Using solid phase adsorption toxin tracking and extended local similarity analysis to monitor lipophilic shellfish toxins in a mussel culture ranch in the Yangtze River Estuary.

Harmful algal blooms (HABs) and their associated phycotoxins are increasing globally, posing great threats to local coastal ecosystems and human health. Nutrients have been carried by the freshwater Yangtze River and have entered the estuary, which was reported to be a biodiversity-rich but HAB-frequent region. Here, in situ solid phase adsorption toxin tracking (SPATT) was used to monitor lipophilic shellfish toxins (LSTs) in seawaters, and extended local similarity analysis (eLSA) was conducted to trace the temporal and special regions of those LSTs in a one-year trail in a mussel culture ranch in the Yangtze River Estuary. Nine analogs of LSTs, including okadaic acid (OA), dinophysistoxin-1 (DTX1), yessotoxin (YTX), homoyessotoxin (homoYTX), 45-OH-homoYTX, pectenotoxin-2 (PTX2), 7-epi-PTX2 seco acid (7-epi-PTX2sa), gymnodimine (GYM) and azaspiracids-3 (AZA3), were detected in seawater (SPATT) or rope farmed mussels. The concentrations of OA + DTX1 and homoYTX in mussels were positively correlated with those in SPATT samplers (Pearson test, p < 0.05), indicating that SPATT (with resin HP20) would be a good monitoring tool and potential indicator for OA + DTX1 and homoYTX in mussel Mytilus coruscus. The eLSA results indicated that late summer and early autumn were the most phycotoxin-contaminated seasons in the Yangtze River Estuary. OA + DTX1, homoYTX, PTX2 and GYM were most likely driven by the local growing HAB species in spring and summer, while Yangtze River diluted water may impact the accumulation of HAB species, causing potential phycotoxin contamination in the Yangtze River Estuary in autumn and winter. Together, the results showed that the mussel harvesting season, late summer and early autumn, would be the season with the greatest phycotoxin risk and would be the most contaminated by local growing toxic algae. Routine monitoring sites should be set up close to the local seawaters.

Subcritical Methanolysis of Starch and Transglycosidation to Produce Dodecyl Polyglucosides.

Biosurfactants based on carbohydrates are of special interest because of their production from renewable resources, nontoxicity, biocompatibility, and environmental friendliness. Dodecyl polyglucosides, a type of nonionic surfactant synthesized with methyl polyglucosides obtained from the eco-friendly subcritical methanolysis of renewable resource of starch, is no doubt good for ecosystem. With the subcritical methanolysis of starch, the methyl polyglucosides were obtained without any catalyst. Under the reaction condition of the weight ratio of methanol to starch of 7.5, temperature of 220 °C, and reaction time of 2 h, the yield of methyl polyglucosides was 85%. Dodecyl polyglucosides were synthesized by transglycosidation with methyl polyglucosides, and the green nonionic surfactant has excellent surface activity. The critical micelle concentration and hydrophilic lipophilic balance are 0.022 wt % and 12, respectively.

Serum lipoprotein and RBC rigidity index to predict cerebral infarction in patients with carotid artery stenosis.

OBJECTIVE: This study aims to determine the risk factors and to predict the occurrence of cerebral infarction in patients with carotid artery stenosis. METHODS: Two hundred and one subjects with carotid artery stenosis were retrospectively selected from Jinshan Branch of Shanghai Sixth People's Hospital, 115 cases of which with cerebral infarction and 86 without it. Clinical tests were performed including coagulation indices, fasting glucose, serum lipid, and blood rheology. Logistic regression analyses were used to identify the risk factors. Regression model was established, and receiver operating characteristic (ROC) curve was applied to analyze its diagnostic value. RESULTS: Our data indicated that apolipoprotein AI (OR = 0.051, 95% CI: 0.009-0.295), lipoprotein (a) (OR = 1.003, 95% CI: 1.001-1.005), and RBC rigidity index (OR = 0.383, 95% CI: 0.209-0.702) were independent risk factors. Area under the curve (AUC) of the regression model = 0.78, with the sensitivity of 73.9% (95% CI: 64.9%-81.7%) and specificity of 69.2% (95% CI: 52.4%-83.0%). Prediction probability was determined while logistic regression score >0.748 defaulted as high-risk status. High-risk ratios were 80% in progressive cerebral infarction and 72% in nonprogressive cerebral infarction (P > .05), respectively, while significant differences were found when both compared with controls (P < .001). CONCLUSIONS: We show herein that the regression model based on apolipoprotein AI, lipoprotein (a), and RBC IR is a promising tool to predict the occurrence of cerebral infarction in patients with carotid artery stenosis. However, identification of novel diagnostic markers for progressive cerebral infarction is still necessary.