Associations of inflammatory cytokines with inflammatory bowel disease: a Mendelian randomization study.

Objectives: Previous studies have confirmed a link between specific inflammatory cytokines and inflammatory bowel disease (IBD), but the causal relationship between them is not completely clear. This Mendelian Randomization (MR) study aims to evaluate the causal relationship between 18 inflammatory cytokines and inflammatory bowel disease. Method: Two-sample Mendelian randomization utilized genetic variances associated with IBD from two extensive publicly available genome-wide association studies (GWAS) (Crohn's Disease (CD): 12,194 cases and 28,072 controls; Ulcerative Colitis (UC): 12,336 cases and 33,609 controls). The data of inflammatory cytokines was acquired from a GWAS including 8,293 healthy participants. We used inverse variance weighted method, MR-Egger, weighted median, simple model and weighted model to evaluate the causal relationship between inflammatory cytokines and IBD. Sensitivity analysis includes heterogeneity and pleiotropy analysis to evaluate the robustness of the results. Results: The findings indicated suggestive positive associations between Interleukin-13 (IL-13) and macrophage migration inhibitory factor (MIF) with CD (odds ratio, OR: 1.101, 95%CI: 1.021-1.188, p = 0.013; OR: 1.134, 95%CI: 1.024-1.255, p = 0.015). IL-13 also displayed a significant positive correlation with UC (OR: 1.099, 95%CI: 1.018-1.186, p = 0.016). Stem cell factor (SCF) was suggested to be associated with the development of both CD and UC (OR: 1.032, 95%CI: 0.973-1.058, p = 0.012; OR: 1.038, 95%CI: 1.005-1.072, p = 0.024). Conclusion: This study proposes that IL-13 may be a factor correlated with the etiology of IBD (CD and UC), while MIF just be specifically associated with CD. Additionally, SCF appears more likely to be involved in the downstream development of IBD (CD and UC).

Design of a Sleep Modulation System with FPGA-Accelerated Deep Learning for Closed-loop Stage-Specific In-Phase Auditory Stimulation.

Closed-loop sleep modulation is an emerging research paradigm to treat sleep disorders and enhance sleep benefits. However, two major barriers hinder the widespread application of this research paradigm. First, subjects often need to be wire-connected to rack-mount instrumentation for data acquisition, which negatively affects sleep quality. Second, conventional real-time sleep stage classification algorithms give limited performance. In this work, we conquer these two limitations by developing a sleep modulation system that supports closed-loop operations on the device. Sleep stage classification is performed using a lightweight deep learning (DL) model accelerated by a low-power field-programmable gate array (FPGA) device. The DL model uses a single channel electroencephalogram (EEG) as input. Two convolutional neural networks (CNNs) are used to capture general and detailed features, and a bidirectional long-short-term memory (LSTM) network is used to capture time-variant sequence features. An 8-bit quantization is used to reduce the computational cost without compromising performance. The DL model has been validated using a public sleep database containing 81 subjects, achieving a state-of-the-art classification accuracy of 85.8% and a F1-score of 79%. The developed model has also shown the potential to be generalized to different channels and input data lengths. Closed-loop in-phase auditory stimulation has been demonstrated on the test bench.