Background: Significant evidence suggests that asthma might originate from low-grade systemic inflammation. Previous studies have established a positive association between the systemic immune-inflammation index (SII) and the systemic inflammation response index (SIRI) levels and the risk of stroke. However, it remains unclear whether SII, SIRI and the prevalence of stroke are related in individuals with asthma.Methods: The present cross-sectional study used data from the National Health and Nutrition Examination Survey (NHANES) conducted between 1999 and 2018. SII was calculated using the following formula: (platelet count × neutrophil count)/lymphocyte count. SIRI was calculated using the following formula: (neutrophil count × monocyte count)/lymphocyte count. The Spearman rank correlation coefficient was used to determine any correlation between SII, SIRI, and the baseline characteristics. Survey-weighted logistic regression was employed to calculate odds ratios (ORs) and 95% confidence intervals (CIs) to determine the association between SII, SIRI, and stroke prevalence. The predictive value of SII and SIRI for stroke prevalence was assessed through receiver operating characteristic (ROC) curve analysis, with the area under the ROC curve (AUC) being indicative of its predictive value. Additionally, clinical models including SIRI, coronary heart disease, hypertension, age, and poverty income ratio were constructed to evaluate their clinical applicability.Results: Between 1999 and 2018, 5,907 NHANES participants with asthma were identified, of which 199 participants experienced a stroke, while the remaining 5,708 participants had not. Spearman rank correlation analysis indicated that neither SII nor SIRI levels exhibited any significant correlation with the baseline characteristics of the participants (r<0.1). ROC curves were used to determine the optimal cut-off values for SII and SIRI levels to classify participants into low- and high-level groups. Higher SII and SIRI levels were associated with a higher prevalence of stroke, with ORs of 1.80 (95% CI, 1.18-2.76) and 2.23 (95% CI, 1.39-3.57), respectively. The predictive value of SIRI (AUC=0.618) for stroke prevalence was superior to that of SII (AUC=0.552). Furthermore, the clinical model demonstrated good predictive value (AUC=0.825), with a sensitivity of 67.1% and specificity of 87.7%.Conclusion: In asthmatics, higher levels of SII and SIRI significantly increased the prevalence of stroke, with its association being more pronounced in individuals with coexisting obesity and hyperlipidaemia. SII and SIRI are relatively stable novel inflammatory markers in the asthmatic population, with SIRI having a better predictive value for stroke prevalence than SII.

Background: Estimated pulse wave velocity (ePWV) has been proposed as a potential approach to estimate carotid-femoral pulse wave velocity. However, the potential of ePWV in predicting all-cause mortality (ACM) and cardiovascular disease mortality (CVM) in the general population is unclear. Methods: We conducted a prospective cohort study using the data of 33,930 adults (age ≥ 20 years) from the National Health and Nutrition Examination Survey (NHANES) from 1999 to 2014 until the end of December 2019. The study outcomes included ACM and CVM. Survey-weighted Cox proportional hazards models were used to assess hazard ratios (HRs) and 95% confidence intervals (CIs) to determine the association between ePWV and ACM and CVM. To further investigate whether ePWV was superior to traditional risk factors in predicting ACM and CVM, comparisons between ePWV and the Framingham Risk Score (FRS) and Pooled Cohort Equations (PCE) models were performed. Integrated Discriminant Improvement (IDI) and Net Reclassification Improvement (NRI) were employed to analyze differences in predictive ability between models. Results: The weighted mean age of the 33,930 adults included was 45.2 years, and 50.28% of all participants were men. In the fully adjusted Cox regression model, each 1 m/s increase in ePWV was associated with 50% and 49% increases in the risk of ACM (HR 1.50; 95% CI, 1.45–1.54) and CVM (HR 1.49; 95% CI, 1.41–1.57), respectively. After adjusting for FRS, each 1 m/s increase in ePWV was still associated with 29% (HR 1.29; 95% CI, 1.24–1.34) and 34% (HR 1.34; 95% CI, 1.23–1.45) increases in the risk of ACM and CVM, respectively. The area under the curve (AUC) predicted by ePWV for 10-year ACM and CVM were 0.822 and 0.835, respectively. Compared with the FRS model, the ePWV model improved the predictive value of ACM and CVM by 5.1% and 3.8%, respectively, with no further improvement in event classification. In comparison with the PCE model, the ePWV model’s ability to predict 10-year ACM and CVM was improved by 5.1% and 3.5%, and event classification improvement was improved by 34.5% and 37.4%. Conclusions: In the U.S. adults, ePWV is an independent risk factor for ACM and CVM and is independent of traditional risk factors. In the general population aged 20 to 85 years, ePWV has a robust predictive value for the risk of ACM and CVM, superior to the FRS and PCE models. The predictive power of ePWV likely originates from the traditional risk factors incorporated into its calculation, rather than from an indirect association with measured pulse wave velocity.

Antibiotics are often used to promote animal growth and prevent diarrhea in weanling piglets. Nevertheless, intestinal pathogenic bacterial resistance and drug residues caused by antibiotic overuse are worthy of concern and demand an urgent solution. Bacillus amyloliquefaciens TL106 has been isolated from cold- and disease-resistant Tibetan pigs in Linzhi, Tibet.