Photonic crystal sensor for melamine based on magnetic molecularly imprinted nanoparticles self-assembled with an amphiphilic random copolymer.

Magnetic molecularly imprinted nanoparticles (MMINPs) were obtained with a one-step process through miniemulsion self-assembly using an amphiphilic random copolymer as both an emulsifier and MMINP coating, oleic acid-modified magnetite nanoparticles as magnetic cores, and melamine (MEL) as the template molecule. MMINPs were assembled under an external magnetic field to construct photonic crystal (PC) sensor for naked-eye detection of MEL. The MMINPs were characterized by FT-IR, TEM, TGA, and VSM. The analytical performances of the magnetic molecularly imprinted PC sensor for MEL (MEL-MMIPCs) were investigated with respect to sensitivity, response time, selectivity, and stability. As the MEL concentration increases from 1.0 to 1.0 × 106 µg/l, the reflection wavelength of MEL-MMIPCs shifted from 497 to 709 nm, and was linear with the logarithm of MEL concentration in this range. The detection limit was 0.21 µg/l (S/N = 3) and response time was within 30 s. The MEL-MMIPC sensor had an imprinting factor of 5.09, and selectivity factors for the analogs cyanuric acid and atrazine were 8.76 and 5.75, respectively, indicating the high sensitivity and selectivity. After 10 cycles of elution/response, MEL-MMIPCs still had a good ability to recognize MEL.

Systemic immune-inflammation index as a potential biomarker of cardiovascular diseases: A systematic review and meta-analysis.

Background: Several studies have investigated the value of the systemic immune-inflammation index (SII) for predicting cardiovascular disease (CVD), but the results were inconsistent. Therefore, a meta-analysis and systematic review were conducted to assess the correlation between SII and risk of CVD. Materials and methods: Two investigators systematically searched PubMed, Embase, Web of Science, Cochrane library, and CINAHL databases to identify all studies that examined the association between SII levels and CVD. The risk estimates of CVD for people with high SII compared to those with low SII levels and the weighted mean difference (WMD) between the CVD and control groups were pooled using fixed- or random-effects models based on the heterogeneity test. We used the Newcastle-Ottawa Scale to assess the risk of bias in eligible studies, and the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) system was applied to rate the certainty of evidence. Results: A total of 13 studies with 152,996 participants were included for analysis. The overall pooled results showed that higher SII was significantly associated with an increased risk of CVD (HR = 1.39, 95%CI: 1.20-1.61, P < 0.001). This increased risk could be observed in almost all CVD subtypes, including ischemic stroke (HR = 1.31, 95%CI: 1.06-1.63, P = 0.013), hemorrhagic stroke (HR = 1.22, 95%CI: 1.10-1.37, P < 0.001), myocardial infarction (HR = 1.11, 95%CI: 1.01-1.23, P = 0.027), and peripheral arterial disease (HR = 1.51, 95%CI: 1.18-1.93, P = 0.001). There were no significant but still similar trends in venous thrombosis (HR = 4.65, 95%CI: 0.66-32.71, P = 0.122), cerebral small vessel disease (HR = 1.09, 95%CI: 0.95-1.25, P = 0.233), and acute coronary syndrome (HR = 1.08, 95%CI: 0.96-1.22, P = 0.200). Furthermore, the pooled results showed that SII levels at the onset of CVD were significantly higher than that in the general population (WMD = 355.2, 95%CI: 234.8-475.6, P < 0.001), which was consistent across different CVD subtypes. The GRADE assessment suggested that the quality of current evidence from observational studies was low or very low. Conclusion: This study indicated that SII may be a potential biomarker for CVD development and elevated SII is associated with an increased risk of CVD. However, the quality of evidence is generally low. Additional well-designed studies are necessary to determine the optimal cutoff value and to characterize the benefited population.