Prostate cancer biomarker identification using MALDI-MS DATA: Initial results

Mantena, Vamsi Krishnam Raju; Jiang, Wenjuan; Jiang, Li; McKenzie, R.

doi:10.1109/lissa.2009.4906723

Cited by 10 publications

(7 citation statements)

References 13 publications

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“…Using a temporal-spatial data-driven approach (Mantena et al, 2009), results indicate fluctuating startle eyeblink amplitude could approximate fluctuating amygdala activity. Previously, startle eyeblink amplitude has had a limited role in measuring fluctuating activity in the amygdala.…”

Section: Discussionmentioning

confidence: 99%

The role of startle fluctuation and non-response startle reflex in tracking amygdala dynamics

Magerman

et al. 2020

Preprint

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The startle reflex is considered a sensitive defensive reaction to potential threats that manifests as a unique eye blink-like pattern in the EMG. Eye blink EMG has a bellshaped potential when startle probes are elicited, that strongly corresponds to amygdala activity. Considering how amygdala activity fluctuates over time in response to emotional and self-threatening stimuli, observing startle eyeblink size fluctuation over time could provide a cost-effective, convenient, and less resource intensive means for examining amygdala activity over time. Yet based on current standards in the literature, often startle evoked eye blink EMG values do not exhibit activity 3SDs from the mean eyeblink response, thus these trials are typically excluded from startle analyses. It stands to reason, however, that these trials may still index amygdala activity in a meaningful way. Through investigating the association between startle eyeblink amplitude, corresponding ERP amplitude, and underlying neural activity, the current study provides evidence that startle amplitudes exhibit a linear relationship with proxies for amygdala activity, e.g., N100 amplitudes and regions heavily interconnected with the amygdala. Specifically, the startle reflex correlates to large amount of brain regions in N100 time window in addition to the N100 amplitude. Thus, both valid and otherwise traditionally non-valid startle reflex responses appear to index amygdala activity and should be included accordingly. This approach could help salvage large amounts of meaningful data traditionally excluded from studies interested in amygdala responses to various stimuli over time.

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Section: Discussionmentioning

confidence: 99%

The role of startle fluctuation and non-response startle reflex in tracking amygdala dynamics

Magerman

et al. 2020

Preprint

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“…25 After the re-binning step, there are 821 peaks resulting from the 974 MALDI-MS spectra available for this study.…”

Section: Maldi Spectra Data Processingmentioning

confidence: 99%

“…Results of peak re-binning. 25 ground truth (either HMGT, LMGT or intersection) and then tested on the rest of the data for classification using a cross-validation technique.…”

Section: Experiments Designmentioning

confidence: 99%

Prostate cancer region prediction by fusing results from MALDI spectra–processing and texture analysis

et al. 2012

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We present a three-step method to predict prostate cancer (PCa) regions on biopsy tissue samples based on high-confidence, low-resolution PCa regions marked by a pathologist. First, we will apply a texture-analysis technique on a high-magnification optical image to predict PCa regions on an adjacent tissue slice. Second, we will design a prediction model for the same purpose, using matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS) tissue-imaging data from the adjacent slice. Finally, we will fuse those two results to obtain the PCa regions that will assist MALDI imaging biomarker identification. Experiment results show that the texture analysis–based prediction is sensitive (87.45%) but less specific (75%), and the prediction based on the MALDI spectra data processing is not sensitive (50.98%) but supremely specific (100%). By combining these two results, an optimized prediction for PCa regions on the adjacent slice can be achieved (sensitivity: 80.39%, specificity: 93.09%).

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“…The raw MALDI data we received from the EVMS needs to undergo a series of pre preprocessing steps [5]. They include a. Baseline adjustment: In general, it is recommended to remove the ion and chemical noise that are usually higher at smaller m/z values.…”

Section: Maldi Spectra Preprocessing Stepsmentioning

confidence: 99%

Prostate cancer region prediction using MALDI mass spectra

et al. 2010

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For the early detection of prostate cancer, the analysis of the Prostate-specific antigen (PSA) in serum is currently the most popular approach. However, previous studies show that 15% of men have prostate cancer even their PSA concentrations are low. MALDI Mass Spectrometry (MS) proves to be a better technology to discover molecular tools for early cancer detection. The molecular tools or peptides are termed as biomarkers. Using MALDI MS data from prostate tissue samples, prostate cancer biomarkers can be identified by searching for molecular or molecular combination that can differentiate cancer tissue regions from normal ones. Cancer tissue regions are usually identified by pathologists after examining H&E stained histological microscopy images. Unfortunately, histopathological examination is currently done on an adjacent slice because the H&E staining process will change tissue's protein structure and it will derogate MALDI analysis if the same tissue is used, while the MALDI imaging process will destroy the tissue slice so that it is no longer available for histopathological exam. For this reason, only the most confident cancer region resulting from the histopathological examination on an adjacent slice will be used to guide the biomarker identification. It is obvious that a better cancer boundary delimitation on the MALDI imaging slice would be beneficial. In this paper, we proposed methods to predict the true cancer boundary, using the MALDI MS data, from the most confident cancer region given by pathologists on an adjacent slice.

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Prostate cancer biomarker identification using MALDI-MS DATA: Initial results

Cited by 10 publications

References 13 publications

The role of startle fluctuation and non-response startle reflex in tracking amygdala dynamics

The role of startle fluctuation and non-response startle reflex in tracking amygdala dynamics

Prostate cancer region prediction by fusing results from MALDI spectra–processing and texture analysis

Prostate cancer region prediction using MALDI mass spectra

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