Automatic removal of soft tissue from 3D dental photo scans; an important step in automating future forensic odontology identification.

The potential of intraoral 3D photo scans in forensic odontology identification remains largely unexplored, even though the high degree of detail could allow automated comparison of ante mortem and post mortem dentitions. Differences in soft tissue conditions between ante- and post mortem intraoral 3D photo scans may cause ambiguous variation, burdening the potential automation of the matching process and underlining the need for limiting inclusion of soft tissue in dental comparison. The soft tissue removal must be able to handle dental arches with missing teeth, and intraoral 3D photo scans not originating from plaster models. To address these challenges, we have developed the grid-cutting method. The method is customisable, allowing fine-grained analysis using a small grid size and adaptation of how much of the soft tissues are excluded from the cropped dental scan. When tested on 66 dental scans, the grid-cutting method was able to limit the amount of soft tissue without removing any teeth in 63/66 dental scans. The remaining 3 dental scans had partly erupted third molars (wisdom teeth) which were removed by the grid-cutting method. Overall, the grid-cutting method represents an important step towards automating the matching process in forensic odontology identification using intraoral 3D photo scans.

The Composition of the Fecal and Mucosa-adherent Microbiota Varies Based on Age and Disease Activity in Ulcerative Colitis.

BACKGROUND: Pediatric-onset ulcerative colitis (pUC) represents a more aggressive disease phenotype compared with adult-onset UC. We hypothesized that this difference can, in part, be explained by the composition of the microbiota. METHODS: In a prospective, longitudinal study, we included pediatric (N = 30) and adult (N = 30) patients with newly or previously (>1 year) diagnosed UC. We analyzed the microbiota composition in the mucosa-adherent microbiota at baseline, using 16S rRNA gene sequencing, and the fecal microbiota at baseline and at 3-month intervals, using shotgun metagenomics. RESULTS: For fecal samples, the bacterial composition differed between pUC and aUC in newly diagnosed patients (ß-diversity, Bray Curtis: R2 = 0.08, P = .02). In colon biopsies, microbial diversity was higher in aUC compared with pUC (α-diversity, Shannon: estimated difference 0.54, P = .006). In the mucosa-adherent microbiota, Alistipes finegoldii was negatively associated with disease activity in pUC while being positively associated in aUC (estimate: -0.255 and 0.098, P = .003 and P = .02 in pUC and aUC, respectively). Finally, we showed reduced stability of the fecal microbiota in pediatric patients, evidenced by a different composition of the fecal microbiota in newly and previously diagnosed pUC, a pattern not found in adults. CONCLUSIONS: Our results indicate that pediatric UC patients have a more unstable fecal microbiota and a lower α diversity than adult patients and that the microbiota composition differs between aUC and pUC patients. These findings offer some explanation for the observed differences between pUC and aUC and indicate that individualized approaches are needed if microbiota modifications are to be used in the future treatment of UC.

In a prospective study, we found substantial differences in the mucosa-adherent and fecal microbiota between patients with pediatric and adult-onset ulcerative colitis. These differences offer some explanation for the severe phenotype reported in pediatric-onset ulcerative colitis.