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POCARED Announces Real Time Identification of Bacterial Species and Antibiotic Resistance Markers Via Intrinsic Fluorescence

POCARED Diagnostics, a leading in-vitro diagnostic and pre-analytical technologies manufacturer announces a breakthrough in real time identification of bacterial species and antibiotic resistance markers utilizing its proprietary technology in the P 1000. POCARED's P 1000™ is a rapid automated platform that employs intrinsic fluorescence; optical data analysis and artificial intelligence to analyze multi-dimensional optical characteristics of microorganisms.

POCARED's P-1000 has successfully identified 5 antimicrobial resistance markers: KPC, NDM, vanB, mecA and OXA.

Performance data will be presented at the  ASM 2015  General Meeting, May 30 to June 2 in New Orleans, LA.

"Rapidly identifying bacterial species and their antimicrobial resistance markers provides the laboratory, clinicians and epidemiologists significant advances that assist in the care and management of the ill patient. POCARED is pleased to lead in the antibiotic stewardship efforts in the Healthcare setting," says Jonathan Gurfinkel, President and CEO of the company.

Increasing antimicrobial resistance is a global problem with surveillance of resistance a priority. The global market for antimicrobial resistance testing is greater than $500 million and is growing 5 to 7% annually. POCARED continues to develop solutions for today's Healthcare issues. The P 1000 is revolutionizing the detection, identification and enumeration of bacteria and yeast together with their antimicrobial resistance markers in a few minutes directly from culture.  It is a fully automated, reagent-free, real-time and easy to operate platform.

POCARED's P-1000™ is an automated rapid system that employs intrinsic fluorescence, optical data analysis and artificial intelligence methods to analyze multi-dimensional optical characteristics of microorganisms. It captures the emitted light from the interaction between photons and molecules to detect the pathogens' unique optical properties and subsequently an algorithm determines results.

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