• Journal of the Korean Burn Society
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• v.23 no.1
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• pp.1-6
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• 2020

Purpose: PL, creatinine and urine output are biomarkers of the suitability and prognosis of fluid therapy in severe burn patients. The purpose of this study is to evaluate the usefulness of predicting mortality by biomarkers and its change during initial fluid therapy for severe burn patients. Methods: A retrograde review was performed on 733 patients from January 2014 to December 2018 who were admitted as severe burn patients to our burn intensive care unit (BICU). Plasma lactate, serum creatinine and urine output were measured at the time of admission to the BICU and after 48 hours. ABSI score, Hangang score, APACHEII, revised Baux index and TBSA were collected after admission. Results: 733 patients were enrolled. PL was the most useful indicators for predicting mortality in burn patients at the time of admission (AUC: 0.813) and after 48 hours (AUC: 0.698). On the other hand, mortality prediction from initial fluid therapy for 48 hours showed different results. Only creatinine showed statistical differences (P<0.05) in mortality prediction. But there were no statistical differences in mortality prediction with PL and UO (P>0.05). Conclusion: In this study, PL was most useful predictor among biomarkers for predicting mortality. Improvement in creatinine levels during the first 48 hours is associated with improved mortality. Therefore, efforts are needed to improve creatinine levels.

• Journal of Korean Neurosurgical Society
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• v.61 no.3
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• pp.376-385
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• 2018

Recent discoveries of brain tumor-related genes and fast advances in genomic testing technologies have led to the era of molecular diagnosis of brain tumor. Molecular profiling of brain tumor became the significant step in the diagnosis, the prediction of prognosis and the treatment of brain tumor. Because traditional molecular testing methods have limitations in time and cost for multiple gene tests, next-generation sequencing technologies are rapidly introduced into clinical practice. Targeted sequencing panels using these technologies have been developed for brain tumors. In this article, focused on pediatric brain tumor, key discoveries of brain tumor-related genes are reviewed and cancer panels used in the molecular profiling of brain tumor are discussed.

• Asian Pacific Journal of Cancer Prevention
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• v.15 no.3
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• pp.1067-1074
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• 2014

In Morocco, breast cancer is the most prevalent cancer in women and a major public health problem. Several Moroccan studies have focused on studying this disease, but more are needed, especially at the genetic and molecular levels. It is therefore interesting to establish the genetic and molecular profile of Moroccan patients with breast cancer. In this paper, we will highlight some pertinent hypotheses that may enhance breast cancer care in Moroccan patients. This review will give a precise description of breast cancer in Morocco and propose some new markers for detection and prediction of breast cancer prognosis.

• Journal of Biomedical Engineering Research
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• v.38 no.5
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• pp.271-276
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• 2017

Cancer biomarkers are using in the diagnosis, staging, prognosis and prediction of disease progression. But, there are not sufficiently profiled and validated in early detection and risk classification of prostate cancer. In this study, we have devoted to finding a panel of serum biomarkers that are able to detect the diagnosis of prostate cancer. The serum samples were consisted of 111 prostate cancer and 343 control samples and examined. Eleven biomarkers were constructed in this study, and then nine biomarkers were relevant to candidate biomarkers by using t test. Finally, four biomarkers, PSA, ApoA2, CYFRA21.1 and TTR, were selected as the prostate cancer biomarker panel, logistic regression was used to identify algorithms for diagnostic biomarker combinations(AUC = 0.9697). A panel of combination biomarkers is less invasive and could supplement clinical diagnostic accuracy.

• Journal of Biomedical Engineering Research
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• v.12 no.4
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• pp.295-302
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• 1991

In this paper, the inter- and intra-frame distortions in the gray levels of a series of fluorescein ocular fundus photographs are corrected. For doing this, the background images are extracted from original images using the image blurring effect by decimation, and then shading corrected images are obtained by subtracting the background images from the original images pixel by pixel. In a series of fluorescein ocular fundus photographs, after the gray scale distoriton is corrected, the intensity volumes of dye leakage are measured and represented by a graph. These data may be useful for the prediction of prognosis and the therapeutic management.

• Journal of Digestive Cancer Research
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• v.11 no.3
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• pp.130-140
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• 2023

Gastric cancer (GC) is one of the most common malignant tumors worldwide, with a 5-year survival rate of < 40%. The diagnosis and treatment decisions of GC rely on human experts' judgments on medical images; therefore, the accuracy can be hindered by image condition, objective criterion, limited experience, and interobserver discrepancy. In recent years, several applications of artificial intelligence (AI) have emerged in the GC field based on improvement of computational power and deep learning algorithms. AI can support various clinical practices in endoscopic examination, pathologic confirmation, radiologic staging, and prognosis prediction. This review has systematically summarized the current status of AI applications after a comprehensive literature search. Although the current approaches are challenged by data scarcity and poor interpretability, future directions of this field are likely to overcome the risk and enhance their accuracy and applicability in clinical practice.

• Asian Pacific Journal of Cancer Prevention
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• v.13 no.1
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• pp.191-194
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• 2012

We conducted a case-control study in China to clarify the association between XRCC1-Arg399Gln polymorphism and HCC risk. A total of 150 cases and 158 controls were selected from the the Affiliated Hospital of Qingdao University from May 2008 to May 2010. XRCC1-Arg399Gln polymorphism was based upon duplex polymerase-chain-reaction with the confronting-two-pairprimer (PCR-CTPP) method. All analyses were performed using the STATA statistical package. A significantly increased risk was associated with the Arg/Gln genotype (adjusted OR 1.78, 95%CI=1.13-2.79) compared with genotype Arg/Arg. In contrast, the Gln/Gln genotype had non-significant increased risk of HCC with adjusted OR (95%CI) of 1.69 (0.93-2.66). A significant association was found between positive HBsAg and Arg/Gln, with an OR of 3.43 (95% CI=1.45-8.13). Patients carrying Gln/Gln genotypes showed significantly lower median survival than Arg/Arg genotypes (HR=1.38, 95% CI=1.04-1.84). Further Kaplan-Meier analysis showed decreased median survival in Arg/Gln+Gln/Gln genotype carriers in comparison to Arg/Arg carriers (HR=1.33, 95% CI=1.02-1.76). In conclusion, we observed that XRCC1-Arg399Cln polymorphism is associated with susceptibility to HCC, and XRCC1 Gln allele genotype showed significant prognostic associations.

• Asian Pacific Journal of Cancer Prevention
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• v.17 no.5
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• pp.2655-2660
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• 2016

Background: Prostate cancer is the second most common male cancer and remains a leading cause of cancer death worldwide. Heterogeneity regarding recurrence, tumor progression and therapeutic response reflects the inadequacy of traditional prognostic factors and underlies interest in new genetic and molecular markers. In this work, we studied the prognostic value of the expression of 9 proteins, Ki-67, p53, Bcl-2, PSA, HER2, E-cadherin, $p21^{WAF1/Cip1}$, $p27^{Kip1}$ and $p16^{ink4a}$ in prostate cancer. Materials and Methods: We conducted a retrospective study of 50 prostate cancers diagnosed in Pathology Department of Farhet Hached Hospital, Sousse, Tunisia, during a period of 12 months. Clinico-pathological data and survival were investigated. Protein expression was analyzed by immunohistochemistry on archived material. Results: Expression or over-expression of Ki-67, p53, Bcl-2, PSA, HER2, E-Cadherin, $p21^{WAF1/Cip1}$, $p27^{Kip1}$ and $p16^{ink4a}$ was observed in 68%, 24%, 32%, 78%, 12%, 90%, 20%, 44% and 56% of cases, respectively. Overall five-year survival was 68%. A statistically significant correlation was observed between death occurrence and advanced age (p=0.018), degree of tumor differentiation (p=0.0001), perineural invasion (p=0.016) and metastasis occurrence (p=0.05). Death occurrence was significantly correlated with the expression of p53 (p=0.007), Bcl-2 (p=0.02), Ki-67 (p=0.05) and $p27^{Kip1}$ (p=0.04). Conclusions: The p53, Bcl-2, Ki-67 and $p27^{Kip1}$ proteins may be useful additional prognostic markers for prostate cancer. The use of these proteins in clinical practice can improve prognosis prediction, disease screening and treatment response of prostatic cancer.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.35 no.10
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• pp.1299-1306
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• 2011

In this study, crack-growth model parameters subjected to variable amplitude loading are estimated in the form of a probability distribution using the method of Bayesian parameter estimation. Huang's model is employed to describe the retardation and acceleration of the crack growth during the loadings. The Markov Chain Monte Carlo (MCMC) method is used to obtain samples of the parameters following the probability distribution. As the conventional MCMC method often fails to converge to the equilibrium distribution because of the increased complexity of the model under variable amplitude loading, an improved MCMC method is introduced to overcome this shortcoming, in which a marginal (PDF) is employed as a proposal density function. The model parameters are estimated on the basis of the data from several test specimens subjected to constant amplitude loading. The prediction is then made under variable amplitude loading for the same specimen, and validated by the ground-truth data using the estimated parameters.

• Nuclear Engineering and Technology
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• v.48 no.5
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• pp.1184-1191
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• 2016

This paper introduces the development of a transient monitoring system to detect the early stage of a transient, to identify the type of the transient scenario, and to inform an operator with the remaining time to turbine trip when there is no operator's relevant control. This study focused on the transients originating from a secondary system in nuclear power plants (NPPs), because the secondary system was recognized to be a more dominant factor to make unplanned turbine-generator trips which can ultimately result in reactor trips. In order to make the proposed methodology practical forward, all the transient scenarios registered in a simulator of a 1,000 MWe pressurized water reactor were archived in the transient pattern database. The transient patterns show plant behavior until turbine-generator trip when there is no operator's intervention. Meanwhile, the operating data periodically captured from a plant computer is compared with an individual transient pattern in the database and a highly matched section among the transient patterns enables isolation of the type of transient and prediction of the expected remaining time to trip. The transient pattern database consists of hundreds of variables, so it is difficult to speedily compare patterns and to draw a conclusion in a timely manner. The transient pattern database and the operating data are, therefore, converted into a smaller dimension using the principal component analysis (PCA). This paper describes the process of constructing the transient pattern database, dealing with principal components, and optimizing similarity measures.