Expression of mTOR conduction pathway in human osteosarcoma MG-63 cells and their stem cells, and the inhibitory effect of different doses of rapamycin

Purpose: To investigate the expressions of rapamycin target protein (mTOR) conduction pathway in human osteosarcoma MG-63 cells and their stem cells, and to examine the inhibitory effect of different doses of rapamycin.Methods: mTOR mRNA in osteosarcoma stem-like cells and human osteosarcoma MG-63 cells were determined by quantitative reverse transcription polymerase chain reaction (qRT-PCR). The cells were treated with different doses of rapamycin and divided into low dose group (0.5 mg), medium dose group (1.0 mg), high dose group (2.0 mg) and blank (control) group. Apoptosis and cell cycle of MG-63 cells were determined by flow cytometry, while proliferation of MG-63 cells up was assessed by CCK-8 kit.Results: mTOR in human osteosarcoma MG-63 cells was significantly lower than that in osteosarcoma stem-like cells. Compared with the control group, mRNA expression levels of mTOR in MG-63 cells and osteosarcoma stem-like cells were significantly decreased after treatment with different concentrations of rapamycin (p < 0.05). MG-63 cells treated with various doses of rapamycin exhibited a significant decrease in their proliferation, compared with control group, while only the high rapamycin concentration group exhibited a significant decrease in osteosarcoma stem-like cell proliferation (p < 0.05). Treatment with rapamycin in MG-63 cells and osteosarcoma stem-like cells resulted in a significant increase in apoptosis, prolonged G0/G1 phase and shortened S phase (p < 0.05).Conclusion: Rapamycin inhibits the expression of mTOR mRNA in osteosarcoma stem-like and MG-63 cells. It also inhibits the proliferation and cell cycle formation of osteosarcoma stem-like cells and MG-63 cells via mTOR signal pathway. These findings may provide a new target for the treatment of osteosarcoma

Development of Structure-Switching Aptamers for Kanamycin Detection Based on Fluorescence Resonance Energy Transfer

The structure-switching aptamers are designed for the simple and rapid detection of kanamycin based on the signal transduction principle of fluorescence resonance energy transfer (FRET). The structure switch is composed of kanamycin-binding aptamers and the complementary strands, respectively labeled with fluorophore and quencher, denoted as FDNA and QDNA. In the absence of kanamycin, FDNA and QDNA form the double helix structure through the complementary pairing of bases. The fluorophore and the quencher are brought into close proximity, which results in the fluorescence quenching because of the FRET mechanism. In the presence of kanamycin, the FDNA specifically bind to the target due to the high affinity of aptamers, and the QDNA are dissociated. The specific recognition between aptamers and kanamycin will obstruct the formation of structure switch and reduce the efficiency of FRET between FDNA and QDNA, thus leading to the fluorescence enhancement. Therefore, based on the structure-switching aptamers, a simple fluorescent assay for rapid detection of kanamycin was developed. Under optimal conditions, there was a good linear relationship between kanamycin concentration and the fluorescence signal recovery. The linear range of this method in milk samples was 100–600 nM with the detection limit of 13.52 nM (3σ), which is well below the maximum residue limit (MRL) of kanamycin in milk. This method shows excellent selectivity for kanamycin over the other common antibiotics. The structure-switching aptamers have been successfully applied to the detection of kanamycin spiked in milk samples with the satisfying recoveries between 101.3 and 109.1%, which is well-consistent with the results from LC-MS/MS. Due to the outstanding advantages of facile operation, rapid detection, high sensitivity, excellent specificity, and low cost, the application and extension of this strategy for rapid determination of antibiotics in food samples may greatly improve the efficiency in food safety and quality supervision

The sialic acid-dependent nematocyst discharge process in relation to its physical-chemical properties is a role model for nanomedical diagnostic and therapeutic tools

Formulas derived from theoretical physics provide important insights about the nematocyst discharge process of Cnidaria (Hydra, jellyfishes, box-jellyfishes and sea-anemones). Our model description of the fastest process in living nature raises and answers questions related to the material properties of the cell- and tubule-walls of nematocysts including their polysialic acid (polySia) dependent target function. Since a number of tumor-cells, especially brain-tumor cells such as neuroblastoma tissues carry the polysaccharide chain polySia in similar concentration as fish eggs or fish skin, it makes sense to use these findings for new diagnostic and therapeutic approaches in the field of nanomedicine. Therefore, the nematocyst discharge process can be considered as a bionic blue-print for future nanomedical devices in cancer diagnostics and therapies. This approach is promising because the physical background of this process can be described in a sufficient way with formulas presented here. Additionally, we discuss biophysical and biochemical experiments which will allow us to define proper boundary conditions in order to support our theoretical model approach. PolySia glycans occur in a similar density on malignant tumor cells than on the cell surfaces of Cnidarian predators and preys. The knowledge of the polySia-dependent initiation of the nematocyst discharge process in an intact nematocyte is an essential prerequisite regarding the further development of target-directed nanomedical devices for diagnostic and therapeutic purposes. The theoretical description as well as the computationally and experimentally derived results about the biophysical and biochemical parameters can contribute to a proper design of anti-tumor drug ejecting vessels which use a stylet-tubule system. Especially, the role of nematogalectins is of interest because these bridging proteins contribute as well as special collagen fibers to the elastic band properties. The basic concepts of the nematocyst discharge process inside the tubule cell walls of nematocysts were studied in jellyfishes and in Hydra which are ideal model organisms. Hydra has already been chosen by Alan Turing in order to figure out how the chemical basis of morphogenesis can be described in a fundamental way. This encouraged us to discuss the action of nematocysts in relation to morphological aspects and material requirements. Using these insights, it is now possible to discuss natural and artificial nematocyst-like vessels with optimized properties for a diagnostic and therapeutic use, e.g., in neurooncology. We show here that crucial physical parameters such as pressure thresholds and elasticity properties during the nematocyst discharge process can be described in a consistent and satisfactory way with an impact on the construction of new nanomedical devices

An Improved Model Predictive Torque Control for PMSM Drives Based on Discrete Space Vector Modulation

In this article, an improved model predictive torque control (MPTC) method based on discrete space vector modulation (DSVM) is proposed for permanent magnet synchronous motor (PMSM) drives. Aiming at solving the two problems of large torque ripples and high computational complexity in conventional MPTC, the proposed method adopts a second optimization and a new simplified search strategy. The key idea of second optimization is to make the output voltage vector closer to the actual optimal solution. In this case, a more suitable voltage vector is applied in each sampling period. The simplified search strategy reduces the calculation time by cutting down the number of candidate voltage vectors without affecting drives performance. Compared to the conventional MPTC without DSVM and with DSVM, the proposed method can produce superior steady-state performance and lower computational complexity. Simulation and experimental results are presented to validate the effectiveness and feasibility of the proposed method

Deep learning assisted diagnosis system: improving the diagnostic accuracy of distal radius fractures

ObjectivesTo explore an intelligent detection technology based on deep learning algorithms to assist the clinical diagnosis of distal radius fractures (DRFs), and further compare it with human performance to verify the feasibility of this method.MethodsA total of 3,240 patients (fracture: n = 1,620, normal: n = 1,620) were included in this study, with a total of 3,276 wrist joint anteroposterior (AP) X-ray films (1,639 fractured, 1,637 normal) and 3,260 wrist joint lateral X-ray films (1,623 fractured, 1,637 normal). We divided the patients into training set, validation set and test set in a ratio of 7:1.5:1.5. The deep learning models were developed using the data from the training and validation sets, and then their effectiveness were evaluated using the data from the test set. Evaluate the diagnostic performance of deep learning models using receiver operating characteristic (ROC) curves and area under the curve (AUC), accuracy, sensitivity, and specificity, and compare them with medical professionals.ResultsThe deep learning ensemble model had excellent accuracy (97.03%), sensitivity (95.70%), and specificity (98.37%) in detecting DRFs. Among them, the accuracy of the AP view was 97.75%, the sensitivity 97.13%, and the specificity 98.37%; the accuracy of the lateral view was 96.32%, the sensitivity 94.26%, and the specificity 98.37%. When the wrist joint is counted, the accuracy was 97.55%, the sensitivity 98.36%, and the specificity 96.73%. In terms of these variables, the performance of the ensemble model is superior to that of both the orthopedic attending physician group and the radiology attending physician group.ConclusionThis deep learning ensemble model has excellent performance in detecting DRFs on plain X-ray films. Using this artificial intelligence model as a second expert to assist clinical diagnosis is expected to improve the accuracy of diagnosing DRFs and enhance clinical work efficiency

Exploring the Spatial Characteristics of Inbound Tourist Flows in China Using Geotagged Photos

As important modern tourist destinations, cities play a critical role in developing agglomerated tourism elements and promoting urban life quality. An in-depth exploration of tourist flow patterns between destination cities can reflect the dynamic trends of the inbound tourist market. This is significant for the development of tourism markets and innovation in tourism products. To this end, photos with geographical and corresponding metadata covering the entire country from 2011 to 2017 are used to explore the spatial characteristics of China’s inbound tourist flow, the spatial patterns of tourist movement, and the tourist destination cities group based on data mining techniques, including the Markov chain, a frequent-pattern-mining algorithm, and a community detection algorithm. Our findings show that: (1) the strongest flow of inbound tourists is between Beijing and Shanghai. These two cities, along with Xi’an and Guiling, form a “double-triangle” framework, (2) the travel between emerging destination cities in Central and Western China have gradually become frequently selected itineraries, and, (3) based on the flow intensity, inbound tourist destination cities can be divided into nine groups. This study provides a valuable reference for the development of China’s inbound tourism market

A Study on the Intelligent Analysis and Pre-warning Platform of Power Grid Video Surveillance Based on “the Integration of Regulation and Control”

In order to strengthen the centralized management and control on the work of substations in all aspects and increase the real-time surveillance and safety level of unattended substations, this paper carries out a study on the intelligent analysis and pre-warning platform of power grid video surveillance based on “the integration of regulation and control”. With the design idea of combining centralization and distribution, this platform screens and analyzes a large amount of videos intelligently through the target characteristic detection method based on vision and the means of pattern classification, realizing accurate warnings of the work of unattended substations