Dysregulation of CD4+ and CD8+ resident memory T, myeloid, and stromal cells in steroid-experienced, checkpoint inhibitor colitis.

BACKGROUND: Colitis caused by checkpoint inhibitors (CPI) is frequent and is treated with empiric steroids, but CPI colitis mechanisms in steroid-experienced or refractory disease are unclear. METHODS: Using colon biopsies and blood from predominantly steroid-experienced CPI colitis patients, we performed multiplexed single-cell transcriptomics and proteomics to nominate contributing populations. RESULTS: CPI colitis biopsies showed enrichment of CD4+resident memory (RM) T cells in addition to CD8+ RM and cytotoxic CD8+ T cells. Matching T cell receptor (TCR) clonotypes suggested that both RMs are progenitors that yield cytotoxic effectors. Activated, CD38+ HLA-DR+ CD4+ RM and cytotoxic CD8+ T cells were enriched in steroid-experienced and a validation data set of steroid-naïve CPI colitis, underscoring their pathogenic potential across steroid exposure. Distinct from ulcerative colitis, CPI colitis exhibited perturbed stromal metabolism (NAD+, tryptophan) impacting epithelial survival and inflammation. Endothelial cells in CPI colitis after anti-TNF and anti-cytotoxic T-lymphocyte-associated antigen 4 (anti-CTLA-4) upregulated the integrin α4ß7 ligand molecular vascular addressin cell adhesion molecule 1 (MAdCAM-1), which may preferentially respond to vedolizumab (anti-α4ß7). CONCLUSIONS: These findings nominate CD4+ RM and MAdCAM-1+ endothelial cells for targeting in specific subsets of CPI colitis patients.

Data-driven autonomous operation of VOCs removal system.

Removal of volatile organic compounds (VOCs) from the air has been an important issue in many industrial fields. Traditionally, the operation of VOCs removal systems has relied on fixed operating conditions determined by domain experts based on their expertise and intuition. In practice, this manual operation cannot respond immediately to changes in the system environment. To facilitate the autonomous operation of the system, the operating conditions should be optimized properly in real time to adapt to the changes in the system environment. Recently, optimization frameworks have been widely applied to real-world industrial systems across various domains using different approaches. The primary motivation for this study is the effective implementation of an optimization framework targeting a VOCs removal system. In this paper, we present a data-driven autonomous operation method for optimizing the operating conditions of a VOCs removal system to enhance the overall performance. An optimization problem is formulated with the decision variables denoting the parameters associated with the operating condition, the environmental variables representing the measurements for the system environment, the constraints specifying the control ranges of the parameters, and the objective function representing the system performance as determined by the operating conditions and environment. Using the previous operation data from the system, a neural network is trained to model the system performance as a function of the decision and environmental variables to approximate the objective function. For the current state of the system environment, the optimal operating condition is derived by solving the optimization problem. A case study of a targeted VOCs removal system demonstrates that the proposed method effectively optimizes the operating conditions for improved system performance without intervention from domain experts.