Seegene and Shimadzu Form Partnership for Pathogen Detection
Seegene, Inc. (Rockville, Maryland) and Shimadzu Corporation (Columbia, Maryland) announced that they had formed a strategic partnership to combine Seegene's Seeplex multipathogen tests with Shimadzu's MultiNA high-speed electrophoresis system.
Seegene, Inc. (Rockville, Maryland) and Shimadzu Corporation (Columbia, Maryland) announced that they had formed a strategic partnership to combine Seegene's Seeplex multipathogen tests with Shimadzu's MultiNA high-speed electrophoresis system. The companies will collaborate on integrating the polymerase chain reaction-based tests with the electrophoresis system to provide a highly sensitive, high-throughput detection and analysis system. The microchip electrophoresis system is designed for DNA and RNA nucleic acid size confirmation and quantification, and the tests reportedly are capable of detecting multiple pathogens in a single tube, including STDs, respiratory viruses, human papillomaviruses, sepsis, and pneumonia.
Investigating the Protective Effects of Frankincense Oil on Wound Healing with GC–MS
April 2nd 2025Frankincense essential oil is known for its anti-inflammatory, antioxidant, and therapeutic properties. A recent study investigated the protective effects of the oil in an excision wound model in rats, focusing on oxidative stress reduction, inflammatory cytokine modulation, and caspase-3 regulation; chemical composition of the oil was analyzed using gas chromatography–mass spectrometry (GC–MS).
Evaluating Natural Preservatives for Meat Products with Gas and Liquid Chromatography
April 1st 2025A study in Food Science & Nutrition evaluated the antioxidant and preservative effects of Epilobium angustifolium extract on beef burgers, finding that the extract influenced physicochemical properties, color stability, and lipid oxidation, with higher concentrations showing a prooxidant effect.
Rethinking Chromatography Workflows with AI and Machine Learning
April 1st 2025Interest in applying artificial intelligence (AI) and machine learning (ML) to chromatography is greater than ever. In this article, we discuss data-related barriers to accomplishing this goal and how rethinking chromatography data systems can overcome them.
