Peter D. Chang

Alterations of gut microbiota are common in chronic kidney disease (CKD) and contribute to increased uremic toxins including indoxyl sulfate (IS), p-cresyl sulfate (pCS) and trimethylamine N-oxide (TMAO), which are linked to cerebrovascular disease risk. This study examined the kidney–gut–brain axis in CKD mice and in dialysis patients. Male and female mice with adenine-induced CKD were fed a high-amino-acid (HAA) diet to increase precursors of gut-derived uremic toxins. A subgroup of mice received antibiotics in drinking water to suppress gut microbiota and evaluate its role in toxin generation. Behavior tests, gut microbiome composition and brain histology for cerebral microhemorrhages were analyzed. CKD mice had higher serum levels of creatinine, cystatin C and gut-derived toxins, a 2.5-fold increase in brain microhemorrhages, and decreased locomotor activity. The HAA diet significantly increased serum TMAO but not IS and pCS, and all three toxins were reduced by antibiotic therapy. Sex differences were observed; in male animals, higher TMAO was associated with increased brain microhemorrhages, whereas in female mice, pCS was associated with brain microhemorrhage burden. The suppression of toxins with antibiotics improved working memory in male animals. Gut microbiota analysis revealed the expansion of Lactobacillus and Ileibacterium in CKD mice. The HAA diet and antibiotics altered gut microbiota composition without changing alpha diversity. The human study utilized biobanked serum samples and a retrospective review of brain imaging scans in a hemodialysis patient cohort; TMAO levels were associated with increased lobar microbleeds. Our study supports a role for bacterial-derived uremic toxins in the kidney–gut–brain axis and cerebral microhemorrhage formation in CKD.

Background/Objectives: While previous studies have evaluated AI algorithms for cervical spine fracture (CSFx) detection on CT, many have lacked validation on diverse, multinational datasets or have focused primarily on overall case-level classification This study aimed to evaluate the performance of an AI application for acute CSFx detection in case-level classification, fracture localization, and spinal level labeling on multicenter, multinational, and multivendor CT data. Methods: Non-enhanced CTs were retrospectively collected from a U.S. teleradiology company, a French teleradiology company, and a U.S. university hospital. Four radiologists independently labeled the presence and location (including the spinal level) of acute CSFx to establish the reference standard. Per-case diagnostic performance, per-bounding box positive predictive value (PPV) for localization, and overall agreement of cervical vertebral level labeling of the AI were assessed. Results: A total of 155 patients (60.6 years ± 21.2 years, 104 men) with acute CSFx and 173 patients (51.9 years ± 22.7 years, 91 men) without acute CSFx were evaluated. Data were acquired using scanners from five manufacturers. For acute CSFx diagnosis, the AI achieved a per-case sensitivity of 90.3%, a specificity of 91.9%, an accuracy of 91.2%, an area under the receiver operating characteristic curve (AUC) of 0.91, and Matthews correlation coefficient of 0.82. Among 192 bounding boxes representing acute CSFx generated for 154 positive cases by the AI, 162 were true positives (per-bounding box PPV, 84.4%). Of the 186 bounding boxes for which the AI displayed cervical spinal level, 181 were labeled correctly (overall agreement, 97.3%). Conclusions: The AI application for detecting acute CSFx demonstrated high diagnostic performance on multicenter, multinational, and multivendor data, with high performance in fracture localization and spinal level labeling.

To assess the feasibility and accuracy of a deep learning (DL) model to identify acute middle cerebral artery (MCA) occlusion using high resolution non-contrast CT (NCCT) imaging data. In this study, a total of 4,648 consecutive exams (July 2021 to December 2023) were retrospectively used for model training and validation, while an additional 1,011 consecutive exams (January 2024 to August 2024) were used for independent testing. Using high-resolution NCCT acquired at 1.0 mm slice thickness or less, MCA thrombus was labeled using same day CTA as ground-truth. A 3D DL model was trained for per-voxel thrombus segmentation, with the sum of positive voxels used to estimate likelihood of acute MCA occlusion. For detection of MCA M1 segment acute occlusion, the model yielded an AUROC of 0.952 [0.904 -1.00], accuracy of 93.6%[88.1 -98.2], sensitivity of 90.9% [83.1 -100], and specificity of 93.6% [88.0 -98.3]. Inclusion of M2 segment occlusions reduced performance only slightly, yielding an AUROC of 0.884 [0.825 -0.942], accuracy of 93.2% [85.1 -97.2], sensitivity of 77.4% [69.3 92.2], and specificity of 93.6% [85.1 -97.8]. A DL model can detect acute MCA occlusion from high resolution NCCT with accuracy approaching that of CTA. Using this tool, a majority of candidate thrombectomy patients may be identified with NCCT alone, which could aid stroke triage in settings that lack CTA or are otherwise resource constrained. DL＝ deep learning.

While fluorescent labeling has been the standard for visualizing fibers within fibrillar scaffold models of the extracellular matrix (ECM), the use of fluorescent dyes can compromise cell viability and photobleach prematurely. The intricate fibrillar composition of ECM is crucial for its viscoelastic properties, which regulate intracellular signaling and provide structural support for cells. Naturally derived biomaterials such as fibrin and collagen replicate these fibrillar structures, but longitudinal confocal imaging of fibers using fluorescent dyes may impact cell function and photobleach the sample long before termination of the experiment. An alternative technique is reflection confocal microscopy (RCM) that provides high-resolution images of fibers. However, RCM is sensitive to fiber orientation relative to the optical axis, and consequently, many fibers are not detected. We aim to recover these fibers. Here, we propose a deep learning tool for predicting fluorescently labeled optical sections from unlabeled image stacks. Specifically, our model is conditioned to reproduce fluorescent labeling using RCM images at 3 laser wavelengths and a single laser transmission image. The model is implemented using a fully convolutional image-to-image mapping architecture with a hybrid loss function that includes both low-dimensional statistical and high-dimensional structural components. Upon convergence, the proposed method accurately recovers 3-dimensional fibrous architecture without substantial differences in fiber length or fiber count. However, the predicted fibers were slightly wider than original fluorescent labels (0.213 ± 0.009 μm). The model can be implemented on any commercial laser scanning microscope, providing wide use in the study of ECM biology.

Background and Objectives: Acute aortic dissection (AD) is a life-threatening condition in which early detection can significantly improve patient outcomes and survival. This study evaluates the clinical benefits of integrating a deep learning (DL)-based application for the automated detection and prioritization of AD on chest CT angiographies (CTAs) with a focus on the reduction in the scan-to-assessment time (STAT) and interpretation time (IT). Materials and Methods: This retrospective Multi-Reader Multi-Case (MRMC) study compared AD detection with and without artificial intelligence (AI) assistance. The ground truth was established by two U.S. board-certified radiologists, while three additional expert radiologists served as readers. Each reader assessed the same CTAs in two phases: assessment unaided by AI assistance (pre-AI arm) and, after a 1-month washout period, assessment aided by device outputs (post-AI arm). STAT and IT metrics were compared between the two arms. Results: This study included 285 CTAs (95 per reader, per arm) with a mean patient age of 58.5 years ±14.7 (SD), of which 52% were male and 37% had a prevalence of AD. AI assistance significantly reduced the STAT for detecting 33 true positive AD cases from 15.84 min (95% CI: 13.37–18.31 min) without AI to 5.07 min (95% CI: 4.23–5.91 min) with AI, representing a 68% reduction (p < 0.01). The IT also reduced significantly from 21.22 s (95% CI: 19.87–22.58 s) without AI to 14.17 s (95% CI: 13.39–14.95 s) with AI (p < 0.05). Conclusions: The integration of a DL-based algorithm for AD detection on chest CTAs significantly reduces both the STAT and IT. By prioritizing urgent cases, the AI-assisted approach outperforms the standard First-In, First-Out (FIFO) workflow.