Comparative in vitro study of the cleaning efficacy of AirFloss ultra and I-Prox Sulcus brushes in an orthodontic phantom model

Boes, Hanna; Brüstle, Sören; Danesh, Gholamreza; Zimmer, Stefan; Bizhang, Mozhgan

doi:10.1038/s41598-021-81603-y

Cited by 1 publication

(2 citation statements)

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“…Refined data are available in the supplemental tables and figures. The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium http://proteomecentral.proteomexchange.org via the iProx partner repository (Boes et al., 2021 ; Ma et al., 2019 ) with the dataset identifier PXD039096 Additional data and details may be available upon request from the authors.…”

Section: Data Availability Statementmentioning

confidence: 99%

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Molecular distinctions of bronchoalveolar and alveolar organoids under differentiation conditions

Yu,

Chen,

Zheng

et al. 2024

Physiological Reports

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The bronchoalveolar organoid (BAO) model is increasingly acknowledged as an ex‐vivo platform that accurately emulates the structural and functional attributes of proximal airway tissue. The transition from bronchoalveolar progenitor cells to alveolar organoids is a common event during the generation of BAOs. However, there is a pressing need for comprehensive analysis to elucidate the molecular distinctions characterizing the pre‐differentiated and post‐differentiated states within BAO models. This study established a murine BAO model and subsequently triggered its differentiation. Thereafter, a suite of multidimensional analytical procedures was employed, including the morphological recognition and examination of organoids utilizing an established artificial intelligence (AI) image tracking system, quantification of cellular composition, proteomic profiling and immunoblots of selected proteins. Our investigation yielded a detailed evaluation of the morphologic, cellular, and molecular variances demarcating the pre‐ and post‐differentiation phases of the BAO model. We also identified of a potential molecular signature reflective of the observed morphological transformations. The integration of cutting‐edge AI‐driven image analysis with traditional cellular and molecular investigative methods has illuminated key features of this nascent model.

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Section: Data Availability Statementmentioning

confidence: 99%

“…prote omexc hange. org via the iProx partner repository (Boes et al, 2021;Ma et al, 2019) with the dataset identifier PXD039096 Additional data and details may be available upon request from the authors.…”

Section: Author Contributionsmentioning

confidence: 99%