As an acidic, ocean colloid polysaccharide, alginate is both a biopolymer and a polyelectrolyte that is considered to be biocompatible, nontoxic, nonimmunogenic, and biodegradable. A significant number of studies have confirmed the potential use of alginate-based platforms as effective vehicles for drug delivery for cancer-targeted treatment. In this review, the focus is on the formation of alginate-based cancer-targeted delivery systems. Specifically, some general chemical and physical properties of alginate and different types of alginate-based delivery systems are discussed, and various kinds of alginate-based carriers are introduced. Finally, recent innovative strategies to functionalize alginate-based vehicles for cancer targeting are described to highlight research towards the optimization of alginate.

Daidzein (DZ) has a broad spectrum of biological activities, including antioxidant, anti-inflammatory and anticancer as well as cardio- and hepatoprotective properties. The present study was designed to elucidate the in-depth mechanism underlying the neuroprotective efficacy of DZ against spinal cord ischemic/reperfusion injury (SCII) in a rat model by comparison with the standard neuroprotective agent methylprednisolone (MP). A total of 48 rats were divided into four groups of twelve rats in each (n=12). In sham-operated group (Control) group, rats received only saline (Fogarty catheter was inserted without balloon inflation), whereas rats in the SCII induction group (SCII) were subjected to SCII insult by insertion of a Fogarty balloon catheter, which was inflated in the descending thoracic aorta to cause an occlusion. A proportion of rats was treated with DZ (20 mg/kg; DZ+SCII group) or MP (50 mg/kg; MP+SCII group) for seven days prior to and after SCII. The locomotor function (neurological activity) and antioxidant levels (superoxide dismutase and catalase) levels were significantly improved upon treatment with DZ and MP in comparison with those in the SCII group. A concomitant decline in edema, inflammatory markers (myeloperoxidase, tumor necrosis factor-α and nuclear factor κB p65), the apoptotic marker caspase-3 and the number of cells with terminal deoxynucleotidyl transferase-mediated dUTP nick-end labeling was also observed in the DZ and MP groups. The protein levels of phosphoinositide-3 kinase (PI3K), the phosphorylated Akt/Akt ratio and B-cell lymphoma 2 (Bcl-2) were substantially downregulated, while Bcl-2-associated X protein levels were upregulated SCII insult group, which was inhibited by treatment with DZ. To conclude, pre-treatment with DZ significantly improved the neurological function by upregulating PI3K/Akt signaling and thereby considerably attenuating the inflammatory response and apoptosis, thus maintaining the neuronal count in an SCII-induced rat model.

Despite much progress achieved in the SPs design, unexpectedly low targeting efficiency, retention and poor cell permeability are still the major obstacles in the development of SPs candidates "

AbstractTraditional precise point positioning (PPP) based on undifferenced ionosphere-free linear combination of observations has many advantages such as high accuracy and easy operation. PPP usually uses the Kalman Filter (KF) to estimate state vector. However, the positioning performance depends on the accuracy of the kinematic model and initial value. The inaccurate kinematic model or initial value will lead to filter performance degradation or even divergence. To overcome this problem, this paper proposes a PPP method with an additional baseline vector constraint, which uses the direction information and length information of the baseline to correct the estimated position of the receiver. By reducing the error covariance matrix of the float solution, the algorithm improves the accuracy of the float solution. By using the collected real GPS static and kinematic data, the performance of the traditional model and the proposed model in this paper are compared. It is shown that the additional baseline vector constraint improves the PPP solution effectively in comparison with that of traditional PPP model. Additionally, the contribution of the additional constraint is up to the accuracy of the prior information.