Deep learning in computed tomography to predict endotype in chronic rhinosinusitis with nasal polyps

Abstract Background As treatment strategies differ according to endotype, rhinologists must accurately determine the endotype in patients affected by chronic rhinosinusitis with nasal polyps (CRSwNP) for the appropriate management. In this study, we aim to construct a novel deep learning model using paranasal sinus computed tomography (CT) to predict the endotype in patients with CRSwNP. Methods We included patients diagnosed with CRSwNP between January 1, 2020, and April 31, 2023. The endotype of patients with CRSwNP in this study was classified as eosinophilic or non-eosinophilic. Sinus CT images (29,993 images) were retrospectively collected, including the axial, coronal, and sagittal planes, and randomly divided into training, validation, and testing sets. A residual network-18 was used to construct the deep learning model based on these images. Loss functions, accuracy functions, confusion matrices, and receiver operating characteristic curves were used to assess the predictive performance of the model. Gradient-weighted class activation mapping was performed to visualize and interpret the operating principles of the model. Results Among 251 included patients, 86 and 165 had eosinophilic or non-eosinophilic CRSwNP, respectively. The median (interquartile range) patient age was 49 years (37–58 years), and 153 (61.0%) were male. The deep learning model showed good discriminative performance in the training and validation sets, with areas under the curves of 0.993 and 0.966, respectively. To confirm the model generalizability, the receiver operating characteristic curve in the testing set showed good discriminative performance, with an area under the curve of 0.963. The Kappa scores of the confusion matrices in the training, validation, and testing sets were 0.985, 0.928, and 0.922, respectively. Finally, the constructed deep learning model was used to predict the endotype of all patients, resulting in an area under the curve of 0.962. Conclusions The deep learning model developed in this study may provide a novel noninvasive method for rhinologists to evaluate endotypes in patients with CRSwNP and help develop precise treatment strategies

Quasi-Steady-State CEST Prediction Based on TCN-LSTM

An important topic in chemical exchange saturation transfer (CEST)-magnetic resonance imaging (MRI) is that certain CEST effects (such as amide proton transfer effects) require sufficiently long saturation time to reach steady state, which makes CEST imaging less practical in clinical application. To address this issue, we develop a deep learning-based model to predict quasi-steady-state (QUASS) CEST from experimentally acquired CEST images with short saturation time. The study described in this paper are outlined as follows: 1) Bloch-McConnell equation is designed to obtain simulated CEST Z-spectra data, in which all possible parameters of the equation were optimized to automatically acquire large amount of training data for reflecting metabolite combinations; 2) tumor-bearing rat model was established on a 7T horizontal diameter small animal MRI scanner, allowing ground-truth generation; 3) by combining the advantages of temporal convolutional network (TCN) and long short-term memory (LSTM) in temporal modelling, a TCN-LSTM model is developed to predict QUASS CEST data. (4) To evaluate the performance of TCN-LSTM, the multilayer perceptron (MLP), recurrent neural network (RNN), LSTM, gated recurrent unit (GRU), BiLSTM and TCN are included in comparison experiment. In terms of absolute error modulus, mutual information (MI), structural similarity (SSIM) and feature similarity (FSIM), the results show that TCN-LSTM provides better prediction results than its counterparts

Epigenetic drug screen identified IOX1 as an inhibitor of Th17-mediated inflammation through targeting TET2

BACKGROUND: Targeting helper T cells, especially Th17 cells, has become a plausible therapy for many autoimmune diseases. METHODS: Using an in vitro culture system, we screened an epigenetics compound library for inhibitors of IFN-γ and IL-17 expression in murine Th1 and Th17 cultures. FINDINGS: This identified IOX1 as an effective suppressor of IL-17 expression in both murine and human CD4(+) T cells. Furthermore, we found that IOX1 suppresses Il17a expression directly by targeting TET2 activity on its promoter in Th17 cells. Using established pre-clinical models of intraocular inflammation, treatment with IOX1 in vivo reduced the migration/infiltration of Th17 cells into the site of inflammation and tissue damage. INTERPRETATION: These results provide evidence of the strong potential for IOX1 as a viable therapy for inflammatory diseases, in particular of the eye. FUNDING: This study was supported by the National Key Research and Development Program of China 2021YFA1101200 (2021YFA1101204) to LW and XW; the National Natural Science Foundation of China 81900844 to XH and 82171041 to LW; the China Postdoctoral Science Foundation 2021M700776 and the Scientific Research Project of Guangdong Provincial Bureau of Traditional Chinese Medicine 20221373 to YZ; and the National Institute for Health Research (NIHR) Biomedical Research Centre at Moorfields Eye Hospital NHS (National Health Service) Foundation Trust and University College London Institute of Ophthalmology, UK (DAC, LPS, PJPL, MS, ADD and RWJL). The views expressed are those of the authors and not necessarily those of the NIHR or the UK's Department of Health and Social Care