Phenotypic heterogeneity of cancer cells, cell biological context, heterotypic crosstalk and the microenvironment are key determinants of the multistep process of tumor development. They sign ...responsible, to a significant extent, for the limited response and resistance of cancer cells to molecular-targeted therapies. Better functional knowledge of the complex intra- and intercellular signaling circuits underlying communication between the different cell types populating a tumor tissue and of the systemic and local factors that shape the tumor microenvironment is therefore imperative. Sophisticated 3D multicellular tumor spheroid (MCTS) systems provide an emerging tool to model the phenotypic and cellular heterogeneity as well as microenvironmental aspects of in vivo tumor growth. In this review we discuss the cellular, chemical and physical factors contributing to zonation and cellular crosstalk within tumor masses. On this basis, we further describe 3D cell culture technologies for growth of MCTS as advanced tools for exploring molecular tumor growth determinants and facilitating drug discovery efforts. We conclude with a synopsis on technological aspects for on-line analysis and post-processing of 3D MCTS models.
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The lack of in vitro models that represent the native tumor microenvironment is a significant challenge for cancer research. Two-dimensional (2D) monolayer culture has long been the standard for in ...vitro cell-based studies. However, differences between 2D culture and the in vivo environment have led to poor translation of cancer research from in vitro to in vivo models, slowing the progress of the field. Recent advances in three-dimensional (3D) culture have improved the ability of in vitro culture to replicate in vivo conditions. Although 3D cultures still cannot achieve the complexity of the in vivo environment, they can still better replicate the cell–cell and cell–matrix interactions of solid tumors. Multicellular tumor spheroids (MCTS) are three-dimensional (3D) clusters of cells with tumor-like features such as oxygen gradients and drug resistance, and represent an important translational tool for cancer research. Accordingly, natural and synthetic polymers, including collagen, hyaluronic acid, Matrigel®, polyethylene glycol (PEG), alginate and chitosan, have been used to form and study MCTS for improved clinical translatability. This review evaluates the current state of biomaterial-based MCTS formation, including advantages and disadvantages of the different biomaterials and their recent applications to the field of cancer research, with a focus on the past five years.
Hepatocellular carcinoma (HCC) is one of the most common malignant tumors worldwide and has poor prognosis. Specially, patients with HCC usually have poor tolerance of systemic chemotherapy, because ...HCCs develop from chronically damaged tissue that contains considerable inflammation, fibrosis, and cirrhosis. Since HCC exhibits highly heterogeneous molecular characteristics, a proper in vitro system is required for the study of HCC pathogenesis. To this end, we have established two new hepatitis B virus (HBV) DNA-secreting HCC cell lines from infected patients.
Based on these two new HCC cell lines, we have developed chemosensitivity assays for patient-derived multicellular tumor spheroids (MCTSs) in order to select optimized anti-cancer drugs to provide more informative data for clinical drug application. To monitor the effect of the interaction of cancer cells and stromal cells in MCTS, we used a 3D co-culture model with patient-derived HCC cells and stromal cells from human hepatic stellate cells, human fibroblasts, and human umbilical vein endothelial cells to facilitate screening for optimized cancer therapy.
To validate our system, we performed a comparison of chemosensitivity of the three culture systems, which are monolayer culture system, tumor spheroids, and MCTSs of patient-derived cells, to sorafenib, 5-fluorouracil, and cisplatin, as these compounds are typically standard therapy for advanced HCC in South Korea.
In summary, these findings suggest that the MCTS culture system is the best methodology for screening for optimized treatment for each patients with HCC, because tumor spheroids not only mirror the 3D cellular context of the tumors but also exhibit therapeutically relevant pathophysiological gradients and heterogeneity of in vivo tumors.
The uncertainty of distributed renewable energy brings significant challenges to economic operation of microgrids. Conventional online optimization approaches require a forecast model. However, ...accurately forecasting the renewable power generations is still a tough task. To achieve online scheduling of a residential microgrid (RM) that does not need a forecast model to predict the future PV/wind and load power sequences, this article investigates the usage of reinforcement learning (RL) approach to tackle this challenge. Specifically, based on the recent development of <inline-formula> <tex-math notation="LaTeX"> {M} </tex-math></inline-formula>odel-Based <inline-formula> <tex-math notation="LaTeX"> {R} </tex-math></inline-formula>einforcement <inline-formula> <tex-math notation="LaTeX"> {L} </tex-math></inline-formula>earning, MuZero (Schrittwieser et al. , 2019) we investigate its application to the RM scheduling problem. To accommodate the characteristics of the RM scheduling application, an optimization framework that combines the model-based RL agent with the mathematical optimization technique is designed, and long short-term memory (LSTM) units are adopted to extract features from the past renewable generation and load sequences. At each time step, the optimal decision is obtained by conducting Monte-Carlo tree search (MCTS) with a learned model and solving an optimal power flow sub-problem. In this way, this approach can sequentially make operational decisions online without relying on a forecast model. The numerical simulation results demonstrate the effectiveness of the proposed algorithm.
•Combined the effects of MCT supplementation and exercise on cognitive performance.•Backed up previous literature that MCTs improve cognitive performance at rest.•Discovered MCTs offset the cognitive ...decline associated with a prolonged bout of exercise.•Used MCT gels with a combination of C8 and C10.
Prolonged exercise has been linked to a decline in cognitive function due to a variety of factors, such as a drop in oxygen in the prefrontal cortex and an increase in stress hormones and neurotransmitters. Medium chain triglycerides (MCTs) may possibly offset this decline as they provide energy for the brain via both direct and indirect pathways, alongside promoting chronic physiological adaptations within the brain.
Participants were divided into two groups; MCT (n = 9) and Placebo (n = 10). The MCT gels contained 6 g of MCT with a C8:C10 ratio of 30:70, whereas the placebo gels contained carbohydrates of similar calorific value to the MCT gels. Participants visited the laboratory on three occasions (familiarisation/fitness test, pre-supplementation, post-supplementation), during which they performed a battery of cognitive tasks assessing domains such as processing speed, working memory, selective attention, decision making and coordination, before and after a prolonged bout of exercise (60 mins at 90% gas exchange threshold (GET). A 2-week supplementation period between visits 2 and 3 involved the ingestion of 2 gels per day.
Exercise resulted in detriments in most cognitive tasks pre-supplementation for both groups, and post-supplementation for the Placebo group (main effect ps< 0.05). Post-supplementation, the effect of exercise was mediated in the MCT group for all cognitive tasks (main effect ps< 0.05), except for the Digit and Spatial Span Backwards test phases (main effect ps> 0.05). Furthermore, MCT supplementation enhanced before-exercise cognitive performance and in some measures, such as working memory, this was maintained after-exercise (interaction effect ps> 0.05).
Chronic MCT supplementation enhanced before-exercise cognitive performance and offset the cognitive decline caused by a prolonged bout of exercise. In some cases, improvements in before-exercise cognitive performance were maintained after-exercise.
The tumor ecosystem evolves with dynamic interactions between cancer and normal cells, and nutrients have emerged as new regulators of cancer hallmarks. Lactate has climbed the rankings as a ...multifunctional molecule orchestrating many aspects of the disease onset and progression. Here, we patchwork and discuss the main recent findings conferred during the EMBO workshop titled ‘Lactate: Unconventional Roles of a Nutrient Along the Tumor Landscape.’
Antenna selection is a promising technology to achieve a good balance between high transmission capacity and low hardware complexity for massive multiple-input multiple-output (MIMO) systems. ...However, the design of a near-optimal antenna selection algorithm with low searching complexity is still a challenge. In this paper, we describe a self-supervised learning based Monte Carlo Tree Search (MCTS) method to solve the antenna selection problem for a massive MIMO system. The search process for selecting antennas with maximal channel capacity is converted to a decision-making based problem. Based on the system model of antenna selection, we map the components of a MIMO system to the basic elements of MCTS such as action, tree state, and reward. To improve the search efficiency of the MCTS, we employ a linear regression module to extract the channel features from the channel state information (CSI) and output the prediction to MCTS as prior probability. Since the data and label are generated by the MCTS process itself, the entire process can be considered as a self-supervised learning process. According to the simulation results, the proposed self-supervised learning MCTS-based antenna search method exhibits a high searching efficiency with near-optimal performance, which archives 40% and 15% outage capacity compared with random selection and greedy search selection, respectively. The bit error rate (BER) performance of the proposed method has about 1-dB gain compared to the greedy search selection method.
We propose a novel method applicable in many scene understanding problems that adapts the Monte Carlo Tree Search (MCTS) algorithm, originally designed to learn to play games of high-state ...complexity. From a generated pool of proposals, our method jointly selects and optimizes proposals that minimize the objective term. In our first application for floor plan reconstruction from point clouds, our method selects and refines the room proposals, modelled as 2D polygons, by optimizing on an objective function combining the fitness as predicted by a deep network and regularizing terms on the room shapes. We also introduce a novel differentiable method for rendering the polygonal shapes of these proposals. Our evaluations on the recent and challenging Structured3D and Floor-SP datasets show significant improvements over the state-of-the-art both in speed and quality of reconstructions, without imposing hard constraints nor assumptions on the floor plan configurations. In our second application, we extend our approach to reconstruct general 3D room layouts from a color image and obtain accurate room layouts. We also show that our differentiable renderer can easily be extended for rendering 3D planar polygons and polygon embeddings. Our method shows high performance on the Matterport3D-Layout dataset, without introducing hard constraints on room layout configurations.
Periodontal ligament stem cells (PDLSCs) play a crucial role in periodontal bone regeneration. Lactate used to be considered a waste product of glucose metabolism. In recent years, a few pieces of ...evidence revealed its roles in regulating the osteogenic differentiation of stem cells, but the standpoints were controversial. This study aims to investigate the effects and the mechanisms of lactate on the osteogenic differentiation of human periodontal ligament stem cells (hPDLSCs).
The hPDLSCs were treated with different concentrations of lactic acid and lactate to differentiate the effects of the acidic PH and ion lactate. Proliferation and cytotoxicity were measured by Cell Counting Kit-8 (CCK8) assay and Live/Dead assay. The osteogenic differentiation of hPDLSCs was analyzed by alizarin red staining, alkaline phosphatase (ALP) staining, and then osteogenic proteins and genes were measured by western blot and reverse transcription-quantitative PCR (qRT-PCR). To investigate the potential signaling pathways, MCT1 inhibitor, G-protein inhibitors, and rapamycin were used, and then autophagy-related proteins and osteogenic proteins were measured by western blot.
The inhibition of lactic acid on the osteogenic differentiation of hPDLSCs was more significant than lactate at the same concentration. Lactate inhibited the expression of ALP which can be rescued by Gα inhibitor. Alizarin red staining, the protein expression levels of osteocalcin (OCN), osteoprotegerin (OPN), osterix (OSX), and beclin1, LC3-II/LC3-I were decreased by lactate and partly rescued by MCT1 inhibitor. Rapamycin restored the protein expression levels of beclin1, LC3-II/LC3-I and OCN, OPN, OSX under the high lactate conditions.
Lactate inhibits the expression of ALP via Gα subunit signaling, and inhibits mineralized nodules formation and the expression of osteogenic-related proteins via reducing autophagy through the MCT1-mTOR signaling pathway.
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•Lactate inhibited osteogenesis of hPDLSCs via Gα and MCT1.•Lactate reduced autophagy of hPDLSCs.•Targeting MCT1 or autophagy rescued the inhibitory effects of lactate.
Recently, as the development of artificial intelligence (AI), data-driven AI methods have shown amazing performance in solving complex problems to support the Internet of Things (IoT) world with ...massive resource-consuming and delay-sensitive services. In this paper, we propose an intelligent resource allocation framework (iRAF) to solve the complex resource allocation problem for the collaborative mobile edge computing (CoMEC) network. The core of iRAF is a multitask deep reinforcement learning algorithm for making resource allocation decisions based on network states and task characteristics, such as the computing capability of edge servers and devices, communication channel quality, resource utilization, and latency requirement of the services, etc. The proposed iRAF can automatically learn the network environment and generate resource allocation decision to maximize the performance over latency and power consumption with self-play training. iRAF becomes its own teacher: a deep neural network (DNN) is trained to predict iRAF's resource allocation action in a self-supervised learning manner, where the training data is generated from the searching process of Monte Carlo tree search (MCTS) algorithm. A major advantage of MCTS is that it will simulate trajectories into the future, starting from a root state, to obtain a best action by evaluating the reward value. Numerical results show that our proposed iRAF achieves 59.27% and 51.71% improvement on service latency performance compared with the greedy-search and the deep <inline-formula> <tex-math notation="LaTeX">Q </tex-math></inline-formula>-learning-based methods, respectively.
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