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  • 1
    Online-Ressource
    Online-Ressource
    American Association for Cancer Research (AACR) ; 2020
    In:  Cancer Research Vol. 80, No. 16_Supplement ( 2020-08-15), p. 2293-2293
    In: Cancer Research, American Association for Cancer Research (AACR), Vol. 80, No. 16_Supplement ( 2020-08-15), p. 2293-2293
    Kurzfassung: Background: Upper tract urinary carcinoma (UTUC) includes renal pelvic cancer and ureteral cancer. For its diagnosis, ureteroscopy is the only method to obtain pathological result before surgery. However, there are also some problems with ureteroscopy. In addition to the trauma and infection risks, the possibility of exudation and adhesion of surrounding tissues after ureteroscopy is significantly increased, hypertension in the cavity may cause tumor spread, and the use of ureteroscopy and biopsy may cause recurrence of bladder tumors after surgery. These unfavorable factors make clinically urgent need for a sensitive and non-invasive method for the diagnosis of UTUC. Materials and Methods: In this study, 150 hematuria patients with upper tract urinary disease and 100 healthy people were enrolled. Among the patients, 75 were pathologically confirmed with upper tract urinary carcinoma while 75 with benign urinary diseases. A Genetron Urinary Assay was designed to maximize the number of unique driver gene variants of UTUC by a limited number of amplicons. Next-generation sequencing libraries were constructed using multiplex PCR methods. We also detected CpG-sites on ONECUT2 gene by performing bisulfite-specific real-time PCR for bisulfite converted DNA. Furthermore, we developed a prediction model based on several significant features to evaluate the risk of UTUC. Results: 234 samples were available for analysis. Our liquid biopsy assay demonstrated a sensitivity of 94%, a specificity of 96%, a positive predication value (PPV) of 92% and a negative predication value(NPV) of 98% in this study. By using a decision tree model, we found age, gene mutations of HRAS, TERT and TP53 and methylation of ONECUT2 did place at some key nodes to classify the risk of patients of UTUC, and age around 48.5, the frequencies of mutated genes above certain value and ΔCt value of ONECUT2 about 7.6 would be good cutoffs. This model was able to stratify patients into a low or high-risk group with an accuracy score at 94%. After further analysis, we found that ONECUT2 methylation and TP53 mutations were more common in high grade UTUC (p & lt;0.01), while AKT1 and FGFR3 (trend, but no statistical significance) mutations were more often shown in low grade group. No significant difference of gene mutations frequency was found between non muscle invasive and muscle invasive group, but FGFR3 as well as TERT and PIK3CA gene showed a trend of higher occurrence. Conclusion: In this study, Genetron Urinary Assay has a good performance in the diagnostic of UTUC. Its high sensitivity, high specificity, and non-invasiveness make it a potential clinical alternative to ureteroscopy. Further validation in a large prospective cohort of wide population is necessary to prove the true clinical value of this newly developed method. Citation Format: Xin Ma, Yansheng Xu, Hongzhao Li, Xing Ai, Jiangping Gao, Yiming Liang, Qiaosong Zheng, Kaisheng Mao, Tonghui Ma, Xu Zhang. A urine-based liquid biopsy method for detection of upper tract urinary carcinoma [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 2293.
    Materialart: Online-Ressource
    ISSN: 0008-5472 , 1538-7445
    RVK:
    RVK:
    Sprache: Englisch
    Verlag: American Association for Cancer Research (AACR)
    Publikationsdatum: 2020
    ZDB Id: 2036785-5
    ZDB Id: 1432-1
    ZDB Id: 410466-3
    Standort Signatur Einschränkungen Verfügbarkeit
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  • 2
    Online-Ressource
    Online-Ressource
    Institute of Electrical and Electronics Engineers (IEEE) ; 2022
    In:  IEEE Transactions on Neural Networks and Learning Systems Vol. 33, No. 9 ( 2022-9), p. 4930-4944
    In: IEEE Transactions on Neural Networks and Learning Systems, Institute of Electrical and Electronics Engineers (IEEE), Vol. 33, No. 9 ( 2022-9), p. 4930-4944
    Materialart: Online-Ressource
    ISSN: 2162-237X , 2162-2388
    Sprache: Unbekannt
    Verlag: Institute of Electrical and Electronics Engineers (IEEE)
    Publikationsdatum: 2022
    ZDB Id: 2644189-5
    Standort Signatur Einschränkungen Verfügbarkeit
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  • 3
    In: Applied Surface Science, Elsevier BV, Vol. 549 ( 2021-05), p. 149327-
    Materialart: Online-Ressource
    ISSN: 0169-4332
    Sprache: Englisch
    Verlag: Elsevier BV
    Publikationsdatum: 2021
    ZDB Id: 2002520-8
    ZDB Id: 52886-9
    Standort Signatur Einschränkungen Verfügbarkeit
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  • 4
    Online-Ressource
    Online-Ressource
    Frontiers Media SA ; 2021
    In:  Frontiers in Oncology Vol. 10 ( 2021-2-9)
    In: Frontiers in Oncology, Frontiers Media SA, Vol. 10 ( 2021-2-9)
    Kurzfassung: Conventional clinical detection methods such as CT, urine cytology, and ureteroscopy display low sensitivity and/or are invasive in the diagnosis of upper tract urinary carcinoma (UTUC), a factor precluding their use. Previous studies on urine biopsy have not shown satisfactory sensitivity and specificity in the application of both gene mutation or gene methylation panels. Therefore, these unfavorable factors call for an urgent need for a sensitive and non-invasive method for the diagnosis of UTUC. Methods In this study, a total of 161 hematuria patients were enrolled with (n = 69) or without (n = 92) UTUC. High-throughput sequencing of 17 genes and methylation analysis for ONECUT2 CpG sites were combined as a liquid biopsy test panel. Further, a logistic regression prediction model that contained several significant features was used to evaluate the risk of UTUC in these patients. Results In total, 86 UTUC− and 64 UTUC+ case samples were enrolled for the analysis. A logistic regression analysis of significant features including age, the mutation status of TERT promoter, and ONECUT2 methylation level resulted in an optimal model with a sensitivity of 94.0%, a specificity of 93.1%, the positive predictive value of 92.2% and a negative predictive value of 94.7%. Notably, the area under the curve (AUC) was 0.957 in the training dataset while internal validation produced an AUC of 0.962. It is worth noting that during follow-up, a patient diagnosed with ureteral inflammation at the time of diagnosis exhibiting both positive mutation and methylation test results was diagnosed with ureteral carcinoma 17 months after his enrollment. Conclusion This work utilized the epigenetic biomarker ONECUT2 for the first time in the detection of UTUC and discovered its superior performance. To improve its sensitivity, we combined the biomarker with high-throughput sequencing of 17 genes test. It was found that the selected logistic regression model diagnosed with ureteral cancer can evaluate upper tract urinary carcinoma risk of patients with hematuria and outperform other existing panels in providing clinical recommendations for the diagnosis of UTUC. Moreover, its high negative predictive value is conducive to rule to exclude patients without UTUC.
    Materialart: Online-Ressource
    ISSN: 2234-943X
    Sprache: Unbekannt
    Verlag: Frontiers Media SA
    Publikationsdatum: 2021
    ZDB Id: 2649216-7
    Standort Signatur Einschränkungen Verfügbarkeit
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  • 5
    Online-Ressource
    Online-Ressource
    Association for the Advancement of Artificial Intelligence (AAAI) ; 2020
    In:  Proceedings of the AAAI Conference on Artificial Intelligence Vol. 34, No. 04 ( 2020-04-03), p. 5117-5124
    In: Proceedings of the AAAI Conference on Artificial Intelligence, Association for the Advancement of Artificial Intelligence (AAAI), Vol. 34, No. 04 ( 2020-04-03), p. 5117-5124
    Kurzfassung: Model compression techniques on Deep Neural Network (DNN) have been widely acknowledged as an effective way to achieve acceleration on a variety of platforms, and DNN weight pruning is a straightforward and effective method. There are currently two mainstreams of pruning methods representing two extremes of pruning regularity: non-structured, fine-grained pruning can achieve high sparsity and accuracy, but is not hardware friendly; structured, coarse-grained pruning exploits hardware-efficient structures in pruning, but suffers from accuracy drop when the pruning rate is high. In this paper, we introduce PCONV, comprising a new sparsity dimension, – fine-grained pruning patterns inside the coarse-grained structures. PCONV comprises two types of sparsities, Sparse Convolution Patterns (SCP) which is generated from intra-convolution kernel pruning and connectivity sparsity generated from inter-convolution kernel pruning. Essentially, SCP enhances accuracy due to its special vision properties, and connectivity sparsity increases pruning rate while maintaining balanced workload on filter computation. To deploy PCONV, we develop a novel compiler-assisted DNN inference framework and execute PCONV models in real-time without accuracy compromise, which cannot be achieved in prior work. Our experimental results show that, PCONV outperforms three state-of-art end-to-end DNN frameworks, TensorFlow-Lite, TVM, and Alibaba Mobile Neural Network with speedup up to 39.2 ×, 11.4 ×, and 6.3 ×, respectively, with no accuracy loss. Mobile devices can achieve real-time inference on large-scale DNNs.
    Materialart: Online-Ressource
    ISSN: 2374-3468 , 2159-5399
    Sprache: Unbekannt
    Verlag: Association for the Advancement of Artificial Intelligence (AAAI)
    Publikationsdatum: 2020
    Standort Signatur Einschränkungen Verfügbarkeit
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  • 6
    Online-Ressource
    Online-Ressource
    Institute of Electrical and Electronics Engineers (IEEE) ; 2018
    In:  IEEE Transactions on Electron Devices Vol. 65, No. 6 ( 2018-6), p. 2670-2674
    In: IEEE Transactions on Electron Devices, Institute of Electrical and Electronics Engineers (IEEE), Vol. 65, No. 6 ( 2018-6), p. 2670-2674
    Materialart: Online-Ressource
    ISSN: 0018-9383 , 1557-9646
    Sprache: Unbekannt
    Verlag: Institute of Electrical and Electronics Engineers (IEEE)
    Publikationsdatum: 2018
    ZDB Id: 2028088-9
    ZDB Id: 241634-7
    Standort Signatur Einschränkungen Verfügbarkeit
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  • 7
    Online-Ressource
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    Oxford University Press (OUP) ; 2018
    In:  Journal of Antimicrobial Chemotherapy Vol. 73, No. 2 ( 2018-02-01), p. 544-546
    In: Journal of Antimicrobial Chemotherapy, Oxford University Press (OUP), Vol. 73, No. 2 ( 2018-02-01), p. 544-546
    Materialart: Online-Ressource
    ISSN: 0305-7453 , 1460-2091
    Sprache: Englisch
    Verlag: Oxford University Press (OUP)
    Publikationsdatum: 2018
    ZDB Id: 1467478-6
    SSG: 15,3
    Standort Signatur Einschränkungen Verfügbarkeit
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  • 8
    In: SSRN Electronic Journal, Elsevier BV
    Materialart: Online-Ressource
    ISSN: 1556-5068
    Sprache: Englisch
    Verlag: Elsevier BV
    Publikationsdatum: 2019
    Standort Signatur Einschränkungen Verfügbarkeit
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  • 9
    Online-Ressource
    Online-Ressource
    Association for Computing Machinery (ACM) ; 2017
    In:  ACM Transactions on Embedded Computing Systems Vol. 16, No. 4 ( 2017-11-30), p. 1-23
    In: ACM Transactions on Embedded Computing Systems, Association for Computing Machinery (ACM), Vol. 16, No. 4 ( 2017-11-30), p. 1-23
    Kurzfassung: Self-powered systems running on scavenged energy will be a key enabler for pervasive computing across the Internet of Things. The variability of input power in energy-harvesting systems limits the effectiveness of static optimizations aimed at maximizing the input-energy-to-computation ratio. We show that the resultant gap between available and exploitable energy is significant, and that energy storage optimizations alone do not significantly close the gap. We characterize these effects on a real, fabricated energy-harvesting system based on a nonvolatile processor. We introduce a unified energy-oriented approach to first optimize the number of backups, by more aggressively using the stored energy available when power failure occurs, and then optimize forward progress via improving the rate of input energy to computation via dynamic voltage and frequency scaling and self-learning techniques. We evaluate combining these schemes and show capture of up to 75.5% of all input energy toward processor computation, an average of 1.54 × increase over the best static “Forward Progress” baseline system. Notably, our energy-optimizing policy combinations simultaneously improve both the rate of forward progress and the rate of backup events (by up to 60.7% and 79.2% for RF power, respectively, and up to 231.2% and reduced to zero, respectively, for solar power). This contrasts with static frequency optimization approaches in which these two metrics are antagonistic.
    Materialart: Online-Ressource
    ISSN: 1539-9087 , 1558-3465
    Sprache: Englisch
    Verlag: Association for Computing Machinery (ACM)
    Publikationsdatum: 2017
    ZDB Id: 2096332-4
    Standort Signatur Einschränkungen Verfügbarkeit
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  • 10
    Online-Ressource
    Online-Ressource
    Frontiers Media SA ; 2021
    In:  Frontiers in Neuroscience Vol. 15 ( 2021-9-3)
    In: Frontiers in Neuroscience, Frontiers Media SA, Vol. 15 ( 2021-9-3)
    Kurzfassung: Cancer pain is one of the main complications in advanced cancer patients, and its management is still challenging. Therefore, there is an urgent need to develop novel pharmacotherapy for cancer pain. Several natural products have attracted the interest of researchers. In previous studies, curcumin has proved to exhibit antitumor, antiviral, antioxidant, anti-inflammatory, and analgesic effects. However, the analgesic mechanism of curcumin has not been elucidated. Thus, in this study, we aimed to elucidate the antinociceptive potency and analgesic mechanism of curcumin in cancer-induced bone pain. Our results showed that consecutive curcumin treatment (30, 60, 120 mg/kg, i.p., twice daily for 11 days) produced significant analgesic activity, but had no effect on the progress of the bone cancer pain. Notably, pretreatment with naloxone, a non-selective opioid receptor antagonist, markedly reversed the antinociceptive effect induced by curcumin. Moreover, in primary cultured rat dorsal root ganglion (DRG) neurons, curcumin significantly up-regulated the expression of proopiomelanocortin ( Pomc ) and promoted the release of β-endorphin and enkephalin. Furthermore, pretreatment with the antiserum of β-endorphin or enkephalin markedly attenuated curcumin-induced analgesia in cancer-induced bone pain. Our present study, for the first time, showed that curcumin attenuates cancer-induced bone pain. The results also suggested that stimulation of expression of DRG neurons β-endorphin and enkephalin mediates the antinociceptive effect of curcumin in pain hypersensitivity conditions.
    Materialart: Online-Ressource
    ISSN: 1662-453X
    Sprache: Unbekannt
    Verlag: Frontiers Media SA
    Publikationsdatum: 2021
    ZDB Id: 2411902-7
    Standort Signatur Einschränkungen Verfügbarkeit
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