Hyaluronic acid-coated, prodrug-based nanostructured lipid carriers for enhanced pancreatic cancer therapy.

CONTEXT: Gemcitabine (GEM) and Baicalein (BCL) are reported to have anti-tumor effects including pancreatic cancer. Hyaluronic acid (HA) can bind to over-expressed receptors in various kinds of cancer cells. OBJECTIVE: The aim of this study is to develop prodrugs containing HA, BCL and GEM, and construct nanomedicine incorporate GEM and BCL in the core and HA on the surface. This system could target the cancer cells and co-deliver the drugs. METHODS: GEM-stearic acid lipid prodrug (GEM-SA) and hyaluronic acid-amino acid-baicalein prodrug (HA-AA-BCL) were synthesized. Then, GEM and BCL prodrug-based targeted nanostructured lipid carriers (HA-GEM-BCL NLCs) were prepared by the nanoprecipitation technique. The in vitro cytotoxicity studies of the NLCs were evaluated on AsPC1 pancreatic cancer cell line. In vivo anti-tumor effects were observed on the murine-bearing pancreatic cancer model. RESULTS: HA-GEM-BCL NLCs were effective in entering pancreatic cancer cells over-expressing HA receptors, and showed cytotoxicity of tumor cells in vitro. In vivo study revealed significant tumor growth inhibition ability of HA-GEM-BCL NLCs in murine pancreatic cancer model. CONCLUSION: It could be concluded that HA-GEM-BCL NLCs could be featured as promising co-delivery, tumor-targeted nanomedicine for the treatment of cancers.

Uncertainty-Aware Source-Free Domain Adaptive Semantic Segmentation.

Source-Free Domain Adaptation (SFDA) is becoming topical to address the challenge of distribution shift between training and deployment data, while also relaxing the requirement of source data availability during target domain adaptation. In this paper, we focus on SFDA for semantic segmentation, in which pseudo labeling based target domain self-training is a common solution. However, pseudo labels generated by the source models are particularly unreliable on the target domain data due to the domain shift issue. Therefore, we propose to use Bayesian Neural Network (BNN) to improve the target self-training by better estimating and exploiting pseudo-label uncertainty. With the uncertainty estimation of BNNs, we introduce two novel self-training based components: Uncertainty-aware Online Teacher-Student Learning (UOTSL) and Uncertainty-aware FeatureMix (UFM). Extensive experiments on two popular benchmarks, GTA 5 → Cityscapes and SYNTHIA → Cityscapes, show the superiority of our proposed method with mIoU gains of 3.6% and 5.7% over the state-of-the-art respectively.

Prediction Calibration for Generalized Few-Shot Semantic Segmentation.

Generalized Few-shot Semantic Segmentation (GFSS) aims to segment each image pixel into either base classes with abundant training examples or novel classes with only a handful of (e. g., 1-5) training images per class. Compared to the widely studied Few-shot Semantic Segmentation (FSS), which is limited to segmenting novel classes only, GFSS is much under-studied despite being more practical. Existing approach to GFSS is based on classifier parameter fusion whereby a newly trained novel class classifier and a pre-trained base class classifier are combined to form a new classifier. As the training data is dominated by base classes, this approach is inevitably biased towards the base classes. In this work, we propose a novel Prediction Calibration Network (PCN) to address this problem. Instead of fusing the classifier parameters, we fuse the scores produced separately by the base and novel classifiers. To ensure that the fused scores are not biased to either the base or novel classes, a new Transformer-based calibration module is introduced. It is known that the lower-level features are useful of detecting edge information in an input image than higher-level features. Thus, we build a cross-attention module that guides the classifier's final prediction using the fused multi-level features. However, transformers are computationally demanding. Crucially, to make the proposed cross-attention module training tractable at the pixel level, this module is designed based on feature-score cross-covariance and episodically trained to be generalizable at inference time. Extensive experiments on PASCAL- 5i and COCO- 20i show that our PCN outperforms the state-the-the-art alternatives by large margins.

Quantification of nardosinone in rat plasma using liquid chromatography-tandem mass spectrometry and its pharmacokinetics application.

A rapid, sensitive and high-throughput liquid chromatography-tandem mass spectrometry (LC-MS-MS) method was established and validated to assay the concentration of nardosinone, a main active compound isolated from Nardostachys chinensis, in rat plasma. Plasma samples were processed by protein precipitation with acetonitrile and separated on a Venusil MP-C18 column (50 × 2.1 mm, 5 µm) at an isocratic flow rate of 0.6 mL/min using methanol-0.1% formic acid in water (55 : 45, v/v) as mobile phase, and total run time was 2.5 min. MS-MS detection was accomplished in selected reaction monitoring mode with positive electrospray ionization. The calibration curve was linear over the concentration range of 9.60-320 ng/mL with lower limit of quantification of 9.60 ng/mL. The intra- and inter-day precisions were below 12.3% in terms of relative standard deviation, and the accuracy was within ±9.0% in terms of relative error. Extraction recovery, matrix effect and stability were also satisfactory in rat plasma. The developed method was successfully applied to a pharmacokinetic study of nardosinone following an intravenous injection at a dose of 1.04 mg/kg to Sprague-Dawley rats.