• BMB Reports
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• v.46 no.3
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• pp.163-168
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• 2013

The AML1 gene is an essential transcription factor regulating the differentiation of hematopoietic stem cells into mature blood cells. Though at least 12 different alternatively spliced AML1 mRNAs are generated, three splice variants (AML1a, AML1b and AML1c) have been characterized. Here, using the reverse transcription-polymerase chain reaction with outward-facing primers, we identified a novel non-polyadenylated transcript from the AML1 gene, with exons 5 and 6 scrambled. The novel transcript resisted RNase R digestion, indicating it is a circular RNA structure that may originate from products of mRNA alternative splicing. The expression of the novel transcript in different cells or cell lines of human and a number of other species matched those of the canonical transcripts. The discovery provides additional evidence that circular RNA could stably exist in vivo in human, and may also help to understand the mechanism of the regulation of the AML1 gene transcription.

• Journal of Convergence Korean Medicine
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• v.6 no.1
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• pp.37-42
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• 2024

Objectives: This study aims to evaluate the clinical validity of the system by conducting a clinical study to assess the diagnostic agreement between the system and Korean medical doctors. Methods: This study was conducted from September 7, 2023, to December 7, 2023, across five Korean medicine institutions, involving 100 adult participants aged 20-64 who consented to participate. Participants first entered their symptoms into a web-based program, which utilized an AI-based algorithm to diagnose 36 types of pattern differentiation. Subsequently, Korean medical doctors conducted face-to-face diagnoses using the same 36 types. The diagnostic agreement between the system and the doctors' diagnoses was analyzed using descriptive statistical analysis, and the results were expressed as a percentage agreement. Results: Analysis of the diagnostic data from 100 participants revealed that the web-based diagnosis support system identified an average of 7.76±0.79 patterns per patient, while Korean medical doctors identified an average of 7.99±0.10 patterns per patient. The diagnostic agreement between the system and the doctors showed an average of 7.08±1.08 patterns per patient, with an overall diagnostic agreement rate of 88.57±13.31%. Conclusion: This study developed a web-based diagnosis support system for traditional Korean medicine and evaluated its clinical validity by assessing diagnostic agreement. Comparing the diagnoses of the system with those of Korean medical doctors for 100 patients, the system showed an approximately 89% agreement rate with the clinical diagnoses. The system holds potential for aiding Korean medical doctors in pattern differentiation diagnosis in clinical practice.

• Korean Journal of Artificial Intelligence
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• v.5 no.1
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• pp.1-9
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• 2017

Recently, the Q-Learning algorithm, which is one kind of reinforcement learning, is mainly used to implement artificial intelligence system in combination with deep learning. Many research is going on to improve the performance of Q-Learning. Therefore, purpose of theory try to improve the performance of Q-Learning algorithm. This Theory apply Cross Entropy Error to the loss function of Q-Learning algorithm. Since the mean squared error used in Q-Learning is difficult to measure the exact error rate, the Cross Entropy Error, known to be highly accurate, is applied to the loss function. Experimental results show that the success rate of the Mean Squared Error used in the existing reinforcement learning was about 12% and the Cross Entropy Error used in the deep learning was about 36%. The success rate was shown.

• International Journal of Advanced Culture Technology
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• v.5 no.2
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• pp.74-81
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• 2017

Recently, there are many companies that use systems based on artificial intelligence. The accuracy of artificial intelligence depends on the amount of learning data and the appropriate algorithm. However, it is not easy to obtain learning data with a large number of entity. Less data set have large generalization errors due to overfitting. In order to minimize this generalization error, this study proposed DGA(Dropout Genetic Algorithm) which can expect relatively high accuracy even though data with a less data set is applied to machine learning based genetic algorithm to deep learning based dropout. The idea of this paper is to determine the active state of the nodes. Using Gradient about loss function, A new fitness function is defined. Proposed Algorithm DGA is supplementing stochastic inconsistency about Dropout. Also DGA solved problem by the complexity of the fitness function and expression range of the model about Genetic Algorithm As a result of experiments using MNIST data proposed algorithm accuracy is 75.3%. Using only Dropout algorithm accuracy is 41.4%. It is shown that DGA is better than using only dropout.

• Journal of Information Processing Systems
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• v.18 no.3
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• pp.302-310
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• 2022

Efficient use of limited blood products is becoming very important in terms of socioeconomic status and patient recovery. To predict the appropriateness of patient-specific transfusions for the intensive care unit (ICU) patients who require real-time monitoring, we evaluated a model to predict the possibility of transfusion dynamically by using the Medical Information Mart for Intensive Care III (MIMIC-III), an ICU admission record at Harvard Medical School. In this study, we developed an explainable machine learning to predict the possibility of red blood cell transfusion for major medical diseases in the ICU. Target disease groups that received packed red blood cell transfusions at high frequency were selected and 16,222 patients were finally extracted. The prediction model achieved an area under the ROC curve of 0.9070 and an F1-score of 0.8166 (LightGBM). To explain the performance of the machine learning model, feature importance analysis and a partial dependence plot were used. The results of our study can be used as basic data for recommendations related to the adequacy of blood transfusions and are expected to ultimately contribute to the recovery of patients and prevention of excessive consumption of blood products.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.3
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• pp.974-992
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• 2021

Recently, the healthcare field has undergone rapid changes owing to the accumulation of health big data and the development of machine learning. Data mining research in the field of healthcare has different characteristics from those of other data analyses, such as the structural complexity of the medical data, requirement for medical expertise, and security of personal medical information. Various methods have been implemented to address these issues, including the machine learning model and cloud platform. However, the machine learning model presents the problem of opaque result interpretation, and the cloud platform requires more in-depth research on security and efficiency. To address these issues, this paper presents a recent technology for Internet-of-Things-based (IoT-based) health big data processing. We present a cloud-based IoT health platform and health big data processing technology that reduces the medical data management costs and enhances safety. We also present a data mining technology for health-risk prediction, which is the core of healthcare. Finally, we propose a study using explainable artificial intelligence that enhances the reliability and transparency of the decision-making system, which is called the black box model owing to its lack of transparency.

• Asian Pacific Journal of Cancer Prevention
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• v.14 no.9
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• pp.5513-5518
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• 2013

IFN-${\gamma}$ plays an indirect anti-cancer role through the immune system but may have direct negative effects on cancer cells. It regulates the viability of gastric cancer cells, so we examined whether it affects their proliferation and how that might be brought about. We exposed AGS, HGC-27 and GES-1 gastric cancer cell lines to IFN-${\gamma}$ and found significantly reduced colony formation ability. Flow cytometry revealed no effect of IFN-${\gamma}$ on apoptosis of cell lines and no effect on cell aging as assessed by ${\beta}$-gal staining. Microarray assay revealed that IFN-${\gamma}$ changed the mRNA expression of genes related to the cell cycle and cell proliferation and migration, as well as chemokines and chemokine receptors, and immunity-related genes. Finally, flow cytometry revealed that IFN-${\gamma}$ arrested the cells in the G1/S phase. IFN-${\gamma}$ may slow proliferation of some gastric cancer cells by affecting the cell cycle to play a negative role in the development of gastric cancer.

• Perinatology
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• v.29 no.4
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• pp.189-194
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• 2018

Congenital neuroblastoma is a rare disease. Placental metastasis is extremely rare and poor prognosis has been reported in neonates. Mirror syndrome could occur in mother with placental metastasis with possibilities of hypertension and edema. We report a case of detection of left suprarenal mass in fetus at $31^{+5}weeks^{\prime}$ gestation. Mother presented with palpitation, edema, headache, and hypertension. Maternal 24 hours urine vanillylmandelic acid (VMA) and normetanephrine (NME) level at 34 weeks' gestation were elevated. Consequently, emergent cesarean section was done. Based on abdominal ultrasonography and whole body magnetic resonance imaging, left adrenal tumor with liver metastasis was suspected. Neuroblastoma was confirmed by liver and placenta biopsy. Chemotherapy was started with Pediatric Oncology Group 9243 at day 7 and changed into Children's Oncology Group 3961 due to cholestasis and poor response during 2nd cycle. Plasma exchange was done for aggravated direct hyperbilirubinemia. The baby expired at 73 days due to multi-organ failure. Maternal symptoms were completely resolved in 2 weeks after delivery along with normalization of the elevated level of VMA and NME. We report a first case of mirror syndrome in Korean mother and fetus resulting from metastatic congenital neuroblastoma to placenta.

• The Journal of the Convergence on Culture Technology
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• v.10 no.2
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• pp.453-458
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• 2024

Optical Character Recognition is the technology that recognizes text in images and converts them into digital format. Deep learning-based OCR is being used in many industries with large quantities of recorded data due to its high recognition performance. To improve medical services, deep learning-based OCR was actively introduced by the medical industry. In this paper, we discussed trends in OCR engines and medical OCR and provided a roadmap for development of medical OCR. By using natural language processing on detected text data, current medical OCR has improved its recognition performance. However, there are limits to the recognition performance, especially for non-standard handwriting and modified text. To develop advanced medical OCR, databaseization of medical data, image pre-processing, and natural language processing are necessary.

• Journal of the Korea Convergence Society
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• v.12 no.4
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• pp.9-21
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• 2021

The study aims to identify convergence and trends in technology-based patent data for the biohealth sector in IP5 countries (KR, EP, JP, US, CN) and present the direction of development in that industry. We used patent co-classification analysis-based network analysis and TF-IDF-based text mining as the principal methodology to understand the current state of technology convergence. As a result, the technology convergence cluster in the biohealth industry was derived in three forms: (A) Medical device for treatment, (B) Medical data processing, and (C) Medical device for biometrics. Besides, as a result of trend analysis based on technology convergence results, it is analyzed that Korea is likely to dominate the market with patents with high commercial value in the future as it is derived as a market leader in (B) medical data processing. In particular, the field is expected to require technology convergence activation policies and R&D support strategies for the technology as the possibility of medical data utilization by domestic bio-health companies expands, along with the policy conversion of the "Data 3 Act" passed by the National Assembly in January 2019.