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A list of all the posts and pages found on the site. For you robots out there is an XML version available for digesting as well.
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Posts
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publications
Differential signaling during macropinocytosis in response to M-CSF and PMA in macrophages
Published in Frontiers in Physiology, 2015
The cellular movements that construct a macropinosome have a corresponding sequence of chemical transitions in the cup-shaped region of plasma membrane that becomes the macropinosome. To determine the relative positions of type I phosphatidylinositol 3-kinase (PI3K) and phospholipase C (PLC) in this pathway, we analyzed macropinocytosis in macrophages stimulated by the growth factor macrophage-colony-stimulating factor (M-CSF) and by the diacylglycerol (DAG) analogue phorbol 12-myristate 13-acetate (PMA). In cells stimulated with M-CSF, microscopic imaging of fluorescent probes for intracellular lipids indicated that the PI3K product phosphatidylinositol (3,4,5)-trisphosphate (PIP3) appeared in cups just prior to DAG. We then tested the hypothesis that PMA and DAG function after PI3K and prior to Ras and protein kinase C (PKC) during macropinosome formation in macrophages. Although the PI3K target Akt was activated by M-CSF, the Akt inhibitor MK-2206 did not inhibit macropinocytosis. The phospholipase C (PLC) inhibitor U73122 blocked macropinocytosis by M-CSF but not PMA. Macropinocytosis in response to M-CSF and PMA was inhibited by the Ras inhibitor farnesyl thiosalicylate (FTS), by the PKC inhibitor Calphostin C and by the broad specificity inhibitor rottlerin. These studies support a model in which M-CSF stimulates PI3K in macropinocytic cups, and the resulting increase in PIP3 activates PLC, which in turn generates DAG necessary for activation of PKC, Ras and the late stages of macropinosome closure.
Recommended citation: S. Yoshida, I. Gaeta, R. Pacitto, L. Krienke, O. Alge, B. Gregorka, J. A. Swanson, "Differential signaling during macropinocytosis in response to M-CSF and PMA in macrophages" Front. Physiol. , vol. 6, Jan. 2015, doi: 10.3389/fphys.2015.00008. https://www.frontiersin.org/articles/10.3389/fphys.2015.00008/full
Classifying Osteosarcoma Using Meta-Analysis of Gene Expression
Published in BIBM 2018, 2018
Meta-analysis of gene expression provides the opportunity to compare gene expression across different platforms. In this paper, we use a meta-analysis of RNA-seq data collected by the SJTU team and publicly available microarray data to build a Random Forest classification model. The Random Forest model had average accuracy of 74.1% for cross-validation in the training set and achieved accuracy of 80.0% on the testing set.
Recommended citation: O. Alge, J. Gryak, Y. Hua, and K. Najarian, "Classifying Osteosarcoma Using Meta-Analysis of Gene Expression", in 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Madrid, Spain, Dec. 2018, pp. 2400–2404. doi: 10.1109/BIBM.2018.8621119. https://ieeexplore.ieee.org/abstract/document/8621119/
Automated Classification of Osteosarcoma and Benign Tumors using RNA-seq and Plain X-ray
Published in EMBC 2020, 2020
Osteosarcoma is a prominent bone cancer that typically affects adolescents or people in late adulthood. Early recognition of this disease relies on imaging technologies such as x-ray radiography to detect tumor size and location. This paper aims to differentiate osteosarcoma from benign tumors by analyzing both imaging and RNA-seq data through a combination of image processing and machine learning. In experimental results, the proposed method achieved an Area Under the Receiver Operator Characteristic Curve (AUC) of 0.7272 in three-fold cross-validation, and an AUC of 0.9015 using leave-one-out cross-validation.
Recommended citation: O. Alge, L. Lu, Z. Li, Y. Hua, J. Gryak and K. Najarian, "Automated Classification of Osteosarcoma and Benign Tumors using RNA-seq and Plain X-ray", 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), Montreal, QC, Canada, 2020, pp. 1165-1168, doi: 10.1109/EMBC44109.2020.9176104. https://ieeexplore.ieee.org/abstract/document/9176104/
Predicting Poor Sleep Quality in Fibromyalgia with Wrist Sensors
Published in EMBC 2020, 2020
Fibromyalgia is a musculoskeletal disorder characterized by chronic, widespread muscle pain. This condition is associated with disturbed sleep, which has a direct impact on patient quality of life. Patient-reported outcomes are frequently used to assess sleep quality, but show modest correlations with objective measures of sleep, such as polysomnography. Working towards our goal of an automated ambulatory system of assessing sleep quality, we use features from blood volume pulse (BVP) and electrodermal activity (EDA) collected with a wearable device during sleep. We compare these measurements between individuals with fibromyalgia who experienced poor sleep and individuals in a control group who experienced refreshing sleep. By applying Learning Using Concave and Convex Kernels (LUCCK) and Support Vector Machines (SVM), we achieve mean Area Under the Receiver Operating Characteristic …
Recommended citation: O. Alge, S. M. Reza Soroushmehr, J. Gryak, A. Kratz, and K. Najarian, "Predicting Poor Sleep Quality in Fibromyalgia with Wrist Sensors", in 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), Montreal, QC, Canada, Jul. 2020, pp. 4290–4293. doi: 10.1109/EMBC44109.2020.9176386. https://ieeexplore.ieee.org/abstract/document/9176386/
Prediction of postoperative cardiac events in multiple surgical cohorts using a multimodal and integrative decision support system
Published in Scientific Reports, 2022
Postoperative patients are at risk of life-threatening complications such as hemodynamic decompensation or arrhythmia. Automated detection of patients with such risks via a real-time clinical decision support system may provide opportunities for early and timely interventions that can significantly improve patient outcomes. We utilize multimodal features derived from digital signal processing techniques and tensor formation, as well as the electronic health record (EHR), to create machine learning models that predict the occurrence of several life-threatening complications up to 4 hours prior to the event. In order to ensure that our models are generalizable across different surgical cohorts, we trained the models on a cardiac surgery cohort and tested them on vascular and non-cardiac acute surgery cohorts. The best performing models achieved an area under the receiver operating characteristic curve (AUROC) of 0 …
Recommended citation: R. B. Kim, O. P. Alge, G. Liu, B. E. Biesterveld, G. Wakam, A. M. Williams, M. R. Mathis, K. Najarian and J. Gryak, "Prediction of postoperative cardiac events in multiple surgical cohorts using a multimodal and integrative decision support system", Sci Rep, vol. 12, no. 1, p. 11347, Dec. 2022, doi: 10.1038/s41598-022-15496-w. https://www.nature.com/articles/s41598-022-15496-w
talks
Observation of LPS-induced Macropinosomes
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Use of Signal Processing Methods to Aid in Clinical Decision-Making
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Signal processing methods can be used to obtain information from noninvasive monitoring, such as electrocardiogram (ECG) and photoplethysmogram (PPG). The information gained can be used as input for machine learning methods, which can provide computer-assisted support to clinicians. This presentation discusses applications of signal processing, such as using a wearable device to assess sleep quality, and using multi-lead ECG to aid in risk stratification for sepsis-induced organ dysfunction.
Application of a Tensor-Based Classification Method with Electrocardiogram Data
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Risk stratification for sepsis-induced organ dysfunction is difficult because of its heterogeneity. Moreover, the information needed to compute the Sequential Organ Failure Assessment (SOFA) defined by Sepsis-3 may not be immediately available for patients that require care. Continuous electrocardiogram (ECG) recording is often available for patients in intensive care, and may be useful to identify those at risk of decompensating to septic shock. Additionally, structuring multi-lead ECG data as a higher-order tensor can preserve the data’s structure and prevent loss of information that can occur in traditional machine learning, which structures data as 1-dimensional vectors. The project discussed here is a first step in this research: the development of a tensorial data classification method, and its testing with publicly available ECG data. Methods: We developed a tensor classification method based on multilinear discriminant analysis via Kempf-Ness Theory (KNMDA). We compare performance of this method to existing tensor methods and traditional machine learning, including support vector machine (SVM) and linear discriminant analysis (LDA), at different noise levels. We constructed 3rd order tensors using 12-lead ECG data from the Physionet PTB database, which contains samples of both normal control and unhealthy subjects. Results: Over thirty iterations, our KNMDA method obtained average area under the receiver operating characteristic curve (AUC) of 0.94 (SD 0.03), an improvement compared to linear SVM with 0.91 (0.03) and LDA with 0.68 (0.07) with no additional noise. When signal-to-noise ratio of the ECG signal was changed to -10, KNMDA achieved average AUC of 0.92 (0.03), compared to SVM with 0.85 (0.06) and LDA with 0.59 (0.07). Impact: The results show that our proposed method can outperform existing ones when using real ECG data. We can therefore apply this method to our larger goal of improving risk-stratification for septic patients, to augment physician decision-making and improve patient care.
Using tensor-reduced physiological signals to predict sepsis trajectory
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Background: The quick sequential organ failure assessment (qSOFA) scoring system, defined by Sepsis-3 in 2016, is a method to identify patients at risk to progress to poor outcomes related to sepsis using variables that can be collected at the bedside.