In this article, we prepared a dual thermoresponsive and pH-responsive self-assembled micellar nanogel for anticancer drug delivery by using a degradable pH-responsive ketal derivative, mPEG2000-Isopropylideneglycerol (mPEG-IS, PI) polymer. The purpose of this study is to develop an injectable dual-responsive micellar nanogel system which has a sol-gel phase transition by the stimulation of body temperature with improved stability and biocompatibility as a controlled drug delivery carrier for cancer therapy. The pH-responsive PI was designed with pH-responsive ketal group as hydrophobic moieties and PEG group as hydrophilic moieties. The PI micelles encapsulated paclitaxel (PTX) was fabricated. Then, the PI micelles were formed in a thermo-nanogel. The micellar nanogel could improve the solubility and stability of PTX. The physiochemical properties of PI micelles and micellar nanogel were characterized. The results showed that dual-responsive micellar nanogel could carry out sol-gel transition at 37 C. The PI polymer can spontaneously self-assemble into micellar structure with size of 100-200 nm. The dual-responsive micellar nanogel could be degraded under lower pH condition. The test in vitro PTX release showed that dual-responsive micellar nanogel could release about 70% for 70 h under pH 5.0 while about 10% release at pH 7.4 and pH 9.0. The dual-responsive micellar nanogel was of lower cytotoxicity and suppressed tumor growth most efficiently. The micellar nanogel will be a new potential dual-responsive drug delivery system for cancer therapy.

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In clinical practice, medical image interpretation often involves multi-labeled classification, since the affected parts of a patient tend to present multiple symptoms or comorbidities. Recently, deep learning based frameworks have attained expertlevel performance on medical image interpretation, which can be attributed partially to large amounts of accurate annotations. However, manually annotating massive amounts of medical images is impractical, while automatic annotation is fast but imprecise (possibly introducing corrupted labels). In this work, we propose a new regularization approach, called Flow-Mixup, for multi-labeled medical image classification with corrupted labels. Flow-Mixup guides the models to capture robust features for each abnormality, thus helping handle corrupted labels effectively and making it possible to apply automatic annotation. Specifically, Flow-Mixup decouples the extracted features by adding constraints to the hidden states of the models. Also, Flow-Mixup is more stable and effective comparing to other known regularization methods, as shown by theoretical and empirical analyses. Experiments on two electrocardiogram datasets and a chest X-ray dataset containing corrupted labels verify that Flow-Mixup is effective and insensitive to corrupted labels.

In this study, we report the novel double pH-sensitive mixed micelles to fabricate multicore niosomes for drug delivery. The double pH-sensitive mixed micelles (PMM) were prepared with different pH-sensitive polymers, mPEG2000-Hz-CHEMS and mPEG2000-IS (2:1 w/w). Ginsenoside Rh2-loaded DPMM was mixed with Pluronic F-68, in the aqueous medium, and multicore niosomes were fabricated. The size of multicore niosomes were around 100-300 nm with a high encapsulation efficiency of G-Rh2. The G-Rh2-MCN could release encapsulated G-Rh2 with an accelerated rate under lower pH conditions with lower cytotoxicity and good antitumor efficacy.