Background: The presence of CAR in diverse tumor types is heterogeneous with implications in tumor transduction efficiency in the context of adenoviral mediated cancer gene therapy. Preliminary studies suggest that CAR transcriptional regulation is modulated through histone acetylation and not through promoter methylation. Furthermore, it has been documented that the pharmacological induction of CAR using histone deacetylase inhibitor (iHDAC) compounds is a viable strategy to enhance adenoviral mediated gene delivery to cancer cells in vitro. The incorporation of HDAC drugs into the overall scheme in adenoviral based cancer gene therapy clinical trials seems rational. However, reports using compounds with iHDAC properties utilized routinely in the clinic are pending. Valproic acid, a short chained fatty acid extensively used in the clinic for the treatment of epilepsy and bipolar disorder has been recently described as an effective HDAC inhibitor at therapeutic concentrations.
The key component of stroke diagnosis is the localization and delineation of brain lesions, especially from MRI studies. Nonetheless, this manual delineation is time-consuming and biased by expert opinion. The main purpose of this study is to introduce an autoencoder architecture that effectively integrates cross-attention mechanisms, together with hierarchical deep supervision to delineate lesions under scenarios of remarked unbalance tissue classes, challenging geometry of the shape, and a variable textural representation. This work introduces a cross-attention deep autoencoder that focuses on the lesion shape through a set of convolutional saliency maps, forcing skip connections to preserve the morphology of affected tissue. Moreover, a deep supervision training scheme was herein adapted to induce the learning of hierarchical lesion details. Besides, a special weighted loss function remarks lesion tissue, alleviating the negative impact of class imbalance. The proposed approach was validated on the public ISLES2017 dataset outperforming state-of-the-art results, achieving a dice score of $0.36$ and a precision of $0.42$. Deeply supervised cross-attention autoencoders, trained to pay more attention to lesion tissue, are better at estimating ischemic lesions in MRI studies. The best architectural configuration was achieved by integrating ADC, TTP and Tmax sequences. The contribution of deeply supervised cross-attention autoencoders allows better support the discrimination between healthy and lesion regions, which in consequence results in favorable prognosis and follow-up of patients.
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