midazole-based pH-sensitive Convertible Liposomes for Anticancer Drug Delivery

Solid tumors possess biological features that are different from those in healthy tissues, which provides opportunities of anticancer treatment by nanomedicines. Due to the presence of the fenestrated tumor vasculatures, nanomedicines can selectively accumulate in tumor tissues by the enhanced permeability and retention (EPR) effect. The acidic pH in tumor interstitium (pH 6.0-7.0) also provides a promising mechanism to trigger the nanomedicines to promote the cellular uptake of cargo drugs. The previously reported stealth liposomes coated with PEG are known to accumulate in tumors owing to their prolonged circulation time. The PEG coating on liposomes can hinder serum protein adsorption and thus prevent rapid elimination by the reticuloendothelial system, thus increasing the liposome circulation time. However, liposomal interaction with cancer cells can also be hindered by the PEG coating. In order to improve the anticancer activity of stealth liposomes, novel synthetic imidazole-based lipids were introduced to the composition of stealth liposomes to develop the pH-sensitive imidazole-based convertible liposomes (ICL). At acidic pH, the imidazole-based lipids would protonate to acquire positive charges, thus clustering with the negatively charged PEGylated lipids. Such lipid-lipid electrostatic interaction would induce phase separation of the bilayer to generate a PEG-free domain that displays excess positive charges. Such newly converted, cationic liposomes at acidic pH in tumor interstitium would have better interaction with negatively charged cancer cells and/or enhanced drug release, therefore overcoming the drawback of traditional stealth liposomes. After synthesizing the imidazole-based lipids DHI, DHMI and DHDMI, we constructed doxorubicin (DOX)-loaded ICL formulations. The physicochemical properties of ICL were characterized, and factors influencing such properties were explored. The pH-triggered acquisition of positive charges of ICL was confirmed by the elevation of ζ- potentials and aggregation with negatively charged model liposomes that mimic bio-membranes at acidic pH 6.0-7.0. Acidic pH-triggered release of ICL was confirmed by drug release assays. It was also found that although the incorporation of cholesterol can remarkably reduce the size and increase the encapsulation efficiency (EE) of ICL, it also hinders the pH-sensitivity of ICL. The morphology of ICL at both pH 7.4 and pH 6.0 was characterized under transmission electron microscopy (TEM), which showed morphological changes in response to acidic pH 6.0, which further supported the proposed pH-sensitivity of ICL. Cytotoxicity assays on 3D MCS of HeLa, A549, MDA-MB-231 and MDA-MB-468 cell lines were conducted to evaluate the anticancer activity of ICL formulations. ICL formulations without cholesterol showed considerably enhanced anticancer activities against MCS compared with the non-sensitive stealth liposomes (NSL). However, incorporation of cholesterol decreased such activities. The IC50 values of cholesterol-free ICL and ICL with cholesterol against MCS strongly suggested that the pH-sensitivity introduced by the imidazole-based lipids would enhance the anticancer activity of stealth liposomes, while the hindrance of the pH-sensitivity by cholesterol would reduce such activities. Taken together, ICL’s pH-sensitivity is correlated with their enhanced anticancer activity than non-sensitive stealth liposomes

midazole-based pH-sensitive Convertible Liposomes for Anticancer Drug Delivery

Solid tumors possess biological features that are different from those in healthy tissues, which provides opportunities of anticancer treatment by nanomedicines. Due to the presence of the fenestrated tumor vasculatures, nanomedicines can selectively accumulate in tumor tissues by the enhanced permeability and retention (EPR) effect. The acidic pH in tumor interstitium (pH 6.0-7.0) also provides a promising mechanism to trigger the nanomedicines to promote the cellular uptake of cargo drugs. The previously reported stealth liposomes coated with PEG are known to accumulate in tumors owing to their prolonged circulation time. The PEG coating on liposomes can hinder serum protein adsorption and thus prevent rapid elimination by the reticuloendothelial system, thus increasing the liposome circulation time. However, liposomal interaction with cancer cells can also be hindered by the PEG coating. In order to improve the anticancer activity of stealth liposomes, novel synthetic imidazole-based lipids were introduced to the composition of stealth liposomes to develop the pH-sensitive imidazole-based convertible liposomes (ICL). At acidic pH, the imidazole-based lipids would protonate to acquire positive charges, thus clustering with the negatively charged PEGylated lipids. Such lipid-lipid electrostatic interaction would induce phase separation of the bilayer to generate a PEG-free domain that displays excess positive charges. Such newly converted, cationic liposomes at acidic pH in tumor interstitium would have better interaction with negatively charged cancer cells and/or enhanced drug release, therefore overcoming the drawback of traditional stealth liposomes. After synthesizing the imidazole-based lipids DHI, DHMI and DHDMI, we constructed doxorubicin (DOX)-loaded ICL formulations. The physicochemical properties of ICL were characterized, and factors influencing such properties were explored. The pH-triggered acquisition of positive charges of ICL was confirmed by the elevation of ζ- potentials and aggregation with negatively charged model liposomes that mimic bio-membranes at acidic pH 6.0-7.0. Acidic pH-triggered release of ICL was confirmed by drug release assays. It was also found that although the incorporation of cholesterol can remarkably reduce the size and increase the encapsulation efficiency (EE) of ICL, it also hinders the pH-sensitivity of ICL. The morphology of ICL at both pH 7.4 and pH 6.0 was characterized under transmission electron microscopy (TEM), which showed morphological changes in response to acidic pH 6.0, which further supported the proposed pH-sensitivity of ICL. Cytotoxicity assays on 3D MCS of HeLa, A549, MDA-MB-231 and MDA-MB-468 cell lines were conducted to evaluate the anticancer activity of ICL formulations. ICL formulations without cholesterol showed considerably enhanced anticancer activities against MCS compared with the non-sensitive stealth liposomes (NSL). However, incorporation of cholesterol decreased such activities. The IC50 values of cholesterol-free ICL and ICL with cholesterol against MCS strongly suggested that the pH-sensitivity introduced by the imidazole-based lipids would enhance the anticancer activity of stealth liposomes, while the hindrance of the pH-sensitivity by cholesterol would reduce such activities. Taken together, ICL’s pH-sensitivity is correlated with their enhanced anticancer activity than non-sensitive stealth liposomes

Investigation and implementation Gene signature development using microarray data – A case study on early stage non-small cell lung cancer

Purpose Gene signature development using microarrays has started more than 15 years ago, yet there are still common mistakes made by researchers. The goal of this research is to investigate and implement gene signature using affymetrix array data. It aims to establish a working flow with well-justified steps for gene signature development. Methods Gene expression data from surgery samples of 62 early stage un-treated NSCLC patients in JBR10 trial was used for training model development. Individual genes were selected using univariate cox regression analysis, and then the gene set was summarized by principle components, which were then served as the inputs to the Cox regression model. A multi-layer internal validation was conducted for modeling evaluation. The performance of the gene signature was evaluated by testing on three independent data sets. Results A signature of 88 genes was developed that can identify patients with significantly different survival prognosis (Hazard Ratio, 95% CI, P). The signature was successfully validated in independent datasets (Hazard Ratio, 95% CI, P; Hazard Ratio, 95% CI, P; Hazard Ratio, 95% CI, P). Conclusion A working flow of gene signature development composed of preliminary gene filtering, individual gene selection, predictive model construction using supervised principle component analysis and further internal/external validation, has been constructed. Using gene expression of 62 patients from affymetrix array data in JBR.10 trials, an 88-gene signature was obtained and validated in independent datasets

Specializing Small Language Models towards Complex Style Transfer via Latent Attribute Pre-Training

In this work, we introduce the concept of complex text style transfer tasks, and constructed complex text datasets based on two widely applicable scenarios. Our dataset is the first large-scale data set of its kind, with 700 rephrased sentences and 1,000 sentences from the game Genshin Impact. While large language models (LLM) have shown promise in complex text style transfer, they have drawbacks such as data privacy concerns, network instability, and high deployment costs. To address these issues, we explore the effectiveness of small models (less than T5-3B) with implicit style pre-training through contrastive learning. We also propose a method for automated evaluation of text generation quality based on alignment with human evaluations using ChatGPT. Finally, we compare our approach with existing methods and show that our model achieves state-of-art performances of few-shot text style transfer models

Panoramic Annular Localizer: Tackling the Variation Challenges of Outdoor Localization Using Panoramic Annular Images and Active Deep Descriptors

Visual localization is an attractive problem that estimates the camera localization from database images based on the query image. It is a crucial task for various applications, such as autonomous vehicles, assistive navigation and augmented reality. The challenging issues of the task lie in various appearance variations between query and database images, including illumination variations, dynamic object variations and viewpoint variations. In order to tackle those challenges, Panoramic Annular Localizer into which panoramic annular lens and robust deep image descriptors are incorporated is proposed in this paper. The panoramic annular images captured by the single camera are processed and fed into the NetVLAD network to form the active deep descriptor, and sequential matching is utilized to generate the localization result. The experiments carried on the public datasets and in the field illustrate the validation of the proposed system.Comment: Accepted by ITSC 201

E2Net: Resource-Efficient Continual Learning with Elastic Expansion Network

Continual Learning methods are designed to learn new tasks without erasing previous knowledge. However, Continual Learning often requires massive computational power and storage capacity for satisfactory performance. In this paper, we propose a resource-efficient continual learning method called the Elastic Expansion Network (E2Net). Leveraging core subnet distillation and precise replay sample selection, E2Net achieves superior average accuracy and diminished forgetting within the same computational and storage constraints, all while minimizing processing time. In E2Net, we propose Representative Network Distillation to identify the representative core subnet by assessing parameter quantity and output similarity with the working network, distilling analogous subnets within the working network to mitigate reliance on rehearsal buffers and facilitating knowledge transfer across previous tasks. To enhance storage resource utilization, we then propose Subnet Constraint Experience Replay to optimize rehearsal efficiency through a sample storage strategy based on the structures of representative networks. Extensive experiments conducted predominantly on cloud environments with diverse datasets and also spanning the edge environment demonstrate that E2Net consistently outperforms state-of-the-art methods. In addition, our method outperforms competitors in terms of both storage and computational requirements

Imidazole-Based pH-Sensitive Convertible Liposomes for Anticancer Drug Delivery

In efforts to enhance the activity of liposomal drugs against solid tumors, three novel lipids that carry imidazole-based headgroups of incremental basicity were prepared and incorporated into the membrane of PEGylated liposomes containing doxorubicin (DOX) to render pH-sensitive convertible liposomes (ICL). The imidazole lipids were designed to protonate and cluster with negatively charged phosphatidylethanolamine-polyethylene glycol when pH drops from 7.4 to 6.0, thereby triggering ICL in acidic tumor interstitium. Upon the drop of pH, ICL gained more positive surface charges, displayed lipid phase separation in TEM and DSC, and aggregated with cell membrane-mimetic model liposomes. The drop of pH also enhanced DOX release from ICL consisting of one of the imidazole lipids, sn-2-((2,3-dihexadecyloxypropyl)thio)-5-methyl-1H-imidazole. ICL demonstrated superior activities against monolayer cells and several 3D MCS than the analogous PEGylated, pH-insensitive liposomes containing DOX, which serves as a control and clinical benchmark. The presence of cholesterol in ICL enhanced their colloidal stability but diminished their pH-sensitivity. ICL with the most basic imidazole lipid showed the highest activity in monolayer Hela cells; ICL with the imidazole lipid of medium basicity showed the highest anticancer activity in 3D MCS. ICL that balances the needs of tissue penetration, cell-binding, and drug release would yield optimal activity against solid tumors

Computation Tree Logic Model Checking of Multi-Agent Systems Based on Fuzzy Epistemic Interpreted Systems

Model checking is an automated formal verification method to verify whether epistemic multi-agent systems adhere to property specifications. Although there is an extensive literature on qualitative properties such as safety and liveness, there is still a lack of quantitative and uncertain property verifications for these systems. In uncertain environments, agents must make judicious decisions based on subjective epistemic. To verify epistemic and measurable properties in multi-agent systems, this paper extends fuzzy computation tree logic by introducing epistemic modalities and proposing a new Fuzzy Computation Tree Logic of Knowledge (FCTLK). We represent fuzzy multi-agent systems as distributed knowledge bases with fuzzy epistemic interpreted systems. In addition, we provide a transformation algorithm from fuzzy epistemic interpreted systems to fuzzy Kripke structures, as well as transformation rules from FCTLK formulas to Fuzzy Computation Tree Logic (FCTL) formulas. Accordingly, we transform the FCTLK model checking problem into the FCTL model checking. This enables the verification of FCTLK formulas by using the fuzzy model checking algorithm of FCTL without additional computational overheads. Finally, we present correctness proofs and complexity analyses of the proposed algorithms. Additionally, we further illustrate the practical application of our approach through an example of a train control system

Speech-to-Speech Translation with Discrete-Unit-Based Style Transfer

Direct speech-to-speech translation (S2ST) with discrete self-supervised representations has achieved remarkable accuracy, but is unable to preserve the speaker timbre of the source speech during translation. Meanwhile, the scarcity of high-quality speaker-parallel data poses a challenge for learning style transfer between source and target speech. We propose an S2ST framework with an acoustic language model based on discrete units from a self-supervised model and a neural codec for style transfer. The acoustic language model leverages self-supervised in-context learning, acquiring the ability for style transfer without relying on any speaker-parallel data, thereby overcoming the issue of data scarcity. By using extensive training data, our model achieves zero-shot cross-lingual style transfer on previously unseen source languages. Experiments show that our model generates translated speeches with high fidelity and style similarity. Audio samples are available at http://stylelm.github.io/ .Comment: 5 pages, 1 figure. submitted to ICASSP 202