Hasil untuk "Nutritional diseases. Deficiency diseases"

Mingjing Xu, Jianheng Zheng, Tao Ying et al.

Abstract Objectives Lifestyle intervention, especially dietary modification, has been the cornerstone in preventing type 2 diabetes (T2D). We aimed to investigate the associations of various protein intake exposures with the risk of incident T2D in adults with or without metabolic diseases. Methods We followed 29517 residents enrolled in the Shanghai Suburban Adult Cohort and Biobank (SSACB) without diabetes at baseline through the electronic information system. Cox proportional hazard models were used to evaluate the associations of various protein intake exposures with the risk of incident T2D, visualized by restricted cubic splines (RCS). Propensity-score matching and subgroup analysis were used to characterize the association between total protein and incident T2D by metabolic diseases. Meta-analysis further explored the association between protein intake and incident T2D in broader populations. Results In SSACB, 1511 (5.1%) participants developed T2D during a median follow-up period of 5.69 years. A U-shaped association between total protein and risk of incident T2D was found (protective range: 12.20-16.85 percentage energy (%E), cut-off point: 14.53%E). The U-shaped association (P-nonlinear < 0.001) remained in adults with hypertension with a narrower protective range (12.20–15.35%E), with a linear association in adults with NAFLD (HR per 1%E: 0.952, 95% CI: [0.910, 0.995]), whereas no significant association in adults with hyperlipidemia or central obesity. A negative association between plant protein and risk of incident T2D was also found in SSACB (HR per 1%E: 0.947, 95% CI: [0.900, 0.996]). In addition, the U-shaped association of total protein with the risk of incident T2D was reaffirmed in the dose-response meta-analysis (cut-off point: 15.10%E). Conclusion In SSACB, a U-shaped association between total protein intake and risk of incident T2D was found, which was reaffirmed in the dose-response meta-analysis, and differed by metabolic diseases, especially hypertension and NAFLD. Moreover, plant protein was inversely associated with the risk of incident T2D.

L Cif, D Demailly, JP Lin et al.

Heterozygous mutations in KMT2B are associated with an early-onset, progressive, and often complex dystonia (DYT28). Key characteristics of typical disease include focal motor features at disease presentation, evolving through a caudocranial pattern into generalized dystonia, with prominent oromandibular, laryngeal, and cervical involvement. Although KMT2B-related disease is emerging as one of the most common causes of early-onset genetic dystonia, much remains to be understood about the full spectrum of the disease. We describe a cohort of 53 patients with KMT2B mutations, with detailed delineation of their clinical phenotype and molecular genetic features. We report new disease presentations, including atypical patterns of dystonia evolution and a subgroup of patients with a non-dystonic neurodevelopmental phenotype. In addition to the previously reported systemic features, our study has identified co-morbidities, including the risk of status dystonicus, intrauterine growth retardation, and endocrinopathies. Analysis of this study cohort (n = 53) in tandem with published cases (n = 80) revealed that patients with chromosomal deletions and protein-truncating variants had a significantly higher burden of systemic disease (with earlier onset of dystonia) than those with missense variants. Eighteen individuals had detailed longitudinal data available after insertion of deep brain stimulation for medically refractory dystonia. Median age at deep brain stimulation was 11.5 years (range: 4.5 to 37.0 years). Follow-up after deep brain stimulation ranged from 0.25 to 22 years. Significant improvement of motor function and disability (as assessed by the Burke-Fahn-Marsden Dystonia Rating Scales, BFMDRS-M and BFMDRS-D) was evident at 6 months, 1 year, and last follow-up (motor, P = 0.001, P = 0.004, and P = 0.012; disability, P = 0.009, P = 0.002, and P = 0.012).

Austin Polanco, M. E. J. Newman

Repurposing existing drugs to treat new diseases is a cost-effective alternative to de novo drug development, but there are millions of potential drug-disease combinations to be considered with only a small fraction being viable. In silico predictions of drug-disease associations can be invaluable for reducing the size of the search space. In this work we present a novel network of drugs and the diseases they treat, compiled using a combination of existing textual and machine-readable databases, natural-language processing tools, and hand curation, and analyze it using network-based link prediction methods to identify potential drug-disease combinations. We measure the efficacy of these methods using cross-validation tests and find that several methods, particularly those based on graph embedding and network model fitting, achieve impressive prediction performance, significantly better than previous approaches, with area under the ROC curve above 0.95 and average precision almost a thousand times better than chance.

Abdullah Al Shafi, Rowzatul Zannat, Abdul Muntakim et al.

Disease-symptom datasets are significant and in demand for medical research, disease diagnosis, clinical decision-making, and AI-driven health management applications. These datasets help identify symptom patterns associated with specific diseases, thus improving diagnostic accuracy and enabling early detection. The dataset presented in this study systematically compiles disease-symptom relationships from various online sources, medical literature, and publicly available health databases. The data was gathered through analyzing peer-reviewed medical articles, clinical case studies, and disease-symptom association reports. Only the verified medical sources were included in the dataset, while those from non-peer-reviewed and anecdotal sources were excluded. The dataset is structured in a tabular format, where the first column represents diseases, and the remaining columns represent symptoms. Each symptom cell contains a binary value, indicating whether a symptom is associated with a disease. Thereby, this structured representation makes the dataset very useful for a wide range of applications, including machine learning-based disease prediction, clinical decision support systems, and epidemiological studies. Although there are some advancements in the field of disease-symptom datasets, there is a significant gap in structured datasets for the Bangla language. This dataset aims to bridge that gap by facilitating the development of multilingual medical informatics tools and improving disease prediction models for underrepresented linguistic communities. Further developments should include region-specific diseases and further fine-tuning of symptom associations for better diagnostic performance

Georgina Noel Marchiori, Elio Andrés Soria, María Eugenia Pasqualini et al.

Background: Cardiovascular risk is modifiable by changes in lifestyle and pharmacological management, with hypertension being a common pathology worldwide. Its treatment must address multiple metabolic targets. Based on the hypothesis that certain antihypertensive medications, such as the commonly used enalapril and losartan, and dietary habits improve hypertension-related changes in carotid structure and cardiometabolic variables, this work aimed to associate these drugs, as well as the Mediterranean diet adherence and non-modifiable biological factors, with changes in carotid intima-media thickness [cIMT] and blood lipids. Methods: Sociodemographic, clinical, biochemical and lifestyle data were collected in a cross-sectional study of 313 subjects under survey due to cardiovascular risk factors, aged 34–83 years (Cordoba, Argentina). Generalised structural equation models were used for analysis. Results: A higher cIMT with age and male sex was confirmed. Women had lower triacylglycerols and saturated fatty acids in serum but higher circulating levels of LDL-C, HDL-C and total cholesterol than men. Also, a higher adherence to the Mediterranean diet was associated with lower triacylglycerols, but higher levels of HDL-C cholesterol and ω-3 polyunsaturated fatty acids (ω-3 PUFAs) in serum. A greater adherence to the Mediterranean diet did not affect cIMT. Enalapril was associated with increased serum ω-3 PUFAs levels, but it did not affect other lipid fractions. Moreover, enalapril may control cIMT, whereas losartan may not. Conclusions: Our data demonstrate that the Mediterranean diet and enalapril are associated with a cardioprotective circulating lipid profile in hypertension. Concerning this, enalapril potentially promotes serum ω-3 PUFAs levels beyond its classical antihypertensive effect, which encourages future clinical studies to confirm it.

Álisson C. Gonçalves, Jéssica F. Vieira, Ana Carolina N. Rodrigues et al.

Background. Benfotiamine, a synthetic analog of thiamine, offers greater bioavailability compared to other thiamine salts and increases thiamine stores upon oral intake. Thiamine is essential for energy metabolism. This study aimed to evaluate the effects of oral benfotiamine supplementation on energy metabolism, particularly the Krebs cycle function, in the muscle of endurance-trained mice, and to assess its impact on endurance performance. Methods. Twenty-five mice were randomly assigned to four groups: a standard diet with sedentary behavior (Sta-Sed), a benfotiamine-supplemented diet with sedentary behavior (Ben-Sed), a standard diet with swimming training (Sta-Tr), and a benfotiamine-supplemented diet with swimming training (Ben-Tr). The trained groups underwent five weekly swimming sessions for six weeks, followed by an exhaustive test. Thiamine and its esters were measured in erythrocytes and gastrocnemius muscle. Gene expression of pyruvate dehydrogenase (PDHa) and alpha-ketoglutarate dehydrogenase (OGDH), along with levels of pyruvic, lactic, and hydroxybutyric acids in muscle, was analyzed. Results. The benfotiamine-supplemented groups had higher thiamine levels in erythrocytes and muscles compared to the standard-diet groups. No differences were observed in PDHa and OGDH gene expression. The Ben-Tr group exhibited increased muscle lactic acid levels and a higher lactic acid to pyruvic acid ratio compared to the sedentary groups. Hydroxybutyric acid levels were also elevated in the Ben-Tr group. No significant differences in exhaustive test duration were found between the groups. Conclusion. Benfotiamine supplementation increases thiamine levels in erythrocytes and muscle but does not affect the gene expression of thiamine-dependent enzymes. Although it alters energy metabolism in trained muscle, it does not enhance endurance performance in mice.

Yuke Zhang, Bohan Zhang, Ru Wang et al.

Abstract Background Observational studies have indicated that the plasma lipid profiles of patients with atopic dermatitis show significant differences compared to healthy individuals. However, the causal relationship between these differences remains unclear due to the inherent limitations of observational studies. Our objective was to explore the causal effects between 179 plasma lipid species and atopic dermatitis, and to investigate whether circulating inflammatory proteins serve as mediators in this causal pathway. Methods We utilized public genome-wide association studies data to perform a bidirectional two-sample, two-step mendelian randomization study. The inverse variance-weighted method was adopted as the primary analysis technique. MR-Egger and the weighted median were used as supplementary analysis methods. MR-PRESSO, Cochran’s Q test, and MR-Egger intercept test were applied for sensitivity analyses to ensure the robustness of our findings. Results The Mendelian randomization analysis revealed that levels of Phosphatidylcholine (PC) (18:1_20:4) (OR: 0.950, 95% CI: 0.929–0.972, p = 6.65 × 10− 6), Phosphatidylethanolamine (O-18:1_20:4) (OR: 0.938, 95% CI: 0.906–0.971, p = 2.79 × 10− 4), Triacylglycerol (TAG) (56:6) (OR: 0.937, 95% CI: 0.906–0.969, p = 1.48 × 10− 4) and TAG (56:8) (OR: 0.918, 95% CI: 0.876–0.961, p = 2.72 × 10− 4) were inversely correlated with the risk of atopic dermatitis. Conversely, PC (18:1_20:2) (OR: 1.053, 95% CI: 1.028–1.079, p = 2.11 × 10− 5) and PC (O-18:1_20:3) (OR: 1.086, 95% CI: 1.039–1.135, p = 2.47 × 10− 4) were positively correlated with the risk of atopic dermatitis. The results of the reverse directional Mendelian randomization analysis indicated that atopic dermatitis exerted no significant causal influence on 179 plasma lipid species. The level of circulating IL-18R1 was identified as a mediator for the increased risk of atopic dermatitis associated with higher levels of PC (18:1_20:2), accounting for a mediation proportion of 9.07%. Conclusion Our research suggests that plasma lipids can affect circulating inflammatory proteins and may serve as one of the pathogenic factors for atopic dermatitis. Targeting plasma lipid levels as a treatment for atopic dermatitis presents a potentially novel approach.

Yidan Shi, Leilei Zeng, Mary E. Thompson et al.

Continuous-time multistate models are widely used for analyzing interval-censored data on disease progression over time. Sometimes, diseases manifest differently and what appears to be a coherent collection of symptoms is the expression of multiple distinct disease subtypes. To address this complexity, we propose a mixture hidden Markov model, where the observation process encompasses states representing common symptomatic stages across these diseases, and each underlying process corresponds to a distinct disease subtype. Our method models both the overall and the type-specific disease incidence/prevalence accounting for sampling conditions and exactly observed death times. Additionally, it can utilize partially available disease-type information, which offers insights into the pathway through specific hidden states in the disease process, to aid in the estimation. We present both a frequentist and a Bayesian way to obtain the estimates. The finite sample performance is evaluated through simulation studies. We demonstrate our method using the Nun Study and model the development and progression of dementia, encompassing both Alzheimer's disease (AD) and non-AD dementia.

Ruoyu Chen, Weiyi Zhang, Bowen Liu et al.

The rising prevalence of vision-threatening retinal diseases poses a significant burden on the global healthcare systems. Deep learning (DL) offers a promising solution for automatic disease screening but demands substantial data. Collecting and labeling large volumes of ophthalmic images across various modalities encounters several real-world challenges, especially for rare diseases. Here, we introduce EyeDiff, a text-to-image model designed to generate multimodal ophthalmic images from natural language prompts and evaluate its applicability in diagnosing common and rare diseases. EyeDiff is trained on eight large-scale datasets using the advanced latent diffusion model, covering 14 ophthalmic image modalities and over 80 ocular diseases, and is adapted to ten multi-country external datasets. The generated images accurately capture essential lesional characteristics, achieving high alignment with text prompts as evaluated by objective metrics and human experts. Furthermore, integrating generated images significantly enhances the accuracy of detecting minority classes and rare eye diseases, surpassing traditional oversampling methods in addressing data imbalance. EyeDiff effectively tackles the issue of data imbalance and insufficiency typically encountered in rare diseases and addresses the challenges of collecting large-scale annotated images, offering a transformative solution to enhance the development of expert-level diseases diagnosis models in ophthalmic field.

Payam Sharifan, Susan Darroudi, Mahdi Rafiee et al.

Abstract Introduction Vitamin D deficiency has been reported to affect liver function biomarkers. This study was aimed to investigate the effect of consuming vitamin D fortified low-fat dairy products on liver function tests in adults with abdominal obesity. Methods This total blinded randomized controlled trial was undertaken on otherwise healthy abdominally obese adults living in Mashhad, Iran. Milk and yogurt were fortified with 1500 IU vitamin D3 nano-capsules. Participants were randomized to receive fortified milk (n = 73), plain milk (n = 73), fortified yogurt (n = 69), and plain yogurt (n = 74) for 10 weeks. Blood samples were taken at baseline and at the end of the study to assess serum levels of vitamin D, alanine aminotransferase, aspartate aminotransferase, alkaline phosphatase (ALP), and Gamma glutamyl transferase. Results A total of 289 participants completed the study (54% female). The groups were homogenous in terms of age, sex, weight, energy intake, and physical activity level (p-value > 0.05). After the trial, vitamin D serum levels were significantly increased in both groups receiving fortified products (both p < 0.001). There was a significant time*group effect only in serum ALP (p < 0.001). Conclusion Consumption of dairy products fortified by 1500 IU vitamin D3 might have detrimental effects on serum levels of some liver enzymes in individuals with abdominal obesity. Further studies needed to determine these effects and underlying mechanisms. Trial registration: IRCT20101130005280N27 .

Maria Góis, Patrícia Batista, Magnólia Araújo et al.

Probiotic products have been the focus of research for several years due to the potential of their biological properties to impact mental health, mood, and cognitive functions. Kombucha is a probiotic drink that has been reported to be beneficial for mental health, particularly at the level of emotion regulation. This study aims to understand the perception of the Portuguese and Brazilian populations regarding the consumption of probiotics and Kombucha, as well as to understand these consumers’ perceptions related to the impact on emotion regulation (and the impact of this consumption on emotion regulation). The research was conducted through an online questionnaire and had a total sample of 256 participants. The results show that there are no statistically significant differences between the consumption of probiotics and Kombucha when comparing the Portuguese and Brazilian samples. Additionally, this study reveals a significant association between probiotic consumption patterns in both the Portuguese and Brazilian samples. However, no statistically significant relationship was found between the consumption of probiotics and Kombucha and emotion regulation. This study intends to contribute to the increase in knowledge about the perception of probiotics and Kombucha consumption in relation to emotion regulation, and to draw attention to the importance of this topic in the community (society, academia, and industry).

Konrad Furmańczyk, Wojciech Niemiro, Mariola Chrzanowska et al.

We propose a new graphical model to describe the comorbidity of allergic diseases. We present our model in two versions. First, we introduce a generative model that correctly reflects the variables' causal relationship. Then we propose an approximation of the generative model by another misspecified model that is computationally more efficient and easily interpretable. We will focus on the misspecified version, which we consider more practical. We include in the model two directed graphs, one graph of known dependency between the main binary variables (diseases), and a second graph of the dependence between the occurrence of the diseases and their symptoms. In the model, we also consider additional auxiliary variables. The proposed model is evaluated on a cross-sectional multicentre study in Poland on the ECAP database (www.ecap.pl). An assessment of the stability of the proposed model was obtained using bootstrap and jackknife techniques.

Yousuf Rayhan Emon, Md Golam Rabbani, Md. Taimur Ahad et al.

Sweet orange leaf diseases are significant to agricultural productivity. Leaf diseases impact fruit quality in the citrus industry. The apparition of machine learning makes the development of disease finder. Early detection and diagnosis are necessary for leaf management. Sweet orange leaf disease-predicting automated systems have already been developed using different image-processing techniques. This comprehensive literature review is systematically based on leaf disease and machine learning methodologies applied to the detection of damaged leaves via image classification. The benefits and limitations of different machine learning models, including Vision Transformer (ViT), Neural Network (CNN), CNN with SoftMax and RBF SVM, Hybrid CNN-SVM, HLB-ConvMLP, EfficientNet-b0, YOLOv5, YOLOv7, Convolutional, Deep CNN. These machine learning models tested on various datasets and detected the disease. This comprehensive review study related to leaf disease compares the performance of the models; those models' accuracy, precision, recall, etc., were used in the subsisting studies

Anish Mall, Sanchit Kabra, Ankur Lhila et al.

This research paper presents AMaizeD: An End to End Pipeline for Automatic Maize Disease Detection, an automated framework for early detection of diseases in maize crops using multispectral imagery obtained from drones. A custom hand-collected dataset focusing specifically on maize crops was meticulously gathered by expert researchers and agronomists. The dataset encompasses a diverse range of maize varieties, cultivation practices, and environmental conditions, capturing various stages of maize growth and disease progression. By leveraging multispectral imagery, the framework benefits from improved spectral resolution and increased sensitivity to subtle changes in plant health. The proposed framework employs a combination of convolutional neural networks (CNNs) as feature extractors and segmentation techniques to identify both the maize plants and their associated diseases. Experimental results demonstrate the effectiveness of the framework in detecting a range of maize diseases, including powdery mildew, anthracnose, and leaf blight. The framework achieves state-of-the-art performance on the custom hand-collected dataset and contributes to the field of automated disease detection in agriculture, offering a practical solution for early identification of diseases in maize crops advanced machine learning techniques and deep learning architectures.

Yixin Wang, Zihao Lin, Haoyu Dong

Knowledge Graph (KG) plays a crucial role in Medical Report Generation (MRG) because it reveals the relations among diseases and thus can be utilized to guide the generation process. However, constructing a comprehensive KG is labor-intensive and its applications on the MRG process are under-explored. In this study, we establish a complete KG on chest X-ray imaging that includes 137 types of diseases and abnormalities. Based on this KG, we find that the current MRG data sets exhibit a long-tailed problem in disease distribution. To mitigate this problem, we introduce a novel augmentation strategy that enhances the representation of disease types in the tail-end of the distribution. We further design a two-stage MRG approach, where a classifier is first trained to detect whether the input images exhibit any abnormalities. The classified images are then independently fed into two transformer-based generators, namely, ``disease-specific generator" and ``disease-free generator" to generate the corresponding reports. To enhance the clinical evaluation of whether the generated reports correctly describe the diseases appearing in the input image, we propose diverse sensitivity (DS), a new metric that checks whether generated diseases match ground truth and measures the diversity of all generated diseases. Results show that the proposed two-stage generation framework and augmentation strategies improve DS by a considerable margin, indicating a notable reduction in the long-tailed problem associated with under-represented diseases.

Quan V. Vuong, Hong Ngoc Thuy Pham, Christopher Negus

Herbal teas, which are a rich and diverse source of polyphenols, have been widely consumed due to their association with various health benefits. Preparation techniques can significantly affect the level of polyphenols in a cup of tea. Thus, this study investigated the impact of different preparation techniques, including brewing time in hot water, microwave-assisted extraction with cold and hot water (cold and hot MAE) for both radiation time and power, and laboratory testing condition on extractability of polyphenols in infusion from a teabag. The results showed that brewing time using hot water significantly affected the extractability of polyphenols and antioxidant activity. Cold and hot MAE conditions also significantly affected the extractability of polyphenols and antioxidant activity from a teabag infusion. Hot brewing at 7 min and cold MAE at full power with second boiled (1.93 min on and 1 min off radiation) are recommended for the preparation of herbal tea from a teabag, as these conditions had comparable extractability of polyphenols and antioxidant activity in comparison with other preparation techniques. There are over 20 major chromatogram peaks, of which 7 were identified as gallic acid, catechin, caffeic acid, ferulic acid, epicatechin gallate, quercetin, and kaempferol, revealing potential health benefits of this herbal tea.

Maisa Hamed Al Kiyumi

Kirstin Roster, Colm Connaughton, Francisco A. Rodrigues

Recent infectious disease outbreaks, such as the COVID-19 pandemic and the Zika epidemic in Brazil, have demonstrated both the importance and difficulty of accurately forecasting novel infectious diseases. When new diseases first emerge, we have little knowledge of the transmission process, the level and duration of immunity to reinfection, or other parameters required to build realistic epidemiological models. Time series forecasts and machine learning, while less reliant on assumptions about the disease, require large amounts of data that are also not available in early stages of an outbreak. In this study, we examine how knowledge of related diseases can help make predictions of new diseases in data-scarce environments using transfer learning. We implement both an empirical and a theoretical approach. Using empirical data from Brazil, we compare how well different machine learning models transfer knowledge between two different disease pairs: (i) dengue and Zika, and (ii) influenza and COVID-19. In the theoretical analysis, we generate data using different transmission and recovery rates with an SIR compartmental model, and then compare the effectiveness of different transfer learning methods. We find that transfer learning offers the potential to improve predictions, even beyond a model based on data from the target disease, though the appropriate source disease must be chosen carefully. While imperfect, these models offer an additional input for decision makers during pandemic response.

Liesbet Trenson, Sander Trenson, Falco van Nes et al.

Introduction: Obesity is a global health challenge, and pharmacologic options are emerging. Once daily subcutaneous administration of 3 mg liraglutide, a glucagon like peptide-1 analogue, has been shown to induce weight loss in clinical trials, but real-world effectiveness data are scarce. Methods: It is a single-centre retrospective cohort study of patients who were prescribed liraglutide on top of lifestyle adaptations after multidisciplinary evaluation. In Belgium, liraglutide is only indicated for weight management if the BMI is &#x3e;30 kg/m2 or ≥27 kg/m2 with comorbidities such as dysglycaemia, dyslipidaemia, hypertension, or obstructive sleep apnoea. No indication is covered by the compulsory health care insurance. Liraglutide was started at 0.6 mg/day and uptitrated weekly until 3 mg/day or the maximum tolerated dose. Treatment status and body weight were evaluated at the 4-month routine visit. Results: Between June 2016 and January 2020, liraglutide was prescribed to 115 patients (77% female), with a median age of 47 (IQR 37.7–54.0) years, a median body weight of 98.4 (IQR 90.0–112.2) kg, a BMI of 34.8 (IQR 32.2–37.4) kg/m2, and an HbA1c level of 5.6%. Five (4%) patients did not actually initiate treatment, 9 (8%) stopped treatment, and 8 (7%) were lost to follow-up. At the 4-month visit, the median body weight had decreased significantly by 9.2% to 90.8 (IQR 82.0–103.5) kg (p &#x3c; 0.001). Patients using 3.0 mg/day (n = 60) had lost 8.0 (IQR 5.8–10.4) kg. The weight loss was similar (p = 0.9622) in patients that used a lower daily dose because of intolerance: 7.4 (IQR 6.2–9.6) kg for 1.2 mg (n = 3), 7.8 (IQR 4.1–7.8) kg for 1.8 mg (n = 16), and 9.0 (IQR 4.8–10.7) kg for 2.4 mg/day (n = 14). Weight loss was minimal if liraglutide treatment was not started or stopped prematurely (median 3.0 [IQR 0.3–4.8] kg, p &#x3c; 0.001, vs. on treatment). Further analysis showed an additional weight reduction of 1.8 kg in the patients that had started metformin &#x3c;3 months before the start of liraglutide (p &#x3c; 0.001). The main reasons for liraglutide discontinuation were gastrointestinal complaints (n = 5/9) and drug cost (n = 2/9). Conclusion: In this selected group of patients, the majority complied with liraglutide treatment over the initial 4-month period and achieved a significant weight loss, irrespective of the maximally tolerated maintenance dose. Addition of metformin induced a small but significant additional weight loss.

Yvyuan Gong, Rui Dong, Kailong Zhang et al.

Oat roll is a traditional Chinese food made of whole oat flour. The purpose of this work was to investigate the role of guar gum on starch retrogradation and water migration of cooked oat roll during freeze-thaw cycles by low-field nuclear magnetic resonance (LF-NMR) measurement, differential scanning calorimetry (DSC) and so on. The results showed that freeze-thaw cycles affected the migration of strong bound water to weak bound water and free water, and thus deteriorated the quality of products. As the addition of guar gum increased, the water mobility and the consequent water loss caused by the freeze-thaw cycles were significantly reduced. The addition of 0.3 % and 0.5 % guar gum could decrease the number and size of pores in cooked oat roll, enhanced the water absorption and gelatinization capacity. Moreover, hindered rearrangement of starch molecular chains to delay retrogradation by restricting the water mobility. Finally the increase of hardness caused by freeze-thaw cycles was decelerated. However, 1 % added amount of guar gum due to its excessive viscosity, caused hindrance to the water absorption and gelatinization of starch granules. Resulted in a greater increase in hardness and adhesiveness, which reduced the edible quality.