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| 1 | Impact of diabetes on promoting the growth of breast cancer显示文摘Background:Type Ⅱ diabetes mellitus(DM2)is a significant risk factor for cancers,including breast cancer.However,a proper diabetic breast cancer mouse model is notwell-established for treatment strategy design.Additionally,the precise diabetic signaling pathways that regulate cancer growth remain unresolved.In the present study,we established a suitable mouse model and demonstrated the pathogenic role of diabetes on breast cancer progression.Methods:We successfully generated a transgenic mouse model of human epidermal growth factor receptor 2 positive(Her2^(+) or ERBB2)breast cancer with DM2 by crossing leptin receptor mutant(Lepr^(db/+))mice with (MMTV-ErbB2/neu)mice.Themousemodelswere administrated with antidiabetic drugs to assess the impacts of controlling DM2 in affecting tumor growth.Magnetic resonance spectroscopic imaging was employed to analyze the tumor metabolism.Results:Treatment with metformin/rosiglitazone in MMTV-ErbB2/Lepr^(db/db) mousemodel reduced serum insulin levels,prolonged overall survival,decreased cumulative tumor incidence,and inhibited tumor progression.Anti-insulin resistance medications also inhibited glycolytic metabolism in tumors in vivo as indicated by the reduced metabolic flux of hyperpolarized ^(13)C pyruvate-to-lactate reaction.The tumor cells from MMTV-ErbB2/Lepr^(db/db) transgenic mice treated with metformin had reprogrammed metabolism by reducing levels of both oxygen consumption and lactate production.Metformin decreased the expression of Myc and pyruvate kinase isozyme 2(PKM2),leading to metabolism reprogramming.Moreover,metformin attenuated the mTOR/AKT signaling pathway and altered adipokine profiles.Conclusions:MMTV-ErbB2/Lepr^(db/db) mouse model was able to recapitulate diabetic HER2^(+) human breast cancer.Additionally,our results defined the signaling pathways deregulated in HER2^(+) breast cancer under diabetic condition,which can be intervened by anti-insulin resistance therapy. | Ping-Chieh Chou Hyun Ho Choi Yizhi Huang Enrique Fuentes-Mattei Guermarie Velazquez-Torres Fanmao Zhang Liem Phan Jaehyuk Lee Yanxia Shi James A.Bankson Yun Wu Huamin Wang Ruiying Zhao Sai-Ching Jim Yeung Mong-Hong Lee | 2021 | Cancer Communications2021，41，5： | 4 |
| 2 | Functional genomics in the rice blast fungus to unravel the fungal pathogenicity显示文摘A rapidly growing number of successful genome sequencing projects in plant pathogenic fungi greatly increase the demands for tools and methodologies to study fungal pathogenicity at genomic scale. Magnaporthe oryzae is an economically important plant pathogenic fungus whose genome is fully sequenced. Recently we have reported the development and application of functional genomics platform technologies in M. oryzae. This model approach would have many practical ramifications in design and implementation of upcoming functional genomics studies of filamentous fungi aimed at understanding fungal pathogenicity. | Junhyun JEON Jaehyuk CHOI Jongsun PARK Yong-Hwan LEE | 2008 | Journal of Zhejiang University-Science B(Biomedicine & Biotechnology)2008，9，10： | 2 |
| 3 | Planar Hall effect in a single GaMnAs film grown on Si substrate显示文摘 | Jaehyuk Won Jinsik Shin Sangyeop Lee | 2012 | J Cryst Growth2012，12，： | 1 |
| 4 | E‐cadherin expression in early gastric carcinoma and correlation with lymph node metastasis显示文摘 | DongYi Kim JaeKyoon Joo YoungKyu Park SeongYeob Ryu HyunSoo Kim BokKyun Noh KyungHwa Lee JaeHyuk Lee | 2007 | J Surg Oncol2007，，5： | 1 |
| 5 | E‐cadherin expression in early gastric carcinoma and correlation with lymph node metastasis显示文摘 | DongYi Kim JaeKyoon Joo YoungKyu Park SeongYeob Ryu HyunSoo Kim BokKyun Noh KyungHwa Lee JaeHyuk Lee | 2007 | J. Surg. Oncol2007，，5： | 1 |
| 6 | TinyML-Based Classification in an ECG Monitoring Embedded System显示文摘Recently, the development of the Internet of Things (IoT) hasenabled continuous and personal electrocardiogram (ECG) monitoring. In theECG monitoring system, classification plays an important role because it canselect useful data (i.e., reduce the size of the dataset) and identify abnormaldata that can be used to detect the clinical diagnosis and guide furthertreatment. Since the classification requires computing capability, the ECGdata are usually delivered to the gateway or the server where the classificationis performed based on its computing resource. However, real-time ECG datatransmission continuously consumes battery and network resources, whichare expensive and limited. To mitigate this problem, this paper proposes atiny machine learning (TinyML)-based classification (i.e., TinyCES), wherethe ECG monitoring device performs the classification by itself based onthe machine-learning model, which can reduce the memory and the networkresource usages for the classification. To demonstrate the feasibility, afterwe configure the convolutional neural networks (CNN)-based model usingECG data from the Massachusetts Institute of Technology (MIT)-Beth IsraelHospital (BIH) arrhythmia and the Physikalisch Technische Bundesanstalt(PTB) diagnostic ECG databases, TinyCES is validated using the TinyMLsupportedArduino prototype. The performance results show that TinyCEScan have an approximately 97% detection ratio, which means that it has greatpotential to be a lightweight and resource-efficient ECG monitoring system. | Eunchan Kim Jaehyuk Kim Juyoung Park Haneul Ko Yeunwoong Kyung | 2023 | Computers, Materials & Continua2023，，4： | 0 |
| 7 | Bug Prioritization Using Average One Dependence Estimator显示文摘Automation software need to be continuously updated by addressing software bugs contained in their repositories.However,bugs have different levels of importance;hence,it is essential to prioritize bug reports based on their sever-ity and importance.Manually managing the deluge of incoming bug reports faces time and resource constraints from the development team and delays the resolu-tion of critical bugs.Therefore,bug report prioritization is vital.This study pro-poses a new model for bug prioritization based on average one dependence estimator;it prioritizes bug reports based on severity,which is determined by the number of attributes.The more the number of attributes,the more the severity.The proposed model is evaluated using precision,recall,F1-Score,accuracy,G-Measure,and Matthew’s correlation coefficient.Results of the proposed model are compared with those of the support vector machine(SVM)and Naive Bayes(NB)models.Eclipse and Mozilla datasetswere used as the sources of bug reports.The proposed model improved the bug repository management and out-performed the SVM and NB models.Additionally,the proposed model used a weaker attribute independence supposition than the former models,thereby improving prediction accuracy with minimal computational cost. | Kashif Saleem Rashid Naseem Khalil Khan Siraj Muhammad Ikram Syed Jaehyuk Choi | 2023 | Intelligent Automation & Soft Computing2023，，6： | 0 |
| 8 | Deep-Net:Fine-Tuned Deep Neural Network Multi-Features Fusion for Brain Tumor Recognition显示文摘Manual diagnosis of brain tumors usingmagnetic resonance images(MRI)is a hectic process and time-consuming.Also,it always requires an expert person for the diagnosis.Therefore,many computer-controlled methods for diagnosing and classifying brain tumors have been introduced in the literature.This paper proposes a novel multimodal brain tumor classification framework based on two-way deep learning feature extraction and a hybrid feature optimization algorithm.NasNet-Mobile,a pre-trained deep learning model,has been fine-tuned and twoway trained on original and enhancedMRI images.The haze-convolutional neural network(haze-CNN)approach is developed and employed on the original images for contrast enhancement.Next,transfer learning(TL)is utilized for training two-way fine-tuned models and extracting feature vectors from the global average pooling layer.Then,using a multiset canonical correlation analysis(CCA)method,features of both deep learning models are fused into a single feature matrix—this technique aims to enhance the information in terms of features for better classification.Although the information was increased,computational time also jumped.This issue is resolved using a hybrid feature optimization algorithm that chooses the best classification features.The experiments were done on two publicly available datasets—BraTs2018 and BraTs2019—and yielded accuracy rates of 94.8%and 95.7%,respectively.The proposedmethod is comparedwith several recent studies andoutperformed inaccuracy.In addition,we analyze the performance of each middle step of the proposed approach and find the selection technique strengthens the proposed framework. | Muhammad Attique Khan Reham R.Mostafa Yu-Dong Zhang Jamel Baili Majed Alhaisoni Usman Tariq Junaid Ali Khan Ye Jin Kim Jaehyuk Cha | 2023 | Computers, Materials & Continua2023，76，9： | 0 |
| 9 | Efficient Autonomous Defense System Using Machine Learning on Edge Device显示文摘As a large amount of data needs to be processed and speed needs to be improved,edge computing with ultra-low latency and ultra-connectivity is emerging as a new paradigm.These changes can lead to new cyber risks,and should therefore be considered for a security threat model.To this end,we constructed an edge system to study security in two directions,hardware and software.First,on the hardware side,we want to autonomically defend against hardware attacks such as side channel attacks by configuring field programmable gate array(FPGA)which is suitable for edge computing and identifying communication status to control the communication method according to priority.In addition,on the software side,data collected on the server performs end-to-end encryption via symmetric encryption keys.Also,we modeled autonomous defense systems on the server by using machine learning which targets to incoming and outgoing logs.Server log utilizes existing intrusion detection datasets that should be used in real-world environments.Server log was used to detect intrusion early by modeling an intrusion prevention system to identify behaviors that violate security policy,and to utilize the existing intrusion detection data set that should be used in a real environment.Through this,we designed an efficient autonomous defense system that can provide a stable system by detecting abnormal signals from the device and converting them to an effective method to control edge computing,and to detect and control abnormal intrusions on the server side. | Jaehyuk Cho | 2022 | Computers, Materials & Continua2022，，2： | 0 |
| 10 | 糖尿病对促进乳腺癌生长的影响显示文摘背景与目的2型糖尿病(typeⅡdiabetes mellitus,DM2)是包括乳腺癌在内的多种癌症的危险因素。目前,尚未建立可用于研究的糖尿病乳腺癌小鼠模型。另外,调节癌症生长的糖尿病信号通路也尚未明确。在本研究中,我们建立了一个糖尿病乳腺癌小鼠模型,并证实了糖尿病在乳腺癌进展中的影响。方法通过将瘦素受体突变(Lepr^(db/+))小鼠和MMTV-ErbB2/neu小鼠杂交,成功构建了人表皮生长因子受体2阳性(human epidermal growth factor receptor 2,Her2^(+)或ERBB2)的乳腺癌转基因小鼠模型。用抗糖尿病药物治疗该小鼠模型来评价治疗DM2对肿瘤生长的影响。用磁共振波谱成像分析肿瘤代谢情况。结果用二甲双胍/罗格列酮治疗MMTV-ErbB2/Lepr^(db/db)小鼠模型可降低血清胰岛素水平,延长生存,降低肿瘤累积发生率,抑制肿瘤进展。超极化^(13)C标记的丙酮酸/乳酸转换代谢流降低,表明抗胰岛素抵抗治疗抑制了体内实验中肿瘤糖酵解。用二甲双胍处理MMTV-ErbB2/Lepr^(db/db)转基因小鼠来源的肿瘤细胞,降低了耗氧量和乳酸产量,使细胞代谢发生了重新编程。二甲双胍可降低Myc和丙酮酸激酶M2型同工酶(pyruvate kinase isozyme 2,PKM2)的表达,引起代谢重编程。另外,二甲双胍可阻断mTOR/AKT信号通路并改变脂肪因子谱。结论MMTV-ErbB2/Lepr^(db/db)转基因小鼠模型可用于糖尿病HER2^(+)人乳腺癌的研究。本研究明确了糖尿病状态下HER2^(+)人乳腺癌中失调的信号通路,该通路可被抗胰岛素抵抗治疗干预。 | Ping-Chieh Chou Hyun Ho Choi Yizhi Huang Enrique Fuentes-Mattei Guermarie Velazquez-Torres Fanmao Zhang Liem Phan Jaehyuk Lee Yanxia Shi James A.Bankson Yun Wu Huamin Wang Ruiying Zhao Sai-Ching Jim Yeung Mong-Hong Lee | 2022 | 癌症2022，41，3： | 0 |
| 11 | Analgesic efficacy of median nerve stimulation in mice with chemotherapy-induced peripheral neuropathy via modulation of brain-derived neurotrophic factor expression显示文摘OBJECTIVE:Chemotherapeutic agents such as docetaxel(DTX)can trigger chemotherapy-induced peripheral neuropathy(CIPN),which is characterized by unbearable pain.This study was designed to investigate the analgesic effect and related neuronal mechanism of low-frequency median nerve stimulation(LFMNS)on DTX-induced tactile hypersensitivity in mice.METHODS:To produce CIPN,DTX was administered intraperitoneally 4 times,once every 2 d,to male ICR mice.LFMNS was performed on the wrist area,and the pain response was measured using von Frey filaments on both hind paws.Western blot and immunofluorescence staining were performed using dorsal root ganglion and spinal cord samples to measure the expression of brainderived neurotrophic factor(BDNF).RESULTS:Repeated LFMNS significantly attenuated the DTX-induced abnormal sensory response and suppressed the enhanced expression of BDNF in the DRG neurons and spinal dorsal area.CONCLUSIONS:LFMNS might be an effective nonpharmaceutical option for treating patients suffering from CIPN via regulating the expression of peripheral and central BDNF. | Dong-Wook Kang Jae-Gyun Choi Hee Ju Song Jaehyuk Kim Miae Lee Taehee Kim Suk-Yun Kang Yeonhee Ryu Hwa Seung Yoo Jin Sun Lee Jin Bong Park Sang Do Lee Hyun-Woo Kim | 2023 | Journal of Traditional Chinese Medicine2023，43，4： | 0 |
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