of this work will help to reveal the rock-breaking mechanisms and optimize the cutter arrangement for the Stinger PDC cutter.

N-Doped rGO-Like Carbon Prepared from Coconut Shell:Structure and Specific Capacitance

An rGO−like carbon compound has been synthesized from biomass,i.e.,old coconut shell,by a carbonization process followed by heating at 400°C for 5 h.The nitrogen doping was achieved by adding the urea(CH4N2O)and stirring at 70°C for 14 h.The morphology and structure of the rGO-like carbon were investigated by electron microscopies and Raman spectroscopy.The presence of C-N functional groups was analyzed by Fourier transform infrared and synchrotron X-ray photoemission spectroscopy,while the particle and the specific capacitance were measured by particle sizer and cyclic voltammetry.The highest specific capacitance of 72.78 F/g is achieved by the sample with 20%urea,having the smallest particles size and the largest surface area.The corresponding sample has shown to be constituted by the appropriate amount of C–N pyrrolic and pyridinic defects.

Asset specificity and farmers' intergenerational succession willingness of apple management

Understanding the factors behind apple farmers’willingness to pass on the management of their farms to their descendants is crucial to the continuity of apple production.Due to the high specificity of the human capital,physical assets,land assets,and geographical location in apple production,this study used a binary logistic regression and a mediating effect model to explore the impact of asset specificity on farmers’intergenerational succession willingness of apple management(FISWAM)and to examine the mediating effects of loss aversion in the impact of asset specificity on the FISWAM.The results showed that about 18.68%of the respondents expressed willingness to transfer their apple business between generations,and the FISWAM was generally weak.In addition to the negative impact of geographical location specificity(GLS),human capital specificity(HCS),physical assets specificity(PAS),and land assets specificity(LAS)can enhance the FISWAM.Loss aversion plays a partial mediating role in the impact of PAS,LAS,and GLS on the FISWAM.

Key elements determining the intestinal region-specific environment of enteric neurons in type 1 diabetes

Diabetes,as a metabolic disorder,is accompanied with several gastrointestinal(GI)symptoms,like abdominal pain,gastroparesis,diarrhoea or constipation.Serious and complex enteric nervous system damage is confirmed in the background of these diabetic motility complaints.The anatomical length of the GI tract,as well as genetic,developmental,structural and functional differences between its segments contribute to the distinct,intestinal region-specific effects of hyperglycemia.These observations support and highlight the importance of a regional approach in diabetes-related enteric neuropathy.Intestinal large and microvessels are essential for the blood supply of enteric ganglia.Bidirectional morpho-functional linkage exists between enteric neurons and enteroglia,however,there is also a reciprocal communication between enteric neurons and immune cells on which intestinal microbial composition has crucial influence.From this point of view,it is more appropriate to say that enteric neurons partake in multidirectional communication and interact with these key players of the intestinal wall.These interplays may differ from segment to segment,thus,the microenvironment of enteric neurons could be considered strictly regional.The goal of this review is to summarize the main tissue components and molecular factors,such as enteric glia cells,interstitial cells of Cajal,gut vasculature,intestinal epithelium,gut microbiota,immune cells,enteroendocrine cells,prooxidants,antioxidant molecules and extracellular matrix,which create and determine a gut region-dependent neuronal environment in diabetes.

Preparation of multicore millimeter-sized spherical alginate capsules to specifically and sustainedly release fish oil

Specific and sustained release of nutrients from capsules to the gastrointestinal tract has attracted many attentions in the field of food and drug delivery.In this work,we reported a monoaxial dispersion electrospraying-ionotropic gelation technique to prepare multicore millimeter-sized spherical capsules for specific and sustained release of fish oil.The spherical capsules had diameters from 2.05 mm to 0.35 mm with the increased applied voltages.The capsules consisted of uniform(at applied voltages of≤10 k V)or nonuniform(at applied voltages of>10 k V)multicores.The obtained capsules had reasonable loading ratios(9.7%-6.3%)due to the multicore structure.In addition,the obtained capsules had specific and sustained release behaviors of fish oil into the small intestinal phase of in vitro gastrointestinal tract and small intestinal tract models.The simple monoaxial dispersion electrospraying-ionotropic gelatin technique does not involve complicated preparation formulations and polymer modification,which makes the technique has a potential application prospect for the fish oil preparations and the encapsulation of functional active substances in the field of food and drug industries.