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Biomedical subjects

C Nicole White

Publications and source records attributed to C Nicole White.

5 recordsLinked to original sources

Assessment of BNP and NT-proBNP in emergency department patients presenting with suspected acute coronary syndromes.

OBJECTIVES: The relationship between BNP and NT-proBNP among physiologically and clinically relevant demographic subgroups has never been clarified in the context of the emergency department (ED). DESIGN AND METHODS: A blood sample taken from patients presenting to the E.D. with suspected acute coronary syndromes (ACS) was analyzed for BNP and NT-proBNP, and correlation between them was examined as an entire group then as subgroups according to gender, ethnicity, age, and comorbidity variables. RESULTS: BNP and NT-proBNP correlate well (0.89, P < 0.0001) in a population of 420 patients and in patient subgroups with a history of various etiologies, including vascular disorders. CONCLUSIONS: In general, BNP and NT-proBNP correlate very well in patients with suspected ACS and may aid in the risk stratification process in emergency departments.

Acute Disease↗

Using proteomic approaches to identify new biomarkers for detection and monitoring of ovarian cancer.

OBJECTIVES: Early detection and monitoring the treatment remain the most important factors in improving long-term survival of ovarian cancer patients. New biomarkers that individually or in combination improve the diagnostic performance of existing tumor markers are critically needed. This study uses proteomic approaches to identify new biomarkers for detection and monitoring of ovarian cancer. METHODS: We analyzed protein profiles of three sets of sera using surface enhanced laser desorption and ionization time-of-light mass spectroscopy (SELDI-TOF-MS) on IMAC ProteinChip arrays and ProPeak software for bioinformatics data analysis. The first set of patients included 21 ovarian cancers, 18 benign diseases, and 20 normal patients. The second set included 32 ovarian cancers, 30 benign ovarian diseases, and 30 age-matched healthy controls. The third set included samples collected before and after chemotherapy from 18 ovarian cancer patients. All samples were collected at the Gynecology and Obstetrics Hospital of Fudan University in Shanghai, China. The datasets from low-intensity and high-intensity spectra were analyzed separately. RESULTS: Seven peaks were selected for their contribution to the separation of ovarian cancers from controls using the first and second set of samples. The same dysregulation trends were confirmed for six of the seven peaks in independent validation using the third set of samples. CONCLUSIONS: Using SELDI-TOF analysis of 195 unique specimens, we discovered with preliminary validation six distinct peaks that may potentially be useful in the detection and monitoring of ovarian cancer. Additional studies are required to determine the protein identities of these peaks and to further validate their performance as biomarkers.

Adult↗

Independent validation of candidate breast cancer serum biomarkers identified by mass spectrometry.

BACKGROUND: We previously selected a panel of 3 breast cancer biomarkers (BC1, BC2, and BC3) from serum samples collected at a single hospital based on their collective contribution to the optimal separation of breast cancer patients and noncancer controls by surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF MS). The identities and general applicability of these markers, however, were unknown. In this study, we performed protein expression profiling on samples obtained from a second hospital, included a greater number of ductal carcinoma in situ (DCIS) cases, and performed purification and identification of the 2 confirmed markers. METHODS: Using a case-control study design, we performed protein expression profiling on serum samples from the National Cancer Institute (Milan, Italy). The validation sample cohort consisted of 61 women with locally invasive breast cancer, 32 with DCIS, 37 with various benign breast diseases (including 13 atypical), and 46 age-matched apparently healthy women (age range, 44-68 years). Validated biomarkers were purified and identified with serial chromatography, 1-dimensional gel electrophoresis, in-gel ASP-N digestion, peptide mass fingerprinting, and tandem mass peptide sequencing. RESULTS: The BC3 and BC2 expression patterns in this sample set were consistent with the first study sample set. BC3 and BC2 were identified to be complement component C3a(desArg) and a C-terminal-truncated form of C3a(desArg), respectively. CONCLUSIONS: Evaluation of biomarkers in independent sample sets can help determine the broader utility of candidate markers, and protein identification permits understanding of their molecular basis. C3a(desArg) appears to lack specificity among patients with benign diseases, limiting its utility as a stand-alone tumor marker, but it may still be useful in a multimarker panel for early detection of breast cancer.

Adolescent↗

Bioinformatics strategies for proteomic profiling.

Clinical proteomics is an emerging field that involves the analysis of protein expression profiles of clinical samples for de novo discovery of disease-associated biomarkers and for gaining insight into the biology of disease processes. Mass spectrometry represents an important set of technologies for protein expression measurement. Among them, surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI TOF-MS), because of its high throughput and on-chip sample processing capability, has become a popular tool for clinical proteomics. Bioinformatics plays a critical role in the analysis of SELDI data, and therefore, it is important to understand the issues associated with the analysis of clinical proteomic data. In this review, we discuss such issues and the bioinformatics strategies used for proteomic profiling.

Computational Biology↗