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Biorefinery-inspired, two-step valorization strategy to manage plant-based recalcitrant organic waste, involving solvent extraction, and fermentation with -a proof of concept study.
Front Microbiol
January 2025
School of Biotechnology, Amrita Vishwa Vidyapeetham, Kollam, India.
Approximately 40-50% of municipal solid waste is organic and causing biogenic malodor and infections, due to inefficient treatment methods. Biorefinery-based bioremediation and valorization is in vogue against these conventional strategies since it combines unit operations for better efficiency and productivity. Deriving inspiration, the proposed strategy puts together a unique and compatible combination of processes.
View Article and Find Full Text PDFAllostatic load and psychological characteristics of transgender individuals. Preliminary case-control findings.
Psychoneuroendocrinology
December 2024
First Department of Psychiatry, Eginition Hospital, National and Kapodistrian University of Athens, 72-74 Vas. Sophias Ave, Athens 11528, Greece.
Despite considerable social progress, transgender people still face multiple chronic stressors. This study evaluated the allostatic load index and assessed specific psychological characteristics between transgender and cisgender participants who were matched based on age and assigned-at-birth gender. Twenty-one transgender people (52.
View Article and Find Full Text PDFRanking Surgeon Performance After ACL Reconstruction Using the Knee Injury and Osteoarthritis Outcome Score (KOOS) Subscales.
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Center for Effectiveness Research in Orthopaedics, Greenville, South Carolina, USA.
Background: Patient-reported outcomes (PROs) are considered the gold standard for evaluating value-based care in orthopaedics. However, there is little evidence to guide implementation of PROs for surgeon performance evaluation.
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Digital Home-Based Self-Monitoring System for People with Heart Failure: Protocol for Development of SmartHeart and Evaluation of Feasibility and Acceptability.
JMIR Res Protoc
January 2025
School of Exercise and Nutrition Sciences, Institute for Physical Activity and Nutrition, Deakin University, Burwood, Australia.
Background: Heart failure (HF) is a chronic, progressive condition where the heart cannot pump enough blood to meet the body's needs. In addition to the daily challenges that HF poses, acute exacerbations can lead to costly hospitalizations and increased mortality. High health care costs and the burden of HF have led to the emerging application of new technologies to support people living with HF to stay well while living in the community.
View Article and Find Full Text PDFUse of AI in Cardiac CT and MRI: A Scientific Statement from the ESCR, EuSoMII, NASCI, SCCT, SCMR, SIIM, and RSNA.
Radiology
January 2025
From the Department of Radiology, University of Washington, UW Medical Center-Montlake, Seattle, Wash (D.M.); Department of Radiology, OncoRad/Tumor Imaging Metrics Core (TIMC), University of Washington, Seattle, Wash (D.M.); Department of Radiology and Imaging Sciences, Emory University, Atlanta, Ga (M.v.A.); Department of Radiology and Nuclear Medicine, Radboud University Medical Center, Nijmegen, the Netherlands (M.H.); Department of Radiology, Mayo Clinic, Rochester, Minn (T.L., E.E.W.); Departments of Cardiology and Radiology, Royal Brompton Hospital, London, United Kingdom (E.D.N.); School of Biomedical Engineering and Imaging Sciences, King's College, London, United Kingdom (E.D.N.); Department of Radiology, Northwestern University Feinberg School of Medicine, Chicago, Ill (B.D.A.); Department of Radiology, University of Cagliari, Cagliari, Italy (L.S.); Department of Radiology, University of Groningen, University Medical Center Groningen, Hanzeplein 1 Postbus 30 001, 9700 RB Groningen, the Netherlands (R.V.); Department of Medical Imaging, University Medical Imaging Toronto, University of Toronto, Toronto, Ontario, Canada (K.H.); and Toronto General Hospital Research Institute, University Health Network, University of Toronto, Toronto, Ontario, Canada (K.H.).
Artificial intelligence (AI) offers promising solutions for many steps of the cardiac imaging workflow, from patient and test selection through image acquisition, reconstruction, and interpretation, extending to prognostication and reporting. Despite the development of many cardiac imaging AI algorithms, AI tools are at various stages of development and face challenges for clinical implementation. This scientific statement, endorsed by several societies in the field, provides an overview of the current landscape and challenges of AI applications in cardiac CT and MRI.
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