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The relationship between diabetes mellitus and short-term exposure to extreme temperatures remains controversial. A systematic review and meta-analysis were performed to assess the association between extreme temperatures and diabetes mellitus morbidity and mortality. PubMed, Embase, the Cochrane Library, Web of Science, and the Cumulative Index to Nursing and Allied Health Literature (CINAHL) were searched since inception to January 1, 2019, and updated on November 17, 2020. The results were combined using random effects model and reported as relative risk (RR) with 95% confidence interval (CI). In total, 32 studies met the inclusion criteria. (1) Both heat and cold exposures have impact on diabetes. (2) For heat exposure, the subgroup analysis revealed that the effect on diabetes mortality (RR=1.139, 95% CI: 1.089–1.192) was higher than morbidity (RR=1.012, 95% CI: 1.004–1.019). (3) With the increase of definition threshold, the impact of heat exposure on diabetes rose. (4) A stronger association between heat exposure and diabetes was observed in the elderly (≥ 60 years old) (RR=1.040, 95% CI: 1.017–1.064). In conclusion, short-term exposure to both heat and cold temperatures has impact on diabetes. The elderly is the vulnerable population of diabetes exposure to heat temperature. Developing definitions of heatwaves at the regional level are suggested.

Research indicates that air pollution is a risk factor of an increased occurrence of diseases. However, evidence is limited on the effects of the pollution index on disease and whether temperature modifies the effects. The objectives were (i) to explore the effects of the Air Pollution Index (API) and specific indices for pollutants (PM 10 , NO 2 , and SO 2 ) on respiratory emergency department (ED) visits in Beijing and (ii) to investigate whether temperature modified the effects of main air pollutants on respiratory ED visits. A quasi-Poisson generalized additive model was employed to examine the association of API and indices for pollutants with respiratory disease. Bivariate response surface model and stratification model (cold days, moderately cold days, moderately hot days, and hot days) were used to analyze the modification effects of temperature on air pollution and respiratory disease. The results showed that (i) the effects of API on respiratory diseases were similar to the index for PM 10 in Beijing. (ii) API and PM 10 were associated with increased respiratory ED visits on cold days and moderately cold days. Furthermore, the effects of PM 10 on respiratory disease on moderately cold days [Relative risk (RR) = 1.006 per 10 μg/m 3 , 95% CI 1.002–1.009] were stronger than on cold days (RR = 1.004 per 10 μg/m 3 , 95% CI 1.000–1.008). (iii) PM 10 (API) had a greater impact on children aged 10 to 17 years and females on moderately cold days, while the elderly had an increased risk of respiratory disease to PM 10 (RR = 1.008 per 10 μg/m 3 , 95% CI 1.002–1.013) and API (RR = 1.013 per 10, 95% CI 1.004–1.022) on cold days. In conclusion, temperature can modify the association between API and respiratory morbidity. A stronger correlation existed between PM 10 and respiratory diseases on moderately cold days, while the effects of cold days were less than that attributable to moderately cold days.

ObjectiveAdverse health effects of fine particles (particulate matter2.5) have been well documented by a series of studies. However, evidences on the impacts of black carbon (BC) or elemental carbon (EC) on health are limited. The objectives were (1) to explored the effects of BC and EC on cardiovascular and respiratory morbidity and mortality, and (2) to verified the reliability of the meta-analysis by drawing p value plots.DesignThe systematic review and meta-analysis using adapted Grading of Recommendations Assessment, Development and Evaluation (GRADE) approach and p value plots approach.Data sourcesPubMed, Embase and Web of Science were searched from inception to 19 July 2021.Eligibility criteria for selecting studiesTime series, case cross-over and cohort studies that evaluated the associations between BC/EC on cardiovascular or respiratory morbidity or mortality were included.Data extraction and synthesisTwo reviewers independently selected studies, extracted data and assessed risk of bias. Outcomes were analysed via a random effects model and reported as relative risk (RR) with 95% CI. The certainty of evidences was assessed by adapted GRADE. The reliabilities of meta-analyses were analysed by p value plots.ResultsSeventy studies met our inclusion criteria. (1) Short-term exposure to BC/EC was associated with 1.6% (95% CI 0.4% to 2.8%) increase in cardiovascular diseases per 1 µg/m3 in the elderly; (2) Long-term exposure to BC/EC was associated with 6.8% (95% CI 0.4% to 13.5%) increase in cardiovascular diseases and (3) The p value plot indicated that the association between BC/EC and respiratory diseases was consistent with randomness.ConclusionsBoth short-term and long-term exposures to BC/EC were related with cardiovascular diseases. However, the impact of BC/EC on respiratory diseases did not present consistent evidence and further investigations are required.PROSPERO registration numberCRD42020186244.

Steviol glycosides (SGs) are high-intensity, zero-calorie natural sweeteners with demonstrated safety and potential health benefits, positioning them as ideal sucrose substitutes for metabolic disorder management. However, their broad application is limited by inherent drawbacks such as bitterness, low solubility, and inefficient production systems. This review provides a comprehensive summary of recent advances in SG research, covering their sources, properties, and bioactivities. A particular focus is placed on innovative bioproduction strategies—including enzyme engineering, metabolic pathway optimization, and sustainable extraction techniques. Strategies to overcome these challenges through sensory-function enhancement—including formulation and structural modification—are discussed. Furthermore, it highlights emerging trends like microbial chassis-based production and next-generation sweetener design, providing actionable insights for overcoming industrial bottlenecks. By integrating multidisciplinary advances in bioengineering, sensory science, and sustainable processing, this review offers a forward-looking perspective on the development and application of SGs as functional sweeteners in the global food industry.

Tartary buckwheat (Fagopyrum tataricum), a medicinal and edible crop, is valued for its richness in flavonoids and polyphenols, which confer antioxidant and hypoglycemic activities. Eurotium cristatum, a dominant fungus crucial for the quality of Fuzhuan tea, produces unique aromas and metabolites. This study developed cookies by replacing 20% of low-gluten flour with Tartary buckwheat flour that had undergone solid-state fermentation with E. cristatum followed by baking. Compared to cookies containing non-inoculated buckwheat flour, the fermented cookies contained significantly higher levels of total flavonoids (4.97 mg/g) and polyphenols (2.31 mg/g), and exhibited markedly enhanced antioxidant activity, as evidenced by a 16.4% higher ABTS radical scavenging rate and a 42.5% greater ferric reducing power. The fermented cookies also exhibited improved textural and sensory properties, a unique aroma profile characterized by pleasant floral notes, and a more homogeneous microstructure. HS-SPME-GC-MS analysis indicated that the optimized flavor resulted from the upregulation of key pleasant aroma compounds (e.g., (E)-2-nonenal, anethole) and the suppression of specific off-odor compounds (e.g., 2-ethyl-3,5-dimethylpyrazine, p-cresol). In conclusion, solid-state fermentation with E. cristatum followed by baking, effectively improves both the nutritional and sensory characteristics of Tartary buckwheat cookies, providing a viable strategy for developing novel, health-promoting bakery products with an appealing compelling flavor profile.

Antioxidant peptides (AOPs) have the natural properties of food preservatives; they are capable of improving the oxidation stability of food while also providing additional benefits such as disease prevention. Traditional experimental methods for identifying antioxidant peptides are time consuming and costly, so effective machine learning models are increasingly being valued by researchers. In this study, we integrated amino acid composition, transformation, and distribution (CTD) and block substitution matrix 62 (BLOSUM62) to develop an SVM-based AOP prediction model called AOPxSVM. This strategy significantly improves the prediction accuracy of the model by comparing 15 feature combinations and feature selection strategies, with their effectiveness being visually verified using UMAP. AOPxSVM achieves high accuracy values of 0.9092 and 0.9330, as well as Matthew’s correlation coefficients (MCCs) of 0.8253 and 0.8670, on two independent test sets, both surpassing the state-of-the-art methods based on the same test sets, thus demonstrating AOPs’ excellent identification capability. We believe that AOPxSVM can serve as a powerful tool for identifying AOPs.

Background An increasing number of systematic reviews (SRs) in the environmental field have been published in recent years as a result of the global concern about the health impacts of air pollution and temperature. However, no study has assessed and compared the methodological and reporting quality of SRs on the health effects of air pollutants and extreme temperatures. This study aims to assess and compare the methodological and reporting quality of SRs on the health effects of ambient air pollutants and extreme temperatures. Methods PubMed, Embase, the Cumulative Index to Nursing and Allied Health Literature (CINAHL), Cochrane Library, Web of Science, and Epistemonikos databases were searched. Two researchers screened the literature and extracted information independently. The methodological quality of the SRs was assessed through A Measurement Tool to Assess Systematic Reviews 2 (AMSTAR 2). The reporting quality was assessed through Preferred Reporting Items of Systematic reviews and Meta-Analyses (PRISMA). Results We identified 405 SRs (286 for air pollution, 108 for temperature, and 11 for the synergistic effects). The methodological and reporting quality of the included SRs were suboptimal, with major deficiencies in protocol registration. The methodological quality of SRs of air pollutants was better than that of temperature, especially in terms of satisfactory explanations for any heterogeneity (69.6% v. 45.4%). The reporting quality of SRs of air pollution was better than temperature, however, adherence to the reporting of the assessment results of risk of bias in all SRs (53.5% v. 34.3%) was inadequate. Conclusions Methodological and reporting quality of SRs on the health effect of air pollutants were higher than those of temperatures. However, deficiencies in protocol registration and the assessment of risk of bias remain an issue for both pollutants and temperatures. In addition, developing a risk-of-bias assessment tool applicable to the temperature field may improve the quality of SRs.

Glycosylation is one of the most common and important modifications in natural products (NPs), which can alter the biological activities and properties of NPs, effectively increase structural diversity, and improve pharmacological activities. The biosynthesis of glycosylation in natural products involves multiple complex biological processes, which are coordinated by many enzymes. UDP-glycosyltransferases (UGTs) play a crucial role in glycosylation modification, and have attracted long-term and widespread research attention. UGTs can catalyze the O-, C-, S-, and N-glycosylation of different substrates, producing a variety of glycosides with broad biological activity, while improving the solubility, stability, bioavailability, pharmacological activity, and other functions of NPs. In recent years, the rapid development of synthetic biology and advanced manufacturing technologies, especially the widespread application of artificial intelligence in the field of synthetic biology, has led to a series of new discoveries in the biosynthesis of NP glycosides by UGT. This work summarizes the latest progress and challenges in the field of NP glycosylation, covering the research results and potential applications of glycosylated derivatives of terpenes, flavonoids, polyphenols, aromatic compounds, and other compounds in terms of biogenesis. Looking to the future, research may leverage artificial intelligence-driven synthetic biology techniques to decipher genes related to the synthetic pathway, which is expected to further promote the large-scale synthesis and application of glycosylated NPs, and increase the diversity of NPs in the pharmaceutical, functional food, and cosmetic industries.

Umami is an important widely-used taste component of food seasoning. Umami peptides are specific structural peptides endowing foods with a favorable umami taste. Laboratory approaches used to identify umami peptides are time-consuming and labor-intensive, which are not feasible for rapid screening. Here, we developed a novel peptide sequence-based umami peptide predictor, namely iUP-BERT, which was based on the deep learning pretrained neural network feature extraction method. After optimization, a single deep representation learning feature encoding method (BERT: bidirectional encoder representations from transformer) in conjugation with the synthetic minority over-sampling technique (SMOTE) and support vector machine (SVM) methods was adopted for model creation to generate predicted probabilistic scores of potential umami peptides. Further extensive empirical experiments on cross-validation and an independent test showed that iUP-BERT outperformed the existing methods with improvements, highlighting its effectiveness and robustness. Finally, an open-access iUP-BERT web server was built. To our knowledge, this is the first efficient sequence-based umami predictor created based on a single deep-learning pretrained neural network feature extraction method. By predicting umami peptides, iUP-BERT can help in further research to improve the palatability of dietary supplements in the future.

Titanium has been reported to have positive effects on crop growth and production in various species. However, the impact of titanium on the Tartary buckwheat crops has not yet been studied. Therefore, an experiment was conducted to investigate the effect of spraying different concentrations of ionic titanium on the growth, photosynthesis, and uptake of mineral nutrients in Tartary buckwheat. The results showed that the application of titanium significantly improved dry matter accumulation, internode diameter, main stem node, root length, root average diameter, root surface area, root volume, grains per plant, and weight of grains per plant. Additionally, chlorophyll and photosynthetic parameters showed improvement regardless of the concentration of titanium used. The study found that titanium accumulation was mainly in leaves. The content of titanium in leaves showed a significant positive correlation with K, Ca, Mg, Mn, Cu, Zn, and B. This suggests a potential synergistic relationship between titanium and minerals in Tartary buckwheat leaves. Furthermore, the study also observed a significant increase in the total accumulation of P, K, Ca, Mg, Mn, Cu, Zn, and B in Tartary buckwheat plants. Overall, this study provides evidence for the positive effects of titanium on Tartary buckwheat and offers a theoretical foundation for practical production.