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Linh Tran, MD

  • Clinical Assistant Professor
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Locations and Hours

Obstetrics & Gynecology

300 Pasteur Dr
Rm HH333 MC 5317
Stanford, CA 94305
Phone: (650) 498-8080
Map, Directions & Parking

Specialties

Obstetrics & Gynecology

Work and Education

Professional Education

UCLA School of Medicine, Los Angeles, CA, 2009

Residency

UCLA School of Medicine, Los Angeles, CA, 2013

Board Certifications

Obstetrics & Gynecology, American Board of Obstetrics and Gynecology

Services

Obstetrics

All Publications

Evaluation of Patient Characteristics Associated With Mode of Hysterectomy and Conversion Rate. Journal of minimally invasive gynecology Pao, S. A., Tran, L. 2015; 22 (6S): S96-?

View details for DOI 10.1016/j.jmig.2015.08.259

View details for PubMedID 27679387

Evaluation of Patient Characteristics Associated With Mode of Hysterectomy and Conversion Rate Abstracts of the 44th AAGL Global Congress of Minimally Invasive Gynecology Pao, S., Tran, L. 2015: 110
Detection of differentially expressed proteins in early-stage melanoma patients using SELDI-TOF mass spectrometry. Annals of the New York Academy of Sciences Wilson, L. L., Tran, L., Morton, D. L., Hoon, D. S. 2004; 1022: 31722

Abstract

Tumor progression is a dynamic sequence of events that involves specific protein changes. We hypothesized that Surface Enhanced Laser Desorption/Ionization (SELDI) mass spectrometric analysis of sera from patients with AJCC stage I and II melanoma with negative loco-regional lymph nodes could identify potential melanoma-associated protein biomarkers of disease recurrence. Serum specimens were collected from 49 patients who developed recurrence (n = 25) or remained free of recurrence (n = 24) without evidence of disease following complete resection (AJCC stage I and II). Follow-up was longer than 5 years. Serum proteins were denatured and applied onto two protein chip chemistry surfaces (weak cationic WCX2; metal-binding, IMAC3-Cu). SELDI ProteinChip mass spectrometry was then performed. SELDI data were analyzed, protein peak clustering and classification were performed, and a supervised classification algorithm was employed to classify the dataset. Multiple protein peaks ranging from 3.3 to 30 kDa were identified between patients with recurrence and those without recurrence, and the expression pattern differences of three proteins were used to generate the discriminating classification tree. The biomarkers were expressed with a high degree of reproducibility. In this early characterization study, melanoma recurrence was predicted with a sensitivity of 72% (18/25) and a specificity of 75% (18/24). This novel pilot study revealed three proteins that accurately identified patients who developed recurrence after curative resection of primary melanoma.

View details for DOI 10.1196/annals.1318.047

View details for PubMedID 15251977