BAP1 and YY1 regulate expression of death receptors in malignant pleural mesothelioma

Malignant pleural mesothelioma (MPM) is a rare, aggressive, and incurable cancer arising from the mesothelial lining of the pleura, with few available treatment options. We recently reported loss of function of the nuclear deubiquitinase BRCA1-associated protein 1 (BAP1), a frequent event in MPM, is associated with sensitivity to tumour necrosis factor-related apoptosis-inducing ligand (TRAIL)-mediated apoptosis. As a potential underlying mechanism, here we report that BAP1 negatively regulates the expression of TRAIL receptors: death receptors 4 (DR4) and 5 (DR5). Using tissue microarrays (TMAs) of tumour samples from MPM patients, we found a strong inverse correlation between BAP1 and TRAIL receptor expression. BAP1 knockdown increased DR4 and DR5 expression, whereas overexpression of BAP1 had the opposite effect. Reporter assays confirmed wild-type BAP1, but not catalytically-inactive mutant BAP1, reduced promoter activities of DR4 and DR5, suggesting deubiquitinase activity is required for the regulation of gene expression. Co-IP studies demonstrated direct binding of BAP1 to the transcription factor Ying Yang 1 (YY1), and ChIP assays revealed BAP1 and YY1 to be enriched in the promoter regions of DR4 and DR5. Knockdown of YY1 also increased DR4 and DR5 expression and sensitivity to TRAIL. These results suggest that BAP1 and YY1 cooperatively repress transcription of TRAIL receptors. Our finding that BAP1 directly regulates the extrinsic apoptotic pathway will provide new insights into the role of BAP1 in the development of MPM and other cancers with frequent BAP1 mutations

Vascular Implications of COVID-19: Role of Radiological Imaging, Artificial Intelligence, and Tissue Characterization: A Special Report

The SARS-CoV-2 virus has caused a pandemic, infecting nearly 80 million people worldwide, with mortality exceeding six million. The average survival span is just 14 days from the time the symptoms become aggressive. The present study delineates the deep-driven vascular damage in the pulmonary, renal, coronary, and carotid vessels due to SARS-CoV-2. This special report addresses an important gap in the literature in understanding (i) the pathophysiology of vascular damage and the role of medical imaging in the visualization of the damage caused by SARS-CoV-2, and (ii) further understanding the severity of COVID-19 using artificial intelligence (AI)-based tissue characterization (TC). PRISMA was used to select 296 studies for AI-based TC. Radiological imaging techniques such as magnetic resonance imaging (MRI), computed tomography (CT), and ultrasound were selected for imaging of the vasculature infected by COVID-19. Four kinds of hypotheses are presented for showing the vascular damage in radiological images due to COVID-19. Three kinds of AI models, namely, machine learning, deep learning, and transfer learning, are used for TC. Further, the study presents recommendations for improving AI-based architectures for vascular studies. We conclude that the process of vascular damage due to COVID-19 has similarities across vessel types, even though it results in multi-organ dysfunction. Although the mortality rate is ~2% of those infected, the long-term effect of COVID-19 needs monitoring to avoid deaths. AI seems to be penetrating the health care industry at warp speed, and we expect to see an emerging role in patient care, reduce the mortality and morbidity rate

Cardiovascular risk assessment in patients with rheumatoid arthritis using carotid ultrasound B-mode imaging

Rheumatoid arthritis (RA) is a systemic chronic inflammatory disease that affects synovial joints and has various extra-articular manifestations, including atherosclerotic cardiovascular disease (CVD). Patients with RA experience a higher risk of CVD, leading to increased morbidity and mortality. Inflammation is a common phenomenon in RA and CVD. The pathophysiological association between these diseases is still not clear, and, thus, the risk assessment and detection of CVD in such patients is of clinical importance. Recently, artificial intelligence (AI) has gained prominence in advancing healthcare and, therefore, may further help to investigate the RA-CVD association. There are three aims of this review: (1) to summarize the three pathophysiological pathways that link RA to CVD; (2) to identify several traditional and carotid ultrasound image-based CVD risk calculators useful for RA patients, and (3) to understand the role of artificial intelligence in CVD risk assessment in RA patients. Our search strategy involves extensively searches in PubMed and Web of Science databases using search terms associated with CVD risk assessment in RA patients. A total of 120 peer-reviewed articles were screened for this review. We conclude that (a) two of the three pathways directly affect the atherosclerotic process, leading to heart injury, (b) carotid ultrasound image-based calculators have shown superior performance compared with conventional calculators, and (c) AI-based technologies in CVD risk assessment in RA patients are aggressively being adapted for routine practice of RA patients

Economics of Artificial Intelligence in Healthcare: Diagnosis vs. Treatment

Motivation: The price of medical treatment continues to rise due to (i) an increasing population; (ii) an aging human growth; (iii) disease prevalence; (iv) a rise in the frequency of patients that utilize health care services; and (v) increase in the price. Objective: Artificial Intelligence (AI) is already well-known for its superiority in various healthcare applications, including the segmentation of lesions in images, speech recognition, smartphone personal assistants, navigation, ride-sharing apps, and many more. Our study is based on two hypotheses: (i) AI offers more economic solutions compared to conventional methods; (ii) AI treatment offers stronger economics compared to AI diagnosis. This novel study aims to evaluate AI technology in the context of healthcare costs, namely in the areas of diagnosis and treatment, and then compare it to the traditional or non-AI-based approaches. Methodology: PRISMA was used to select the best 200 studies for AI in healthcare with a primary focus on cost reduction, especially towards diagnosis and treatment. We defined the diagnosis and treatment architectures, investigated their characteristics, and categorized the roles that AI plays in the diagnostic and therapeutic paradigms. We experimented with various combinations of different assumptions by integrating AI and then comparing it against conventional costs. Lastly, we dwell on three powerful future concepts of AI, namely, pruning, bias, explainability, and regulatory approvals of AI systems. Conclusions: The model shows tremendous cost savings using AI tools in diagnosis and treatment. The economics of AI can be improved by incorporating pruning, reduction in AI bias, explainability, and regulatory approvals. © 2022 by the authors

Engineered human mesenchymal stem cells for neuroblastoma therapeutics

Drug-resistant neuroblastoma remains a major challenge in paediatric oncology and novel and less toxic therapeutic approaches are urgently needed to improve survival and reduce the side effects of traditional therapeutic interventions. Mesenchymal stem cells (MSCs) are an attractive candidate for cell and gene therapy since they are recruited by and able to infiltrate tumours. This feature has been exploited by creating genetically modified MSCs that are able to combat cancer by delivering therapeutic molecules. Whether neuroblastomas attract systemically delivered MSCs is still controversial. We investigated whether MSCs engineered to express tumour necrosis factor-related apoptosis-inducing ligand (TRAIL) could: i) cause death of classic and primary neuroblastoma cell lines in vitro; ii) migrate to tumour sites in vivo; and iii) reduce neuroblastoma growth in xenotransplantation experiments. We observed that classic and primary neuroblastoma cell lines expressing death receptors could be killed by TRAIL-loaded MSCs in vitro. When injected in the peritoneum of neuroblastoma-bearing mice, TRAIL-MSCs migrated to tumour sites, but were unable to change the course of cancer development. These results indicated that MSCs have the potential to be used to deliver drugs in neuroblastoma patients, but more effective biopharmaceuticals should be used instead of TRAIL.SPARK

Structure of the TPR Domain of AIP: Lack of Client Protein Interaction with the C-Terminal alpha-7 Helix of the TPR Domain of AIP Is Sufficient for Pituitary Adenoma Predisposition

PMCID: PMC3534021This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited

Simulation of the thermally induced austenitic phase transition in NiTi nanoparticles

The reverse martensitic ("austenitic") transformation upon heating of equiatomic nickel-titanium nanoparticles with diameters between 4 and 17 nm is analyzed by means of molecular-dynamics simulations with a semi-empirical model potential. After constructing an appropriate order parameter to distinguish locally between the monoclinic B19' at low and the cubic B2 structure at high temperatures, the process of the phase transition is visualized. This shows a heterogeneous nucleation of austenite at the surface of the particles, which propagates to the interior by plane sliding, explaining a difference in austenite start and end temperatures. Their absolute values and dependence on particle diameter are obtained and related to calculations of the surface induced size dependence of the difference in free energy between austenite and martensite.Comment: 6 pages, 4 figures, accepted for publication in "The European Physical Journal B

Multimodality carotid plaque tissue characterization and classification in the artificial intelligence paradigm: a narrative review for stroke application

Cardiovascular disease (CVD) is one of the leading causes of morbidity and mortality in the United States of America and globally. Carotid arterial plaque, a cause and also a marker of such CVD, can be detected by various non-invasive imaging modalities such as magnetic resonance imaging (MRI), computer tomography (CT), and ultrasound (US). Characterization and classification of carotid plaque-type in these imaging modalities, especially into symptomatic and asymptomatic plaque, helps in the planning of carotid endarterectomy or stenting. It can be challenging to characterize plaque components due to (I) partial volume effect in magnetic resonance imaging (MRI) or (II) varying Hausdorff values in plaque regions in CT, and (III) attenuation of echoes reflected by the plaque during US causing acoustic shadowing. Artificial intelligence (AI) methods have become an indispensable part of healthcare and their applications to the non-invasive imaging technologies such as MRI, CT, and the US. In this narrative review, three main types of AI models (machine learning, deep learning, and transfer learning) are analyzed when applied to MRI, CT, and the US. A link between carotid plaque characteristics and the risk of coronary artery disease is presented. With regard to characterization, we review tools and techniques that use AI models to distinguish carotid plaque types based on signal processing and feature strengths. We conclude that AI-based solutions offer an accurate and robust path for tissue characterization and classification for carotid artery plaque imaging in all three imaging modalities. Due to cost, user-friendliness, and clinical effectiveness, AI in the US has dominated the most

Integrated mapping of pharmacokinetics and pharmacodynamics in a patient-derived xenograft model of glioblastoma

Therapeutic options for the treatment of glioblastoma remain inadequate despite concerted research efforts in drug development. Therapeutic failure can result from poor permeability of the blood-brain barrier, heterogeneous drug distribution, and development of resistance. Elucidation of relationships among such parameters could enable the development of predictive models of drug response in patients and inform drug development. Complementary analyses were applied to a glioblastoma patient-derived xenograft model in order to quantitatively map distribution and resulting cellular response to the EGFR inhibitor erlotinib. Mass spectrometry images of erlotinib were registered to histology and magnetic resonance images in order to correlate drug distribution with tumor characteristics. Phosphoproteomics and immunohistochemistry were used to assess protein signaling in response to drug, and integrated with transcriptional response using mRNA sequencing. This comprehensive dataset provides simultaneous insight into pharmacokinetics and pharmacodynamics and indicates that erlotinib delivery to intracranial tumors is insufficient to inhibit EGFR tyrosine kinase signaling.National Institutes of Health (U.S.) (U54 CA210180)MIT/Mayo Physical Sciences Center for Drug Distribution and Drug Efficacy in Brain TumorsDana-Farber Cancer Institute (PLGA Fund)Lundbeck FoundationNovo Nordisk Foundatio