Background Bone erosions are common in allergic fungal rhinosinusitis (AFRS). This study aimed at developing an image-based grading and scoring system for paranasal sinus (PNS) wall erosion in AFRS. Methods A retrospective review of all confirmed AFRS cases based on the Bent and Kuhn criteria was conducted. Preoperative computed tomography (CT) images were studied to detect PNS wall erosion with expansion. Based on our observation, we described a grading system based on the proportion of PNS wall erosion, with 1 if less thanone-third, 2 if between one-third and two-thirds, and 3 if more than two-thirds of the wall is eroded. This method provides a new scoring system ranging from 0 to 72. The inter-observer reliability of this scoring system was tested and the percent of agreement was found to be 90%. Results Among 142 AFRS cases, 82 patients (57.7%) had bone erosion. Orbital extension via lamina papyracea erosion occurred in 28.2% and 17.6% of the anterior and posterior ethmoid sinuses respectively, via floor erosion in 8.3% of the frontal sinuses, and via roof erosion in 2.1% of the maxillary sinuses. Intracranial extension caused by the anterior skull base erosion occurred in 19.4%, 10.9%, and 6% of the posterior ethmoid, anterior ethmoid, and frontal sinuses, respectively. The middle and posterior cranial fossa skull base was eroded in 14.4% and 9.2% of the sphenoid sinuses, respectively. Infratemporal extension occurred via erosion of the sphenoid sinus lateral wall in 17.3% of the sphenoid sinuses and via erosion of the maxillary sinus posterior wall in 6.7% of the maxillary sinuses. The mean of bone erosion score was 9.52, and the highest score was 34/72. Conclusion The orbit is the most common extra-sinus extension site via the lamina papyracea erosion. We propose a new grading and scoring system to assess disease severity and progress.
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http://dx.doi.org/10.7759/cureus.6395 | DOI Listing |
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December 2024
Department of Medical Oncology, Medical University of Sofia, University Hospital "Tsaritsa Yoanna", 1527 Sofia, Bulgaria.
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December 2024
Associate Laboratory for Animal and Veterinary Sciences (AL4AnimalS), Interdisciplinary Center for Research in Animal Health (CIISA), Faculty of Veterinary Medicine, University of Lisbon, 1300-477 Lisbon, Portugal.
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December 2024
Department of Biomedical Engineering, University of Connecticut, Storrs, CT 06269, USA.
The field of emotion recognition from physiological signals is a growing area of research with significant implications for both mental health monitoring and human-computer interaction. This study introduces a novel approach to detecting emotional states based on fractal analysis of electrodermal activity (EDA) signals. We employed detrended fluctuation analysis (DFA), Hurst exponent estimation, and wavelet entropy calculation to extract fractal features from EDA signals obtained from the CASE dataset, which contains physiological recordings and continuous emotion annotations from 30 participants.
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December 2024
Instituto de Estudios de Género, Universidad Carlos III de Madrid, Calle Madrid, 126, 28903 Getafe, Spain.
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December 2024
Department of General Medicine, Shizuoka Saiseikai General Hospital, Shizuoka 422-8527, Japan.
(1) Background: Delirium is a serious condition in patients undergoing treatment for somatic diseases, leading to poor prognosis. However, the pathophysiology of delirium is not fully understood and should be clarified for its adequate treatment. This study analyzed the relationship between confusion symptoms in delirium and resting-state electroencephalogram (EEG) power spectrum (PS) profiles to investigate the heterogeneity.
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