Inhaled delivery of cetuximab-conjugated immunoliposomes loaded with afatinib: A promising strategy for enhanced non-small cell lung cancer treatment.

Afatinib (AT), an FDA-approved aniline-quinazoline derivative, is a first-line treatment for metastatic non-small cell lung cancer (NSCLC). Combining it with cetuximab (CX), a chimeric human-murine derivative immunoglobulin-G1 monoclonal antibody (mAb) targeting the extracellular domain of epidermal growth factor receptor (EGFR), has shown significant improvements in median progression-free survival. Previously, we developed cetuximab-conjugated immunoliposomes loaded with afatinib (AT-MLP) and demonstrated their efficacy against NSCLC cells (A549 and H1975). In this study, we aimed to explore the potential of pulmonary delivery to mitigate adverse effects associated with oral administration and intravenous injection. We formulated AT-MLP dry powders (AT-MLP-DPI) via freeze drying using tert-butanol and mannitol as cryoprotectants in the hydration medium. The physicochemical and aerodynamic properties of dry powders were well analyzed firstly. In vitro cellular uptake and cytotoxicity study revealed concentration- and time-dependent cellular uptake behavior and antitumor efficacy of AT-MLP-DPI, while Transwell assay demonstrated the superior inhibitory effects on NSCLC cell invasion and migration. Furthermore, in vivo pharmacokinetic study showed that pulmonary delivery of AT-MLP-DPI significantly increased bioavailability, prolonged blood circulation time, and exhibited higher lung concentrations compared to alternative administration routes and formulations. The in vivo antitumor efficacy study carried on tumor-bearing nude mice indicated that inhaled AT-MLP-DPI effectively suppressed lung tumor growth.

Spherical Centralized Quantization for Fast Image Retrieval.

Existing supervised quantization methods usually learn the quantizers from pair-wise, triplet, or anchor-based losses, which only capture their relationship locally without aligning them globally. This may cause an inadequate use of the entire space and a severe intersection among different semantics, leading to inferior retrieval performance. Furthermore, to enable quantizers to learn in an end-to-end way, current practices usually relax the non-differentiable quantization operation by substituting it with softmax, which unfortunately is biased, leading to an unsatisfying suboptimal solution. To address the above issues, we present Spherical Centralized Quantization (SCQ), which contains a Priori Knowledge based Feature (PKFA) module for the global alignment of feature vectors, and an Annealing Regulation Semantic Quantization (ARSQ) module for low-biased optimization. Specifically, the PKFA module first applies Semantic Center Allocation (SCA) to obtain semantic centers based on prior knowledge, and then adopts Centralized Feature Alignment (CFA) to gather feature vectors based on corresponding semantic centers. The SCA and CFA globally optimize the inter-class separability and intra-class compactness, respectively. After that, the ARSQ module performs a partial-soft relaxation to tackle biases, and an Annealing Regulation Quantization loss for further addressing the local optimal solution. Experimental results show that our SCQ outperforms state-of-the-art algorithms by a large margin (2.1%, 3.6%, 5.5% mAP respectively) on CIFAR-10, NUS-WIDE, and ImageNet with a code length of 8 bits. Codes are publicly available:https://github.com/zzb111/Spherical-Centralized-Quantization.

End-to-End Pre-Training With Hierarchical Matching and Momentum Contrast for Text-Video Retrieval.

Lately, video-language pre-training and text-video retrieval have attracted significant attention with the explosion of multimedia data on the Internet. However, existing approaches for video-language pre-training typically limit the exploitation of the hierarchical semantic information in videos, such as frame semantic information and global video semantic information. In this work, we present an end-to-end pre-training network with Hierarchical Matching and Momentum Contrast named HMMC. The key idea is to explore the hierarchical semantic information in videos via multilevel semantic matching between videos and texts. This design is motivated by the observation that if a video semantically matches a text (can be a title, tag or caption), the frames in this video usually have semantic connections with the text and show higher similarity than frames in other videos. Hierarchical matching is mainly realized by two proxy tasks: Video-Text Matching (VTM) and Frame-Text Matching (FTM). Another proxy task: Frame Adjacency Matching (FAM) is proposed to enhance the single visual modality representations while training from scratch. Furthermore, momentum contrast framework was introduced into HMMC to form a multimodal momentum contrast framework, enabling HMMC to incorporate more negative samples for contrastive learning which contributes to the generalization of representations. We also collected a large-scale Chinese video-language dataset (over 763k unique videos) named CHVTT to explore the multilevel semantic connections between videos and texts. Experimental results on two major Text-video retrieval benchmark datasets demonstrate the advantages of our methods. We release our code at https://github.com/cheetah003/HMMC.

Revisiting Multi-Codebook Quantization.

Multi-Codebook Quantization (MCQ) is a generalized version of existing codebook-based quantizations for Approximate Nearest Neighbor (ANN) search. Specifically, MCQ picks one codeword for each sub-codebook independently and takes the sum of picked codewords to approximate the original vector. The objective function involves no constraints, therefore, MCQ theoretically has the potential to achieve the best performance because solutions of other codebook-based quantization methods are all covered by MCQ's solution space under the same codebook size setting. However, finding the optimal solution to MCQ is proved to be NP-hard due to its encoding process, i.e., converting an input vector to a binary code. To tackle this, researchers apply constraints to it to find near-optimal solutions or employ heuristic algorithms that are still time-consuming for encoding. Different from previous approaches, this paper takes the first attempt to find a deep solution to MCQ. The encoding network is designed to be as simple as possible, so the very complex encoding problem becomes simply a feed-forward. Compared with other methods on three datasets, our method shows state-of-the-art performance. Notably, our method is 11× - 38× faster than heuristic algorithms for encoding, which makes it more practical for the real scenery of large-scale retrieval. Our code is publicly available: https://github.com/DeepMCQ/DeepQ.

Tanshinone IIA-Loaded Micelles Functionalized with Rosmarinic Acid: A Novel Synergistic Anti-Inflammatory Strategy for Treatment of Atherosclerosis.

Rosmarinic acid (RA) and tanshinone IIA (TA) which are effective components in Salvia miltiorrhiza show anti-inflammatory potential against atherosclerosis. Based on polysulfated propylene-polyethylene glycol (PPS-PEG), RA was grafted onto this polymer via amide bonds to form a micelle carrier for TA encapsulation: PPS-PEG-RA@TA. A potent inhibitory effect on lipopolysaccharide (LPS) -induced proliferation of endothelial cells with significant intracellular uptake was observed with this system. This could have been the result of release of TA in a reactive oxygen species (ROS) environment and stronger antioxidant effect of RA. The synergistic effect was optimized when the combination was used in a molar ratio of 1:1. Mechanistic studies showed that, compared with PPS-PEG-RA and TA+RA, PPS-PEG-RA@TA micelles could more effectively regulate the nuclear factor-kappa B (NF-κB) pathway to reduce expression of vascular cell adhesion molecule-1 (VCAM-1), inhibit the inflammatory cascade and reduce endothelial-cell injury. One month after intravenous injection of PPS-PEG-RA@TA micelles, the plaque area in murine aortic vessels was reduced significantly, and serious toxic side-effects were not observed in vivo, along with excellent biocompatibility. In summary, PPS-PEG-RA@TA micelles could achieve synergistic treatment of atherosclerosis.

Engineering of Stimulus-Responsive Pirfenidone Liposomes for Pulmonary Delivery During Treatment of Idiopathic Pulmonary Fibrosis.

Idiopathic pulmonary fibrosis (IPF) is an interstitial lung disease characterized by progressive and irreversible loss of lung function. Clinically safe and efficacious drug treatments for IPF are lacking. Pirfenidone (an anti-inflammatory, antioxidant and anti-fibrotic small-molecule drug) is considered a promising treatment for IPF. Unfortunately, several disadvantages of pirfenidone caused by traditional administration (e.g., gastrointestinal reactions, short elimination half-life) hinder its implementation. We designed pirfenidone pH-sensitive liposomes (PSLs) to target the acidic microenvironment of IPF and act directly at the disease site through pulmonary administration. Pirfenidone was encapsulated in liposomes to extend its half-life, and modified with polyethylene glycol on the surface of liposomes to improve the permeability of the mucus layer in airways. In vitro, the cytotoxicity of pirfenidone PSLs to pulmonary fibroblasts was increased significantly at 48 h compared with that using pirfenidone. In a murine and rat model of bleomycin-induced pulmonary fibrosis, pirfenidone PSLs inhibited IPF development and increased PSL accumulation in the lungs compared with that using pirfenidone solution or phosphate-buffered saline. Pirfenidone PSLs had potentially fewer side effects and stronger lung targeting. These results suggest that pirfenidone PSLs are promising preparations for IPF treatment.