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  • 1
    Online Resource
    Online Resource
    Relation between blood pressure and pulse wave velocity for human arteries
    Proceedings of the National Academy of Sciences ; 2018
    In:  Proceedings of the National Academy of Sciences Vol. 115, No. 44 ( 2018-10-30), p. 11144-11149
    Details
    In: Proceedings of the National Academy of Sciences, Proceedings of the National Academy of Sciences, Vol. 115, No. 44 ( 2018-10-30), p. 11144-11149
    Abstract: Continuous monitoring of blood pressure, an essential measure of health status, typically requires complex, costly, and invasive techniques that can expose patients to risks of complications. Continuous, cuffless, and noninvasive blood pressure monitoring methods that correlate measured pulse wave velocity (PWV) to the blood pressure via the Moens−Korteweg (MK) and Hughes Equations, offer promising alternatives. The MK Equation, however, involves two assumptions that do not hold for human arteries, and the Hughes Equation is empirical, without any theoretical basis. The results presented here establish a relation between the blood pressure P and PWV that does not rely on the Hughes Equation nor on the assumptions used in the MK Equation. This relation degenerates to the MK Equation under extremely low blood pressures, and it accurately captures the results of in vitro experiments using artificial blood vessels at comparatively high pressures. For human arteries, which are well characterized by the Fung hyperelastic model, a simple formula between P and PWV is established within the range of human blood pressures. This formula is validated by literature data as well as by experiments on human subjects, with applicability in the determination of blood pressure from PWV in continuous, cuffless, and noninvasive blood pressure monitoring systems.
    Type of Medium: Online Resource
    ISSN: 0027-8424 , 1091-6490
    URL: Article
    DOI: 10.1073/pnas.1814392115
    RVK:
    TA 1000
    RVK:
    WA 15000
    Language: English
    Publisher: Proceedings of the National Academy of Sciences
    Publication Date: 2018
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  • 2
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    Online Resource
    Abstract 3779: Evaluation of anti-cancer efficacy of lipid nanoparticles containing siRNA against HPV16 E6 and E7 combined with cisplatin in xenograft model of cervical cancer
    American Association for Cancer Research (AACR) ; 2023
    In:  Cancer Research Vol. 83, No. 7_Supplement ( 2023-04-04), p. 3779-3779
    Details
    In: Cancer Research, American Association for Cancer Research (AACR), Vol. 83, No. 7_Supplement ( 2023-04-04), p. 3779-3779
    Abstract: Background: High-risk human papillomavirus (HPV) type 16 is the major etiological agent found in more than 60% of patients with cervical cancer and associated with cancer development and progression. Persistent expression HPV E6 and E7 oncoproteins are essential for the initiation and maintenance of cervical cancer. The therapeutic approaches targeting HPV E6 and E7 have been proved to be highly efficient in killing malignant cells. The suppression of HPV 16 E6/E7 expression by the short interfering RNA (siRNA) was more effective in combination therapy with cisplatin (CDDP) for cervical cancer. ENB101-LNP is an ionizable lipid nanoparticles (LNPs) encapsulating siRNA against E6/E7 of HPV 16 for delivery to the tumor cells. Methods: To investigate the anticancer efficacy and mechanism of ENB101-LNP combined with cisplatin in cervical cancer; the xenograft model was generated by the injection of CaSki cells subcutaneously into BALB/c nude mice. Mice were randomly divided into six groups: 1) blank control; 2) 1mg/kg ENB101-LNP; 3) 3mg/kg ENB101-LNP; 4) CDDP; 5) 1mg/kg ENB101-LNP-CDDP; 6) 3mg/kg ENB101-LNP-CDDP. ENB101-LNP was treated with intravenous injection three times weekly for 3 weeks and CDDP was treated with IP injection once a week for 3 weeks. Tumor growth curves were plotted to calculate the tumor inhibition rate. Mice were sacrificed one week after the last treatment and the tumor tissues were investigated using histopathology, immunohistochemical staining and western blotting. Results: Compared with the control and the other treatment groups, the tumor growth was significantly inhibited (P & lt; 0.05). The tumor inhibition rate was 29.7% in 1mg/kg ENB101-LNP group, 29.6% in 3mg/kg ENB101-LNP group, 34.0% in CDDP group, 47.0% in 1mg/kg ENB101-LNP-CDDP group. At the dose of 3mg ENB101-LNP/kg combined with CDDP exhibited superior antitumor efficacy, the tumor inhibition rate was 68.8%. The successful ENB101-LNP mediated knockdown (with up to 80%) of HPV16 E6/E7 in tumors of both 1mg/kg and 3mg/kg groups was confirmed by RT-PCR. The ENB101-LNP increased p53 and p21 expression in tumors compared to control and CDDP alone groups and combination treatment with CDDP showed decreased expression of PD-L1 expression compared to CDDP alone group. The treatment of ENB101-LNP and combination with CDDP showed significant increase in apoptotic cells compared to control and single-agent groups respectively. Conclusions: The ENB101-LNP inhibiting E6 and E7 of HPV 16 in combination with CDDP showed promising anticancer activity in the cervical cancer cells xenograft mouse model. Citation Format: Sung Wan Kang, Shin-Wha Lee, Ji-young Lee, Ok Ju Kang, Hyejeong Kim, Yisak Kim, Yong-Man Kim. Evaluation of anti-cancer efficacy of lipid nanoparticles containing siRNA against HPV16 E6 and E7 combined with cisplatin in xenograft model of cervical cancer. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 3779.
    Type of Medium: Online Resource
    ISSN: 1538-7445
    URL: Article
    DOI: 10.1158/1538-7445.AM2023-3779
    Language: English
    Publisher: American Association for Cancer Research (AACR)
    Publication Date: 2023
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  • 3
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    Deep Learning-Based Robust Actuator Fault Detection and Isolation Scheme for Highly Redundant Multirotor UAVs
    MDPI AG ; 2023
    In:  Drones Vol. 7, No. 7 ( 2023-07-01), p. 437-
    Details
    In: Drones, MDPI AG, Vol. 7, No. 7 ( 2023-07-01), p. 437-
    Abstract: This article presents a novel approach for detecting and isolating faulty actuators in highly redundant Multirotor UAVs using cascaded Deep Neural Network (DNN) models. The proposed Fault Detection and Isolation (FDI) framework combines Long Short-Term Memory (LSTM)-based fault detection and faulty actuator locator models to achieve real-time monitoring. The study focuses on a Hexadecarotor multirotor UAV equipped with sixteen rotors. To tackle the complexity of FDI resulting from redundancy, a partitioning technique is introduced based on system dynamics. The proposed FDI scheme is composed of a region classifier model responsible for detecting faults and fault locator models that precisely determine the location of the failed actuator. Extensive training and testing of the models demonstrate high accuracy, with the regional classifier model achieving 98.97% accuracy and the fault locator model achieving 99.107% accuracy. Furthermore, the scheme was integrated into the flight control system of the UAV, before being tested via both real-time monitoring in the simulation environment and analysis of recorded real flight data. The models exhibit remarkable performance in detecting and localizing injected faults. Therefore, using DNN models and the partitioning technique, this research offers a promising method for accurately detecting and isolating faulty actuators, thereby improving the overall performance and dependability of highly redundant Multirotor UAVs in various operational scenarios.
    Type of Medium: Online Resource
    ISSN: 2504-446X
    URL: Article
    DOI: 10.3390/drones7070437
    Language: English
    Publisher: MDPI AG
    Publication Date: 2023
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  • 4
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    Online Resource
    Improved Attitude and Heading Accuracy with Double Quaternion Parameters Estimation and Magnetic Disturbance Rejection
    MDPI AG ; 2021
    In:  Sensors Vol. 21, No. 16 ( 2021-08-13), p. 5475-
    Details
    In: Sensors, MDPI AG, Vol. 21, No. 16 ( 2021-08-13), p. 5475-
    Abstract: The use of unmanned aerial vehicle (UAV) applications has grown rapidly over the past decade with the introduction of low-cost microelectromechanical system (MEMS)-based sensors that measure angular velocity, gravity, and magnetic field, which are important for an object orientation determination. However, the use of low-cost sensors has also been limited because their readings are easily distorted by unwanted internal and/or external noise signals such as environmental magnetic disturbance, which lead to errors in attitude and heading estimation results. In an extended Kalman filter (EKF) process, this study proposes a method for mitigating the effect of magnetic disturbance on attitude determination by using a double quaternion parameters for representation of orientation states, which decouples the magnetometer from attitude computation. Additionally, an online measurement error covariance matrix tuning system was implemented to reject the impact of magnetic disturbance on the heading estimation. Simulation and experimental tests were conducted to verify the performance of the proposed methods in resolving the magnetic noise effect on attitude and heading. The results showed that the proposed method performed better than complimentary, gradient descent, and single quaternion-based EKF.
    Type of Medium: Online Resource
    ISSN: 1424-8220
    URL: Article
    DOI: 10.3390/s21165475
    Language: English
    Publisher: MDPI AG
    Publication Date: 2021
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  • 5
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    Multirotor Unmanned Aerial Vehicle Configuration Optimization Approach for Development of Actuator Fault-Tolerant Structure
    MDPI AG ; 2022
    In:  Applied Sciences Vol. 12, No. 13 ( 2022-07-04), p. 6781-
    Details
    In: Applied Sciences, MDPI AG, Vol. 12, No. 13 ( 2022-07-04), p. 6781-
    Abstract: Presently, multirotor unmanned aerial vehicles (UAV) are utilized in numerous applications. Their design governs the system’s controllability and operation performance by influencing the achievable forces and moments produced. However, unexpected causalities, such as actuator failure, adversely affect their controllability, which raises safety concerns about their service. On the other hand, their design flexibility allows further design optimization for various performance requirements, including actuator failure tolerance. Thus, this study proposed an optimization framework that can be employed to design a novel actuator fault-tolerant multirotor UAV configuration. The approach used an attainable moment set (AMS) to evaluate the achievable moment from a multirotor configuration; similarly, standard deviation geometries (SDG) were employed to define performance requirements. Therefore, given a UAV configuration, actuator fault situation, and SDG derived from the designed mission requirement, the suggested optimization framework maximizes the scaling factor of SDG and fits it into the AMS by adjusting the design parameters up to a sufficient margin. The framework is implemented to optimize selected parameters of the Hexacopter-type of parcel delivery multirotor UAV developed by the PNU drone, and a simulation was conducted. The result showed that the optimized configuration of the UAV achieved actuator fault tolerance and operation-performing capability in the presence of a failed actuator.
    Type of Medium: Online Resource
    ISSN: 2076-3417
    URL: Article
    DOI: 10.3390/app12136781
    Language: English
    Publisher: MDPI AG
    Publication Date: 2022
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