| 1. |
- Dzidic, Majda, et al.
(författare)
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Aberrant IgA responses to the gut microbiota during infancy precede asthma and allergy development
- 2017
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Ingår i: Journal of Allergy and Clinical Immunology. - : MOSBY-ELSEVIER. - 0091-6749 .- 1097-6825. ; 139:3, s. 1017-
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Tidskriftsartikel (refereegranskat)abstract
- Background: Although a reduced gut microbiota diversity and low mucosal total IgA levels in infancy have been associated with allergy development, IgA responses to the gut microbiota have not yet been studied. Objective: We sought to determine the proportions of IgA coating together with the characterization of the dominant bacteria, bound to IgA or not, in infant stool samples in relation to allergy development. Methods: A combination of flow cytometric cell sorting and deep sequencing of the 16S rDNA gene was used to characterize the bacterial recognition patterns by IgA in stool samples collected at 1 and 12 months of age from children staying healthy or having allergic symptoms up to 7 years of age. Results: The children with allergic manifestations, particularly asthma, during childhood had a lower proportion of IgA bound to fecal bacteria at 12months of age compared with healthy children. These alterations cannot be attributed to differences in IgA levels or bacterial load between the 2 groups. Moreover, the bacterial targets of early IgA responses (including coating of the Bacteroides genus), as well as IgA recognition patterns, differed between healthy children and children with allergic manifestations. Altered IgA recognition patterns in children with allergy were observed already at 1 month of age, when the IgA antibodies are predominantly maternally derived in breast-fed children. Conclusion: An aberrant IgAresponsiveness to the gutmicrobiota during infancy precedes asthma and allergy development, possibly indicating an impaired mucosal barrier function in allergic children.
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| 2. |
- Dzidic, Majda, et al.
(författare)
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Allergy development is associated with consumption of breastmilk with a reduced microbial richness in the first month of life
- 2020
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Ingår i: Pediatric Allergy and Immunology. - : WILEY. - 0905-6157 .- 1399-3038. ; 31, s. 250-257
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Tidskriftsartikel (refereegranskat)abstract
- Background Early colonization with a diverse microbiota seems to play a crucial role for appropriate immune maturation during childhood. Breastmilk microbiota is one important source of microbes for the infant, transferred together with maternal IgA antibodies. We previously observed that allergy development during childhood was associated with aberrant IgA responses to the gut microbiota already at 1 month of age, when the IgA antibodies are predominantly maternally derived in breastfed infants. Objective To determine the microbial composition and IgA-coated bacteria in breastmilk in relation to allergy development in children participating in an intervention trial with pre- and post-natal Lactobacillus reuteri supplementation. Methods A combination of flow cytometric cell sorting and 16S rRNA gene sequencing was used to characterize the bacterial recognition patterns by IgA in breastmilk samples collected one month post-partum from 40 mothers whose children did or did not develop allergic and asthmatic symptoms during the first 7 years of age. Results The milk fed to children developing allergic manifestations had significantly lower bacterial richness, when compared to the milk given to children that remained healthy. Probiotic treatment influenced the breastmilk microbiota composition. However, the proportions of IgA-coated bacteria, the total bacterial load and the patterns of IgA-coating were similar in breastmilk between mothers of healthy children and those developing allergies. Conclusion Consumption of breastmilk with a reduced microbial richness in the first month of life may play an important role in allergy development during childhood.
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| 3. |
- Dzidic, Majda, et al.
(författare)
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Oral microbiome development during childhood: an ecological succession influenced by postnatal factors and associated with tooth decay
- 2018
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Ingår i: The ISME Journal. - : NATURE PUBLISHING GROUP. - 1751-7362 .- 1751-7370. ; 12:9, s. 2292-2306
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Tidskriftsartikel (refereegranskat)abstract
- Information on how the oral microbiome develops during early childhood and how external factors influence this ecological process is scarce. We used high-throughput sequencing to characterize bacterial composition in saliva samples collected at 3, 6, 12, 24 months and 7 years of age in 90 longitudinally followed children, for whom clinical, dietary and health data were collected. Bacterial composition patterns changed through time, starting with "early colonizers", including Streptococcus and Veillonella; other bacterial genera such as Neisseria settled after 1 or 2 years of age. Dental caries development was associated with diverging microbial composition through time. Streptococcus cristatus appeared to be associated with increased risk of developing tooth decay and its role as potential biomarker of the disease should be studied with species-specific probes. Infants born by C-section had initially skewed bacterial content compared with vaginally delivered infants, but this was recovered with age. Shorter breastfeeding habits and antibiotic treatment during the first 2 years of age were associated with a distinct bacterial composition at later age. The findings presented describe oral microbiota development as an ecological succession where altered colonization pattern during the first year of life may have long-term consequences for childs oral and systemic health.
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| 4. |
- Ravikumar, Sadhana, et al.
(författare)
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Improved Segmentation of Deep Sulci in Cortical Gray Matter Using a Deep Learning Framework Incorporating Laplace’s Equation
- 2023
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Ingår i: Information Processing in Medical Imaging - 28th International Conference, IPMI 2023, Proceedings. - 0302-9743 .- 1611-3349. - 9783031340475 ; 13939 LNCS, s. 692-704
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Konferensbidrag (refereegranskat)abstract
- When developing tools for automated cortical segmentation, the ability to produce topologically correct segmentations is important in order to compute geometrically valid morphometry measures. In practice, accurate cortical segmentation is challenged by image artifacts and the highly convoluted anatomy of the cortex itself. To address this, we propose a novel deep learning-based cortical segmentation method in which prior knowledge about the geometry of the cortex is incorporated into the network during the training process. We design a loss function which uses the theory of Laplace’s equation applied to the cortex to locally penalize unresolved boundaries between tightly folded sulci. Using an ex vivo MRI dataset of human medial temporal lobe specimens, we demonstrate that our approach outperforms baseline segmentation networks, both quantitatively and qualitatively.
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