A new combination of analytical chemistry and mathematical data analysis techniques allows the rapid identification of the species, strain and infectious phase of a potential biological terrorism agent. Coxiella burnetii causes the human disease Q fever, which can cause serious illness and even death.
A new combination of analytical chemistry and mathematical data analysis techniques allows the rapid identification of the species, strain and infectious phase of a potential biological terrorism agent. Coxiella burnetii causes the human disease Q fever, which can cause serious illness and even death.
Research by the Georgia Institute of Technology (USA) and the Centers for Disease Control and Prevention (CDC) has yielded a method that proved to be 95.2% accurate in identifying and classifying Coxiella burnetii. The laboratory test delivers results in about five minutes compared with about two hours for the laboratory technique currently used to detect the bacterium.
Because the bacterium has the potential to be used as a bioweapon, it is important to detect Coxiella burnetii at an early stage, and to determine which strain it is to determine the geographic area it came from.
Facundo Fernandez from Georgia Tech, and his PhD student Carrie Young, collaborated with CDC researchers in the National Center for Environmental Health and the National Center for Infectious Diseases. They combined mass spectrometry and a mathematical data analysis technique called partial least squares analysis. Mass spectrometry allows researchers to look at the profiles of different proteins expressed in a microorganism, and partial least squares analysis separates important information from "noise" or biological baseline shifts caused by sample preparation variations that could corrupt a predictive model. The combination of these techniques is a novel concept and researchers believe the technique will also work with other pathogens. The technique can detect Coxiella burnetii strains at very low concentrations, specifically at the attomole level, which is equivalent to 1 × 10–17 moles.
Coxiella burnetii is a species of concern because it causes the highly infective human disease Q fever, which is transmitted primarily by cattle, sheep and goats. A human can be infected by just one bacterium. Symptoms can include high fever, severe headache, vomitting, diarrhoea, abdominal pain and chest pain. Q fever can also lead to pneumonia and hepatitis. According to the CDC, the bacterium is considered a bioterrorism agent because of its long-term environmental stability, resistance to heat and drying, extremely low infectious dose, aerosol infectious route and history of weaponization by various countries.
Thermodynamic Insights into Organic Solvent Extraction for Chemical Analysis of Medical Devices
April 16th 2025A new study, published by a researcher from Chemical Characterization Solutions in Minnesota, explored a new approach for sample preparation for the chemical characterization of medical devices.
Sorbonne Researchers Develop Miniaturized GC Detector for VOC Analysis
April 16th 2025A team of scientists from the Paris university developed and optimized MAVERIC, a miniaturized and autonomous gas chromatography (GC) system coupled to a nano-gravimetric detector (NGD) based on a NEMS (nano-electromechanical-system) resonator.
Miniaturized GC–MS Method for BVOC Analysis of Spanish Trees
April 16th 2025University of Valladolid scientists used a miniaturized method for analyzing biogenic volatile organic compounds (BVOCs) emitted by tree species, using headspace solid-phase microextraction coupled with gas chromatography and quadrupole time-of-flight mass spectrometry (HS-SPME-GC–QTOF-MS) has been developed.
A Guide to (U)HPLC Column Selection for Protein Analysis
April 16th 2025Analytical scientists are faced with the task of finding the right column from an almost unmanageable range of products. This paper focuses on columns that enable protein analysis under native conditions through size exclusion, hydrophobic interaction, and ion exchange chromatography. It will highlight the different column characteristics—pore size, particle size, base matrices, column dimensions, ligands—and which questions will help decide which columns to use.