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Background: Assessment and selection of donor lungs remain largely subjective and experience based. Criteria to accept or decline lungs are poorly standardized and are not compliant with the current donor pool. Using ex vivo computed tomography (CT) images, we investigated the use of a CT-based machine learning algorithm for screening donor lungs before transplantation., Methods: Clinical measures and ex situ CT scans were collected from 100 cases as part of a prospective clinical trial. Following procurement, donor lungs were inflated, placed on ice according to routine clinical practice, and imaged using a clinical CT scanner before transplantation while stored in the icebox. We trained and tested a supervised machine learning method called dictionary learning, which uses CT scans and learns specific image patterns and features pertaining to each class for a classification task. The results were evaluated with donor and recipient clinical measures., Results: Of the 100 lung pairs donated, 70 were considered acceptable for transplantation (based on standard clinical assessment) before CT screening and were consequently implanted. The remaining 30 pairs were screened but not transplanted. Our machine learning algorithm was able to detect pulmonary abnormalities on the CT scans. Among the patients who received donor lungs, our algorithm identified recipients who had extended stays in the intensive care unit and were at 19 times higher risk of developing chronic lung allograft dysfunction within 2 years posttransplant., Conclusions: We have created a strategy to ex vivo screen donor lungs using a CT-based machine learning algorithm. As the use of suboptimal donor lungs rises, it is important to have in place objective techniques that will assist physicians in accurately screening donor lungs to identify recipients most at risk of posttransplant complications., (Copyright © 2023 International Society for the Heart and Lung Transplantation. Published by Elsevier Inc. All rights reserved.)

Therapeutic resistance remains a major obstacle to preventing progression of H3K27M-altered Diffuse Midline Glioma (DMG). Resistance is driven in part by ALDH-positive cancer stem cells (CSC), with high ALDH1A3 expression observed in H3K27M-mutant DMG biopsies. We hypothesized that ALDH-mediated stemness and resistance may in part be driven by the oncohistone itself. Upon deletion of H3K27M, ALDH1A3 expression decreased dramatically and was accompanied by a gain in astrocytic marker expression and a loss of neurosphere forming potential, indicative of differentiation. Here we show that the oncohistone regulates histone acetylation through ALDH1A3 in a Wnt-dependent manner and that loss of H3K27M expression results in sensitization of DMGs to radiotherapy. The observed elevated Wnt signaling in H3K27M-altered DMG likely stems from a dramatic suppression of mRNA and protein expression of the Wnt inhibitor EYA4 driven by the oncohistone. Thus, our findings identify EYA4 as a bona fide tumor suppressor in DMG that upon suppression, results in aberrant Wnt signaling to orchestrate stemness and differentiation. Future studies will explore whether overexpression of EYA4 in DMG can impede growth and invasion. In summary, we have gained mechanistic insight into H3K27M-mediated regulation of cancer stemness and differentiation, which provides rationale for exploring new therapeutic targets for DMG., Competing Interests: Declaration of Competing Interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: CAL has received consulting fees from Astellas Pharmaceuticals, Odyssey Therapeutics, and T-Knife Therapeutics, and is an inventor on patents pertaining to Kras regulated metabolic pathways, redox control pathways in pancreatic cancer, and targeting the GOT1-pathway as a therapeutic approach (US Patent No: 2015126580-A1, 05/07/2015; US Patent No: 20190136238, 05/09/2019; International Patent No: WO2013177426-A2, 04/23/2015). All other authors declare that no conflicts of interest exist., (Copyright © 2023. Published by Elsevier Inc.)

Early detection of lung cancer is critical for improvement of patient survival. To address the clinical need for efficacious treatments, genetically engineered mouse models (GEMM) have become integral in identifying and evaluating the molecular underpinnings of this complex disease that may be exploited as therapeutic targets. Assessment of GEMM tumor burden on histopathological sections performed by manual inspection is both time consuming and prone to subjective bias. Therefore, an interplay of needs and challenges exists for computer-aided diagnostic tools, for accurate and efficient analysis of these histopathology images. In this paper, we propose a simple machine learning approach called the graph-based sparse principal component analysis (GS-PCA) network, for automated detection of cancerous lesions on histological lung slides stained by hematoxylin and eosin (H&E). Our method comprises four steps: 1) cascaded graph-based sparse PCA, 2) PCA binary hashing, 3) block-wise histograms, and 4) support vector machine (SVM) classification. In our proposed architecture, graph-based sparse PCA is employed to learn the filter banks of the multiple stages of a convolutional network. This is followed by PCA hashing and block histograms for indexing and pooling. The meaningful features extracted from this GS-PCA are then fed to an SVM classifier. We evaluate the performance of the proposed algorithm on H&E slides obtained from an inducible K-ras G12D lung cancer mouse model using precision/recall rates, F β -score, Tanimoto coefficient, and area under the curve (AUC) of the receiver operator characteristic (ROC) and show that our algorithm is efficient and provides improved detection accuracy compared to existing algorithms., Competing Interests: Declaration of Competing Interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Dr. Charles Hatt is employed by and has stock options in Imbio, Inc. Dr. Craig Galban is co-inventor of Parametric Response Mapping, which the University of Michigan has licensed to Imbio, Inc., and has a financial interest in Imbio, Inc., (Copyright © 2023. Published by Elsevier Inc.)

Diffuse midline glioma (DMG) is the leading cause of brain tumor-related deaths in children. DMG typically presents with variable neurologic symptoms between ages 3 and 10. Currently, radiation remains the standard therapy for DMG to halt progression and reduce tumor bulk to minimize symptoms. However, tumors recur in almost 100% of patients and thus, DMG is still considered an incurable cancer with a median survival of 9-12 months. Surgery is generally contraindicated due to the delicate organization of the brainstem, where DMG is located. Despite extensive research efforts, no chemotherapeutic agents, immune therapies, or molecularly targeted therapies have been approved to provide survival benefit. Furthermore, the efficacy of therapies is limited by poor blood-brain barrier penetration and inherent resistance mechanisms of the tumor. However, novel drug delivery approaches, along with recent advances in molecularly targeted therapies and immunotherapies, have advanced to clinical trials and may provide viable future treatment options for DMG patients. This review seeks to evaluate current therapeutics at the preclinical stage and those that have advanced to clinical trials and to discuss the challenges of drug delivery and inherent resistance to these therapies., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023. Published by Elsevier Inc.)

Parametric response mapping (PRM) is a novel computed tomography (CT) technology that has shown potential for assessment of bronchiolitis obliterans syndrome (BOS) after hematopoietic stem cell transplantation (HCT). The primary aim of this study was to evaluate whether variations in image acquisition under real-world conditions affect the PRM measurements of clinically diagnosed BOS. CT scans were obtained retrospectively from 72 HCT recipients with BOS and graft-versus-host disease from Fred Hutchinson Cancer Research Center, Karolinska Institute, and the University of Michigan. Whole lung volumetric scans were performed at inspiration and expiration using site-specific acquisition and reconstruction protocols. PRM and pulmonary function measurements were assessed. Patients with moderately severe BOS at diagnosis (median forced expiratory volume at 1 second [FEV1] 53.5% predicted) had similar characteristics between sites. Variations in site-specific CT acquisition protocols had a negligible effect on the PRM-derived small airways disease (SAD), that is, BOS measurements. PRM-derived SAD was found to correlate with FEV1% predicted and FEV1/ forced vital capacity (R = -0.236, P = .046; and R = -0.689, P < .0001, respectively), which suggests that elevated levels in the PRM measurements are primarily affected by BOS airflow obstruction and not CT scan acquisition parameters. Based on these results, PRM may be applied broadly for post-HCT diagnosis and monitoring of BOS., (© 2020 The American Society of Transplantation and the American Society of Transplant Surgeons.)

Extracellular signal-regulated kinases (ERK) 1 and 2 as well as ERK-5 were previously suggested to phosphorylate connexin-43 and to contribute to the modulation of gap junctional intercellular communication (GJC). Exposure of rat liver epithelial cells to epidermal growth factor (EGF) or the redox cycling and alkylating agent menadione resulted in phosphorylation of connexin-43 and loss in GJC, both of which were abrogated by pharmacological inhibitors of ERK-1/2 activation, if used in concentrations that selectively abrogate phosphorylation of ERK-1/2 but not of ERK-5. Thus, EGF- or menadione-induced loss of GJC is mediated by ERK-1/2 but not ERK-5 in rat liver epithelial cells.