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Disease-modifying effects of natural Δ9-tetrahydrocannabinol in endometriosis-associated pain

  1. Alejandra Escudero-Lara
  2. Josep Argerich
  3. David Cabañero  Is a corresponding author
  4. Rafael Maldonado  Is a corresponding author
  1. Universitat Pompeu Fabra, Spain
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Cite this article as: eLife 2020;9:e50356 doi: 10.7554/eLife.50356

Abstract

Endometriosis is a chronic painful disease highly prevalent in women that is defined by growth of endometrial tissue outside the uterine cavity and lacks adequate treatment. Medical use of cannabis derivatives is a current hot topic and it is unknown whether phytocannabinoids may modify endometriosis symptoms and development. Here we evaluate the effects of repeated exposure to Δ9-tetrahydrocannabinol (THC) in a mouse model of surgically-induced endometriosis. In this model, female mice develop mechanical hypersensitivity in the caudal abdomen, mild anxiety-like behavior and substantial memory deficits associated with the presence of extrauterine endometrial cysts. Interestingly, daily treatments with THC (2 mg/kg) alleviate mechanical hypersensitivity and pain unpleasantness, modify uterine innervation and restore cognitive function without altering the anxiogenic phenotype. Strikingly, THC also inhibits the development of endometrial cysts. These data highlight the interest of scheduled clinical trials designed to investigate possible benefits of THC for women with endometriosis.

Data availability

All data supporting the findings of this study are available within the manuscript and its source data files. Source data files have been provided for Figures 1, 2, 3 and 4 and their figure supplements.

Article and author information

Author details

  1. Alejandra Escudero-Lara

    Laboratory of Neuropharmacology, Department of Experimental and Health Sciences, Universitat Pompeu Fabra, Barcelona, Spain
    Competing interests
    The authors declare that no competing interests exist.
  2. Josep Argerich

    Laboratory of Neuropharmacology, Department of Experimental and Health Sciences, Universitat Pompeu Fabra, Barcelona, Spain
    Competing interests
    The authors declare that no competing interests exist.
  3. David Cabañero

    Laboratory of Neuropharmacology, Department of Experimental and Health Sciences, Universitat Pompeu Fabra, Barcelona, Spain
    For correspondence
    david.cabanero@upf.edu
    Competing interests
    The authors declare that no competing interests exist.
  4. Rafael Maldonado

    Laboratory of Neuropharmacology, Department of Experimental and Health Sciences, Universitat Pompeu Fabra, Barcelona, Spain
    For correspondence
    rafael.maldonado@upf.edu
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-4359-8773

Funding

Instituto de Salud Carlos III (RD16/0017/0020)

  • Rafael Maldonado

Ministerio de Ciencia, Innovacion y Universidades (SAF2017-84060-R-AEI/FEDER-UE)

  • Rafael Maldonado

Generalitat de Catalunya - Agencia de Gestio d'Ajuts Universitaris i de Recerca (2017-SGR-669)

  • Rafael Maldonado

Generalitat de Catalunya - Agencia de Gestio d'Ajuts Universitaris i de Recerca (ICREA Academia 2015)

  • Rafael Maldonado

Generalitat de Catalunya - Agencia de Gestio d'Ajuts Universitaris i de Recerca (2019FI_B2_00111)

  • Alejandra Escudero-Lara

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Ethics

Animal experimentation: All animal procedures were conducted in accordance with standard ethical guidelines (European Communities Directive 2010/63/EU and NIH Guide for Care and Use of Laboratory Animals, 8th Edition) and approved by autonomic (Generalitat de Catalunya, Departament de Territori i Sostenibilitat) and local (Comitè Ètic d'Experimentació Animal, CEEA-PRBB) ethical committees.

Reviewing Editor

  1. Allan Basbaum, University of California, San Francisco, United States

Publication history

  1. Received: July 19, 2019
  2. Accepted: December 26, 2019
  3. Accepted Manuscript published: January 14, 2020 (version 1)
  4. Version of Record published: January 23, 2020 (version 2)

Copyright

© 2020, Escudero-Lara et al.

This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.

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    An early-warning model to predict in-hospital mortality on admission of COVID-19 patients at an emergency department (ED) was developed and validated using a machine-learning model. In total, 2782 patients were enrolled between March 2020 and December 2020, including 2106 patients (first wave) and 676 patients (second wave) in the COVID-19 outbreak in Italy. The first-wave patients were divided into two groups with 1474 patients used to train the model, and 632 to validate it. The 676 patients in the second wave were used to test the model. Age, 17 blood analytes, and Brescia chest X-ray score were the variables processed using a random forests classification algorithm to build and validate the model. Receiver operating characteristic (ROC) analysis was used to assess the model performances. A web-based death-risk calculator was implemented and integrated within the Laboratory Information System of the hospital. The final score was constructed by age (the most powerful predictor), blood analytes (the strongest predictors were lactate dehydrogenase, D-dimer, neutrophil/lymphocyte ratio, C-reactive protein, lymphocyte %, ferritin std, and monocyte %), and Brescia chest X-ray score (https://bdbiomed.shinyapps.io/covid19score/). The areas under the ROC curve obtained for the three groups (training, validating, and testing) were 0.98, 0.83, and 0.78, respectively. The model predicts in-hospital mortality on the basis of data that can be obtained in a short time, directly at the ED on admission. It functions as a web-based calculator, providing a risk score which is easy to interpret. It can be used in the triage process to support the decision on patient allocation.

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    Background:

    Cryptococcal meningitis has high mortality. Flucytosine is a key treatment but is expensive and rarely available. The anticancer agent tamoxifen has synergistic anti-cryptococcal activity with amphotericin in vitro. It is off-patent, cheap, and widely available. We performed a trial to determine its therapeutic potential.

    Methods:

    Open label randomized controlled trial. Participants received standard care – amphotericin combined with fluconazole for the first 2 weeks – or standard care plus tamoxifen 300 mg/day. The primary end point was Early Fungicidal Activity (EFA) – the rate of yeast clearance from cerebrospinal fluid (CSF). Trial registration https://clinicaltrials.gov/ct2/show/NCT03112031.

    Results:

    Fifty patients were enrolled (median age 34 years, 35 male). Tamoxifen had no effect on EFA (−0.48log10 colony-forming units/mL/CSF control arm versus −0.49 tamoxifen arm, difference −0.005log10CFU/ml/day, 95% CI: −0.16, 0.15, p=0.95). Tamoxifen caused QTc prolongation.

    Conclusions:

    High-dose tamoxifen does not increase the clearance rate of Cryptococcus from CSF. Novel, affordable therapies are needed.

    Funding:

    The trial was funded through the Wellcome Trust Asia Programme Vietnam Core Grant 106680 and a Wellcome Trust Intermediate Fellowship to JND grant number WT097147MA.