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BACKGROUND: The association of single nucleotide polymorphism of KCNQ1 gene rs2237895 with type 2 diabetes mellitus (T2DM) is currently controversial. It is unknown whether this association can be gene realized across different populations. AIM: To determine the association of KCNQ1 rs2237895 with T2DM and provide reliable evidence for genetic susceptibility to T2DM. METHODS: We searched PubMed, Embase, Web of Science, Cochrane Library, Medline, Baidu Academic, China National Knowledge Infrastructure, China Biomedical Liter-ature Database, and Wanfang to investigate the association between KCNQ1 gene rs2237895 and the risk of T2DM up to January 12, 2022. Review Manager 5.4 was used to analyze the association of the KCNQ1 gene rs2237895 polymorphism with T2DM and to evaluate the publication bias of the selected literature. RESULTS: Twelve case-control studies (including 11273 cases and 11654 controls) met our inclusion criteria. In the full population, allelic model [odds ratio (OR): 1.19; 95% confidence interval (95%CI): 1.09-1.29; P < 0.0001], recessive model (OR: 1.20; 95%CI: 1.11-1.29; P < 0.0001), dominant model (OR: 1.27. 95%CI: 1.14-1.42; P < 0.0001), and codominant model (OR: 1.36; 95%CI: 1.15-1.60; P = 0.0003) (OR: 1.22; 95%CI: 1.10-1.36; P = 0.0002) indicated that the KCNQ1 gene rs2237895 polymorphism was significantly correlated with susceptibility to T2DM. In stratified analysis, this association was confirmed in Asian populations: allelic model (OR: 1.25; 95%CI: 1.13-1.37; P < 0.0001), recessive model (OR: 1.29; 95%CI: 1.11-1.49; P = 0.0007), dominant model (OR: 1.35; 95%CI: 1.20-1.52; P < 0.0001), codominant model (OR: 1.49; 95%CI: 1.22-1.81; P < 0.0001) (OR: 1.26; 95%CI: 1.16-1.36; P < 0.0001). In non-Asian populations, this association was not significant: Allelic model (OR: 1.06, 95%CI: 0.98-1.14; P = 0.12), recessive model (OR: 1.04; 95%CI: 0.75-1.42; P = 0.83), dominant model (OR: 1.06; 95%CI: 0.98-1.15; P = 0.15), codominant model (OR: 1.08; 95%CI: 0.82-1.42; P = 0.60. OR: 1.15; 95%CI: 0.95-1.39; P = 0.14). CONCLUSION: KCNQ1 gene rs2237895 was significantly associated with susceptibility to T2DM in an Asian population. Carriers of the C allele had a higher risk of T2DM. This association was not significant in non-Asian populations.
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Introduction: Continuous strawberry cropping often causes soil-borne diseases, with 20 calcium cyanamide being an effective soil fumigant, pig manure can often be used as soil organic fertilizer. Its impact on soil microorganisms structure, however, remains unclear. Methods: This study investigated the effectiveness of calcium cyanamide and pig manure in treating strawberry soil, specifically against strawberry anthracnose. We examined the physical and chemical properties of the soil and the rhizosphere microbiome and performed a network analysis. Results: Results showed that calcium cyanamide treatment significantly reduces the mortality rate of strawberry in seedling stage by reducing pathogen abundance, while increasing actinomycetes and Alphaproteobacteria during the harvest period. This treatment also enhanced bacterial network connectivity, measured by the average connectivity of each Operational Taxonomic Unit (OTU), surpassing other treatments. Moreover, calcium cyanamide notably raised the levels of organic matter, available potassium, and phosphorus in the soil-key factors for strawberry disease resistance and yield. Discussion: Overall, applying calcium cyanamide to soil used for continuous strawberry cultivation can effectively decrease anthracnose incidence. It may be by changing soil physical and chemical properties and enhancing bacterial network stability, thereby reducing the copy of anthracnose. This study highlights the dual benefit of calcium cyanamide in both disease control and soil nutrient enhancement, suggesting its potential as a valuable tool in sustainable strawberry farming.
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This study aimed to examine the interaction mechanism of polyphenol protein in a heat-treated aqueous solution system using epigallocatechin gallate (EGCG) and whey protein (WP) as raw materials. Further, we hypothesized the binding characteristics of these two compounds. The results were as follows: The quenching mechanism between WP and EGCG was characterized as static quenching. As the temperature increased, the binding constant and the binding force between EGCG and WP both increased. The number of binding sites (denoted as n) between WP and EGCG was approximately 1. Hence, WP provided a single site to bind to EGCG to form a complex. The main binding modes between WP and EGCG were hydrophobic and electrostatic interactions, and they were spontaneously combined into complexes (ΔG < 0). This study provided a basis for the interaction between WP and EGCG under different heating conditions and their combination mode.
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OBJECTIVES: Cerebral venous sinus thrombosis (CVST) can cause sinus obstruction and stenosis, with potentially fatal consequences. High-resolution magnetic resonance imaging (HRMRI) can diagnose CVST qualitatively, although quantitative screening methods are lacking for patients refractory to anticoagulation therapy and who may benefit from endovascular treatment (EVT). Thus, in this study, we used radiomic features (RFs) extracted from HRMRI to build machine learning models to predict response to drug therapy and determine the appropriateness of EVT. MATERIALS AND METHODS: RFs were extracted from three-dimensional T1-weighted motion-sensitized driven equilibrium (MSDE), T2-weighted MSDE, T1-contrast, and T1-contrast MSDE sequences to build radiomic signatures and support vector machine (SVM) models for predicting the efficacy of standard drug therapy and the necessity of EVT. RESULTS: We retrospectively included 53 patients with CVST in a prospective cohort study, among whom 14 underwent EVT after standard drug therapy failed. Thirteen RFs were selected to construct the RF signature and CVST-SVM models. In the validation dataset, the sensitivity, specificity, and area under the curve performance for the RF signature model were 0.833, 0.937, and 0.977, respectively. The radiomic score was correlated with days from symptom onset, history of dyslipidemia, smoking, fibrin degradation product, and D-dimer levels. The sensitivity, specificity, and area under the curve for the CVST-SVM model in the validation set were 0.917, 0.969, and 0.992, respectively. CONCLUSIONS: The CVST-SVM model trained with RFs extracted from HRMRI outperformed the RF signature model and could aid physicians in predicting patient responses to drug treatment and identifying those who may require EVT.
