Raymond J Langley from 10345 Belgrove Ave, Daphne, AL 36526, age 53

Resumes

Assistant Professor

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Location:

Albuquerque, NM

Industry:

Higher Education

Work:

University of South Alabama
Assistant Professor
Lovelace Respiratory Research Institute Jul 2010 - Jul 2015
Associate Research Scientist
Ncgr May 2008 - May 2010
Post-Doctoral Fellow

Education:

University of New Mexico 2003 - 2007
Doctorates, Doctor of Philosophy

Skills:

Molecular Biology
Biotechnology
Pcr
Qpcr
Life Sciences
Systems Biology
Research
Genomics
Cell Biology
Sequencing
Cell
Biochemistry
Animal Models
Clinical Research
Genetics
Bioinformatics
Transcriptomics
Predictive Modeling
Metabolomics

Raymond Langley

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Location:

United States

Raymond Langley

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Location:

United States

Us Patents

Methods For Diagnosis Of Sepsis And Risk Of Death
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US Patent:

20100273207, Oct 28, 2010
Filed:

Apr 24, 2010
Appl. No.:

12/766882
Inventors:

Raymond Langley - Albuquerque NM, US
Stephen Kingsmore - St. Augustine FL, US
Bo Chen - Durham NC, US
Lawrence Carin - Durham NC, US
International Classification:

C12Q 1/04
US Classification:

435 34
Abstract:

The present invention is directed to methods for diagnosis of sepsis and high risk of sepsis death. Such methods are directed to subjects presenting to healthcare facilities with symptoms for sepsis. Clinico-metabolomic classifiers are tested and compared in the methods of the present invention.

Sepsis Prognosis Biomarkers
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US Patent:

20150024969, Jan 22, 2015
Filed:

Sep 12, 2012
Appl. No.:

14/344534
Inventors:

Raymond Langley - Albuquerque NM, US
Stephen Kingsmore - Overland Park KS, US
Assignee:

THE CHILDREN'S MERCY HOSPITAL - Kansas City MO
International Classification:

G01N 33/68
G01N 33/49
US Classification:

506 12, 506 15, 436 70
Abstract:

A method for determining prognosis, diagnosis and theronosis of a sepsis infection in a patient is disclosed. The method involves measuring the age, mean arterial pressure, hematocrit, patient temperature, and the concentration of one or more metabolites that are predictive of sepsis severity. The method can involve obtaining a blood sample from said patient and determining the concentration of the metabolite in the patient's blood; and then determining the severity of sepsis infection by analyzing the measured values in a weighted logistic regression equation.