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

alexandrossfakianakis shared this article with you from Inoreader Role of craniofacial phenotypes in the response to oral appliance therapy for obstructive sleep apnea via Journal of Oral Rehabilitation 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. View on Web

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Single-cell transcriptomic analyses provide insights into the cellular origins and drivers of brain metastasis from lung adenocarcinoma

alexandrossfakianakis shared this article with you from Inoreader Single-cell transcriptomic analyses provide insights into the cellular origins and drivers of brain metastasis from lung adenocarcinoma via Neuro-Oncology Advance Access Abstract Background Brain metastasis (BM) is the most common intracranial malignancy causing significant mortality, and lung cancer is the most common origin of BM. However, the cellular origins and drivers of BM from lung adenocarcinoma (LUAD) have yet to be defined. Methods The cellular constitutions were characterized by single-cell transcriptomic profiles of 11 LUAD primary tumor (PT) and 10 BM samples (GSE131907). Copy number variation (CNV) and clonality analysis were applied to illustrate cellular origins of BM tumors. Brain metastasis-associated epithelial cells (BMAECs) were identified by pseudotime trajectory analysis. By using machine-learning algorithms, we developed the BM-index representing the relative abundance of BMAECs in the bulk RNA-seq data, indicating high risk of BM. Therapeutic drugs targeting BMAECs were predicted based on the drug sensitivity data of cancer cell lines. Results Differences in macrophage s and T cells between PTs and BMs were investigated by single-cell RNA (scRNA) and immunohistochemistry and immunofluorescence data. CNV analysis demonstrated BM was derived from subclones of PT with a gain of chromosome 7. We then identified BMAECs and its biomarker, S100A9. Immunofluorescence indicated strong correlations of BMAECs with metastasis and prognosis evaluated by the paired PT and BM samples from Peking Union Medical College Hospital (PUMCH). We further evaluated the clinical significance of BM-index and identified 7 drugs that potentially target BMAECs. Conclusions This study clarified possible cellular origins and drivers of metastatic LUAD at single cell level, and laid a foundation for early detections of LUAD patients with a high risk of BM. View on Web

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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 GBMdeconvoluteR accurately infers proportions of neoplastic and immune cell populations from bulk glioblastoma transcriptomics data via Neuro-Oncology Advance Access 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 View on Web

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