Alendronate/folic acid-decorated polymeric nanoparticles for hierarchically targetable chemotherapy against bone metastatic breast cancer.

To considerably enhance treatment efficacy for bone metastatic breast cancer via dual bone/tumor-targeted chemotherapy, a nanoparticle-based delivery system comprising poly(lactic-co-glycolic acid) (PLGA) as the hydrophobic core coated with alendronate-modified d-α-tocopheryl polyethylene glycol succinate (ALN-TPGS) and folic acid-conjugated TPGS (FA-TPGS) was developed as a vehicle for paclitaxel (PTX) in this work. The ALN/FA-decorated nanoparticles not only showed superior ALN-mediated binding affinity for hydroxyapatite abundant in bone tissue but also promoted uptake of payloads by folate receptor-overexpressing cancer cells to significantly augment PTX cytotoxicity. Notably, through dual-targetable delivery to the bone matrix and folate receptor-overexpressing 4T1 tumors, the PTX-loaded nanoparticles substantially accumulated in bone metastases in vivo and inhibited 4T1 tumor growth and lung metastasis, leading to significant improvement of the survival rate of treated mice. Upon treatment with the ALN/FA-decorated PTX-loaded nanoparticles, the bone destruction and bone loss of the tumor-bearing mice were appreciably retarded, and the adverse effects on normal tissues were alleviated. These results demonstrate that the ALN/FA-decorated PTX-loaded delivery system developed in this study shows great promise for the effective treatment of bone metastatic breast cancer.

Sampling strategies for occupational exposure assessment under generalized linear model.

OBJECTIVES: Occupational exposure assessment is a major task in industrial hygiene studies. Although statistical analyses for magnitudes and variations of exposures to various types of working populations based on existing data sets are extensive, relatively few discussions on study designs appear in the literature, especially for sample size determination and number of repeated measurements. METHODS: In this paper, we propose a general framework of sampling strategies on sample size requirement together with the number of repeated measurements using the mixed-effects generalized linear model (GLM). As illustrative examples, we discuss sampling strategies separately under the log-normal assumption for hypotheses testing on (i) mean exposure differences of multiple worker groups and (ii) presence of a long-term exposure trend. RESULTS: Given a specified alternative hypothesis, the desired significance level and statistical power, the number of repeated measurements, within-worker and between-worker variances, and a correlation structure, we have derived and tabulated an explicit sample size requirement of the two hypothetical cases under log-normal distribution assumption. CONCLUSIONS: On the basis of the tabulated outcomes, the sample size requirement is much more dominant than the number of repeated measurements for a group exposure comparison. Thus, in this case, recruiting more workers with fewer repeated measurements may be more economical than the opposite approach. For testing the presence of a long-term exposure trend, the sample size required decreases substantially with the number of repeated measurements. Also, equally spaced sampling times would be optimal because the effect of between-worker variance is algebraically cancelled out in sample size calculations.