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Shuang-Yan Huan

Publications and source records attributed to Shuang-Yan Huan.

2 recordsLinked to original sources

Quartz crystal microbalance bioaffinity sensor for biotin based on mixed self-assembled monolayers and metastable molecular complex receptor.

A quartz crystal microbalance (QCM) sensor was proposed for the detection of small molecule biotin based on the mixed self-assembled monolayer (SAM) of thiols on gold substrate and the bioaffinity difference between an analyte (biotin) and an analogue compound (HABA) in binding avidin. Avidin formed a metastable complex with 2-[(4-hydroxyphenyl)azo]benzoic acid (HABA) immobilized on the crystal surface. When the sensor contacts a sample solution containing biotin, the avidin was released from the sensor surface to form a more stable complex with biotin in solution. The frequency change recorded is proportional to the desorbed mass of avidin, and there is a clear mathematic relationship between the frequency change and the biotin concentration. The use of mixed SAMs allows the stable attachment of bioreceptor molecules on the QCM, and enhances the amount of the immobilized molecules on the QCM, as a longer "space arm" in the mixed SAMs makes this monolayer membrane more accessible to capture the immobilized molecules. The proposed bioaffinity sensor has nice response to biotin in the range of 0.017-1.67 microg/mL. The sensor could be regenerated under very mild conditions simply by reimmersion of the sensor into a biotin solution to desorb the surplus avidin.

Azo Compounds↗

Optimized partition of minimum spanning tree for piecewise modeling by particle swarm algorithm. QSAR studies of antagonism of angiotensin II antagonists.

In quantitative structure-activity relationship (QSAR) modeling, when compounds in a training set exhibit a significant structural distinction between each other, in particular when chemicals of biological interest interacting on the receptor involve a different mechanism, it might be difficult to construct a single linear model for the whole population of compounds of interest with desired residuals. Developing a piecewise linear local model can be effective to circumvent the aforementioned problem. In this paper, piecewise modeling by the particle swarm optimization (PMPSO) approach is applied to QSAR study. The minimum spanning tree is used for clustering all compounds in the training set to form a tree, and the modified discrete PSO is applied to divide the tree to find satisfactory piecewise linear models. A new objective function is formulated for searching the appropriate piecewise linear models. The proposed PMPSO algorithm was used to predict the antagonism of angiotensin II. The results demonstrated that PMPSO is useful for improvement of the performance of regression models.

Algorithms↗