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Sökning: WFRF:(Luca Stijn)

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1.
  • Sartelli, Massimo, et al. (författare)
  • Ten golden rules for optimal antibiotic use in hospital settings: the WARNING call to action
  • 2023
  • Ingår i: WORLD JOURNAL OF EMERGENCY SURGERY. - 1749-7922. ; 18:1
  • Forskningsöversikt (refereegranskat)abstract
    • Antibiotics are recognized widely for their benefits when used appropriately. However, they are often used inappropriately despite the importance of responsible use within good clinical practice. Effective antibiotic treatment is an essential component of universal healthcare, and it is a global responsibility to ensure appropriate use. Currently, pharmaceutical companies have little incentive to develop new antibiotics due to scientific, regulatory, and financial barriers, further emphasizing the importance of appropriate antibiotic use. To address this issue, the Global Alliance for Infections in Surgery established an international multidisciplinary task force of 295 experts from 115 countries with different backgrounds. The task force developed a position statement called WARNING (Worldwide Antimicrobial Resistance National/International Network Group) aimed at raising awareness of antimicrobial resistance and improving antibiotic prescribing practices worldwide. The statement outlined is 10 axioms, or "golden rules," for the appropriate use of antibiotics that all healthcare workers should consistently adhere in clinical practice.
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2.
  • Getachew, Merkebu, et al. (författare)
  • Within and among farm variability of coffee quality of smallholders in southwest Ethiopia
  • 2023
  • Ingår i: Agroforestry Systems. - : Springer Science and Business Media LLC. - 0167-4366 .- 1572-9680. ; 97:5, s. 883-905
  • Tidskriftsartikel (refereegranskat)abstract
    • The biophysical drivers that affect coffee quality vary within and among farms. Quantifying their relative importance is crucial for making informed decisions concerning farm management, marketability and profit for coffee farmers. The present study was designed to quantify the relative importance of biophysical variables affecting coffee bean quality within and among coffee farms and to evaluate a near infrared spectroscopy-based model to predict coffee quality. Twelve coffee plants growing under low, intermediate and dense shade were studied in twelve coffee farms across an elevational gradient (1470–2325 m asl) in Ethiopia. We found large within farm variability, demonstrating that conditions varying at the coffee plant-level are of large importance for physical attributes and cupping scores of green coffee beans. Overall, elevation appeared to be the key biophysical variable influencing all the measured coffee bean quality attributes at the farm level while canopy cover appeared to be the most important biophysical variable driving the above-mentioned coffee bean quality attributes at the coffee plant level. The biophysical variables driving coffee quality (total preliminary and specialty quality) were the same as those driving variations in the near-infrared spectroscopy data, which supports future use of this technology to assess green bean coffee quality. Most importantly, our findings show that random forest is computationally fast and robust to noise, besides having comparable prediction accuracy. Hence, it is a useful machine learning tool for regression studies and has potential for modeling linear and nonlinear multivariate calibrations. The study also confirmed that near-infrared spectroscopic-based predictions can be applied as a supplementary approach for coffee cup quality evaluations.
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