Research Keyword: hierarchical clustering

Metabolomics Profiling of White Button, Crimini, Portabella, Lion’s Mane, Maitake, Oyster, and Shiitake Mushrooms Using Untargeted Metabolomics and Targeted Amino Acid Analysis

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Researchers analyzed seven popular mushroom varieties to understand their chemical makeup. They found over 10,000 different compounds across all mushrooms, with each variety having its own unique set of chemicals. Lion’s mane and oyster mushrooms were particularly rich in L-ergothioneine, a special amino acid thought to have antioxidant and anti-aging properties. The common white button, crimini, and portabella mushrooms had similar nutrient profiles, while specialty mushrooms had distinct chemical signatures.

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Extraction, Characterization, Biological Properties, and X-Ray Fluorescence Analysis of Functional Polysaccharides Derived from Limnospira platensis

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Spirulina (Limnospira platensis) is a nutrient-dense microalga that contains beneficial polysaccharides with multiple health benefits. Researchers extracted and analyzed these polysaccharides, finding they have strong antioxidant properties and can help regulate blood sugar levels by inhibiting α-glucosidase enzymes. These compounds also support beneficial gut bacteria growth, making spirulina a promising natural ingredient for functional foods and health supplements.

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Headspace Solid-Phase Microextraction Followed by Gas Chromatography-Mass Spectrometry as a Powerful Analytical Tool for the Discrimination of Truffle Species According to Their Volatiles

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This study analyzed the aromatic compounds in two types of Greek truffles to distinguish between them. Researchers used a technique called headspace solid-phase microextraction combined with gas chromatography to identify 45 different volatile compounds. The study found specific aromatic markers that uniquely identify each truffle species, demonstrating that this analytical approach can reliably differentiate between truffle types based on their smell.

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Integrating Machine Learning and Molecular Methods for Trichophyton indotineae Identification and Resistance Profiling Using MALDI-TOF Spectra

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A new type of fungus called Trichophyton indotineae is causing stubborn skin infections that don’t respond well to standard antifungal treatments. Researchers used advanced laboratory techniques combined with computer analysis to better identify this fungus from MALDI-TOF spectra, which is a quick fingerprinting method for microorganisms. The study showed that machine learning could accurately distinguish this problematic fungus from similar species and found specific markers that could help clinics detect it faster, potentially improving patient treatment outcomes.

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