Chronic rhinosinusitis with nasal polyps in older adults : clinical presentation, pathophysiology, and comorbidity

Purpose of Review Chronic rhinosinusitis and nasal polyps (CRSwNP) is a common condition that significantly affects patients' life. This work aims to provide an up-to-date overview of CRSwNP in older adults, focusing on its aging-related clinical presentations, pathophysiology, and comorbidity associations including asthma. Recent Findings Recent large population-based studies using nasal endoscopy have shown that CRSwNP is a mostly late-onset disease. Age-related changes in physiologic functions, including nasal epithelial barrier dysfunction, may underlie the incidence and different clinical presentations of CRSwNP in older adults. However, there is still a paucity of evidence on the effect of aging on phenotypes and endotypes of CRSwNP. Meanwhile, late-onset asthma is a major comorbid condition in patients with CRSwNP; they frequently present with type 2 inflammatory signatures that are refractory to conventional treatments when they are comorbid. However, as they are more commonly non-atopic, causative factors other than classical atopic sensitization, such as Staphylococcus aureus specific IgE sensitization, are suggested to drive the type 2 inflammation. There are additional comorbidity associations in older patients with CRSwNP, including those with chronic otitis media and head and neck malignancy. Age is a major determinant for the incidence and clinical presentations of CRSwNP. Given the heterogeneity in phenotypes and endotypes, longitudinal investigations are warranted to elucidate the effects of aging on CRSwNP

Alteration of Tremor Dominant and Postural Instability Gait Difficulty Subtypes During the Progression of Parkinson's Disease: Analysis of the PPMI Cohort

Background: Classifying PD into tremor dominant (TD) and postural instability gait difficulty (PIGD) subtypes may have several limitations, such as its diagnostic inconsistency and inability to reflect disease stage. In this study, we investigated the patterns of progression and dopaminergic denervation, by prospective evaluation at regular time intervals.Methods: 325 PD dopamine replacement drug-naïve patients (age 61.2 ± 9.7, M:F = 215:110) were enrolled. Patients were grouped into TD, indeterminant, and PIGD subtypes. Clinical parameters and I-123 FP-CIT SPECT images of each groups were analyzed and compared at baseline, 1, 2, and 4 years of follow up periods.Results: Baseline I-123 FP-CIT uptakes of the striatum were significantly higher in the TD group compared with the indeterminant group and PIGD group (p < 0.01). H & Y stage and MDS-UPDRS part III scores of the indeterminant group were significantly worse at baseline, compared with the TD and PIGD groups (p < 0.001 and p < 0.01, respectively), and MDS-UPDRS part II scores of the indeterminant group were significantly worse than the PIGD group (p < 0.001). There were no other significant differences of age, gender, weight, duration of PD, SCOPA-AUT, MOCA, usage of dopamine agonists, and levodopa equivalent daily doses at baseline. After 4 years of follow up, there were no differences of I-123 FP-CIT uptakes or clinical parameters, except for the MDS-UPDRS part II between the TD and indeterminant group (p < 0.05). The motor-subtypes were reevaluated at the 4 years period, and the proportion of patients grouped to the PIGD subtype increased. In the reevaluated PIGD group, MDS-UPDRS part II score (p < 0.001), SCOPA-AUT (p < 0.001), the proportion of patients who developed levodopa induced dyskinesia were higher than the reevaluated TD group, and the striatal I-123 FP-CIT uptakes were significantly lower (p < 0.01).Conclusion: There are no significant differences of symptoms and dopaminergic innervation between the TD and PIGD group after a certain period of follow up. Significant portion of patients switched from the TD subtype to the PIGD subtype during disease progression, and had a worse clinical prognosis

GaIA: Graphical Information Gain based Attention Network for Weakly Supervised Point Cloud Semantic Segmentation

While point cloud semantic segmentation is a significant task in 3D scene understanding, this task demands a time-consuming process of fully annotating labels. To address this problem, recent studies adopt a weakly supervised learning approach under the sparse annotation. Different from the existing studies, this study aims to reduce the epistemic uncertainty measured by the entropy for a precise semantic segmentation. We propose the graphical information gain based attention network called GaIA, which alleviates the entropy of each point based on the reliable information. The graphical information gain discriminates the reliable point by employing relative entropy between target point and its neighborhoods. We further introduce anchor-based additive angular margin loss, ArcPoint. The ArcPoint optimizes the unlabeled points containing high entropy towards semantically similar classes of the labeled points on hypersphere space. Experimental results on S3DIS and ScanNet-v2 datasets demonstrate our framework outperforms the existing weakly supervised methods. We have released GaIA at https://github.com/Karel911/GaIA.Comment: WACV 2023 accepted pape