PubMed İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/11727/4810
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Item A deep learning approach for sepsis monitoring via severity score estimation(2021) Asuroglu, Tunc; Ogul, Hasan; 33157471Background and objective: Sepsis occurs in response to an infection in the body and can progress to a fatal stage. Detection and monitoring of sepsis require multi-step analysis, which is time-consuming, costly and requires medically trained personnel. A metric called Sequential Organ Failure Assessment (SOFA) score is used to determine the severity of sepsis. This score depends heavily on laboratory measurements. In this study, we offer a computational solution for quantitatively monitoring sepsis symptoms and organ systems state without laboratory test. To this end, we propose to employ a regression-based analysis by using only seven vital signs that can be acquired from bedside in Intensive Care Unit (ICU) to predict the exact value of SOFA score of patients before sepsis occurrence. Methods: A model called Deep SOFA-Sepsis Prediction Algorithm (DSPA) is introduced. In this model, we combined Convolutional Neural Networks (CNN) features with Random Forest (RF) algorithm to predict SOFA scores of sepsis patients. A subset of Medical Information Mart in Intensive Care (MIMIC) III dataset is used in experiments. 5154 samples are extracted as input. Ten-fold cross validation test are carried out for experiments. Results: We demonstrated that our model has achieved a Correlation Coefficient (CC) of 0.863, a Mean Absolute Error (MAE) of 0.659, a Root Mean Square Error (RMSE) of 1.23 for predictions at sepsis onset. The accuracies of SOFA score predictions for 6 hours before sepsis onset were 0.842, 0.697, and 1.308, in terms of CC, MAE and RMSE, respectively. Our model outperformed traditional machine learning and deep learning models in regression analysis. We also evaluated our model's prediction performance for identifying sepsis patients in a binary classification setup. Our model achieved up to 0.982 AUC (Area Under Curve) for sepsis onset and 0.972 AUC for 6 hours before sepsis, which are higher than those reported by previous studies. Conclusions: By utilizing SOFA scores, our framework facilitates the prognose of sepsis and infected organ systems state. While previous studies focused only on predicting presence of sepsis, our model aims at providing a prognosis solution for sepsis. SOFA score estimation process in ICU depends on laboratory environment. This dependence causes delays in treating patients, which in turn may increase the risk of complications. By using easily accessible non-invasive vital signs that are routinely collected in ICU, our framework can eliminate this delay. We believe that the estimation of the SOFA score will also help health professionals to monitor organ states. (C) 2020 Elsevier B.V. All rights reserved.Item MicroRNA-17, MicroRNA-19b, MicroRNA-146a, MicroRNA-302d Expressions in Hepatoblastoma and Clinical Importance(2019) Ecevit, Cigdem O.; Aktas, Safiye; Yildirim, Hulya Tosun; Demirag, Bengu; Erbay, Ayse; Karaca, Irfan; Celik, Ahmet; Demir, Ayse Banu; Ercetin, Ayse Pinar; Olgun, Nur; 29889802Hepatoblastoma (HB) is the most common liver malignancy in children. The prognosis changes according to the histologic subtypes of HB. In the present study, we aimed to characterize the expression level of selected microRNAs (miRNAs) in HB as well as in histologic subtypes, and to consider the association with the prognosis. A total of 22 HB tumor samples, subtyped as fetal (n= 16) and embryonal (n= 6), and 10 nontumorous surrounding liver samples were evaluated in this study. Expressions of miR-17, miR-146a, miR-302d, and miR-19b were analyzed in 22 HB tumor samples and 10 nontumorous surrounding liver samples by quantitative real-time polymerase chain reaction. Lower miRNA-17 expression levels were obtained in tumor samples in comparison with nontumorous surrounding liver samples (P= 0.028). Lower miRNA-17 expression was significant for predicting prognosis in HB patients (area under receiver-operator characteristic curve= 0.875, P= 0.044). A higher-level of miR-19b was found in embryonal samples (P= 0.008). Overall and event-free survival was not found to correlate with miRNA expression levels (P> 0.05). This research finds miRNA-17 and miRNA-19b expression levels can provide important data on diagnosis and prognosis in HB showing different clinical behaviors.Item Associations between the expression of mucins (MUC1, MUC2, MUC5AC and MUC6) and clinicopathologic parameters of human breast carcinomas(2020) Oral, Onur; Unverdi, Hatice; Kumcu, Emrah; Turkbey, Duygu; Dogan, Serdar; Hucumenoglu, Sema; 33154304Aims: The aim of this study is to evaluate the relationships between the expression of mucins in invasive breast carcinomas and clinicopathologic parameters. Materials and Methods: We examined 150 cases of invasive breast carcinoma, using the 2012 World Health Organization (WHO) classification of the tumors of the breast. We studied the expression of MUC1, MUC2, MUC5AC, and MUC6 by immunohistochemistry. We also evaluated normal breast tissue and ductal carcinoma in situ (DCIS) lesions in nearby invasive tumor areas. Results: In invasive breast carcinomas, MUC1, MUC2, MUC5AC, and MUC6 were expressed in 98.6%, 11.3%, 9.9, and 8.5% of cases, respectively. MUC2, MUC5AC, and MUC6 were overexpressed in invasive tumors and DCIS lesions were compared with normal breast tissue. The apical pattern of MUC1 was correlated with low grade and ER expression. MUC2 was correlated with mucinous carcinoma and an inverse association with invasive ductal carcinoma, not otherwise specified (NOS). MUC6 expression was associated with lymphovascular invasion. Conclusions: Most invasive breast tumors express MUC1 and the apical pattern of MUC1 is correlated with low grade and ER expression. MUC6 expression is associated with indicators of poor prognosis. Further comprehensive studies need to evaluate the role of mucins as a potential biomarker and to be used as a specific therapeutic target against breast cancer.Item On computer-aided prognosis of septic shock from vital signs(2019) Ogul, Hasan; Baldominos, Alejandro; Asuroglu, Tunc; Colomo-Palacios, Ricardo; AAC-7834-2020Sepsis is a life-threatening condition due to the reaction to an infection. With certain changes in circulatory system, sepsis may progress to septic shock if it is left untreated. Therefore, early prognosis of septic shock may facilitate implementing correct treatment and prevent more serious complications. In this study, we assess the feasibility of applying a computer-aided prognosis system for septic shock. The system is envisaged as a tool to predict septic shock at the time of sepsis onset using only vital signs which are collected routinely in intensive care units (ICUs). To this end, we evaluate the performances of computational methods that take the sequence of vital signs acquired until sepsis onset as input and report the possibility of progressing to a septic shock before any further clinical analysis is performed. Results show that an adaptation of multivariate dynamic time warping can reveal higher accuracy than other known time-series classification methods on a new dataset built from a public ICU database. We argue that the use of computational intelligence methods can promote computer-aided prognosis of septic shock in hospitalized environment to a certain degree.Item The relation of presenting symptoms with staging, grading, and postoperative 3-year mortality in patients with stage I-III non-metastatic colon cancer(2016) Bedir, Osman; Kiziltas, Safak; Kostek, Osman; Ozkanli, Seyma; 27210779Background/Aims: To evaluate the association of presenting symptoms with staging, grading, and postoperative 3-year mortality in patients with colon cancer. Materials and Methods: A total of 132 patients-with a mean (standard deviation; SD) age of 63.0 (10.0) years and of whom 56.0% were males-with non-metastatic stage I-III colon cancer were included. Symptoms prior to diagnosis were evaluated with respect to tumor localization, tumor node metastasis (TNM) stage, histological grade, and postoperative 3-year mortality. Results: Constipation and abdominal pain were the two most common symptoms appearing first (29.5% and 16.7%, respectively) and remained most predominant (25.0% and 20.0%, respectively) up to diagnosis. The frequency of admission symptoms significantly differed with respect to tumor location, TNM stage and histological grade. The postoperative 3-year survival rate was 61.4%. Multivariate logistic regression revealed that melena and rectal bleeding increased the likelihood of 3-year mortality by 13.6-fold (p=0.001) and 4.08-fold (p=0.011), respectively. Conclusion: Our findings revealed differences in presenting symptom profiles with respect to the time of manifestation and predominance as well as to the TNM stage, histological grade, and tumor location. Given that melena and rectal bleeding increased the 3-year mortality risk by 13.6-fold and 4.08-fold, respectively, our findings indicate the association of admission symptoms with outcome among patients with colon cancer.