Baicalin-loaded PEGylated lipid nanoparticles: characterization,pharmacokinetics, and protective effects on acute myocardial ischemia in rats

Context: Baicalin has many pharmacological activities, including protective function against myocardial ischemia by antioxidant effects and free radical scavenging activity. However, its rapid elimination half-life in plasma and poor water solubility limits its clinical efficacy.

Objective: Novel baicalin-loaded PEGylated nanostructured lipid carriers (BN-PEG-NLC) were developed to improve bioavailability of BN, to prolong retention time in vivo and to enhance its protective effect.

Methods: In this study, BN-PEG-NLC were prepared by the emulsion-evaporation and low temperature-solidification method using a mixture of glycerol monostearate and polyethylene glycol monostearate as solid lipids, and oleic acid as the liquid lipid. The physicochemical properties of NLC were characterized. The pharmacokinetic and pharmacodynamic behaviors of BN-PEG-NLC or BN-NLC were evaluated in acute MI rats.

Results and discussion: The particle size, zeta potential, and entrapment efficiency for BN-PEG-NLC were observed as 83.9?nm, ?32.1?mV, and 83.5%, respectively. The release profiles of BN from both BN-PEG-NLC and BN-NLC were fitted to the Ritger–Peppas modal, which presented burst release initially and prolonged release afterwards. Pharmacokinetics results indicated that BN-PEG-NLC exhibited a 7.2-fold increase in AUC in comparison to BN solution, while a 3-fold increase in comparison to BN-NLC. Biodistribution results revealed that BN-PEG-NLC exhibited higher heart drug concentration compared with BN-NLC as well as BN solution. In the present study, BN-PEG-NLC significantly ameliorated infarct size.

Conclusion: The results of the present study imply that PEG-NLC could be the biocompatible carriers for heart-targeted drug delivery to improve myocardial ischemia.     

In Vivo Kinematics of Functional Ankle Instability Patients and Lateral Ankle Sprain Copers During Stair Descent

Patients with mechanic ankle instability experience increased tibiotalar and subtalar joint laxity. However, in vivo joint kinematics in functional ankle instability (FAI) patients and lateral ankle sprain (LAS) copers, especially during dynamic activities, are poorly understood. Ten FAI patients, 10 LAS copers, and 10 healthy controls were included in this study. A dual fluoroscopic imaging system was used to analyze the tibiotalar and subtalar joint kinematics during stair descent. Five key poses of stair descent were analyzed. Kinematic data from six degrees of freedom were calculated utilizing a solid modeling software. The range of motion and joint positions in each degree of freedom were compared among the three groups. The tibiotalar joints of FAI patients and LAS copers were significantly more inverted than those of healthy controls during the foot strike (p = 0.016, = 0.264). The subtalar joints of FAI patients were significantly more anteriorly translated (pose 2, p = 0.003, = 0.352; pose 3, p < 0.001, = 0.454; pose 4, p = 0.004, = 0.334), inverted (pose 4, p = 0.027, = 0.234; pose 5,p = 0.034, = 0.221), and externally rotated (pose 4, p = 0.037, = 0.217; pose 5; p = 0.004, = 0.331) than those of healthy controls during the mid‐stance and the heel off. The FAI patients showed excessive tibiotalar inversion and subtalar joint hypermobility during stair descent. Meanwhile, the LAS copers maintained subtalar joint stability, and only showed excessive tibiotalar inversion in foot strike. These data provide insight into the mechanisms behind the development of FAI after initial LAS. © 2019 Orthopaedic Research Society. Published by Wiley Periodicals, Inc. J Orthop Res 37:1860–1867, 2019     

基于剂量组学预测肺癌患者放射性肺炎发生的研究

目的 探讨剂量组学在预测肺癌根治性放疗患者放射性肺炎发生中的应用潜能。方法 回顾性收集行根治性放疗的314例肺癌患者的临床资料、放疗剂量文件、定位及随访CT图像,根据临床资料及影像学随访资料对放射性肺炎进行分级,提取全肺的剂量组学特征,构建机器学习模型。应用1000次自助抽样法（bootstrap）的最小绝对值收敛和选择算子嵌套逻辑回归（LASSO‐LR）及1000次bootstrap的赤池信息量准则（AIC）向后法筛选与放射性肺炎相关的剂量组学特征,随机按照7∶3划分为训练集及验证集,应用逻辑回归建立预测模型,并应用ROC曲线及校正曲线评价模型的性能。结果 共提取120个剂量组学特征,经LASSO‐LR降维筛选得到12个特征进入“特征池”,再经过AIC向后法筛选,最终筛选出6个剂量组学特征进行模型构建,训练集AUC为0.77（95%CI为0.65~0.87）,独立验证集AUC为0.72（95%CI为0.64~0.81）。结论 利用剂量组学建立的预测模型具有预测放射性肺炎发生的潜力,但仍需继续纳入多中心数据及前瞻性数据进一步挖掘剂量组学的应用潜能。