Using Computer Vision to Improve Endoscopic Disease Quantification in Therapeutic Clinical Trials of Ulcerative Colitis.

BACKGROUND & AIMS: Endoscopic assessment of ulcerative colitis (UC) typically reports only the maximum severity observed. Computer vision methods may better quantify mucosal injury detail, which varies among patients. METHODS: Endoscopic video from the UNIFI clinical trial (A Study to Evaluate the Safety and Efficacy of Ustekinumab Induction and Maintenance Therapy in Participants With Moderately to Severely Active Ulcerative Colitis) comparing ustekinumab and placebo for UC were processed in a computer vision analysis that spatially mapped Mayo Endoscopic Score (MES) to generate the Cumulative Disease Score (CDS). CDS was compared with the MES for differentiating ustekinumab vs placebo treatment response and agreement with symptomatic remission at week 44. Statistical power, effect, and estimated sample sizes for detecting endoscopic differences between treatments were calculated using both CDS and MES measures. Endoscopic video from a separate phase 2 clinical trial replication cohort was performed for validation of CDS performance. RESULTS: Among 748 induction and 348 maintenance patients, CDS was lower in ustekinumab vs placebo users at week 8 (141.9 vs 184.3; P < .0001) and week 44 (78.2 vs 151.5; P < .0001). CDS was correlated with the MES (P < .0001) and all clinical components of the partial Mayo score (P < .0001). Stratification by pretreatment CDS revealed ustekinumab was more effective than placebo (P < .0001) with increasing effect in severe vs mild disease (-85.0 vs -55.4; P < .0001). Compared with the MES, CDS was more sensitive to change, requiring 50% fewer participants to demonstrate endoscopic differences between ustekinumab and placebo (Hedges' g = 0.743 vs 0.460). CDS performance in the JAK-UC replication cohort was similar to UNIFI. CONCLUSIONS: As an automated and quantitative measure of global endoscopic disease severity, the CDS offers artificial intelligence enhancement of traditional MES capability to better evaluate UC in clinical trials and potentially practice.

Learning using privileged information with logistic regression on acute respiratory distress syndrome detection.

The advanced learning paradigm, learning using privileged information (LUPI), leverages information in training that is not present at the time of prediction. In this study, we developed privileged logistic regression (PLR) models under the LUPI paradigm to detect acute respiratory distress syndrome (ARDS), with mechanical ventilation variables or chest x-ray image features employed in the privileged domain and electronic health records in the base domain. In model training, the objective of privileged logistic regression was designed to incorporate data from the privileged domain and encourage knowledge transfer across the privileged and base domains. An asymptotic analysis was also performed, yielding sufficient conditions under which the addition of privileged information increases the rate of convergence in the proposed model. Results for ARDS detection show that PLR models achieve better classification performances than logistic regression models trained solely on the base domain, even when privileged information is partially available. Furthermore, PLR models demonstrate performance on par with or superior to state-of-the-art models under the LUPI paradigm. As the proposed models are effective, easy to interpret, and highly explainable, they are ideal for other clinical applications where privileged information is at least partially available.

COP9 signalosome-mediated deneddylation of CULLIN1 is necessary for SCFEBF1 assembly in Arabidopsis thaliana.

Functions of the SKP1-CUL1-F box (SCF) ubiquitin E3 ligases are essential in plants. The F box proteins (FBPs) are substrate receptors that recruit substrates and assemble an active SCF complex, but the regulatory mechanism underlying the FBPs binding to CUL1 to activate the SCF cycle is not fully understood. We show that Arabidopsis csn1-10 is defective in SCFEBF1-mediated PIF3 degradation during de-etiolation, due to impaired association of EBF1 with CUL1 in csn1-10. EBF1 preferentially associates with un-neddylated CUL1 that is deficient in csn1-10 and the EBF1-CUL1 binding is rescued by the neddylation inhibitor MLN4924. Furthermore, we identify a subset of FBPs with impaired binding to CUL1 in csn1-10, indicating their assembly to form SCF complexes may depend on COP9 signalosome (CSN)-mediated deneddylation of CUL1. This study reports that a key role of CSN-mediated CULLIN deneddylation is to gate the binding of the FBP-substrate module to CUL1, thus initiating the SCF cycle of substrate ubiquitination.

Continuous sepsis trajectory prediction using tensor-reduced physiological signals.

The quick Sequential Organ Failure Assessment (qSOFA) system identifies an individual's risk to progress to poor sepsis-related outcomes using minimal variables. We used Support Vector Machine, Learning Using Concave and Convex Kernels, and Random Forest to predict an increase in qSOFA score using electronic health record (EHR) data, electrocardiograms (ECG), and arterial line signals. We structured physiological signals data in a tensor format and used Canonical Polyadic/Parallel Factors (CP) decomposition for feature reduction. Random Forests trained on ECG data show improved performance after tensor decomposition for predictions in a 6-h time frame (AUROC 0.67 ± 0.06 compared to 0.57 ± 0.08, p = 0.01 ). Adding arterial line features can also improve performance (AUROC 0.69 ± 0.07, p < 0.01 ), and benefit from tensor decomposition (AUROC 0.71 ± 0.07, p = 0.01 ). Adding EHR data features to a tensor-reduced signal model further improves performance (AUROC 0.77 ± 0.06, p < 0.01 ). Despite reduction in performance going from an EHR data-informed model to a tensor-reduced waveform data model, the signals-informed model offers distinct advantages. The first is that predictions can be made on a continuous basis in real-time, and second is that these predictions are not limited by the availability of EHR data. Additionally, structuring the waveform features as a tensor conserves structural and temporal information that would otherwise be lost if the data were presented as flat vectors.

AI-Based Decision Support System for Traumatic Brain Injury: A Survey.

Traumatic brain injury (TBI) is one of the major causes of disability and mortality worldwide. Rapid and precise clinical assessment and decision-making are essential to improve the outcome and the resulting complications. Due to the size and complexity of the data analyzed in TBI cases, computer-aided data processing, analysis, and decision support systems could play an important role. However, developing such systems is challenging due to the heterogeneity of symptoms, varying data quality caused by different spatio-temporal resolutions, and the inherent noise associated with image and signal acquisition. The purpose of this article is to review current advances in developing artificial intelligence-based decision support systems for the diagnosis, severity assessment, and long-term prognosis of TBI complications.

Subcellular trafficking and post-translational modification regulate PIN polarity in plants.

Auxin regulates plant growth and tropism responses. As a phytohormone, auxin is transported between its synthesis sites and action sites. Most natural auxin moves between cells via a polar transport system that is mediated by PIN-FORMED (PIN) auxin exporters. The asymmetrically localized PINs usually determine the directionality of intercellular auxin flow. Different internal cues and external stimuli modulate PIN polar distribution and activity at multiple levels, including transcription, protein stability, subcellular trafficking, and post-translational modification, and thereby regulate auxin-distribution-dependent development. Thus, the different regulation levels of PIN polarity constitute a complex network. For example, the post-translational modification of PINs can affect the subcellular trafficking of PINs. In this review, we focus on subcellular trafficking and post-translational modification of PINs to summarize recent progress in understanding PIN polarity.