Maillard reaction between oligopeptides and reducing sugar at body temperature: The putative anti-glycation agents

Wang, Qiang; Xiang, Xin; Xie, Yuejie; Wang, Kai; Wang, Cao; Nie, Xin; Wang, Puzhi

doi:10.3389/fnut.2022.1062777

Cited by 5 publications

(1 citation statement)

References 33 publications

(39 reference statements)

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“…According to the International Diabetes Federation (IDF), the number of people with diabetes reached approximately 537 million worldwide by 2021 (1 in 10 adults live with diabetes), and approximately 90%-95% of cases of diabetes suffer from type 2 diabetes (T2D) (Sun et al, 2022). T2D has become a global threat to public health in the 21st century (Wang et al, 2022). Fasting blood glucose (FBG) and random blood glucose (RBG) have been the traditional method for assessing the risk of T2D, but they have obvious shortcomings-change over short periods of time due to behavioral changes (Christine et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Development and economic assessment of machine learning models to predict glycosylated hemoglobin in type 2 diabetes

et al. 2023

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Background: Glycosylated hemoglobin (HbA1c) is recommended for diagnosing and monitoring type 2 diabetes. However, the monitoring frequency in real-world applications has not yet reached the recommended frequency in the guidelines. Developing machine learning models to screen patients with poor glycemic control in patients with T2D could optimize management and decrease medical service costs.Methods: This study was carried out on patients with T2D who were examined for HbA1c at the Sichuan Provincial People’s Hospital from April 2018 to December 2019. Characteristics were extracted from interviews and electronic medical records. The data (excluded FBG or included FBG) were randomly divided into a training dataset and a test dataset with a radio of 8:2 after data pre-processing. Four imputing methods, four screening methods, and six machine learning algorithms were used to optimize data and develop models. Models were compared on the basis of predictive performance metrics, especially on the model benefit (MB, a confusion matrix combined with economic burden associated with therapeutic inertia). The contributions of features were interpreted using SHapley Additive exPlanation (SHAP). Finally, we validated the sample size on the best model.Results: The study included 980 patients with T2D, of whom 513 (52.3%) were defined as positive (need to perform the HbA1c test). The results indicated that the model trained in the data (included FBG) presented better forecast performance than the models that excluded the FBG value. The best model used modified random forest as the imputation method, ElasticNet as the feature screening method, and the LightGBM algorithms and had the best performance. The MB, AUC, and AUPRC of the best model, among a total of 192 trained models, were 43475.750 (¥), 0.972, 0.944, and 0.974, respectively. The FBG values, previous HbA1c values, having a rational and reasonable diet, health status scores, type of manufacturers of metformin, interval of measurement, EQ-5D scores, occupational status, and age were the most significant contributors to the prediction model.Conclusion: We found that MB could be an indicator to evaluate the model prediction performance. The proposed model performed well in identifying patients with T2D who need to undergo the HbA1c test and could help improve individualized T2D management.

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Section: Introductionmentioning

confidence: 99%

Development and economic assessment of machine learning models to predict glycosylated hemoglobin in type 2 diabetes

et al. 2023

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Extracellular Silica Nanomatrices Promote In Vitro Maturation of Anti‐tumor Dendritic Cells via Activation of Focal Adhesion Kinase

Tam,

Cheung,

Qin

et al. 2024

Advanced Materials

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The efficacy of dendritic cell (DC)‐based cancer vaccines is critically determined by the functionalities of in vitro maturated DCs. The maturation of DCs typically relies on chemicals that are cytotoxic or hinder the ability of DCs to efficiently activate the antigen‐specific cytotoxic T‐lymphocytes (CTLs) against tumors. Herein, the maturation chemicals are replaced with extracellular silica nanomatrices, fabricated by glancing angle deposition, to promote in vitro maturation of murine bone marrow‐derived DCs (mBMDCs). The extracellular nanomatrices composed of silica nanozigzags (NZs) enable the generation of mature mBMDCs with upregulated levels of co‐stimulatory molecules, C‐C chemokine receptor type‐7, X‐C motif chemokine recetpor‐1, DC‐specific ICAM‐3 grabbing nonintegrin, and enhanced endocytic capacity. The in vitro maturation is partially governed by focal adhesion kinase (FAK) that is mechanically activated in the curved cell adhesions formed at the DC‐NZ interfaces. The NZ‐maturated mBMDCs can prime the antigen‐specific CTLs into programmed cell death protein‐1 (PD‐1)lowCD44high memory phenotypes in vitro and suppress the growth of tumors in vivo. Meanwhile, the NZ‐mediated beneficial effects are also observed in human monocyte‐derived DCs. This work demonstrates that the silica NZs promote the anti‐tumor capacity of in vitro maturated DCs via the mechanoactivation of FAK, supporting the potential of silica NZs being a promising biomaterial for cancer immunotherapy.

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Advances in Maillard reaction products on color, flavor and biological activity of specialty malt

Wang,

Lu,

Cai

et al. 2023

Syst Microbiol and Biomanuf

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Maillard reaction between oligopeptides and reducing sugar at body temperature: The putative anti-glycation agents

Cited by 5 publications

References 33 publications

Development and economic assessment of machine learning models to predict glycosylated hemoglobin in type 2 diabetes

Development and economic assessment of machine learning models to predict glycosylated hemoglobin in type 2 diabetes

Extracellular Silica Nanomatrices Promote In Vitro Maturation of Anti‐tumor Dendritic Cells via Activation of Focal Adhesion Kinase

Advances in Maillard reaction products on color, flavor and biological activity of specialty malt

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