Effects of Sous-Vide on Quality, Structure and Flavor Characteristics of Tilapia Fillets.

To investigate the effects of traditional high-temperature cooking and sous-vide cooking on the quality of tilapia fillets, muscle microstructure, texture, lipid oxidation, protein structure, and volatile compounds were analyzed. In comparison with samples subjected to traditional high-temperature cooking, sous-vide-treated samples exhibited less protein denaturation, a secondary structure dominated by α-helices, a stable and compact structure, a significantly higher moisture content, and fewer gaps in muscle fibers. The hardness of the sous-vide-treated samples was higher than that of control samples, and the extent of lipid oxidation was significantly reduced. The sous-vide cooking technique resulted in notable changes in the composition and relative content of volatile compounds, notably leading to an increase in the presence of 1-octen-3-ol, α-pinene, and dimethyl sulfide, and a decrease in the levels of hexanal, D-limonene, and methanethiol. Sous-vide treatment significantly enhanced the structural stability, hardness, and springiness of muscle fibers in tilapia fillets and reduced nutrient loss, enriched flavor, and mitigated effects on taste and fishy odor.

Machine Learning Prediction Models for Gestational Diabetes Mellitus: Meta-analysis.

BACKGROUND: Gestational diabetes mellitus (GDM) is a common endocrine metabolic disease, involving a carbohydrate intolerance of variable severity during pregnancy. The incidence of GDM-related complications and adverse pregnancy outcomes has declined, in part, due to early screening. Machine learning (ML) models are increasingly used to identify risk factors and enable the early prediction of GDM. OBJECTIVE: The aim of this study was to perform a meta-analysis and comparison of published prognostic models for predicting the risk of GDM and identify predictors applicable to the models. METHODS: Four reliable electronic databases were searched for studies that developed ML prediction models for GDM in the general population instead of among high-risk groups only. The novel Prediction Model Risk of Bias Assessment Tool (PROBAST) was used to assess the risk of bias of the ML models. The Meta-DiSc software program (version 1.4) was used to perform the meta-analysis and determination of heterogeneity. To limit the influence of heterogeneity, we also performed sensitivity analyses, a meta-regression, and subgroup analysis. RESULTS: A total of 25 studies that included women older than 18 years without a history of vital disease were analyzed. The pooled area under the receiver operating characteristic curve (AUROC) for ML models predicting GDM was 0.8492; the pooled sensitivity was 0.69 (95% CI 0.68-0.69; P<.001; I2=99.6%) and the pooled specificity was 0.75 (95% CI 0.75-0.75; P<.001; I2=100%). As one of the most commonly employed ML methods, logistic regression achieved an overall pooled AUROC of 0.8151, while non-logistic regression models performed better, with an overall pooled AUROC of 0.8891. Additionally, maternal age, family history of diabetes, BMI, and fasting blood glucose were the four most commonly used features of models established by the various feature selection methods. CONCLUSIONS: Compared to current screening strategies, ML methods are attractive for predicting GDM. To expand their use, the importance of quality assessments and unified diagnostic criteria should be further emphasized.

Association between retinol binding protein 4 and diabetic retinopathy among type 2 diabetic patients: a meta-analysis.

AIMS: The aim of this study was to investigate the association between retinol-binding protein 4 (RBP4) and diabetic retinopathy (DR) among patients with type 2 diabetes mellitus (T2DM). METHODS: Databases PubMed, Embase, Web of Science, Chinese National Knowledge Infrastructure, VIP, and Wangfang were searched to July 30, 2019. The Newcastle-Ottawa Scale was applied to assess the quality of all identified studies, and those qualified were included in the meta-analysis. The Chi squared Q test and I2 statistics were conducted to evaluate heterogeneity. Standardized mean differences (SMD) and 95% confidence intervals (CI) among RBP4 within the DR and T2DM without retinopathy (DWR) groups were pooled using the random effects model depending on the heterogeneity. Subgroup analyses were conducted among the groups having different diabetes duration, detection methods, body mass index, and total cholesterol and triglyceride levels. The funnel plot was used to assess publication bias. RESULTS: Nineteen observational studies were included in our meta-analysis. RBP4 was significantly higher in both nonproliferative DR (SMD: 0.72, 95% CI 0.48-0.95, P < 0.00001) and proliferative DR (SMD: 2.68, 95% CI 1.69-3.67, P < 0.00001) groups despite high heterogeneity (I2 = 87 and 97% in DR and PDR groups, respectively). Significant differences were noted among most subgroups (P < 0.05). Among those accompanied by hypercholesterolemia, the association between RBP4 and DR were unclear (P = 0.09). CONCLUSIONS: Elevated RBP4 is strongly associated with DR and may play an essential role in its progression. Additional large-scale controlled studies are needed to confirm these findings.