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Sentinel bleeds in head and neck cancer patients present as an ominous symptom often necessitating urgent endovascular embolization. However, this approach can be complicated in patients who have previously undergone head and neck cancer ablation and reconstruction, thus altering the standard arterial vascular supply. Herein we describe an innovative method of internal maxillary artery (IMA) access in a patient with a sentinel bleed who previously underwent proximal external carotid artery (ECA) rerouting for free flap reconstruction. The open retrograde superficial temporal artery approach for IMA embolization is minimally invasive and effective and should be considered for head and neck cancer patients at risk of hemorrhage from distal ECA branches without a proximal ECA embolization option. [ABSTRACT FROM AUTHOR]

Motivation Although single-cell sequencing is becoming more widely available, many tissue samples such as intracranial aneurysms are both fibrous and minute, and therefore not easily dissociated into single cells. To account for the cell type heterogeneity in such tissues therefore requires a computational method. We present a computational deconvolution method, deconvSeq, for sequencing data (RNA and bisulfite) obtained from bulk tissue. This method can also be applied to single-cell RNA sequencing data. Results DeconvSeq utilizes a generalized linear model to model effects of tissue type on feature quantification, which is specific to the data structure of the sequencing type used. Estimated model coefficients can then be used to predict the cell type mixture within a tissue. Predicted cell type mixtures were validated against actual cell counts in whole blood samples. Using this method, we obtained a mean correlation of 0.998 (95% CI 0.995–0.999) from the RNA sequencing data of 35 whole blood samples and 0.95 (95% CI 0.91–0.98) from the reduced representation bisulfite sequencing data from 35 whole blood samples. Using symmetric balances to obtain the correlation between compositional parts, we found that the lowest correlation occurred for monocytes for both RNA and bisulfite sequencing. Comparison with other methods of decomposition such as deconRNAseq, CIBERSORT, MuSiC and EpiDISH showed that deconvSeq is able to achieve good prediction using mean correlation with far fewer genes or CpG sites in the signature set. Availability and implementation Software implementing deconvSeq is available at https://github.com/rosedu1/deconvSeq. Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]