11β hydroxysteroid dehydrogenase type 1 transgenic mesenchymal stem cells attenuate inflammation in models of sepsis

Background: Human bone marrow mesenchymal stem cell (MSC) administration reduces inflammation in pre-clinical models of sepsis and sepsis-related lung injury, however clinical efficacy in patients has not yet been demonstrated. We previously showed that Alveolar Macrophage (AM) 11β-hydroxysteroid dehydrogenase type-1 (HSD-1) autocrine signalling is impaired in critically ill sepsis patients, which promotes inflammatory injury. Administration of transgenic MSCs (tMSCs) which overexpress HSD-1 may enhance the anti-inflammatory effects of local glucocorticoids and be more effective at reducing inflammation in sepsis than cellular therapy alone.Methods: MSCs were transfected using a recombinant lentiviral vector containing the HSD-1 and GPF transgenes under the control of a tetracycline promoter. Thin layer chromatography assessed HSD-1 reductase activity in tMSCs. Mesenchymal stem cell phenotype was assessed by flow cytometry and bi-lineage differentiation. HSD-1 tMSCs were co-cultured with LPS-stimulated monocyte-derived macrophages (MDMs) from healthy volunteers prior to assessment of pro-inflammatory cytokine release. HSD-1 tMSCs were administered intravenously to mice undergoing caecal ligation and puncture (CLP).Results: MSCs were transfected with an efficiency of 91.1%, and maintained an MSC phenotype. Functional HSD-1 activity was demonstrated in tMSCs, with predominant reductase cortisol activation (peak 8.23 pM/hour/100,000 cells). HSD-1 tMSC co-culture with LPS-stimulated MDMs suppressed TNFα and IL-6 release. Administration of transgene activated HSD-1 tMSCs in a murine model of CLP attenuated neutrophilic inflammation more effectively than transgene inactive tMSCs (medians 0.403 v 1.36 × 106/ml, p = 0.033).Conclusion: The synergistic impact of HSD-1 transgene expression and MSC therapy attenuated neutrophilic inflammation in a mouse model of peritoneal sepsis more effectively than MSC therapy alone. Future studies investigating the anti-inflammatory capacity of HSD-1 tMSCs in models of sepsis-related direct lung injury and inflammatory diseases are required

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

The Anti-Inflammatory Drug Leflunomide Is an Agonist of the Aryl Hydrocarbon Receptor

The aryl hydrocarbon receptor (AhR) is a ligand-activated transcription factor that mediates the toxicity and biological activity of dioxins and related chemicals. The AhR influences a variety of processes involved in cellular growth and differentiation, and recent studies have suggested that the AhR is a potential target for immune-mediated diseases.During a screen for molecules that activate the AhR, leflunomide, an immunomodulatory drug presently used in the clinic for the treatment of rheumatoid arthritis, was identified as an AhR agonist. We aimed to determine whether any biological activity of leflunomide could be attributed to a previously unappreciated interaction with the AhR. The currently established mechanism of action of leflunomide involves its metabolism to A771726, possibly by cytochrome P450 enzymes, followed by inhibition of de novo pyrimidine biosynthesis by A771726. Our results demonstrate that leflunomide, but not its metabolite A771726, caused nuclear translocation of AhR into the nucleus and increased expression of AhR-responsive reporter genes and endogenous AhR target genes in an AhR-dependent manner. In silico Molecular Docking studies employing AhR ligand binding domain revealed favorable binding energy for leflunomide, but not for A771726. Further, leflunomide, but not A771726, inhibited in vivo epimorphic regeneration in a zebrafish model of tissue regeneration in an AhR-dependent manner. However, suppression of lymphocyte proliferation by leflunomide or A771726 was not dependent on AhR.These data reveal that leflunomide, an anti-inflammatory drug, is an agonist of the AhR. Our findings link AhR activation by leflunomide to inhibition of fin regeneration in zebrafish. Identification of alternative AhR agonists is a critical step in evaluating the AhR as a therapeutic target for the treatment of immune disorders

Non-invasive longitudinal bioluminescence imaging of human mesoangioblasts in bioengineered esophagi

Esophageal engineering aims to create replacement solutions by generating hollow organs using a combination of cells, scaffolds and regeneration-stimulating factors. Currently, the fate of cells on tissue-engineered grafts is generally determined retrospectively by histological analyses. Unfortunately, quality-controlled cell seeding protocols for application in human patients are not standard practice. As such, the field requires simple, fast and reliable techniques for non-invasive, highly specific cell tracking. Here, we show that bioluminescence imaging is a suitable method to track human mesoangioblast seeding of an esophageal tubular construct at every stage of the pre-clinical bioengineering pipeline. In particular, validation of bioluminescence imaging as longitudinal quantitative assessment of cell density, proliferation, seeding efficiency, bioreactor culture and cell survival upon implantation in vivo was performed against standard methods in 2D cultures and in 3D decellularized esophageal scaffolds. The technique is simple, non-invasive and provides information on mesangioblast distribution over entire scaffolds. Bioluminescence is an invaluable tool in the development of complex bioartificial organs and can assist in the development of standardized cell seeding protocols, with the ability to track cells from bioreactor through to implantation

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

The Post-thrombotic Syndrome-Prevention and Treatment: VAS-European Independent Foundation in Angiology/Vascular Medicine Position Paper.

Importance: The post-thrombotic syndrome (PTS) is the most common long-term complication of deep vein thrombosis (DVT), occurring in up to 40-50% of cases. There are limited evidence-based approaches for PTS clinical management. Objective: To provide an expert consensus for PTS diagnosis, prevention, and treatment. Evidence-review: MEDLINE, Cochrane Database review, and GOOGLE SCHOLAR were searched with the terms "post-thrombotic syndrome" and "post-phlebitic syndrome" used in titles and abstracts up to September 2020. Filters were: English, Controlled Clinical Trial / Systematic Review / Meta-Analysis / Guideline. The relevant literature regarding PTS diagnosis, prevention and treatment was reviewed and summarized by the evidence synthesis team. On the basis of this review, a panel of 15 practicing angiology/vascular medicine specialists assessed the appropriateness of several items regarding PTS management on a Likert-9 point scale, according to the RAND/UCLA method, with a two-round modified Delphi method. Findings: The panelists rated the following as appropriate for diagnosis: 1-the Villalta scale; 2- pre-existing venous insufficiency evaluation; 3-assessment 3-6 months after diagnosis of iliofemoral or femoro-popliteal DVT, and afterwards periodically, according to a personalized schedule depending on the presence or absence of clinically relevant PTS. The items rated as appropriate for symptom relief and prevention were: 1- graduated compression stockings (GCS) or elastic bandages for symptomatic relief in acute DVT, either iliofemoral, popliteal or calf; 2-thigh-length GCS (30-40 mmHg at the ankle) after ilio-femoral DVT; 3- knee-length GCS (30-40 mmHg at the ankle) after popliteal DVT; 4-GCS for different length of times according to the severity of periodically assessed PTS; 5-catheter-directed thrombolysis, with or without mechanical thrombectomy, in patients with iliofemoral obstruction, severe symptoms, and low risk of bleeding. The items rated as appropriate for treatment were: 1- thigh-length GCS (30-40 mmHg at the ankle) after iliofemoral DVT; 2-compression therapy for ulcer treatment; 3- exercise training. The role of endovascular treatment (angioplasty and/or stenting) was rated as uncertain, but it could be considered for severe PTS only in case of stenosis or occlusion above the inguinal ligament, followed by oral anticoagulation. Conclusions and relevance: This position paper can help practicing clinicians in PTS management

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

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

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