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Feasibility analysis of non-electrocardiogram-triggered chest low-dose computed tomography using a kV-independent reconstruction algorithm for predicting cardiovascular disease risk in patients receiving maintenance hemodialysis.
BMC Cardiovasc Disord
January 2025
Department of Computed Tomography and Magnetic Resonance, Fourth Hospital of Hebei Medical University, Shijiazhuang, China.
Objectives: This study aimed to evaluate the feasibility and accuracy of non-electrocardiogram (ECG)-triggered chest low-dose computed tomography (LDCT) with a kV-independent reconstruction algorithm in assessing coronary artery calcification (CAC) degree and cardiovascular disease risk in patients receiving maintenance hemodialysis (MHD).
Methods: In total, 181 patients receiving MHD who needed chest CT and coronary artery calcium score (CACS) scannings sequentially underwent non-ECG-triggered, automated tube voltage selection, high-pitch chest LDCT with a kV-independent reconstruction algorithm and ECG-triggered standard CACS scannings. Then, the image quality, radiation doses, Agatston scores (ASs), and cardiac risk classifications of the two scans were compared.
Towards a common terminology of ventilation during cardiopulmonary resuscitation.
Resuscitation
January 2025
Institute for Emergency Medicine, University Hospital Schleswig-Holstein, Arnold-Heller-Straße 3, Haus 808, Kiel, 24105, Schleswig-Holstein, Germany; Department of Anesthesiology and Intensive Care Medicine, Medical University of Graz, Auenbruggerplatz 5, Graz, 8036, Styria, Austria. Electronic address:
Manual and mechanical ventilation during cardiopulmonary resuscitation are critical yet poorly understood components of resuscitation care. In recent years, intra-arrest ventilation has been the subject of a growing number of laboratory and clinical investigations. Essential components to accurately interpret or reproduce original investigations are the exact measurement and transparent reporting of key ventilation parameters, such as volumes and airway pressures obtained during ongoing cardiopulmonary resuscitation.
View Article and Find Full Text PDFEvaluation of Tele-Education in Malawi for Detection of Macular Features Using Optical Coherence Tomography.
Klin Monbl Augenheilkd
January 2025
Department of Ophthalmology, University Hospital Tübingen, Germany.
Purpose: The study aimed to assess the reliability of tele-education in training a Malawian ophthalmology resident to interpret optical coherence tomography (OCT) images of patients with macular conditions.
Methods: This was a retrospective analysis of 1000 macula-centered OCT image series from 1000 eyes of 1000 consecutive patients from Malawi, which involved initial interpretation by a German retina specialist (observer 1) (T). Observer 1 then trained a Malawian resident (observer 2) via email, and observer 2 independently interpreted images at T, followed by face-to-face training in Malawi and reinterpretation at T and T (3-month intervals).
Idempotents and homology of diagram algebras.
Math Ann
August 2024
Mathematical Institute, Utrecht University, Heidelberglaan 8, 3584 CS Utrecht, The Netherlands.
This paper provides a systematization of some recent results in homology of algebras. Our main theorem gives criteria under which the homology of a diagram algebra is isomorphic to the homology of the subalgebra on diagrams having the maximum number of left-to-right connections. From this theorem, we deduce the 'invertible-parameter' cases of the Temperley-Lieb and Brauer results of Boyd-Hepworth and Boyd-Hepworth-Patzt.
View Article and Find Full Text PDFOptimal Machine Learning Models for Developing Prognostic Predictions in Patients With Advanced Cancer.
Cureus
December 2024
Mathematics, Keio University, Yokohama, JPN.
Context: Accurate prognosis prediction for cancer patients in palliative care is critical for clinical decision-making and personalized care. Traditional statistical models have been complemented by machine learning approaches; however, their comparative effectiveness remains underexplored.
Objectives: To assess the prognostic accuracy of statistical and machine learning models in predicting 30-day survival in patients with advanced cancer using objective data, such as the result of the blood test.
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