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Δευτέρα 23 Ιανουαρίου 2023

Role of craniofacial phenotypes in the response to oral appliance therapy for obstructive sleep apnea

alexandrossfakianakis shared this article with you from Inoreader

Abstract

Background

Mandibular advancement device (MAD) is a good alternative for patients with obstructive sleep apnea (OSA). However, the treatment response varies among individuals.

Objective

This study aimed to explore the role of craniofacial features in the response to MADs to improve prognostication and patient selection.

Methods

The retrospective trial contained 42 males aged 41.5±9.0 years, and with an apnea-hypopnea index (AHI) of 21.5±13.8 events/h. According to the mandibular plane angle, participants were divided into three groups: low angle (n=13), average angle (n=14), and high angle (n=15). Under monitor of home sleep testing, adjustable MADs were used to titrate the mandible forward from 0 mm with an increment of 0.5 mm every day. The polysomnography outcomes, mandibular protrusion amounts, changes in upper airway MRI measurements, and nasal resistance were compared among three groups.

Results

The normalization rate (AHI < 5 /h) was 92.3%, 57.1%, and 46.7% respectively in the low, average, and high angle groups (p=0.027). The effective protrusion where AHI was reduced by half was 20 (11.3~37.5) %, 31.3 (23.6~50) %, and 50 (36.9~64.9) % of the maximal mandibular protrusion, in the low, average, and high angle groups (p=0.004). Multivariate logistic regression revealed that increased gonion angle (OR=0.878) and baseline AHI(OR=0.868) can reduce the probability of normalization.

Conclusion

The high mandibular plane angle might be an unfavorable factor to MAD treatment and more protrusion was needed to achieve a 50% reduction in AHI. Vertical craniofacial pattern (gonion angle) and baseline AHI constituted the model for predicting the effect of MADs.

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GBMdeconvoluteR accurately infers proportions of neoplastic and immune cell populations from bulk glioblastoma transcriptomics data

alexandrossfakianakis shared this article with you from Inoreader
Abstract
Background
Characterising and quantifying cell types within glioblastoma (GBM) tumours at scale will facilitate a better understanding of the association between the cellular landscape and tumour phenotypes or clinical correlates. We aimed to develop a tool that deconvolutes immune and neoplastic cells within the GBM tumour microenvironment from bulk RNA sequencing data.
Methods
We developed an IDH wild-type (IDHwt) GBM-specific single immune cell reference consisting of B cells, T cells, NK cells, microglia, tumour associated macrophages, monocytes, mast and DC cells. We used this alongside an existing neoplastic single cell-type reference for astrocyte-like, oligodendrocyte- and neuronal-progenitor like and mesenchymal GBM cancer cells to create both marker and gene signature matrix-based deconvolution tools. We applied single-cell resolution imaging mass cytometry (IMC) to ten IDHwt GBM samples, five paired primary and recur rent tumours, to determine which deconvolution approach performed best.
Results
Marker based deconvolution using GBM tissue specific markers was most accurate for both immune cells and cancer cells, so we packaged this approach as GBMdeconvoluteR. We applied GBMdeconvoluteR to bulk GBM RNAseq data from The Cancer Genome Atlas and recapitulated recent findings from multi-omics single cell studies with regards associations between mesenchymal GBM cancer cells and both lymphoid and myeloid cells. Furthermore, we expanded upon this to show that these associations are stronger in patients with worse prognosis.
Conclusions
GBMdeconvoluteR accurately quantifies immune and neoplastic cell proportions in IDHwt GBM bulk RNA sequencing data and is accessible here: https : // gbmdeconvoluter.leeds.ac.uk
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