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  • Frontiers Media SA  (2)
  • 1
    In: Frontiers in Medicine, Frontiers Media SA, Vol. 8 ( 2021-8-27)
    Kurzfassung: Background and Study Aims: Deep learning (DL) for video capsule endoscopy (VCE) is an emerging research field. It has shown high accuracy for the detection of Crohn's disease (CD) ulcers. Non-steroidal anti-inflammatory drugs (NSAIDS) are commonly used medications. In the small bowel, NSAIDs may cause a variety of gastrointestinal adverse events including NSAID-induced ulcers. These ulcers are the most important differential diagnosis for small bowel ulcers in patients evaluated for suspected CD. We evaluated a DL network that was trained using CD VCE ulcer images and evaluated its performance for NSAID ulcers. Patients and Methods: The network was trained using CD ulcers and normal mucosa from a large image bank created from VCE of diagnosed CD patients. NSAIDs-induced enteropathy images were extracted from the prospective Bifidobacterium breve (BIf95) trial dataset. All images were acquired from studies performed using PillCam SBIII. The area under the receiver operating curve (AUC) was used as a metric. We compared the network's AUC for detecting NSAID ulcers to that of detecting CD ulcers. Results: Overall, the CD training dataset included 17,640 CE images. The NSAIDs testing dataset included 1,605 CE images. The DL network exhibited an AUC of 0.97 (95% CI 0.97–0.98) for identifying images with NSAID mucosal ulcers. The diagnostic accuracy was similar to that obtained for CD related ulcers (AUC 0.94–0.99). Conclusions: A network trained on VCE CD ulcers similarly identified NSAID findings. As deep learning is transforming gastrointestinal endoscopy, this result should be taken into consideration in the future design and analysis of VCE deep learning applications.
    Materialart: Online-Ressource
    ISSN: 2296-858X
    Sprache: Unbekannt
    Verlag: Frontiers Media SA
    Publikationsdatum: 2021
    ZDB Id: 2775999-4
    Standort Signatur Einschränkungen Verfügbarkeit
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  • 2
    In: Frontiers in Pediatrics, Frontiers Media SA, Vol. 10 ( 2022-8-31)
    Kurzfassung: Anti-TNFα is measurable in infants exposed in utero up to 12 months of age. Data about the exposure effect on the infant’s adaptive immunity are limited. We aimed to prospectively evaluate the distribution and function of T and B cells, in infants of females with inflammatory bowel disease, in utero exposed to anti-TNFα or azathioprine. Methods A prospective multi-center study conducted 2014–2017. Anti-TNFα levels were measured in cord blood, and at 3 and 12 months. T-cell repertoire and function were analyzed at 3 and 12 months by flow-cytometry, expression of diverse T cell receptors (TCR) and T-cell receptor excision circles (TREC) quantification assay. Serum immunoglobulins and antibodies for inactivated vaccines were measured at 12 months. Baseline clinical data were retrieved, and 2-monthly telephonic interviews were performed regarding child infections and growth. Results 24 pregnant females, age 30.6 (IQR 26.5–34.5) years were recruited, 20 with anti-TNFα (infliximab 8, adalimumab 12), and 4 with azathioprine treatment. Cord blood anti-TNFα was higher than maternal blood levels [4.3 (IQR 2.3–9.2) vs. 2.5 (IQR 1.3–9.7) mcg/ml], declining at 3 and 12 months. All infants had normal number of B-cells ( n = 17), adequate levels of immunoglobulins ( n = 14), and protecting antibody levels to Tetanus, Diphtheria, Hemophilus influenza-B and hepatitis B ( n = 17). All had normal CD4+, CD8+ T-cells, and TREC numbers. TCR repertoire was polyclonal in 18/20 and slightly skewed in 2/20 infants. No serious infections requiring hospitalization were recorded. Conclusion We found that T-cell and B-cell immunity is fully mature and immune function is normal in infants exposed in utero to anti-TNFα, as in those exposed to azathioprine. Untreated controls and large-scale studies are needed to confirm these results.
    Materialart: Online-Ressource
    ISSN: 2296-2360
    Sprache: Unbekannt
    Verlag: Frontiers Media SA
    Publikationsdatum: 2022
    ZDB Id: 2711999-3
    Standort Signatur Einschränkungen Verfügbarkeit
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