IMIM - Institut Hospital del Mar d'Investigacions Mèdiques IMIM - Institut Hospital del Mar d'Investigacions Mèdiques

COVID-19 Research

A great deal of research is necessary to understand how the SARS-CoV-2 virus works and to find an effective treatment for COVID-19.

That is why our healthcare professionals -virologists, immunologists, internal medicine specialists, molecular biologists, radiation oncologists, epidemiologists, bioinformaticians, pneumologists, nephrologists, pharmacologists, engineers, neurologists, psychiatrists, cardiologists, pathologists, oncologists, intensivists, anaesthetists, surgeons, etc.- are once again demonstrating their commitment to the progress of science and medicine, and are working on more than 80 research initiatives, seeking the necessary resources and funding to be able to develop these.

What we are working on:

  • Studies to understand the disease mechanism, prognostic factors and evolution
  • The clinical validation of devices for clinical use or follow-up in the population during the easing of lockdown
  • Trials to validate the efficacy of different drugs in treating COVID-19 patients
  • Studies to try to stratify patients according to their progress or the effect on specific groups, such as pregnant women or people with Down syndrome
  • Research into complementary methods for treating COVID-19, such as radiotherapy.
  • Research into new markers for better diagnosing the disease.
  • Research into the effects of the pandemic on mental health
  • In addition, we are involved in important multi-centre clinical trials with other large hospitals: Puerta de Hierro (Madrid), Hospital de Sant Pau, Hospital Germans Trias i Pujol, etc.

Do you want to know more? Here are some of our projects

MIND/COVID, a project studying the impact of the COVID-19 pandemic on mental health

The nationwide MIND/COVID study, headed up by researchers from the Hospital del Mar Medical Research Institute (IMIM) and Hospital del Mar, is one of the few projects funded so far by the Carlos III Health Institute (ISCIII) of the Ministry of Science and Technology. The aim is to study the mental health of healthcare workers and other key groups, as well as COVID-19 patients and a sample of the general Spanish population. Natural disasters such as severe hurricanes, floods or earthquakes, and major epidemic outbreaks -such as SARS, MERS or Ebola- lead to an increase in acute stress, symptoms of anxiety and depression, and other mental health problems. This impact can affect the most vulnerable populations in particular, and lead to the emergence of mental disorders and addictions. Healthcare workers are a vulnerable population because of the risk of contagion and the enormous workload involved in trying to manage the disease.

New diagnostic and prognostic technique for COVID-19

The idea behind the project is to find an affordable, fast and reliable method for the diagnosis and prognosis of COVID-19 using artificial intelligence. This disease mainly affects the lungs, and the extent to which it does so has both diagnostic and prognostic implications, which is why almost all patients suspected of having the disease are given chest X-rays. We are collecting as many simple chest x-rays as possible to compare these with the results of genetic tests for the SARS-CoV-2 virus, in order to train a deep learning algorithm that will be able to differentiate positive from negative cases before the test results are back, and, in positive cases, make an approximation of the patient's prognosis.

Study of the immune response in patients who have overcome the infection and the incidence of immunosuppressive treatments

The control of SARS-CoV-2 infection is based on developing an effective immune response to viruses and in producing and maintaining memory T-lymphocytes that can be activated if the virus re-emerges. The generation of memory T-lymphocytes is also necessary to ensure the effectiveness of vaccination strategies. Using cellular techniques (ELISPOT, flow cytometry) we will study immune response characteristics in patients who have overcome the infection, regardless of its clinical manifestations. In addition, we will analyse the evolution of the infection and the immune response in patients with chronic inflammatory diseases to study whether immunosuppressive treatments (biological or pharmacological) could have had any impact, positive or negative, on the course of the infection and on the establishment of the immune memory.

COVID-19 remote patient monitoring system

Remote monitoring of people with COVID-19 symptoms by means of a wearable low-cost device that captures, analyses, and displays physiological data (Data Analytics), and the use of artificial intelligence to calculate and forecast the behaviour, spread, and progression of the disease. We want to know the status of COVID-19 sufferers in both real time and telematically. This collection and processing of georeferenced data would provide the health and civil authorities with a tool for monitoring the status of the current pandemic, forecasting its future spread and generating a database that would help in behavioural studies and solutions to the pandemic generated by COVID-19.

Characterisation of virus-specific antibody responses in COVID-19 patients and generation of monoclonal antibodies specific to SARS-CoV-2

The available data seem to indicate that there are protective antibody responses generated during COVID-19 infection. However, other studies show the fluctuating nature of these responses. In fact, follow-up reports on patients infected during the 2003 SARS epidemic indicate that antibodies and memory B cells were specific to the virus and were largely undetectable 6 years after infection. In light of this, it is important to generate alternatives to vaccines for preventing and treating COVID-19.

Gene profiling dynamics and identification of gene expression in patients at high risk of severe COVID-19

The main objective of this project is to determine the dynamic transcriptomic profile of adult patients admitted to hospital due to COVID-19 and to characterise the subgroup that develops severe disease. The secondary objective is to establish a gene signature capable of discriminating, at the baseline, the group that will develop complications and, therefore, contribute to the phylogeny of the virus. In addition, through specific tools for assessing virus sequences, we want to contribute to the genomic epidemiological study of SARS-CoV-2.

Dual strategy to inhibit the SARSCoV-2 infection cycle

A major challenge is to find therapeutic solutions that quickly reach affected patients to reduce the morbidity and mortality of the highly contagious SARS-CoV2. The aim of this study is to identify antiviral drugs that interfere with the SARS-CoV2 infection cycle by blocking two different phases of viral entry into human cells. We are trying to accelerate the discovery of antiviral drugs through the "drug re-profiling" strategy that allows the reassignment of existing drugs for treating COVID-19.

Prediction of respiratory complications in patients with COVID-19 using -omics strategies

The aim is to obtain an effective clinical tool by means of metabolomics and transcriptomics techniques to evaluate the possible appearance of respiratory complications in patients with COVID-19. This tool will enable patients to be stratified according to their risk of respiratory deterioration, which could have a favourable impact on their prognosis, assisting in clinical and therapeutic decisions, as well as optimising the use of available resources. Additionally, the results of the project will provide mechanistic information on the development of acute respiratory distress syndrome in COVID-19 sufferers.

Clinical characterisation of COVID-19 infection: prognostic stratification and complications

We want to create risk-stratification scales for poor evolution in COVID-19 patients and develop the evolutionary profile of the patients. We also want to assess the effectiveness of treatments and diagnostic tests, evaluating accessibility, equity, variability and costs. This study will be carried out using information extracted manually and automatically from electronic medical records, including parameters such as epidemiological history, onset of symptoms, clinical manifestations, tests performed, treatments and evolution up to 3 months after discharge. This will then be analysed using classic models of survival, logistic regression, generalised linear models and also analyses based on artificial intelligence techniques that evaluate the risk of poor evolution.


Marta López(ELIMINAR)

93 316 05 76

93 316 04 10

Doctor Aiguader, 88
08003 Barcelona

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