Engineered Shellac Beads-on-the-String Fibers Using Triaxial Electrospinning for Improved Colon-Targeted Drug Delivery.

Colon-targeted drug delivery is gradually attracting attention because it can effectively treat colon diseases. Furthermore, electrospun fibers have great potential application value in the field of drug delivery because of their unique external shape and internal structure. In this study, a core layer of hydrophilic polyethylene oxide (PEO) and the anti-colon-cancer drug curcumin (CUR), a middle layer of ethanol, and a sheath layer of the natural pH-sensitive biomaterial shellac were used in a modified triaxial electrospinning process to prepare beads-on-the-string (BOTS) microfibers. A series of characterizations were carried out on the obtained fibers to verify the process-shape/structure-application relationship. The results of scanning electron microscopy and transmission electron microscopy indicated a BOTS shape and core-sheath structure. X-ray diffraction results indicated that the drug in the fibers was in an amorphous form. Infrared spectroscopy revealed the good compatibility of the components in the fibers. In vitro drug release revealed that the BOTS microfibers provide colon-targeted drug delivery and zero-order drug release. Compared to linear cylindrical microfibers, the obtained BOTS microfibers can prevent the leakage of drugs in simulated gastric fluid, and they provide zero-order release in simulated intestinal fluid because the beads in BOTS microfibers can act as drug reservoirs.

Estimation of a Human-Maneuvered Target Incorporating Human Intention.

This paper presents a new approach for estimating the motion state of a target that is maneuvered by an unknown human from observations. To improve the estimation accuracy, the proposed approach associates the recurring motion behaviors with human intentions, and models the association as an intention-pattern model. The human intentions relate to labels of continuous states; the motion patterns characterize the change of continuous states. In the preprocessing, an Interacting Multiple Model (IMM) estimation technique is used to infer the intentions and extract motions, which eventually construct the intention-pattern model. Once the intention-pattern model has been constructed, the proposed approach incorporate the intention-pattern model to estimation using any state estimator including Kalman filter. The proposed approach not only estimates the mean using the human intention more accurately but also updates the covariance using the human intention more precisely. The performance of the proposed approach was investigated through the estimation of a human-maneuvered multirotor. The result of the application has first indicated the effectiveness of the proposed approach for constructing the intention-pattern model. The ability of the proposed approach in state estimation over the conventional technique without intention incorporation has then been demonstrated.