Self-assembling nanoparticles containing dexamethasone as a novel therapy in allergic airways inflammation.

Nanocarriers can deliver a wide variety of drugs, target them to sites of interest, and protect them from degradation and inactivation by the body. They have the capacity to improve drug action and decrease undesirable systemic effects. We have previously developed a well-defined non-toxic PEG-dendritic block telodendrimer for successful delivery of chemotherapeutics agents and, in these studies, we apply this technology for therapeutic development in asthma. In these proof-of-concept experiments, we hypothesized that dexamethasone contained in self-assembling nanoparticles (Dex-NP) and delivered systemically would target the lung and decrease allergic lung inflammation and airways hyper-responsiveness to a greater degree than equivalent doses of dexamethasone (Dex) alone. We found that ovalbumin (Ova)-exposed mice treated with Dex-NP had significantly fewer total cells (2.78 ± 0.44 × 10(5) (n = 18) vs. 5.98 ± 1.3 × 10(5) (n = 13), P<0.05) and eosinophils (1.09 ± 0.28 × 10(5) (n = 18) vs. 2.94 ± 0.6 × 10(5) (n = 12), p<0.05) in the lung lavage than Ova-exposed mice alone. Also, lower levels of the inflammatory cytokines IL-4 (3.43 ± 1.2 (n = 11) vs. 8.56 ± 2.1 (n = 8) pg/ml, p<0.05) and MCP-1 (13.1 ± 3.6 (n = 8) vs. 28.8 ± 8.7 (n = 10) pg/ml, p<0.05) were found in lungs of the Dex-NP compared to control, and they were not lower in the Dex alone group. In addition, respiratory system resistance was lower in the Dex-NP compared to the other Ova-exposed groups suggesting a better therapeutic effect on airways hyperresponsiveness. Taken together, these findings from early-stage drug development studies suggest that the encapsulation and protection of anti-inflammatory agents such as corticosteroids in nanoparticle formulations can improve efficacy. Further development of novel drugs in nanoparticles is warranted to explore potential treatments for chronic inflammatory diseases such as asthma

Genome-wide estimation of firing efficiencies of origins of DNA replication from time-course copy number variation data

<p>Abstract</p> <p>Background</p> <p>DNA replication is a fundamental biological process during S phase of cell division. It is initiated from several hundreds of origins along whole chromosome with different firing efficiencies (or frequency of usage). Direct measurement of origin firing efficiency by techniques such as DNA combing are time-consuming and lack the ability to measure all origins. Recent genome-wide study of DNA replication approximated origin firing efficiency by indirectly measuring other quantities related to replication. However, these approximation methods do not reflect properties of origin firing and may lead to inappropriate estimations.</p> <p>Results</p> <p>In this paper, we develop a probabilistic model - Spanned Firing Time Model (SFTM) to characterize DNA replication process. The proposed model reflects current understandings about DNA replication. Origins in an individual cell may initiate replication randomly within a time window, but the population average exhibits a temporal program with some origins replicated early and the others late. By estimating DNA origin firing time and fork moving velocity from genome-wide time-course S-phase copy number variation data, we could estimate firing efficiency of all origins. The estimated firing efficiency is correlated well with the previous studies in fission and budding yeasts.</p> <p>Conclusions</p> <p>The new probabilistic model enables sensitive identification of origins as well as genome-wide estimation of origin firing efficiency. We have successfully estimated firing efficiencies of all origins in S.cerevisiae, S.pombe and human chromosomes 21 and 22.</p

Multifunctional targeting micelle nanocarriers with both imaging and therapeutic potential for bladder cancer.

BackgroundWe previously developed a bladder cancer-specific ligand (PLZ4) that can specifically bind to both human and dog bladder cancer cells in vitro and in vivo. We have also developed a micelle nanocarrier drug-delivery system. Here, we assessed whether the targeting micelles decorated with PLZ4 on the surface could specifically target dog bladder cancer cells.Materials and methodsMicelle-building monomers (ie, telodendrimers) were synthesized through conjugation of polyethylene glycol with a cholic acid cluster at one end and PLZ4 at the other, which then self-assembled in an aqueous solution to form micelles. Dog bladder cancer cell lines were used for in vitro and in vivo drug delivery studies.ResultsCompared to nontargeting micelles, targeting PLZ4 micelles (23.2 ± 8.1 nm in diameter) loaded with the imaging agent DiD and the chemotherapeutic drug paclitaxel or daunorubicin were more efficient in targeted drug delivery and more effective in cell killing in vitro. PLZ4 facilitated the uptake of micelles together with the cargo load into the target cells. We also developed an orthotopic invasive dog bladder cancer xenograft model in mice. In vivo studies with this model showed the targeting micelles were more efficient in targeted drug delivery than the free dye (14.3×; P &lt; 0.01) and nontargeting micelles (1.5×; P &lt; 0.05).ConclusionTargeting micelles decorated with PLZ4 can selectively target dog bladder cancer cells and potentially be developed as imaging and therapeutic agents in a clinical setting. Preclinical studies of targeting micelles can be performed in dogs with spontaneous bladder cancer before proceeding with studies using human patients

User-Controllable Recommendation via Counterfactual Retrospective and Prospective Explanations

Modern recommender systems utilize users' historical behaviors to generate personalized recommendations. However, these systems often lack user controllability, leading to diminished user satisfaction and trust in the systems. Acknowledging the recent advancements in explainable recommender systems that enhance users' understanding of recommendation mechanisms, we propose leveraging these advancements to improve user controllability. In this paper, we present a user-controllable recommender system that seamlessly integrates explainability and controllability within a unified framework. By providing both retrospective and prospective explanations through counterfactual reasoning, users can customize their control over the system by interacting with these explanations. Furthermore, we introduce and assess two attributes of controllability in recommendation systems: the complexity of controllability and the accuracy of controllability. Experimental evaluations on MovieLens and Yelp datasets substantiate the effectiveness of our proposed framework. Additionally, our experiments demonstrate that offering users control options can potentially enhance recommendation accuracy in the future. Source code and data are available at \url{https://github.com/chrisjtan/ucr}.Comment: Accepted for presentation at 26th European Conference on Artificial Intelligence (ECAI2023

GT2010-22061 SECONDARY FLOW REDUCTION BY BLADE REDESIGN AND ENDWALL CONTOURING USING AN ADJOINT OPTIMIZATION METHOD

ABSTRACT For low-aspect-ratio turbine blades secondary loss reduction is important for improving performance. This paper presents the application of a viscous adjoint method to reduce secondary loss of a linear cascade. A scalable wall function is implemented in an existing Navier-Stokes flow solver to simulate the secondary flow with reduced requirements on grid density. The simulation result is in good agreement with the experimental data. Entropy production through a blade row is used as the objec GT2010-22061 Copyright © 2010 by ASME and Alstom Technology Ltd. INTRODUCTION At present, further performance improvement of turbomachinery is difficult through traditional design procedures because significant efficiency gains have already been obtained. However, with the fast growth of computational power and advances in numerical methods, Computational Fluid Dynamics (CFD) coupled with advanced optimization algorithms provides a new cost-effective way to improve and optimize turbomachinery design as compared to classical methods based on manual iteration. Many design optimization approaches such as response surface methodology [1, 2], genetic algorithms [3] and finite difference methods A flow solver which can support physically accurate flow solutions is required in the optimization design based on CFD. Not all turbulence models can sufficiently model complex flow. Wilcox gave an overview of turbulence models in his paper In the past several decades, research has been done to improve the efficiency of flow solvers. However, to maintain high accuracy of turbulent flow solution, a fine mesh with a large number of grid points is needed. Kalitzin [6] and Shih In the turbulent flow with low Mach number of a low-aspectratio turbine blade, the flow loss may be categorized as profile loss and secondary loss. In a rather low aspect ratio blade, the secondary flow can influence the entire flow field along the spanwise direction. The theory of secondary flow was described in Horlock&apos;s paper Besides the flow solver, an important component in a typ

Secondary Flow Reduction by Blade Redesign and Endwall Contouring Using an Adjoint Optimization Method

For low-aspect-ratio turbine blades secondary loss reduc-tion is important for improving performance. This paper presents the application of a viscous adjoint method to reduce secondary loss of a linear cascade. A scalable wall function is implemented in an existing Navier-Stokes flow solver to simulate the sec-ondary flow with reduced requirements on grid density. The sim-ulation result is in good agreement with the experimental data. Entropy production through a blade row is used as the objec-tive function in the optimization of blade redesign and endwall contouring. With the adjoint method, the complete gradient in-formation needed for optimization can be obtained by solving the governing flow equations and their corresponding adjoint equa-tions only once, regardless of the number of design parameters. Three design cases are performed with a low-aspect-ratio steam turbine blade tested by Perdichizzi and Dossena. The results demonstrate that it is feasible to reduce flow loss through the redesign of the blade while maintaining the same mass-averaged turning angle. The effects on the profile loss and secondary loss due to the geometry modification of stagger angle, blade shape and endwall profile are presented and analyzed

EEG-based emotion classification using spiking neural networks

A novel method of using the spiking neural networks (SNNs) and the electroencephalograph (EEG) processing techniques to recognize emotion states is proposed in this paper. Three algorithms including discrete wavelet transform (DWT), variance and fast Fourier transform (FFT) are employed to extract the EEG signals, which are further taken by the SNN for the emotion classification. Two datasets, i.e., DEAP and SEED, are used to validate the proposed method. For the former dataset, the emotional states include arousal, valence, dominance and liking where each state is denoted as either high or low status. For the latter dataset, the emotional states are divided into three categories (negative, positive and neutral). Experimental results show that by using the variance data processing technique and SNN, the emotion states of arousal, valence, dominance and liking can be classified with accuracies of 74%, 78%, 80% and 86.27% for the DEAP dataset, and an overall accuracy is 96.67% for the SEED dataset, which outperform the FFT and DWT processing methods. In the meantime, this work achieves a better emotion classification performance than the benchmarking approaches, and also demonstrates the advantages of using SNN for the emotion state classifications

Label-free microfluidic paper-based electrochemical aptasensor for ultrasensitive and simultaneous multiplexed detection of cancer biomarkers

Simultaneous detection of multiple tumor biomarkers in body fluids could facilitate early diagnosis of lung cancer, so as to provide scientific reference for clinical treatment. This paper depicted a multi-parameter paper-based electrochemical aptasensor for simultaneous detection of carcinoembryonic antigen (CEA) and neuron-specific enolase (NSE) in a clinical sample with high sensitivity and specificity. The paper-based device was fabricated through wax printing and screen-printing, which enabled functions of sample filtration and sample auto injection. Amino functional graphene (NG)-Thionin (THI)- gold nanoparticles (AuNPs) and Prussian blue (PB)- poly (3,4- ethylenedioxythiophene) (PEDOT)- AuNPs nanocomposites were synthesized respectively. They were used to modify the working electrodes not only for promoting the electron transfer rate, but also for immobilization of the CEA and NSE aptamers. A label-free electrochemical method was adopted, enabling a rapid simple point-of-care testing. Experimental results showed that the proposed multi-parameter aptasensor exhibited good linearity in ranges of 0.01-500 ng mL for CEA (R  = 0.989) and 0.05-500 ng mL for NSE (R  = 0.944), respectively. The limit of detection (LOD) was 2 pg mL for CEA and 10 pg mL for NSE. In addition, the device was evaluated using clinical serum samples and received a good correlation with large electrochemical luminescence (ECL) equipment, which would offer a new platform for early cancer diagnostics, especially in those resource-limit areas

High-Resolution Imaging of Dendrimers Used in Drug Delivery via Scanning Probe Microscopy

Dendrimers and telodendrimer micelles represent two new classes of vehicles for drug delivery that have attracted much attention recently. Their structural characterization at the molecular and submolecular level remains a challenge due to the difficulties in reaching high resolution when imaging small particles in their native media. This investigation offers a new approach towards this challenge, using scanning tunneling microscopy (STM) and atomic force microscopy (AFM). By using new sample preparation protocols, this work demonstrates that (a) intramolecular features such as drug molecules and dendrimer termini can be resolved; and (b) telodendrimer micelles can be immobilized on the surface without compromising structural integrity, and as such, high resolution AFM imaging may be performed to attain 3D information. This high-resolution structural information should enhance our knowledge of the nanocarrier structure and nanocarrier-drug interaction and, therefore, facilitate design and optimization of the efficiency in drug delivery