Crohn’s disease (CD) is a chronic inflammatory disorder affecting the gastrointestinal (GI) tract. Although the GI tract is the primary site of involvement, many patients, particularly in pediatric cases, first present with non-intestinal manifestations, including oral lesions. Oral manifestations of CD in children occur in around 50-80% of cases, and about 30% of CD cases in children occur first in the mouth. Recognizing such oral lesions in the pediatric population, and requesting a biopsy, may expedite the diagnosis of CD. We describe a 15 year old male who presented with oral findings of multiple aphthous ulcers and plaques of pink papules of the buccal vestibule. We highlight the initial pathology findings, including non-caseating granulomas, sialadenitis, and a notable plasmacytosis, from biopsy of the left retromolar pad area, which triggered further testing for CD. We provide discussion of how CD was eventually diagnosed and treated and highlight the significance of the pathological findings in this case as they relate to the pathogenesis of CD. Key words:Crohn’s disease, Inflammatory bowel disease, Oral manifestations, Pediatric, Granulomatous inflammation, Monotypic plasma cells.

ObjectiveThe pupillary light reflex (PLR) and the pupillary diameter over time (the PLR curve) is an important biomarker of neurological disease, especially in the diagnosis of traumatic brain injury (TBI). We investigated whether PLR curves generated by a novel smartphone pupillometer application could be easily and accurately interpreted to aid in the diagnosis of TBI.MethodsA total of 120 PLR curves from 42 healthy subjects and six patients with TBI were generated by PupilScreen. Eleven clinician raters, including one group of physicians and one group of neurocritical care nurses, classified 48 randomly selected normal and abnormal PLR curves without prior training or instruction. Rater accuracy, sensitivity, specificity, and interrater reliability were calculated.ResultsClinician raters demonstrated 93% accuracy, 94% sensitivity, 92% specificity, 92% positive predictive value, and 93% negative predictive value in identifying normal and abnormal PLR curves. There was high within-group reliability (k = 0.85) and high interrater reliability (K = 0.75).ConclusionThe PupilScreen smartphone application-based pupillometer produced PLR curves for clinical provider interpretation that led to accurate classification of normal and abnormal PLR data. Interrater reliability was greater than previous studies of manual pupillometry. This technology may be a good alternative to the use of subjective manual penlight pupillometry or digital pupillometry.

Introduction: The pupillary light reflex (PLR) is a well-validated biomarker for neurologic monitoring and a decision-making tool for traumatic brain injury patients. We studied a machine learning-based mobile pupillometry platform in patients with acute ischemic stroke (AIS) with a large-vessel occlusion (LVO) prior to thrombectomy, compared to healthy volunteers. Methods: Pupillometry measurements were conducted with a mobile pupillometry platform (PupilScreen) and a digital infrared pupillometer (NeurOptics) for both pre-thrombectomy AIS subjects and healthy volunteers at an academic medical center. To correct for subject age differences, comparisons used the absolute inter-eye difference in each parameter for each subject by measuring the right:left (R:L) eye ratio absolute distance away from 1. Inter-eye difference means across subjects between AIS and healthy cohorts were analyzed for PLR changes in the presence of acute LVO using a t-test for independent means. Results: Seven AIS patients (4 female, 3 male; 1 Hispanic, 6 Caucasian; mean age 60.9 years) and 32 healthy patients (19 female, 13 male; 1 Hispanic, 2 African American, 3 Native American, 5 Asian, 21 Caucasian; mean age 34.4) were enrolled. All LVOs were of the middle cerebral artery, with 3 on the left and 4 on the right. NPi value was above 3 (briskly reactive) for all subjects. However, the smartphone pupillometer demonstrated a statistically significant (p<0.05) increase in mean inter-eye differences for maximum diameter (mean inter-eye difference of 0.14 vs 0.03), minimum diameter (0.21 vs 0.03), and percent change (0.23 vs 0.05). The mean inter-eye differences were lateralized to the side of the LVO for maximum and minimum diameters (right LVO, max pre vs post R:L 0.95 vs 1.0, min 0.91 vs 0.98) (left LVO, max 0.98 vs 0.96, min 1.15 vs 0.93), and were increased in latency and mean constriction velocity whereas they were decreased in maximum constriction velocity and mean dilation velocity, but this was not statistically significant in this cohort. Conclusions: Patients with AIS from LVO demonstrate inter-eye differences in PLR parameters, and mobile pupillometry could be used as a pre-intervention biomarker of AIS in future study. Further investigation in a larger cohort is necessary.

Objective. Defining a clinician's ability to perceptually identify mass from voice will inform the feasibility, design priorities, and performance standards for tools developed to screen for laryngeal mass from voice. This study defined clinician ability of and examined the impact of expertise on screening for laryngeal mass from voice.Study Design. Task comparison study between experts and nonexperts rating voices for the probability of a laryngeal mass.Setting. Online, remote.Methods. Experts (voice-focused speech-language pathologists and otolaryngologists) and nonexperts (general medicine providers) rated 5-s/i/voice samples (with pathology defined by laryngoscopy) for the probability of laryngeal mass via an online survey. The intraclass correlation coefficient (ICC) estimated interrater and intrarater reliability. Diagnostic performance metrics were calculated. A linear mixed effects model examined the impact of expertise and pathology on ratings.Results. Forty clinicians (21 experts and 19 nonexperts) evaluated 344 voice samples. Experts outperformed nonexperts, with a higher area under the curve (70% vs 61%), sensitivity (49% vs 36%), and specificity (83% vs 77%) (all comparisons p < .05). Interrater reliability was fair for experts and poor for nonexperts (ICC: 0.48 vs 0.34), while intrarater reliability was excellent and good, respectively (ICC: 0.9 and 0.6). The main effects of expertise and underlying pathology were significant in the linear model (p < .001). Conclusion.Clinicians demonstrate inadequate performance screening for laryngeal mass from voice to use auditory perception for dysphonia triage. Experts' superior performance indicates that there is acoustic information in a voice that may be utilized to detect laryngeal mass based on voice.