Tree peony seed oil alleviates hyperlipidemia and hyperglycemia by modulating gut microbiota and metabolites in high-fat diet mice.

With the changes of people's lifestyle, hyperlipidemia and hyperglycemia which were induced from a diet high in both fat and sugar have become serious health concerns. Tree peony seed oil (PSO) is a novel kind of edible oil that shows great potential in the food industry because of its high constituent of unsaturated fatty acids. Based 16S rRNA and gut untargeted metabolomics, this study elucidated that the mechanism of PSO regulating blood glucose (Glu) and lipids. The impact of PSO on gut microbiota balance and gut metabolites of mice with a high-fat diet (HFD) was evaluated. The findings indicated that PSO decreased HFD mice's body weight and fat accumulation, ameliorating the levels of blood lipid, reduced liver fat vacuole levels. What's more PSO modulated the proportion of gut microbiota in HFD mice and enhanced the abundance of probiotics. Furthermore, untargeted metabolomic analysis revealed that PSO not only impacted the generation of short-chain fatty acids (SCFAs) by gut microorganism and altered metabolic pathway but exerted influence on secondary bile acids (BA), amino acid metabolism, and various other metabolites. These results suggested that PSO has the potential function for mitigating HFD-induced hyperlipidemia and hyperglycemia by regulating gut microbiota and host metabolism.

Asymmetric impact of climatic parameters on hemorrhagic fever with renal syndrome in Shandong using a nonlinear autoregressive distributed lag model.

Hemorrhagic fever with renal syndrome (HFRS) poses a major threat in Shandong. This study aimed to investigate the long- and short-term asymmetric effects of meteorological factors on HFRS and establish an early forecasting system using autoregressive distributed lag (ARDL) and nonlinear ARDL (NARDL) models. Between 2004 and 2019, HFRS exhibited a declining trend (average annual percentage change = - 9.568%, 95% CI - 16.165 to - 2.451%) with a bimodal seasonality. A long-term asymmetric influence of aggregate precipitation (AP) (Wald long-run asymmetry [WLR] = - 2.697, P = 0.008) and aggregate sunshine hours (ASH) (WLR = 2.561, P = 0.011) on HFRS was observed. Additionally, a short-term asymmetric impact of AP (Wald short-run symmetry [WSR] = - 2.419, P = 0.017), ASH (WSR = 2.075, P = 0.04), mean wind velocity (MWV) (WSR = - 4.594, P < 0.001), and mean relative humidity (MRH) (WSR = - 2.515, P = 0.013) on HFRS was identified. Also, HFRS demonstrated notable variations in response to positive and negative changes in ∆MRH(-), ∆AP(+), ∆MWV(+), and ∆ASH(-) at 0-2 month delays over the short term. In terms of forecasting, the NARDL model demonstrated lower error rates compared to ARDL. Meteorological parameters have substantial long- and short-term asymmetric and/or symmetric impacts on HFRS. Merging NARDL model with meteorological factors can enhance early warning systems and support proactive measures to mitigate the disease's impact.

Application of an ARFIMA Model to Estimate Hepatitis C Epidemics in Henan, China.

Hepatitis C (HC) presents a substantial burden, and a goal has been established for ending HC epidemics by 2030. This study aimed to monitor HC epidemics by designing a paradigmatic autoregressive fractionally integrated moving average (ARFIMA) for projections until 2030, and evaluating its efficacy compared with the autoregressive integrated moving average (ARIMA). Monthly HC incidence data in Henan from January 2004 to June 2023 were obtained. Two periods (January 2004 to June 2022 and January 2004 to December 2015) were treated as training sets to build both models, whereas the remaining periods served as test sets to perform performance evaluation. There were 465,196 HC cases, with an escalation in incidence (average annual percentage change = 8.64, 95% CI: 3.71-13.80) and a peak in March and a trough in February. For both the 12 and 90 holdout data forecasts, ARFIMA generated lower errors than ARIMA across various metrics: mean absolute deviation (252.93 versus 262.28 and 235.37 versus 1,689.65), mean absolute percentage error (0.17 versus 0.18 and 0.14 versus 0.87), root mean square error (350.31 versus 362.31 and 311.96 versus 1,905.71), mean error rate (0.14 versus 0.15 and 0.11 versus 0.82), and root mean square percentage error (0.26 versus 0.26 and 0.24 versus 1.01). Autoregressive fractionally integrated moving average predicted 181,650 (95% CI: 46,518-316,783) HC cases, averaging 22,706 (95% CI: 5,815-39,598) cases annually during 2023-2030. Henan faces challenges in eliminating HC epidemics, emphasizing the need for strengthened strategies. Autoregressive fractionally integrated moving average can offer evidence-based insights for public health measures.

Evaluating the long-term impact of COVID-19-associated public health interventions on zoonotic and vector-borne diseases in China: an interrupted time series analysis.

BACKGROUND: The long-term impact of COVID-19-associated public health interventions on zoonotic and vector-borne infectious diseases (ZVBs) remains uncertain. This study sought to examine the changes in ZVBs in China during the COVID-19 pandemic and predict their future trends. METHODS: Monthly incidents of seven ZVBs (Hemorrhagic fever with renal syndrome [HFRS], Rabies, Dengue fever [DF], Human brucellosis [HB], Leptospirosis, Malaria, and Schistosomiasis) were gathered from January 2004 to July 2023. An autoregressive fractionally integrated moving average (ARFIMA) by incorporating the COVID-19-associated public health intervention variables was developed to evaluate the long-term effectiveness of interventions and forecast ZVBs epidemics from August 2023 to December 2025. RESULTS: Over the study period, there were 1,599,647 ZVBs incidents. HFRS and rabies exhibited declining trends, HB showed an upward trajectory, while the others remained relatively stable. The ARFIMA, incorporating a pulse pattern, estimated the average monthly number of changes of - 83 (95% confidence interval [CI] - 353-189) cases, - 3 (95% CI - 33-29) cases, - 468 (95% CI - 1531-597) cases, 2191 (95% CI 1056-3326) cases, 7 (95% CI - 24-38) cases, - 84 (95% CI - 222-55) cases, and - 214 (95% CI - 1036-608) cases for HFRS, rabies, DF, HB, leptospirosis, malaria, and schistosomiasis, respectively, although these changes were not statistically significant besides HB. ARFIMA predicted a decrease in HB cases between August 2023 and December 2025, while indicating a relative plateau for the others. CONCLUSIONS: China's dynamic zero COVID-19 strategy may have exerted a lasting influence on HFRS, rabies, DF, malaria, and schistosomiasis, beyond immediate consequences, but not affect HB and leptospirosis. ARFIMA emerges as a potent tool for intervention analysis, providing valuable insights into the sustained effectiveness of interventions. Consequently, the application of ARFIMA contributes to informed decision-making, the design of effective interventions, and advancements across various fields.

Time series analysis-based seasonal autoregressive fractionally integrated moving average to estimate hepatitis B and C epidemics in China.

BACKGROUND: Hepatitis B (HB) and hepatitis C (HC) place the largest burden in China, and a goal of eliminating them as a major public health threat by 2030 has been set. Making more informed and accurate forecasts of their spread is essential for developing effective strategies, heightening the requirement for early warning to deal with such a major public health threat. AIM: To monitor HB and HC epidemics by the design of a paradigmatic seasonal autoregressive fractionally integrated moving average (SARFIMA) for projections into 2030, and to compare the effectiveness with the seasonal autoregressive integrated moving average (SARIMA). METHODS: Monthly HB and HC incidence cases in China were obtained from January 2004 to June 2023. Descriptive analysis and the Hodrick-Prescott method were employed to identify trends and seasonality. Two periods (from January 2004 to June 2022 and from January 2004 to December 2015, respectively) were used as the training sets to develop both models, while the remaining periods served as the test sets to evaluate the forecasting accuracy. RESULTS: There were incidents of 23400874 HB cases and 3590867 HC cases from January 2004 to June 2023. Overall, HB remained steady [average annual percentage change (AAPC) = 0.44, 95% confidence interval (95%CI): -0.94-1.84] while HC was increasing (AAPC = 8.91, 95%CI: 6.98-10.88), and both had a peak in March and a trough in February. In the 12-step-ahead HB forecast, the mean absolute deviation (15211.94), root mean square error (18762.94), mean absolute percentage error (0.17), mean error rate (0.15), and root mean square percentage error (0.25) under the best SARFIMA (3, 0, 0) (0, 0.449, 2)12 were smaller than those under the best SARIMA (3, 0, 0) (0, 1, 2)12 (16867.71, 20775.12, 0.19, 0.17, and 0.27, respectively). Similar results were also observed for the 90-step-ahead HB, 12-step-ahead HC, and 90-step-ahead HC forecasts. The predicted HB incidents totaled 9865400 (95%CI: 7508093-12222709) cases and HC totaled 1659485 (95%CI: 856681-2462290) cases during 2023-2030. CONCLUSION: Under current interventions, China faces enormous challenges to eliminate HB and HC epidemics by 2030, and effective strategies must be reinforced. The integration of SARFIMA into public health for the management of HB and HC epidemics can potentially result in more informed and efficient interventions, surpassing the capabilities of SARIMA.

HTPdb and HTPtools: Exploiting maize haplotype-tag polymorphisms for germplasm resource analyses and genomics-informed breeding.

Along with rapid advances in high-throughput-sequencing technology, the development and application of molecular markers has been critical for the progress that has been made in crop breeding and genetic research. Desirable molecular markers should be able to rapidly genotype tens of thousands of breeding accessions with tens to hundreds of markers. In this study, we developed a multiplex molecular marker, the haplotype-tag polymorphism (HTP), that integrates Maize6H-60K array data from 3,587 maize inbred lines with 6,375 blocks from the recombination block map. After applying strict filtering criteria, we obtained 6,163 highly polymorphic HTPs, which were evenly distributed in the genome. Furthermore, we developed a genome-wide HTP analysis toolkit, HTPtools, which we used to establish an HTP database (HTPdb) covering the whole genomes of 3,587 maize inbred lines commonly used in breeding. A total of 172,921 non-redundant HTP allelic variations were obtained. Three major HTPtools modules combine seven algorithms (e.g., chain Bayes probability and the heterotic-pattern prediction algorithm) and a new plotting engine named "BCplot" that enables rapid visualization of the background information of multiple backcross groups. HTPtools was designed for big-data analyses such as complex pedigree reconstruction and maize heterotic-pattern prediction. The HTP-based analytical strategy and the toolkit developed in this study are applicable for high-throughput genotyping and for genetic mapping, germplasm resource analyses, and genomics-informed breeding in maize.