Nanoparticles--particles in the size range 1-100 nm--are emerging as a class of therapeutics for cancer. Early clinical results suggest that nanoparticle therapeutics can show enhanced efficacy, ...while simultaneously reducing side effects, owing to properties such as more targeted localization in tumours and active cellular uptake. Here, we highlight the features of nanoparticle therapeutics that distinguish them from previous anticancer therapies, and describe how these features provide the potential for therapeutic effects that are not achievable with other modalities. While large numbers of preclinical studies have been published, the emphasis here is placed on preclinical and clinical studies that are likely to affect clinical investigations and their implications for advancing the treatment of patients with cancer.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, UILJ, UKNU, UL, UM, UPUK
Cancer nanotherapeutics are rapidly progressing and are being implemented to solve several limitations of conventional drug
delivery systems such as nonspecific biodistribution and targeting, lack of ...water solubility, poor oral bioavailability, and
low therapeutic indices. To improve the biodistribution of cancer drugs, nanoparticles have been designed for optimal size
and surface characteristics to increase their circulation time in the bloodstream. They are also able to carry their loaded
active drugs to cancer cells by selectively using the unique pathophysiology of tumors, such as their enhanced permeability
and retention effect and the tumor microenvironment. In addition to this passive targeting mechanism, active targeting strategies
using ligands or antibodies directed against selected tumor targets amplify the specificity of these therapeutic nanoparticles.
Drug resistance, another obstacle that impedes the efficacy of both molecularly targeted and conventional chemotherapeutic
agents, might also be overcome, or at least reduced, using nanoparticles. Nanoparticles have the ability to accumulate in
cells without being recognized by P-glycoprotein, one of the main mediators of multidrug resistance, resulting in the increased
intracellular concentration of drugs. Multifunctional and multiplex nanoparticles are now being actively investigated and
are on the horizon as the next generation of nanoparticles, facilitating personalized and tailored cancer treatment.
Natural musculoskeletal systems have been widely recognized as an advanced robotic model for designing robust yet flexible microbots. However, the development of artificial musculoskeletal systems at ...micro-nanoscale currently remains a big challenge, since it requires precise assembly of two or more materials of distinct properties into complex 3D micro/nanostructures. In this study, we report femtosecond laser programmed artificial musculoskeletal systems for prototyping 3D microbots, using relatively stiff SU-8 as the skeleton and pH-responsive protein (bovine serum albumin, BSA) as the smart muscle. To realize the programmable integration of the two materials into a 3D configuration, a successive on-chip two-photon polymerization (TPP) strategy that enables structuring two photosensitive materials sequentially within a predesigned configuration was proposed. As a proof-of-concept, we demonstrate a pH-responsive spider microbot and a 3D smart micro-gripper that enables controllable grabbing and releasing. Our strategy provides a universal protocol for directly printing 3D microbots composed of multiple materials.
Recent studies in deep learning-based speech separation have proven the superiority of time-domain approaches to conventional time-frequency-based methods. Unlike the time-frequency domain ...approaches, the time-domain separation systems often receive input sequences consisting of a huge number of time steps, which introduces challenges for modeling extremely long sequences. Conventional recurrent neural networks (RNNs) are not effective for modeling such long sequences due to optimization difficulties, while one-dimensional convolutional neural networks (1-D CNNs) cannot perform utterance-level sequence modeling when its receptive field is smaller than the sequence length. In this paper, we propose dual-path recurrent neural network (DPRNN), a simple yet effective method for organizing RNN layers in a deep structure to model extremely long sequences. DPRNN splits the long sequential input into smaller chunks and applies intra- and inter-chunk operations iteratively, where the input length can be made proportional to the square root of the original sequence length in each operation. Experiments show that by replacing 1-D CNN with DPRNN and apply sample-level modeling in the time-domain audio separation network (TasNet), a new state-of-the-art performance on WSJ0-2mix is achieved with a 20 times smaller model than the previous best system.
Muscles and joints make highly coordinated motion, which can be partly mimicked to drive robots or facilitate activities. However, most cases primarily employ actuators enabling simple deformations. ...Therefore, a mature artificial motor system requires many actuators assembled with jointed structures to accomplish complex motions, posing limitations and challenges to the fabrication, integration, and applicability of the system. Here, a holistic artificial muscle with integrated light‐addressable nodes, using one‐step laser printing from a bilayer structure of poly(methyl methacrylate) and graphene oxide compounded with gold nanorods (AuNRs), is reported. Utilizing the synergistic effect of the AuNRs with high plasmonic property and wavelength‐selectivity as well as graphene with good flexibility and thermal conductivity, the artificial muscle can implement full‐function motility without further integration, which is reconfigurable through wavelength‐sensitive light activation. A biomimetic robot and artificial hand are demonstrated, showcasing functionalized control, which is desirable for various applications, from soft robotics to human assists.
A holistic artificial muscle with integrated light‐addressable nodes, using one‐step laser printing from a bilayer structure of poly(methyl methacrylate) and graphene oxide compounded with gold nanorods, is reported. The artificial muscle can implement full‐function motility without further integration, which is reconfigurable through wavelength‐sensitive light activation. A biomimetic robot and artificial hand is demonstrated, showcasing functionalized control, which is desirable for various applications.
Despite the overwhelming success of deep learning in various speech processing tasks, the problem of separating simultaneous speakers in a mixture remains challenging. Two major difficulties in such ...systems are the arbitrary source permutation and unknown number of sources in the mixture. We propose a novel deep learning framework for single channel speech separation by creating attractor points in high dimensional embedding space of the acoustic signals which pull together the time-frequency bins corresponding to each source. Attractor points in this study are created by finding the centroids of the sources in the embedding space, which are subsequently used to determine the similarity of each bin in the mixture to each source. The network is then trained to minimize the reconstruction error of each source by optimizing the embeddings. The proposed model is different from prior works in that it implements an end-to-end training, and it does not depend on the number of sources in the mixture. Two strategies are explored in the test time, K-means and fixed attractor points, where the latter requires no post-processing and can be implemented in real-time. We evaluated our system on Wall Street Journal dataset and show 5.49% improvement over the previous state-of-the-art methods.
Abstract Luminous red novae and their connection to common envelope evolution (CEE) remain elusive in astrophysics. Here, we present a radiation hydrodynamic model capable of simulating the light ...curves of material ejected during a CEE. For the first time, the radiation hydrodynamic model incorporates complete recombination physics for hydrogen and helium. The radiation hydrodynamic equations are solved with Guangqi . With time-independent ejecta simulations, we show that the peaks in the light curves are attributed to radiation-dominated ejecta, while the extended plateaus are produced by matter-dominated ejecta. To showcase our model’s capability, we fit the light curve of AT 2019zhd. The central mass object of 6 M ⊙ is assumed based on observations and scaling relations. Our model demonstrates that the ejecta mass of AT 2019zhd falls within the range of 0.04–0.1 M ⊙ . Additionally, we demonstrate that recombination energy and radiation force acceleration significantly impact the light curves, whereas dust formation has a limited effect during the peak and plateau phases.
Cancer prevention (chemoprevention) by using naturally occurring dietary agents has gained immense interest because of the broad safety window of these compounds. However, many of these compounds are ...hydrophobic and poorly soluble in water. They frequently display low bioavailability, poor systemic delivery, and low efficacy. To circumvent this problem, we explored a novel approach toward chemoprevention using nanotechnology to deliver luteolin, a natural compound present in green vegetables. We formulated water-soluble polymer-encapsulated Nano-Luteolin from hydrophobic luteolin, and studied its anticancer activity against lung cancer and head and neck cancer. In vitro studies demonstrated that, like luteolin, Nano-Luteolin inhibited the growth of lung cancer cells (H292 cell line) and squamous cell carcinoma of head and neck (SCCHN) cells (Tu212 cell line). In Tu212 cells, the IC50 value of Nano-Luteolin was 4.13 μmol/L, and that of luteolin was 6.96 μmol/L. In H292 cells, the IC50 of luteolin was 15.56 μmol/L, and Nano-Luteolin was 14.96 μmol/L. In vivo studies using a tumor xenograft mouse model demonstrated that Nano-Luteolin has a significant inhibitory effect on the tumor growth of SCCHN in comparison to luteolin. Our results suggest that nanoparticle delivery of naturally occurring dietary agents like luteolin has many advantages and may have potential application in chemoprevention in clinical settings.
Using nanoparticles for the delivery of small molecules in anticancer therapy is a rapidly growing area of research. The advantages of using nanoparticles for drug delivery include enhanced water ...solubility, tumor-specific accumulation and improved antitumor efficacy, while reducing nonspecific toxicity. Current research in this field focuses on understanding precisely how small molecules are released from nanoparticles and delivered to the targeted tumor tissues or cells, and how the unique biodistribution of the drug-carrying nanoparticles limits toxicity in major organs. Here, we discuss existing nanoparticles for the delivery of small-molecule anticancer agents and recent advances in this field.
•Landslide susceptibility mapping models in a loess area were evaluated.•ADTree, ADTree with AdaBoost, and ADTree with Bagging were applied.•The impact of each factor on the landslide occurrence was ...detailed analyzed.•Ensemble models enhance the accuracy of solely applied ADTree model.•ADTree with AdaBoost shows the best result in landslide prediction.
Landslides are a common type of natural disaster that brings great threats to the human lives and economic development around the world, especially in the Chinese Loess Plateau. Longxian County (Shaanxi Province, China), a landslide-prone area located in the southwest part of the Loess Plateau, was selected as the study area. The main purpose of this paper is to map landslide susceptibility using Alternating decision tree (ADTree) as well as GIS-based new ensemble techniques involving ADTree with bootstrap aggregation (Bagging) and ADTree with adaptive boosting (AdaBoost). Initially, a landslide inventory map was prepared with 171 determined historical landslides events in the study area, 120 landslides (70%) were randomly selected for training dataset and the remaining 51 landslides (30%) were used for validation dataset. Subsequently, eleven landslide conditioning factors were considered in the landslide susceptibility mapping. Then, an optimization operation on selection of landslide conditioning factors was performed using correlation attribute evaluation method and Spearman’s rank correlation coefficient. Afterwards, landslide susceptibility maps were generated with the three models. Finally, receiver operating characteristic (ROC) curve, area under the ROC curve (AUC) and statistical measures were applied to evaluate and validate the performance of the models. The results show success rates of the ADTree model, the ADTree with Bagging (ADTree-Bagging) model and the ADTree with AdaBoost (ADTree-AdaBoost) model were 0.872, 0.917, and 0.984, respectively, while prediction rates of the three models were 0.696, 0.752 and 0.787, respectively. In sum, the two ensemble models proposed prohibited better performance than the ADTree model did, and the ADTree-AdaBoost model was selected as the best model in the study. Hence, ensemble techniques can provide new and promising methods for spatial prediction and zonation of landslide susceptibility.
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