PLASMA BIOMARKER ANALYSIS IN PATIENTS WITH CARDIOMYOPATHY SECONDARY TO FABRY DISEASE : INSIGHT INTO THE PATHOPHYSIOLOGY OF FABRY CARDIOMYOPATHY

2014 ◽  
Vol 30 (10) ◽  
pp. S207-S208
Author(s):  
H. Yogasundaram ◽  
B.N. Putko ◽  
I. Paterson ◽  
R.B. Thompson ◽  
A. Khan ◽  
...  
Keyword(s):  
Fabry Disease ◽  
Plasma Biomarker ◽  
Biomarker Analysis ◽  
Insight Into

scholarly journals Advanced Neuromonitoring and Imaging in Pediatric Traumatic Brain Injury

2012 ◽  
Vol 2012 ◽  
pp. 1-11 ◽  
Author(s):  
Stuart H. Friess ◽  
Todd J. Kilbaugh ◽  
Jimmy W. Huh
Keyword(s):  
Traumatic Brain Injury ◽  
Brain Injury ◽  
Intracranial Pressure ◽  
Early Detection ◽  
Pediatric Patients ◽  
Vital Signs ◽  
Pediatric Traumatic Brain Injury ◽  
Pressure Monitoring ◽  
Biomarker Analysis ◽  
Insight Into

While the cornerstone of monitoring following severe pediatric traumatic brain injury is serial neurologic examinations, vital signs, and intracranial pressure monitoring, additional techniques may provide useful insight into early detection of evolving brain injury. This paper provides an overview of recent advances in neuromonitoring, neuroimaging, and biomarker analysis of pediatric patients following traumatic brain injury.

scholarly journals Standardization procedure for plasma biomarker analysis in rat models of epileptogenesis: Focus on circulating microRNAs

Epilepsia ◽  
2017 ◽  
Vol 58 (12) ◽  
pp. 2013-2024 ◽  
Author(s):  
Erwin A. van Vliet ◽  
Noora Puhakka ◽  
James D. Mills ◽  
Prashant K. Srivastava ◽  
Michael R. Johnson ◽  
...  
Keyword(s):  
Plasma Biomarker ◽  
Circulating Micrornas ◽  
Rat Models ◽  
Biomarker Analysis ◽  
Standardization Procedure

scholarly journals Globotriaosylceramide leads to KCa3.1 channel dysfunction: a new insight into endothelial dysfunction in Fabry disease

2010 ◽  
Vol 89 (2) ◽  
pp. 290-299 ◽  
Author(s):  
S. Park ◽  
J. A. Kim ◽  
K. Y. Joo ◽  
S. Choi ◽  
E.-N. Choi ◽  
...  
Keyword(s):  
Endothelial Dysfunction ◽  
Fabry Disease ◽  
Insight Into

Long term biomarker analysis to assess cardiac involvement in Fabry disease

2019 ◽  
Vol 126 (2) ◽  
pp. S99
Author(s):  
Lauren M. Mason ◽  
Renuka Limgala ◽  
Ozlem Goker-Alpan
Keyword(s):  
Fabry Disease ◽  
Cardiac Involvement ◽  
Biomarker Analysis

Plasma biomarker analysis of ramucirumab in Japanese patients with advanced gastric cancer.

2018 ◽  
Vol 36 (4_suppl) ◽  
pp. 81-81 ◽  
Author(s):  
Daisuke Takahari ◽  
Takeru Wakatsuki ◽  
Tetsuo Mashima ◽  
Keisho Chin ◽  
Takashi Ichimura ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Multivariate Analysis ◽  
Prognostic Factor ◽  
Advanced Gastric Cancer ◽  
Base Line ◽  
Plasma Biomarker ◽  
Negative Prognostic Factor ◽  
Biomarker Analysis ◽  
Single Use ◽  
Independent Negative Prognostic Factor

81 Background: Ramucirumab, anti-VEGFR2 receptor antibody, showed significantly improved survivals of gastric cancer in the second line with paclitaxel or single use. Recently we reported early elevation of plasma VEGF-A was associated with shorter survival as a preliminary result. We will report the results of final analysis. Methods: Patients with advanced gastric cancer who received ramucirumab combined with paclitaxel or single use were enrolled. Plasma samples were collected at pre-treatment and day 8 after administration. Nine kinds of plasma biomarker involved in angiogenesis, VEGF-A, C, D, PlGF, VEGFR1, 2, Angiopoietin 1, stromal cell derived factor-1α (SDF1α), and Neuropirin-1, were measured by means of ELISA. Patients were dichotomized by optimal cut-off value. Univariate and multivariate analysis were done by Cox proportion hazard model. Results: Forty-one patients were enrolled. Thirty-nine patients (95.1%) received ramucirumab with paclitaxel. Plasma VEGF-A, D, PlGF, and VEGFR2 levels were significantly increased one week after administration compared with baseline levels, while plasma VEGFR1 and NRP1levels were significantly decreased. Median PFS and OS were 5.6 (95% CI 4.66-6.54) and 9.8 (95% CI 5.41-14.19) months, respectively. In univariate analysis, higher baseline SDF1α and PlGF levels resulted in shorter OS with HR 2.71 95% CI 1.23-6.00, p =0.013 for SDF1α and HR 2.78 95% CI 1.16-6.65, p =0.022 for PlGF. Higher D8 VEGF-A was associated with shorter PFS with HR 2.77 95% CI 1.39-5.51, p =0.004. While higher D8 VEGF-D was associated with better PFS with HR 0.39 95% CI 0.20-0.77, p =0.007. In multivariate analysis, higher baseline SDF1α and PlGF were independent negative prognostic factor for OS with HR 2.45 95% CI 1.10-5.42, p =0.028 for SDF1α and HR 2.48 95% CI 1.03-5.96, p =0.043. With respect to PFS, higher D8 VEGF-A was also independent negative prognostic factor with HR 2.32 95% CI 1.13-4.77, p =0.022, while higher D8 VEGF-D was favorable predictor for PFS with HR 0.47 95% CI 0.23-0.96, p =0.038. Conclusions: Higher base line SDF1α and PlGF levels may be negative prognostic marker. While early VEGF-A and D elevation after ramucirumab administration may be predictive marker of ramucirumab.

scholarly journals White Matter Tract Neuroimaging and Serum Biomarker Analysis in Sports-related Concussion

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Michael J. McGill ◽  
Qiuting Wen ◽  
Ho-Ching Yang ◽  
Salman Shahid ◽  
Yu-Chien Wu
Keyword(s):  
Diffusion Tensor Imaging ◽  
White Matter ◽  
Diffusion Tensor ◽  
Serum Biomarkers ◽  
Post Injury ◽  
Biomarker Analysis ◽  
The Relationship ◽  
Insight Into ◽  
Diffusion Tensor Imaging Dti ◽  
Time Point

Background:  Traumatic brain injury (TBI) is a leading cause of death and disability throughout the world, estimated to carry an annual global incidence of over 27 million cases. Mild TBI (mTBI), commonly known as concussion, is the mildest form of TBI and accounts for roughly 90% of all head injuries. Sports-related concussion (SRC) contributes significantly to this statistic with millions of athletes sustaining high-impact injuries in contact sports such as football, soccer, and lacrosse. By examining the white-matter microstructure, diffusion tensor imaging (DTI) has shown excellent capabilities for detecting pathophysiologic changes after SRC and monitoring symptom progression. Biomarkers including neurofilament light (NfL) and tau have been implicated in SRC and may provide insight into the duration of post-concussive symptoms. At this time, very few studies have been published evaluating the relationship between these serum biomarkers and alterations to DTI metrics.     Methods:  In the present study, we examined the association between serum biomarkers NfL and tau to further understand the relationship between these biomarkers and neuroimaging findings seen with diffusion tensor imaging (DTI) after exposure to a sports-related concussive event.     Results:  Serum tau levels decreased significantly at the 24-48h post-injury time point compared to 6h post-injury. Serum tau levels then elevated significantly at the asymptomatic time point in comparison to the 24-48h post-injury time point. The serum tau level was significantly associated with higher mean diffusivity (MD) in the white-matter tracts. Serum NfL had minimal associations with white matter diffusion metrics.     Conclusion and Potential Impact:  This research serves to better inform future investigations into the relationship between DTI metrics and serum biomarkers in the context of mTBI and SRC. This information may contribute to the development of a simple bedside serum analysis with potential to offer tremendous insight into the comprehensive brain health of patients who are being evaluated for SRC, thereby streamlining the therapeutic process and providing more accessible healthcare to patients in locations where advanced imaging techniques are not readily accessible.  

Biomarker analysis of Fabry disease patient cell fractions using tandem mass spectrometry

2016 ◽  
Vol 117 (2) ◽  
pp. S114
Author(s):  
Amanda Toupin ◽  
Pamela Lavoie ◽  
Michel Boutin ◽  
Mona Abaoui ◽  
Anne-Marie Côté ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Tandem Mass Spectrometry ◽  
Fabry Disease ◽  
Disease Patient ◽  
Tandem Mass ◽  
Biomarker Analysis ◽  
Fabry Disease Patient ◽  
Cell Fractions ◽  
Patient Cell
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