Background Elderly-onset inflammatory bowel disease (IBD), defined as age ≥60 at diagnosis, is increasing worldwide. We aimed to compare clinical characteristics and natural history of elderly-onset IBD patients to adult-onset IBD patients. Methods Patients with a confirmed diagnosis of IBD from 1981 to 2016 were identified from a territory-wide Hong Kong IBD registry involving 13 hospitals. Demographics, comorbidities, clinical features and outcomes of elderly-onset IBD patients were compared to adult-onset IBD patients. Results A total of 2413 patients were identified, of whom 270 (11.2%) had elderly-onset IBD. Median follow-up duration was 111 months (Interquartile range [IQR]: 68-165 months). Ratio of ulcerative colitis (UC): Crohn’s disease (CD) was higher in elderly-onset IBD than adult-onset IBD patients (3.82:1 vs. 1.39:1; p&0.001). Elderly-onset CD had less perianal involvement (5.4% vs. 25.4%; p&.001) than adult-onset. Elderly-onset IBD patients had significantly lower cumulative use of immunomodulators (p=0.001) and biologics (p=0.04). Elderly-onset IBD was associated with higher risks of cytomegalovirus colitis (Odds ratio [OR]: 3.07; 95% Confidence Interval (CI) 1.92-4.89; p&0.001); herpes zoster (OR: 2.42; 95% CI: 1.22-4.80; p=0.12) and all cancer development (Hazard ratio: 2.97; 95% CI: 1.84-4.79; p&0.001). They also had increased number of overall hospitalization (OR: 1.14; 95% CI 1.09-1.20; p&0.001), infections-related hospitalization (OR: 1.87; 95% CI 1.47-2.38; p&0.001) and IBD-related hospitalization (OR: 1.09; 95% CI: 1.04- 1.15; p=0.001) compared to adult-onset IBD. Conclusion Elderly-onset IBD patients were associated with increased risk of infections, cancer development and increased infections- and IBD-related hospitalizations. Specific therapeutic strategies to target this special population is needed.

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In this paper, we address the "dual problem" of multi-view scene reconstruction in which we utilize single-view images captured under different point lights to learn a neural scene representation. Different from existing single-view methods which can only recover a 2.5D scene representation (i.e., a normal / depth map for the visible surface), our method learns a neural reflectance field to represent the 3D geometry and BRDFs of a scene. Instead of relying on multi-view photoconsistency, our method exploits two information-rich monocular cues, namely shading and shadow, to infer scene geometry. Experiments on multiple challenging datasets show that our method is capable of recovering 3D geometry, including both visible and invisible parts, of a scene from single-view images. Thanks to the neural reflectance field representation, our method is robust to depth discontinuities. It supports applications like novel-view synthesis and relighting. Our code and model can be found at https://ywq.github.io/s3nerf.