Publications by authors named "Marjeta Tusek Jelenc"

The growing threat of antibiotic resistance necessitates accurate differentiation between bacterial and viral infections for proper antibiotic administration. In this study, a Virus vs. Bacteria machine learning model was developed to distinguish between these infection types using 16 routine blood test results, C-reactive protein concentration (CRP), biological sex, and age.

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Aging is associated with changes in muscle energy metabolism. Proton (H) and phosphorous (P) magnetic resonance spectroscopy (MRS) has been successfully applied for non-invasive investigation of skeletal muscle metabolism. The aim of this study was to detect differences in adenosine triphosphate (ATP) production in the aging muscle by P-MRS and to identify potential changes associated with buffer capacity of muscle carnosine by H-MRS.

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Phosphorus ((31) P) MRS, combined with saturation transfer (ST), provides non-invasive insight into muscle energy metabolism. However, even at 7 T, the standard ST method with T1 (app) measured by inversion recovery takes about 10 min, making it impractical for dynamic examinations. An alternative method, i.

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Dynamic (31) P-MRS with sufficiently high temporal resolution enables the non-invasive evaluation of oxidative muscle metabolism through the measurement of phosphocreatine (PCr) recovery after exercise. Recently, single-voxel localized (31) P-MRS was compared with surface coil localization in a dynamic fashion, and was shown to provide higher anatomical and physiological specificity. However, the relatively long TE needed for the single-voxel localization scheme with adiabatic pulses limits the quantification of J-coupled spin systems [e.

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Synopsis of recent research by authors named "Marjeta Tusek Jelenc"

  • - Marjeta Tusek Jelenc's research primarily focuses on the intersection of machine learning and medical imaging techniques, particularly in understanding the biochemical processes and metabolic changes in human muscles as well as differentiating between types of infections based on laboratory data.
  • - In her recent work, she developed a machine learning model that leverages routine blood test values to distinguish between viral and bacterial infections, responding to the critical need for accurate diagnostics amid rising antibiotic resistance.
  • - Additionally, her studies utilizing advanced magnetic resonance spectroscopy (MRS) techniques, particularly at high field strengths (7T), have provided insights into age-related differences in energy metabolism in skeletal muscles, further contributing to the understanding of muscle physiology and potential adaptations related to exercise.