Search

In recent decades, the rapid expansion of phytoplankton blooms caused by lake eutrophication has led to severe ecological destruction and impeded the sustainable economic development of local regions. Chlorophyll-a (Chl-a) is commonly used as a biological indicator to detect phytoplankton blooms due to its ease of detection. To improve the accuracy of Chl-a estimation in aquatic systems, an accurate understanding of its true spectral characteristics is imperative. In this study, a comprehensive and realistic experimental scheme was designed from the perspective of real algal strains and real water states. Both in situ and laboratory-based hyperspectral data were collected and analyzed. The results show that there are huge spectral differences not only between laboratory-cultured and real algae strains, but also between static and disturbed water surface conditions. A total of ten different categories of spectral characteristics were selected in both disturbed and static states. Then, six parameters with the best models to the Chl-a concentration were identified. Finally, two linear models of the Chl-a concentration at peaks of 810 nm and 700 nm were identified as the best estimation models for the static and disturbed states, respectively. The results provide a scientific reference for the large-scale retrieval of the Chl-a concentration using satellite remote sensing data. This advancement benefits inland water monitoring and management efforts. [ABSTRACT FROM AUTHOR]

Background: Complications such as cognitive impairment are common in stroke victims. The goal of this study was to see if there was a link between blood iron levels and post-stroke cognitive impairment (PSCI) within 2 weeks after stroke. Methods: A total of 313 patients with ischemic stroke were recruited and separated into two groups: PSCI (n = 202) and non-PSCI (n = 111). The Mini-mental state examination scale was used to evaluate the cognitive status within 2 weeks after stroke (acute phase). The serum iron levels were divided into 4 layers: Q1 ≤ 11.7 μmol/L, Q2 11.8-15.1 μmol/, Q3 15.2-19.3 μmol/L, Q4 ≥ 19.4 μmol/L, respectively. The connection between serum iron and PSCI was then investigated further using binary logistic regression, which was adjusted for confounders. Results: The difference in serum iron levels between the PSCI and non-PSCI group was initially conducted by the Mann-Whitney test, and a significant difference was found (14.5 (11.0-17.8) vs. 16.9 (13.7-21.8), p <.001), with no confounders being adjusted. After adjusting for confounding factors, the binary regression analysis showed that the Q4 layer showed the lowest risk of PSCI, with the Q1 layer being the reference. (odds ratio (OR) = 0.297, 95% confidence interval (CI) = 0.136-0.649, p = 0.002). Conclusion: A decreased risk of early-onset PSCI was linked to high serum iron levels. Low serum iron levels were found to be a risk factor for acute cognitive impairment following stroke, which could help physicians identify and take intervention measures early to reduce the risk of cognitive impairment after stroke. [ABSTRACT FROM AUTHOR]

Introduction: Several studies have examined the crucial role of inflammatory indexes such as the ratio of monocyte and lymphocyte (MLR), systemic-immune-inflammation-index, and the ratio of neutrophil and lymphocyte (NLR) in stroke-associated pneumonia (SAP). However, the function of the systemic inflammation response index (SIRI) in SAP is not known. This study investigated whether SIRI at admission could predict the incidence of SAP in patients with acute ischemic stroke (AIS). Patients and Methods: 2,802 AIS patients collected from 2013 to 2021 were divided into the SAP and non-SAP groups. The predictive performance of SIRI in SAP was evaluated by the receiver operating characteristic curve. Multivariate regression analysis and the restricted cubic spline (RCS) were performed to explore the relationship between SIRI and SAP risk. Results: The SIRI at admission in SAP patients was significantly higher than that in non-SAP patients (median [IQR]: 3.75 [2.05, 6.99] vs. 1.51 [0.94, 2.62], p < 0.001). SIRI had a predictive ability for predicting the incidence of SAP with area under the curve of 0.757, better than NLR and MLR (both p < 0.05). SIRI ≥2.74 was an independent risk factor for the incidence of SAP (odds ratio: 5.82, 95% confidence interval: 4.54, 7.49, p < 0.001). The RCS model showed an increasing trend of the SAP risk with the increase of SIRI. Conclusion: SIRI showed a good predictive value for SAP. In clinical practice, AIS patients with high SIRI levels (SIRI ≥2.74) should be aware of the risk of SAP. [ABSTRACT FROM AUTHOR]