Differential Cathelicidin Expression in Duodenal and Gastric Biopsies from Tanzanian and German Patients

Epithelial surfaces such as the gastrointestinal mucosa depend on expression of antimicrobial peptides like cathelicidin for immune defence against pathogens. The mechanisms behind mucosal cathelicidin regulation are incompletely understood. Cathelicidin expression was analysed in duodenal, antral and corpus/fundic mucosal biopsies from African and German patients. Additionally, cathelicidin expression was correlated with Helicobacter pylori (HP) infection and the inflammatory status of the mucosa. High cathelicidin transcript abundance was detected in duodenal biopsies from African subjects. On the contrary, cathelicidin mRNA expression was either undetectable or very low in tissue specimens from German patients. Also, in the antrum and corpus/fundus regions of the stomach significantly higher cathelicidin transcript levels were measured in Tanzanian compared to German patients. In gastric biopsies from African patients cathelicidin expression was increased in HP positive compared to HP negative subjects. Additionally, the inflammatory status measured by IL-8 expression correlated well with the HP infection status. A higher duodenal and gastric cathelicidin expression in African (compared with European) individuals may be due to upregulation by antigenic stimulation and may confer a higher resistance against enteric infections

Detection of duodenal villous atrophy on endoscopic images using a deep learning algorithm

Background and aims Celiac disease with its endoscopic manifestation of villous atrophy is underdiagnosed worldwide. The application of artificial intelligence (AI) for the macroscopic detection of villous atrophy at routine esophagogastroduodenoscopy may improve diagnostic performance. Methods A dataset of 858 endoscopic images of 182 patients with villous atrophy and 846 images from 323 patients with normal duodenal mucosa was collected and used to train a ResNet 18 deep learning model to detect villous atrophy. An external data set was used to test the algorithm, in addition to six fellows and four board certified gastroenterologists. Fellows could consult the AI algorithm’s result during the test. From their consultation distribution, a stratification of test images into “easy” and “difficult” was performed and used for classified performance measurement. Results External validation of the AI algorithm yielded values of 90 %, 76 %, and 84 % for sensitivity, specificity, and accuracy, respectively. Fellows scored values of 63 %, 72 % and 67 %, while the corresponding values in experts were 72 %, 69 % and 71 %, respectively. AI consultation significantly improved all trainee performance statistics. While fellows and experts showed significantly lower performance for “difficult” images, the performance of the AI algorithm was stable. Conclusion In this study, an AI algorithm outperformed endoscopy fellows and experts in the detection of villous atrophy on endoscopic still images. AI decision support significantly improved the performance of non-expert endoscopists. The stable performance on “difficult” images suggests a further positive add-on effect in challenging cases

Effect of real-time computer-aided polyp detection system (ENDO-AID) on adenoma detection in endoscopists-in-training: a randomized trial

Background The effect of computer-aided polyp detection (CADe) on adenoma detection rate (ADR) among endoscopists-in-training remains unknown. Methods We performed a single-blind, parallel-group, randomized controlled trial in Hong Kong between April 2021 and July 2022 (NCT04838951). Eligible subjects undergoing screening/surveillance/diagnostic colonoscopies were randomized 1:1 to receive colonoscopies with CADe (ENDO-AID(OIP-1), Olympus Co., Japan) or not (control) during withdrawal. Procedures were performed by endoscopists-in-training with <500 procedures and <3 years’ experience. Randomization was stratified by patient age, sex, and endoscopist experience (beginner vs intermediate-level, <200 vs 200-500 procedures). Image enhancement and distal attachment devices were disallowed. Subjects with incomplete colonoscopies or inadequate bowel preparation were excluded. Treatment allocation was blinded to outcome assessors. The primary outcome was ADR. Secondary outcomes were ADR for different adenoma sizes and locations, mean number of adenomas, and non-neoplastic resection rate. Results 386 and 380 subjects were randomized to CADe and control groups, respectively. The overall ADR was significantly higher in CADe than control group (57.5% vs 44.5%, adjusted relative risk 1.41, 95%CI 1.17-1.72, p<0.001). The ADRs for <5mm (40.4% vs 25.0%) and 5-10mm adenomas (36.8% vs 29.2%) were higher in CADe group. The ADRs were higher in CADe group in both right (42.0% vs 30.8%) and left colon (34.5% vs 27.6%), but there was no significant difference in advanced ADR. The ADRs were higher in CADe group among beginners (60.0% vs 41.9%) and intermediate-level endoscopists (56.5% vs 45.5%). Mean number of adenomas (1.48 vs 0.86) and non-neoplastic resection rate were higher in CADe group (52.1% vs 35.0%). Conclusions Among endoscopists-in-training, the use of CADe during colonoscopies was associated with increased overall ADR. (ClinicalTrials.gov: NCT04838951

Vessel and tissue recognition during third-space endoscopy using a deep learning algorithm

In this study, we aimed to develop an artificial intelligence clinical decision support solution to mitigate operator-dependent limitations during complex endoscopic procedures such as endoscopic submucosal dissection and peroral endoscopic myotomy, for example, bleeding and perforation. A DeepLabv3-based model was trained to delineate vessels, tissue structures and instruments on endoscopic still images from such procedures. The mean cross-validated Intersection over Union and Dice Score were 63% and 76%, respectively. Applied to standardised video clips from third-space endoscopic procedures, the algorithm showed a mean vessel detection rate of 85% with a false-positive rate of 0.75/min. These performance statistics suggest a potential clinical benefit for procedure safety, time and also training

MTOR-mediated podocyte hypertrophy regulates glomerular integrity in mice and humans

The cellular origins of glomerulosclerosis involve activation of parietal epithelial cells (PECs) and progressive podocyte depletion. While mammalian target of rapamycin-mediated (mTORmediated) podocyte hypertrophy is recognized as an important signaling pathway in the context of glomerular disease, the role of podocyte hypertrophy as a compensatory mechanism preventing PEC activation and glomerulosclerosis remains poorly understood. In this study, we show that glomerular mTOR and PEC activation-related genes were both upregulated and intercorrelated in biopsies from patients with focal segmental glomerulosclerosis (FSGS) and diabetic nephropathy, suggesting both compensatory and pathological roles. Advanced morphometric analyses in murine and human tissues identified podocyte hypertrophy as a compensatory mechanism aiming to regulate glomerular functional integrity in response to somatic growth, podocyte depletion, and even glomerulosclerosis - all of this in the absence of detectable podocyte regeneration. In mice, pharmacological inhibition of mTOR signaling during acute podocyte loss impaired hypertrophy of remaining podocytes, resulting in unexpected albuminuria, PEC activation, and glomerulosclerosis. Exacerbated and persistent podocyte hypertrophy enabled a vicious cycle of podocyte loss and PEC activation, suggesting a limit to its beneficial effects. In summary, our data highlight a critical protective role of mTOR-mediated podocyte hypertrophy following podocyte loss in order to preserve glomerular integrity, preventing PEC activation and glomerulosclerosis