• Title/Summary/Keyword: Diagnostic Prediction

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Identification of a conservative site in the African swine fever virus p54 protein and its preliminary application in a serological assay

  • Xu, Lingyu;Cao, Chenfu;Yang, Zhiyi;Jia, Weixin
    • Journal of Veterinary Science
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    • v.23 no.4
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    • pp.55.1-55.12
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    • 2022
  • Background: ASF was first reported in Kenya in 1910 in 1921. In China, ASF spread to 31 provinces including Henan and Jiangsu within six months after it was first reported on August 3, 2018. The epidemic almost affected the whole China, causing direct economic losses of tens of billions of yuan. Cause great loss to our pig industry. As ELISA is cheap and easy to operate, OIE regards it as the preferred serological method for ASF detection. P54 protein has good antigenicity and is an ideal antigen for detection. Objective: To identify a conservative site in the African swine fever virus (ASFV) p54 protein and perform a Cloth-enzyme-linked immunosorbent assay (ELISA) for detecting the ASFV antibody in order to reduce risks posed by using the live virus in diagnostic assays. Method: We used bioinformatics methods to predict the antigen epitope of the ASFV p54 protein in combination with the antigenic index and artificially synthesized the predicted antigen epitope peptides. Using ASFV-positive serum and specific monoclonal antibodies (mAbs), we performed indirect ELISA and blocking ELISA to verify the immunological properties of the predicted epitope polypeptide. Results: The results of our prediction revealed that the possible antigen epitope regions were A23-29, A36-45, A72-94, A114-120, A124-130, and A137-150. The indirect ELISA showed that the peptides A23-29, A36-45, A72-94, A114-120, and A137-150 have good antigenicity. Moreover, the A36-45 polypeptide can react specifically with the mAb secreted by hybridoma cells, and its binding site contains a minimum number of essential amino acids in the sequence 37DIQFINPY44. Conclusions: Our study confirmed a conservative antigenic site in the ASFV p54 protein and its amino acid sequence. A competitive ELISA method for detecting ASFV antibodies was established based on recombinant p54 and matching mAb. Moreover, testing the protein sequence alignment verified that the method can theoretically detect antibodies produced by pigs affected by nearly all ASFVs worldwide.

Convolutional Neural Network-based Prediction of Bolt Clamping Force in Initial Bolt Loosening State Using Frequency Response Similarity (초기 볼트풀림 상태의 볼트 체결력 예측을 위한 주파수응답 유사성 기반의 합성곱 신경망)

  • Jea Hyun Lee;Jeong Sam Han
    • Journal of the Computational Structural Engineering Institute of Korea
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    • v.36 no.4
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    • pp.221-232
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    • 2023
  • This paper presents a novel convolutional neural network (CNN)-based approach for predicting bolt clamping force in the early bolt loosening state of bolted structures. The approach entails tightening eight bolts with different clamping forces and generating frequency responses, which are then used to create a similarity map. This map quantifies the magnitude and shape similarity between the frequency responses and the initial model in a fully fastened state. Krylov subspace-based model order reduction is employed to efficiently handle the large amount of frequency response data. The CNN model incorporates a regression output layer to predict the clamping forces of the bolts. Its performance is evaluated by training the network by using various amounts of training data and convolutional layers. The input data for the model are derived from the magnitude and shape similarity map obtained from the frequency responses. The results demonstrate the diagnostic potential and effectiveness of the proposed approach in detecting early bolt loosening. Accurate bolt clamping force predictions in the early loosening state can thus be achieved by utilizing the frequency response data and CNN model. The findings afford valuable insights into the application of CNNs for assessing the integrity of bolted structures.

EEG Feature Engineering for Machine Learning-Based CPAP Titration Optimization in Obstructive Sleep Apnea

  • Juhyeong Kang;Yeojin Kim;Jiseon Yang;Seungwon Chung;Sungeun Hwang;Uran Oh;Hyang Woon Lee
    • International journal of advanced smart convergence
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    • v.12 no.3
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    • pp.89-103
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    • 2023
  • Obstructive sleep apnea (OSA) is one of the most prevalent sleep disorders that can lead to serious consequences, including hypertension and/or cardiovascular diseases, if not treated promptly. Continuous positive airway pressure (CPAP) is widely recognized as the most effective treatment for OSA, which needs the proper titration of airway pressure to achieve the most effective treatment results. However, the process of CPAP titration can be time-consuming and cumbersome. There is a growing importance in predicting personalized CPAP pressure before CPAP treatment. The primary objective of this study was to optimize the CPAP titration process for obstructive sleep apnea patients through EEG feature engineering with machine learning techniques. We aimed to identify and utilize the most critical EEG features to forecast key OSA predictive indicators, ultimately facilitating more precise and personalized CPAP treatment strategies. Here, we analyzed 126 OSA patients' PSG datasets before and after the CPAP treatment. We extracted 29 EEG features to predict the features that have high importance on the OSA prediction index which are AHI and SpO2 by applying the Shapley Additive exPlanation (SHAP) method. Through extracted EEG features, we confirmed the six EEG features that had high importance in predicting AHI and SpO2 using XGBoost, Support Vector Machine regression, and Random Forest Regression. By utilizing the predictive capabilities of EEG-derived features for AHI and SpO2, we can better understand and evaluate the condition of patients undergoing CPAP treatment. The ability to predict these key indicators accurately provides more immediate insight into the patient's sleep quality and potential disturbances. This not only ensures the efficiency of the diagnostic process but also provides more tailored and effective treatment approach. Consequently, the integration of EEG analysis into the sleep study protocol has the potential to revolutionize sleep diagnostics, offering a time-saving, and ultimately more effective evaluation for patients with sleep-related disorders.

Analysis of achievement predictive factors and predictive AI model development - Focused on blended math classes (학업성취도 예측 요인 분석 및 인공지능 예측 모델 개발 - 블렌디드 수학 수업을 중심으로)

  • Ahn, Doyeon;Lee, Kwang-Ho
    • The Mathematical Education
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    • v.61 no.2
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    • pp.257-271
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    • 2022
  • As information and communication technologies are being developed so rapidly, education research is actively conducted to provide optimal learning for each student using big data and artificial intelligence technology. In this study, using the mathematics learning data of elementary school 5th to 6th graders conducting blended mathematics classes, we tried to find out what factors predict mathematics academic achievement and developed an artificial intelligence model that predicts mathematics academic performance using the results. Math learning propensity, LMS data, and evaluation results of 205 elementary school students had analyzed with a random forest model. Confidence, anxiety, interest, self-management, and confidence in math learning strategy were included as mathematics learning disposition. The progress rate, number of learning times, and learning time of the e-learning site were collected as LMS data. For evaluation data, results of diagnostic test and unit test were used. As a result of the analysis it was found that the mathematics learning strategy was the most important factor in predicting low-achieving students among mathematics learning propensities. The LMS training data had a negligible effect on the prediction. This study suggests that an AI model can predict low-achieving students with learning data generated in a blended math class. In addition, it is expected that the results of the analysis will provide specific information for teachers to evaluate and give feedback to students.

Development of smart car intelligent wheel hub bearing embedded system using predictive diagnosis algorithm

  • Sam-Taek Kim
    • Journal of the Korea Society of Computer and Information
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    • v.28 no.10
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    • pp.1-8
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    • 2023
  • If there is a defect in the wheel bearing, which is a major part of the car, it can cause problems such as traffic accidents. In order to solve this problem, big data is collected and monitoring is conducted to provide early information on the presence or absence of wheel bearing failure and type of failure through predictive diagnosis and management technology. System development is needed. In this paper, to implement such an intelligent wheel hub bearing maintenance system, we develop an embedded system equipped with sensors for monitoring reliability and soundness and algorithms for predictive diagnosis. The algorithm used acquires vibration signals from acceleration sensors installed in wheel bearings and can predict and diagnose failures through big data technology through signal processing techniques, fault frequency analysis, and health characteristic parameter definition. The implemented algorithm applies a stable signal extraction algorithm that can minimize vibration frequency components and maximize vibration components occurring in wheel bearings. In noise removal using a filter, an artificial intelligence-based soundness extraction algorithm is applied, and FFT is applied. The fault frequency was analyzed and the fault was diagnosed by extracting fault characteristic factors. The performance target of this system was over 12,800 ODR, and the target was met through test results.

Development and Validation of 18F-FDG PET/CT-Based Multivariable Clinical Prediction Models for the Identification of Malignancy-Associated Hemophagocytic Lymphohistiocytosis

  • Xu Yang;Xia Lu;Jun Liu;Ying Kan;Wei Wang;Shuxin Zhang;Lei Liu;Jixia Li;Jigang Yang
    • Korean Journal of Radiology
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    • v.23 no.4
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    • pp.466-478
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    • 2022
  • Objective: 18F-fluorodeoxyglucose (FDG) PET/CT is often used for detecting malignancy in patients with newly diagnosed hemophagocytic lymphohistiocytosis (HLH), with acceptable sensitivity but relatively low specificity. The aim of this study was to improve the diagnostic ability of 18F-FDG PET/CT in identifying malignancy in patients with HLH by combining 18F-FDG PET/CT and clinical parameters. Materials and Methods: Ninety-seven patients (age ≥ 14 years) with secondary HLH were retrospectively reviewed and divided into the derivation (n = 71) and validation (n = 26) cohorts according to admission time. In the derivation cohort, 22 patients had malignancy-associated HLH (M-HLH) and 49 patients had non-malignancy-associated HLH (NM-HLH). Data on pretreatment 18F-FDG PET/CT and laboratory results were collected. The variables were analyzed using the Mann-Whitney U test or Pearson's chi-square test, and a nomogram for predicting M-HLH was constructed using multivariable binary logistic regression. The predictors were also ranked using decision-tree analysis. The nomogram and decision tree were validated in the validation cohort (10 patients with M-HLH and 16 patients with NM-HLH). Results: The ratio of the maximal standardized uptake value (SUVmax) of the lymph nodes to that of the mediastinum, the ratio of the SUVmax of bone lesions or bone marrow to that of the mediastinum, and age were selected for constructing the model. The nomogram showed good performance in predicting M-HLH in the validation cohort, with an area under the receiver operating characteristic curve of 0.875 (95% confidence interval, 0.686-0.971). At an appropriate cutoff value, the sensitivity and specificity for identifying M-HLH were 90% (9/10) and 68.8% (11/16), respectively. The decision tree integrating the same variables showed 70% (7/10) sensitivity and 93.8% (15/16) specificity for identifying M-HLH. In comparison, visual analysis of 18F-FDG PET/CT images demonstrated 100% (10/10) sensitivity and 12.5% (2/16) specificity. Conclusion: 18F-FDG PET/CT may be a practical technique for identifying M-HLH. The model constructed using 18F-FDG PET/CT features and age was able to detect malignancy with better accuracy than visual analysis of 18F-FDG PET/CT images.

Association between Initial Chest CT or Clinical Features and Clinical Course in Patients with Coronavirus Disease 2019 Pneumonia

  • Zhe Liu;Chao Jin;Carol C. Wu;Ting Liang;Huifang Zhao;Yan Wang;Zekun Wang;Fen Li;Jie Zhou;Shubo Cai;Lingxia Zeng;Jian Yang
    • Korean Journal of Radiology
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    • v.21 no.6
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    • pp.736-745
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    • 2020
  • Objective: To identify the initial chest computed tomography (CT) findings and clinical characteristics associated with the course of coronavirus disease 2019 (COVID-19) pneumonia. Materials and Methods: Baseline CT scans and clinical and laboratory data of 72 patients admitted with COVID-19 pneumonia (39 men, 46.2 ± 15.9 years) were retrospectively analyzed. Baseline CT findings including lobar distribution, presence of ground glass opacities, consolidation, linear opacities, and lung severity score were evaluated. The outcome event was recovery with hospital discharge. The time from symptom onset to discharge or the end of follow-up (for those remained hospitalized) was recorded. Data were censored in events such as death or discharge without recovery. Multivariable Cox proportional hazard regression was used to explore the association between initial CT, clinical or laboratory findings, and discharge with recovery, whereby hazard ratio (HR) values < 1 indicated a lower rate of discharge at four weeks and longer time until discharge. Results: Thirty-two patients recovered and were discharged during the study period with a median length of admission of 16 days (range, 9 to 25 days), while the rest remained hospitalized at the end of this study (median, 17.5 days; range, 4 to 27 days). None died during the study period. After controlling for age, onset time, lesion characteristics, number of lung lobes affected, and bilateral involvement, the lung severity score on baseline CT (> 4 vs. ≤ 4 [reference]: adjusted HR = 0.41 [95% confidence interval, CI = 0.18-0.92], p = 0.031) and initial lymphocyte count (reduced vs. normal or elevated [reference]: adjusted HR = 0.14 [95% CI = 0.03-0.60], p = 0.008) were two significant independent factors that influenced recovery and discharge. Conclusion: Lung severity score > 4 and reduced lymphocyte count at initial evaluation were independently associated with a significantly lower rate of recovery and discharge and extended hospitalization in patients admitted for COVID-19 pneumonia.

Response Prediction after Neoadjuvant Chemotherapy for Colon Cancer Using CT Tumor Regression Grade: A Preliminary Study (대장암 환자의 수술 전 항암화학요법의 반응을 CT 종양퇴행등급을 이용한 반응 예측: 예비 연구)

  • Hwan Ju Je;Seung Hyun Cho;Hyun Seok Oh;An Na Seo;Byung Geon Park;So Mi Lee;See Hyung Kim;Gab Chul Kim;Hunkyu Ryeom;Gyu-Seog Choi
    • Journal of the Korean Society of Radiology
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    • v.84 no.5
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    • pp.1094-1109
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    • 2023
  • Purpose To investigate whether CT-based tumor regression grade (ctTRG) can be used to predict the response to neoadjuvant chemotherapy (NAC) in colon cancer. Materials and Methods A total of 53 patients were enrolled. Two radiologists independently assessed the ctTRG using the length, thickness, layer pattern, and luminal and extraluminal appearance of the tumor. Changes in tumor volume were also analyzed using the 3D Slicer software. We evaluated the association between pathologic TRG (pTRG) and ctTRG. Patients with Rödel's TRG of 2, 3, or 4 were classified as responders. In terms of predicting responder and pathologic complete remission (pCR), receiver operating characteristic was compared between ctTRG and tumor volume change. Results There was a moderate correlation between ctTRG and pTRG (ρ = -0.540, p < 0.001), and the interobserver agreement was substantial (weighted κ = 0.672). In the prediction of responder, there was no significant difference between ctTRG and volumetry (Az = 0.749, criterion: ctTRG ≤ 3 for ctTRG, Az = 0.794, criterion: ≤ -27.1% for volume, p = 0.53). Moreover, there was no significant difference between the two methods in predicting pCR (p = 0.447). Conclusion ctTRG might predict the response to NAC in colon cancer. The diagnostic performance of ctTRG was comparable to that of CT volumetry.

Manganese and Iron Interaction: a Mechanism of Manganese-Induced Parkinsonism

  • Zheng, Wei
    • Proceedings of the Korea Environmental Mutagen Society Conference
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    • 2003.10a
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    • pp.34-63
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    • 2003
  • Occupational and environmental exposure to manganese continue to represent a realistic public health problem in both developed and developing countries. Increased utility of MMT as a replacement for lead in gasoline creates a new source of environmental exposure to manganese. It is, therefore, imperative that further attention be directed at molecular neurotoxicology of manganese. A Need for a more complete understanding of manganese functions both in health and disease, and for a better defined role of manganese in iron metabolism is well substantiated. The in-depth studies in this area should provide novel information on the potential public health risk associated with manganese exposure. It will also explore novel mechanism(s) of manganese-induced neurotoxicity from the angle of Mn-Fe interaction at both systemic and cellular levels. More importantly, the result of these studies will offer clues to the etiology of IPD and its associated abnormal iron and energy metabolism. To achieve these goals, however, a number of outstanding questions remain to be resolved. First, one must understand what species of manganese in the biological matrices plays critical role in the induction of neurotoxicity, Mn(II) or Mn(III)? In our own studies with aconitase, Cpx-I, and Cpx-II, manganese was added to the buffers as the divalent salt, i.e., $MnCl_2$. While it is quite reasonable to suggest that the effect on aconitase and/or Cpx-I activites was associated with the divalent species of manganese, the experimental design does not preclude the possibility that a manganese species of higher oxidation state, such as Mn(III), is required for the induction of these effects. The ionic radius of Mn(III) is 65 ppm, which is similar to the ionic size to Fe(III) (65 ppm at the high spin state) in aconitase (Nieboer and Fletcher, 1996; Sneed et al., 1953). Thus it is plausible that the higher oxidation state of manganese optimally fits into the geometric space of aconitase, serving as the active species in this enzymatic reaction. In the current literature, most of the studies on manganese toxicity have used Mn(II) as $MnCl_2$ rather than Mn(III). The obvious advantage of Mn(II) is its good water solubility, which allows effortless preparation in either in vivo or in vitro investigation, whereas almost all of the Mn(III) salt products on the comparison between two valent manganese species nearly infeasible. Thus a more intimate collaboration with physiochemists to develop a better way to study Mn(III) species in biological matrices is pressingly needed. Second, In spite of the special affinity of manganese for mitochondria and its similar chemical properties to iron, there is a sound reason to postulate that manganese may act as an iron surrogate in certain iron-requiring enzymes. It is, therefore, imperative to design the physiochemical studies to determine whether manganese can indeed exchange with iron in proteins, and to understand how manganese interacts with tertiary structure of proteins. The studies on binding properties (such as affinity constant, dissociation parameter, etc.) of manganese and iron to key enzymes associated with iron and energy regulation would add additional information to our knowledge of Mn-Fe neurotoxicity. Third, manganese exposure, either in vivo or in vitro, promotes cellular overload of iron. It is still unclear, however, how exactly manganese interacts with cellular iron regulatory processes and what is the mechanism underlying this cellular iron overload. As discussed above, the binding of IRP-I to TfR mRNA leads to the expression of TfR, thereby increasing cellular iron uptake. The sequence encoding TfR mRNA, in particular IRE fragments, has been well-documented in literature. It is therefore possible to use molecular technique to elaborate whether manganese cytotoxicity influences the mRNA expression of iron regulatory proteins and how manganese exposure alters the binding activity of IPRs to TfR mRNA. Finally, the current manganese investigation has largely focused on the issues ranging from disposition/toxicity study to the characterization of clinical symptoms. Much less has been done regarding the risk assessment of environmenta/occupational exposure. One of the unsolved, pressing puzzles is the lack of reliable biomarker(s) for manganese-induced neurologic lesions in long-term, low-level exposure situation. Lack of such a diagnostic means renders it impossible to assess the human health risk and long-term social impact associated with potentially elevated manganese in environment. The biochemical interaction between manganese and iron, particularly the ensuing subtle changes of certain relevant proteins, provides the opportunity to identify and develop such a specific biomarker for manganese-induced neuronal damage. By learning the molecular mechanism of cytotoxicity, one will be able to find a better way for prediction and treatment of manganese-initiated neurodegenerative diseases.

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Impacts of Two Types of El Niño on Hydrologic Variability in Annual Maximum Flow and Low Flow in the Han River Basin (두 가지 El Niño 형태에 따른 한강 유역의 연최대홍수량 및 저유량의 변화 분석)

  • Kim, Jong-Suk;Yoon, Sun-Kwon;Lee, Joo-Heon
    • Journal of Korea Water Resources Association
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    • v.45 no.10
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    • pp.969-981
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    • 2012
  • In this study, we analysed hydrologic variability in quantity and onset of annual maximum flow and low flow by impacts of the different phases of ENSO (El Ni$\tilde{n}$o Southern Oscillation) over the Han River Basin. The results show that annual maximum flow has increased statistically significant about 48.3% of all over the watershed. The onset of annual maximum flow was delayed in the west of the Han River basins and in the east of the basins was likely to be rapid onset. Also, this study shows that 7-day low flow was deceased statistically significant about 26.0% of the total area in the Han River Basin, and onset of 7-day low flow tends to be faster in the upper-middle basins of the Han River. The onset of annual maximum flow shows similar pattern during the CT (Cold tongue)/WP (Warm-pool) El Ni$\tilde{n}$o years, but annual maximum flow appeared less in 89.0% of all basins during the CT El Ni$\tilde{n}$o years. In addition, the onset of 7-day low flow tended to be faster about 17 days on average during the WP El Ni$\tilde{n}$o years, and 72.7% of the basins show significant increase during the CT El Ni$\tilde{n}$o years. Consequently, it was found that the different phases of CT/WP El Ni$\tilde{n}$o have effects on sensitivity to variability in quantity and onset of water resources over the Han River Basin. We expect that the present diagnostic study on hydrological variability during different phases of ENSO will provide useful information for long-term prediction and water resources management.