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Category: Health technologies

Tools: Sensory Organs in Their Own Right ?

Posted on by Lea Roy

©Photo by Adi Goldstein on Unsplash

What if by holding a tool we could perceive our environment through touch – using the whole tool, and not just the tip? A study by Inserm researchers at the Lyon Neuroscience Research Center (Inserm/Université Jean Monnet Saint-Etienne/Université Claude Bernard Lyon 1/CNRS) has shown just that – the capacity of the human brain to incorporate a tool as an actual sensory organ. This research, published in Nature, raises the question of a new paradigm concerning the sense of touch, its interpretation when developing our use of tools, and in its medical applications – particularly prosthetics.

The sense of touch is essential to the control we have over our hands and, by extension, over the tools we use to perceive our environment through touch.

Inserm researchers at the Lyon Neuroscience Research Center (Inserm/Université Jean Monnet Saint-Etienne/Université Claude Bernard Lyon 1/CNRS) examined the mechanisms which enable the brain to locate touch through tools. To do this, they used three complementary approaches which involved tapping a wooden rod held in the hand.

The first approach involved tapping different locations of a rod held by a volunteer whose vision was obstructed and then asking him or her to locate the impact. Irrespective of where the rod was tapped, the volunteer was able to sense the location of the impact with the same accuracy as when it was his or her own arm which was tapped.

These results demonstrate the human capacity to “incorporate” the entirety of a tool held in the hand as if it was part of the body, with the brain integrating it as a sensory organ in its own right.

The second approach involved recording the vibrations of the rod perceived at the base of the wrist and on the skin of the hand holding it. The researchers observed that the characteristics of the rod’s vibrations transmitted to the hand predictably depended on the location of the impact.

Finally, in the third approach, the characteristics of the vibrations recorded in the second approach were processed by a computerized simulator of skin responses, thereby modeling the responses, to the vibrations, of the mechanoreceptors (sensory neurons of the skin) in contact with the rod. The research team observed that the mechanoreceptors were able to very precisely decode the vibratory motifs of the rod. Since these motifs strictly depend on the location of the impact, the brain is able to interpret their “profile” sent by the mechanoreceptors and, as a result, locate the area of impact.

This study shows that the human brain treats the tools as sensory extensions to the body, a mechanism which the research team suggests calling “sensing with tools“. The phenomenon newly-described here represents a new paradigm which could improve knowledge of tool-incorporation phenomena in humans and the sensory perception of the visually-impaired, as well as the understanding of prosthesis use in amputees.

20% of reactions to radiologic contrast media are real allergies

Posted on by Lea Roy

©Adobestock

A team of Pole-Imaging Research Explorations-European Hospital Georges Pompidou AP-HP, Paris Descartes University and INSERM led by Professor Olivier Clément, and a team from Caen University Hospital and the University of Caen Normandy, led by Dr Dominique Laroche, conducted the first national prospective multicenter study on allergic reactions to contrast media in radiology. 31 centers in France bringing together radiologists investigators, allergists, anesthetists and biologists have investigated 245 cases of hypersensitivity to contrast media.

Promoted by the AP-HP, the study, funded by the Hospital Regional Program Clinical Research, 2003, shows that allergy is responsible for over 20% of hypersensitivity reactions to contrast media and recommends that patients diagnosed allergic, having a high risk of recurrence, are subject to monitoring based on skin tests performed in an allergist specializes in drug allergy.

This work was published in the journal EClinicalMedicine the Lancet in its issue of July 2018.

In radiology, patients may experience immediate hypersensitivity reactions to iodinated contrast media (for scanners) and gadolinés (for MRI) is injected them in the examination. The reactions such as hives, angioedema, bronchospasm, hypotension and anaphylactic shock. Severe reactions, rare, occur within minutes after injection and require from the imaging team a quick diagnosis and management.

For iodinated contrast agents, reactions have long been falsely labeled “iodine allergy” and mistaken reactions to seafood or skin disinfectants.

But the real allergy to contrast medium is diagnosed by elevated plasma markers tryptase and histamine in the first hour of reaction and intradermal skin tests to make between six weeks and six months after it. The few retrospective studies post on the performance of this type of skin test showed that between 13 and 65% of the responses were truly allergic in origin, according to the populations tested. However, these studies suffered from a lack of clinical data, in particular the name of the injected product, or incomplete or late tests performed, or they mixed the immediate reactions and delayed reactions.

A team of Pole-imaging research explorations-European Hospital Georges Pompidou AP-HP, Paris Descartes University and Inserm, led by Professor Olivier Clément, and a team from Caen University Hospital and the University of Caen Normandy, led by Dr Dominique Laroche studied prospectively immediate hypersensitivity reactions to iodinated products and gadolinés. This multicenter study was conducted in 31 French centers equipped to perform skin tests six weeks to six months after a reaction.

After receiving contrast media for radiology review, 245 patients with immediate reaction took a blood sample in the first hour after it to measure the levels of histamine and tryptase in their plasma. They are seen to offer six weeks after a visit to the allergist to test all existing contrast agents (10 gadolinés iodinated or 5).

Skin testing revealed three types of reactions: allergic (if positive test contrast diluted); potentially allergic (if positive test only to pure product) and nonallergic. They identified 41 patients allergic to iodine products and 10 patients allergic to gadolinés products.

The results showed that over the reaction was severe, the more allergic mechanism revealed by the skin test was frequent : 9.5% in the skin reactions; 22.9% in the moderate reactions; 52.9% in reactions involving life-threatening, and 100% when there was cardiac arrest. Similarly, the levels of histamine and tryptase plasma increased with the severity of the reaction. The presence of cardiovascular signs were also very strongly linked to allergic mechanism.

The group of potentially allergic patients showed clinical symptoms and histamine assays and tryptase intermediate between the group of patients allergic and non-allergic people. This suggests that some of them are truly allergic to the contrast material.

The teams also studied cross-reactions with other different contrast the one responsible for the reaction products: 62.7% of patients had allergic cross-reaction to one or more pure products tested.

This study shows that 21% of radiology hypersensitivity reactions are actually caused by an allergy to contrast media.

Allergic patients have a greater risk of recurrence if their is reinjected contrast agent giving a positive skin test.

Patients exhibited severe symptoms (anaphylactic or cardiovascular symptoms) should benefit from a dose of histamine and tryptase the waning of resuscitation and allergy testing in the six months to determine the allergic or not of their reaction, and especially to know which products will be shown against or authorized for future injections.

Myositis: A New Classification Representing a Decisive Step Towards Improved Diagnosis and Personalized Treatment

Posted on by Priscille Rivière

Coupe transversale de muscle humain, régénération de fibres musculaires après un traitement de la myopathie de Duchenne. Crédits: Inserm/Fardeau, Michel

Prof. Olivier Benveniste’s Inflammatory myopathies and innovative targeted therapies team at the Institute of Myology has produced a new classification of the different forms of myositis (rare inflammatory muscle diseases). Four new types of myositis taking into account the various clinical criteria of patients have now been defined. This research, involving teams from the Institute of Myology, Inserm, the Paris public hospitals system (AP-HP) and Sorbonne Université, was published in September in JAMA and paves the way for reliable diagnosis and personalized treatments.

Myositis (rare inflammatory muscle diseases) is a group of rare autoimmune muscle diseases in which the immune system, in charge of protecting the body from external aggressions (bacteria, viruses…), dysfunctions and attacks the body (in this case, the muscle). These diseases affect between 3,000 and 5,000 adults and children in France.

While the different forms of myositis all have an autoimmune component, each has its own specific triggering mechanisms. Until now, three types of myositis (polymyositis, dermatomyositis, inclusion body myositis) had been identified according to a classification system established in 1975 and updated in 2017 (ACR/EULAR rheumatologist criteria) based essentially on clinical and histological criteria. Prof. Olivier Benveniste, head of the Inflammatory myopathies and innovative targeted therapies team at the Institute of Myology who has been monitoring patients daily at Pitié-Salpêtrière Hospital AP-HP over the past 20 years, had identified major diagnostic errors related to this incomplete and, as a consequence, non-homogeneous, classification which would sometimes even lead to errors in patient treatment. Some patients mistakenly diagnosed with inclusion body myositis were given high-dose steroids which made their condition worse.

That is why, together with his team and the Center of Reference for Neuromuscular Diseases of the Institute of Myology, Prof. Benveniste launched a study on 260 patients in whom he collected and analyzed the various clinical characteristics –and particularly the presence of autoantibodies, which are sometimes causes or consequences of the disease. Using innovative statistical methods, without a priori, in which the mathematical algorithm works unsupervised to aggregate similar patients into subgroups (cluster analysis), the researchers revealed a new classification with four major types of myositis: inclusion body myositis, dermatomyositis, immune-mediated necrotizing myopathy, anti-synthetase syndrome (with polymyositis no longer forming a type of myositis as such).

Characteristics of the four forms of myositis:

▪ Inclusion body myositis: This form of myositis more often affects men over 60 years of age. It progresses slowly but ultimately leads to a highly disabling motor deficit. It particularly affects the quadriceps (thigh muscles used to climb stairs, get up from a chair, maintain stability when walking…), the muscles used to close and shake hands and the muscles used for swallowing. This disease is resistant to standard immunosuppressive treatments such as steroids. It is due to the presence of an inflammatory reaction (myositis) in the muscle and a neurodegenerative process related to Alzheimer’s disease (presence of inclusions).

▪ Dermatomyositis: This form more often affects women. Children can also be affected. There is an associated cancer risk in the most elderly subjects (usually after 60 years). In addition to myositis, which causes predominant muscle weakness in the shoulders, this disease is characterized by the presence of typical dermatological lesions. Dermatomyositis is due to an imbalance of the immune system involving type 1 interferon that helps protect against viruses. New therapies specifically targeting this interferon pathway are under development. Dermatomyositis-specific antibodies are anti-Mi2, anti-SAE, anti-NXP2, and anti-TIF1 gamma.

▪ Immune-mediated necrotizing myopathy: This is characterized by muscular weakness affecting patients of all ages. In the absence of treatment, this type of myositis leads to the most severe and disabling muscle atrophy. This disease is related to the presence of two specific anti-SRP or anti-HMGCR antibodies which directly attack and destroy the muscles. Anti-HMGCR may appear after taking statins. Treatment aims to remove these antibodies.

▪ Anti-synthetase syndrome: This disease affects muscles but also joints (leading to rheumatism), and the lungs (leading to shortness of breath that is sometimes severe). Here too, certain antibodies appear to be responsible: anti-Jo1, anti-PL7 and anti-PL12.

This new classification is decisive in establishing a diagnosis and offering personalized treatment to patients.

“We realized that the current myositis classification was unsuitable and could often lead to the failure of a potential treatment due to non-homogeneous patient groups within a given trial. So our aim was to define a classification based on phenotypic, biological and immunological criteria in order to better diagnose the different types of myositis and ultimately find suitable treatments for patients. This new classification is becoming a reference because even the FDA, which up until then was using the US classification, recommends using our research as a basis. ” explains Prof. Benveniste.

Predicting The Response To Immunotherapy Using Artificial Intelligence

Posted on by Priscille Rivière

Photo by Ken Treloar on Unsplash

A study published in The Lancet Oncology establishes for the first time that artificial intelligence can process medical images to extract biological and clinical information. By designing an algorithm and developing it to analyse CT scan images, medical researchers at Gustave Roussy, CentraleSupélec, Inserm, Paris-Sud University and TheraPanacea (spin-off from CentraleSupélec specialising in artificial intelligence in oncology-radiotherapy and precision medicine) have created a so-called radiomic signature. This signature defines the level of lymphocyte infiltration of a tumour and provides a predictive score for the efficacy of immunotherapy in the patient.     

In the future, physicians might thus be able to use imaging to identify biological phenomena in a tumour located in any part of the body without having to perform a biopsy.  

Up to now, no marker can accurately identify those patients who will respond to anti-PD-1/PD-L1 immunotherapy in a situation where only 15 to 30% of patients do respond to such treatment. It is known that the richer the tumour environment is immunologically (presence of lymphocytes) the greater the chance that immunotherapy will be effective, so the researchers have tried to characterise this environment using imaging and correlate this with the patients’ clinical response. Such is the objective of the radiomic signature designed and validated in the study published in The Lancet Oncology.

In this retrospective study, the radiomic signature was captured, developed and validated in 500 patients with solid tumours (all sites) from four independent cohorts. It was validated genomically, histologically and clinically, making it particularly robust. 

Using an approach based on machine learning, the team first taught the algorithm to use relevant information extracted from CT scans of patients participating in the MOSCATO study[1], which also held tumor genome data. Thus, based solely on images, the algorithm learned to predict what the genome might have revealed about the tumour immune infiltrate, in particular with respect to the presence of cytotoxic T-lymphocytes (CD8) in the tumour, and it established a radiomic signature.   

This signature was tested and validated in other cohorts including that of TCGA (The Cancer Genome Atlas) thus showing that imaging could predict a biological phenomenon, providing an estimation of the degree of immune infiltration of a tumour.  

Then, to test the applicability of this signature in a real situation and correlate it to the efficacy of immunotherapy, it was evaluated using CT scans performed before the start of treatment in patients participating in 5 phase I trials of anti-PD-1/PD-L1 immunotherapy. It was found that the patients in whom immunotherapy was effective at 3 and 6 months had higher radiomic scores as did those with better overall survival.     

The next clinical study will assess the signature both retrospectively and prospectively, will use larger numbers of patients and will stratify them according to cancer type in order to refine the signature.        

This will also employ more sophisticated automatic learning and artificial intelligence algorithms to predict patient response to immunotherapy. To that end, the researchers are intending to integrate data from imaging, molecular biology and tissue analysis. This is the objective of the collaboration between Gustave Roussy, Inserm, Université Paris-Sud, CentraleSupélec and TheraPanacea to identify those patients who are the most likely to respond to treatment, thus improving the efficacy/cost ratio of the treatment.

[1] Results of the MOSCATO study published in Cancer Discovery : http://cancerdiscovery.aacrjournals.org/content/early/2017/03/26/2159-8290.CD-16-1396

// About radiomics

In radiomics, it is considered that imaging (CT, MRI, ultrasound, etc.) not only reveals the organisation and architecture of tissues but also their molecular or cellular composition. The technique involves the use of algorithms to analyse a medical image objectively in order to extract from it information which is invisible to the naked eye, such as the texture of a tumour, its micro-environment, its heterogeneity, etc. For the patient this represents a non-invasive approach that can be repeated over the course of the disease to follow its progress. 

Yellow fever: a new method for testing vaccine safety

Posted on by Lea Roy

A minibrain triculture. © Marie-Christine Cumont and Monique Lafon – Institut Pasteur.

Scientists from the Institut Pasteur, the CNRS and Sanofi Pasteur have recently developed a novel alternative method to animal testing that can be used to verify the safety of vaccines such as the yellow fever vaccine. This original approach is based on the development of an in cellulo device using a 3D culture model, the “BBB-Minibrain”, to evaluate the safety of live vaccines for human use. The model was developed by the Institut Pasteur and a patent application has been filed by the Institut Pasteur and Inserm. It raises hopes for a reduction in the use of animals in quality control, especially in the tests carried out by the pharmaceutical industry to meet the requirements of regulatory authorities. The results of this research were published in the journal Biologicals in May 2018, and online on March 24th.

For several years now, following the adoption of EU Directive 2010/63/EU,1 the scientific community has been actively seeking to reduce the practice of animal testing. But in many cases, these efforts are hindered by a lack of acceptable alternatives that satisfy regulatory authorities. This is particularly the case for the regulatory testing required for live viral vaccines, such as the yellow fever vaccine; suppliers must demonstrate that the seed lots used to produce vaccine batches sold on the market do not represent a risk of neurotoxicity. These tests are currently performed on animals, which are monitored for the emergence of any clinical signs in the central nervous system that may suggest neurotoxic side effects.

Against this backdrop, Institut Pasteur scientists developed a 3D culture model mimicking the human blood-brain interface, the “BBB-Minibrain”, in 2014. This model, formed of a blood-brain barrier (BBB) associated with a mixed culture of neurons, astrocytes and microglia (a “minibrain”), can be used to detect when viruses enter the brain through the BBB, their multiplication in the minibrain and the emergence of any neurotoxic effects. A patent application (WO2016038123) was filed for the model.

The scientists set out to test the BBB-Minibrain’s ability to pinpoint and amplify any rare mutant particles with neuroinvasive and neurovirulent properties that are found in seed lots for live viral vaccines. They chose to use two yellow fever virus vaccine strains, including the strain currently used to produce the vaccine, which does not cause neurotoxicity.

Working with Sanofi Pasteur research teams, they demonstrated that the BBB-Minibrain can be used to identify any rare viral particles in vaccine preparations that have acquired the ability to enter the brain and multiply there. This test therefore paves the way for the rejection of any seed lots containing mutant viruses capable of entering the brain and becoming neurovirulent.

As Monique Lafon, lead author of the study and Director of the Virology Department at the Institut Pasteur, explains, “replacing animal testing is a major challenge for research. The BBB-Minibrain model is an ingenious tool that will facilitate our analysis of the basis for neurovirulence in these viruses, which colonize the brain via the bloodstream.”

These findings represent a first proof of concept and feasibility for the development of an alternative test that complies with the “3Rs” principle. Work to develop this test is ongoing. The long-term aim is to secure approval for the new test from regulatory authorities.

The BBB-Minibrain model raises hopes for the development of an alternative method that can be used by the pharmaceutical industry to perform regulatory tests on live viral vaccines. The aim of this method is to reduce the use of animals while ensuring strict monitoring of any scientific benefits and breakthroughs in the area of human health.

 

1 This Directive enshrines the 3Rs approach: the treatment and use of living animals for scientific purposes are governed by the principles of Replacement, Reduction and Refinement as established at international level.

Anaelle Da Costa received the 2017 Hub France R&D Award (November 2017), Sanofi Pasteur, and the 2017 Global R&D Awards – We R Hope Award for Innovative Postdoctoral Research (May 2018), Sanofi Pasteur.

A Computer Program Able to Automatically Detect and Identify Brain Lesions

Posted on by Lea Roy

 ©Emmanuel Barbier – Inserm/Inria/Univ. Grenobles Alpes  – Figure d’IRM chez l’homme obtenue ici en présence d’une tumeur cérébrale. En gris, des IRM classiques, en couleur, des IRM quantitatives.

Will the radiology of the future come from machine learning? That is the view of Inserm and Inria researchers working in collaboration at the Université Grenoble Alpes who have developed a program able to localize and diagnose various types of brain tumors via MRI image analysis. These analyses have produced highly reliable results, with tumor localizations and tumor-type diagnoses accurate in 100% and over 90% of cases, respectively. This innovative method and its results are the subject of a study published in IEEE-TMI.

MRI – or magnetic resonance imaging – with its ability to reveal various brain tissue characteristics is the medical imaging technique of reference when it comes to obtaining highly-detailed images of the brain. It can produce what is known as “quantitative” images, which each map a measurable brain parameter (such as blood flow or blood vessel diameter). Although the quality of these quantitative images is less dependent on the calibration of the measuring apparatus than that of the standard images obtained with MRI – and so is more reliable – this type of technique is still infrequently used in the clinical MRI setting.

Inserm researchers have been working in conjunction with a research team from Inria on the analysis protocols of these quantitative images at the Université Grenoble Alpes.  The researchers combined various innovative mathematical tools in order to teach a computer program how to analyze quantitative brain MRI images and diagnose any tumors present.

First of all, the program learned how to recognize the characteristics of a healthy brain. Then, when it was shown images of brains with cancer, it became able to automatically localize the regions whose characteristics diverge from those of healthy tissues and to extract the distinguishing characteristics.

Finally, in order to teach the artificial intelligence how to discriminate between the different types of tumor, the researchers then gave it the diagnosis associated with each of the pathological brain images which had been presented to it.

In order to test the ability of the program to differentiate healthy from diseased tissue, the research team provided it with images that it had not seen before – sometimes of healthy brains, sometimes of pathological brains. The program had to indicate whether a tumor was present in these images and, if so, be able to characterize it. And, by succeeding in localizing the lesions perfectly (100%) and diagnosing them very reliably (over 90%), the artificial intelligence turned out to be a very quick study.

“At present, the acquisition of quantitative images does not correspond to what is happening in routine clinical practice in the MRI departments”, specifies Emmanuel Barbier, Inserm researcher leading the study. “But this research shows the value of acquiring these types of images and informs radiologists of the analytical tools that could be available to them in the near future to aid their interpretations. “

In the meantime, the research team will focus on the most relevant images to acquire in order to diagnose brain tumors as precisely as possible and with the greatest possible reliability. It will therefore continue to develop mathematical tools with the aim of improving the program’s self-learning abilities, with the ultimate objective being to extend the diagnostic potential of this artificial intelligence to other brain diseases, such as Parkinson’s.

These quantitative MRI machine learning tools applied to brain tumors are currently being evaluated as part of the Cancer Plan driven by Inserm, within the Tumor Heterogeneity and Ecosystem program.

Their development in the context of Parkinson’s disease diagnosis is also underway via the NeuroCoG multidisciplinary project funded by the Université Grenoble Alpes IDEX.

A new gelling molecule for growing neurons in 3D

Posted on by Priscille Rivière

A multidisciplinary team of researchers from CNRS, INSERM and Université Toulouse III – Paul Sabatier has developed a hydrogel that can grow, develop and differentiate neural stem cells. This biomaterial could provide new paths for the development of in vitro cellular models of brain tissue or of in vivo tissue reconstruction. This work is published in ACS Applied Materials & Interfaces on May 14, 2018.

Although we know how to culture cells on a two-dimensional surface, that is not representative of the actual cell environment in a live organism. In brain tissue, cells are organized and interact in three dimensions in a soft structure. The researchers’ main goal was to imitate this tissue as closely as possible. They developed a hydrogel that meets suitable criteria for permeability, rigidity and biocompatibility; on that, they cultured human neural stem cells[1].

N-heptyl-galactonamide is a new molecule synthesized by these scientists, which is part of a family of gelling agents that usually produces unstable gels. It is biocompatible, has a very simple structure, and can be made quickly, so has many advantages. By working on the parameters for forming the gel, the researchers at the Laboratoire Interactions Moléculaires et Réactivité Chimique et Photochimique (CNRS/Université Toulouse III-Paul Sabatier), Toulouse Neuro Imaging Center (INSERM/Université Toulouse III-Paul Sabatier) and the CNRS Laboratoire d’Analyse et d’Architecture des Systèmes obtained a stable hydrogel with very low density and very low rigidity. Because of that, neural stem cells can penetrate and develop in three dimensions in the hydrogel. It also has a network composed of different types of fibers, some straight and rigid, others curved and flexible. This diversity allows neurons to develop a network of short- and long-distance connections like those in brain tissue.

This new biomaterial could therefore lead to the development of three-dimensional brain tissue models that function in a manner approaching in vivo conditions. In the long run, it could be used to evaluate the effect of a medicine or to enable cells to be transplanted with their matrix to repair brain damage.

[1] Neural stem cells came from patient biopsies (CHU Toulouse – Pôle Neurosciences). These cells are capable of differentiating into neurons and glial cells, the main types of cells in brain tissue.
A new gelling molecule for growing neurons in 3D

Hepatitis C: a novel point-of-care assay

Posted on by Lea Roy

Darragh Duffy and Alba Libre, Immunobiology of Dendritic Cells Unit, Institut Pasteur / Inserm, using the Genedrive HCV assay. ©Institut Pasteur

One of the major challenges identified by the WHO in efforts to eradicate the hepatitis C virus (HCV) is the diagnosis of chronic cases that are generally asymptomatic. Major progress is required for new diagnostic techniques that can be “decentralized”, in other words accessed by populations and countries with limited resources. Scientists from the Institut Pasteur and Inserm, in collaboration with the company genedrive, have developed and validated a rapid, reliable, point-of-care HCV assay. This new screening assay means that patients can begin treatment for the disease as soon as they are diagnosed. The results have been published in the journal Gut on April 4th, 2018.

Hepatitis C is a liver disease caused by the hepatitis C virus (HCV). The virus can result in chronic infection, which may lead to severe complications such as cirrhosis and liver cancer many years later. Chronic infection with the hepatitis C virus affects approximately 1% of the global population (71 million people) and claims 400,000 lives every year when it develops into severe disease.

New direct-acting antivirals can successfully treat more than 95% of patients with chronic HCV infection if they are taken in time. In 2016, the WHO therefore published a plan to eliminate this major threat for public health by 2030. But the main challenge in meeting this ambitious target remains the diagnosis of asymptomatic patients, especially in low- or middle-income countries, where access to traditional screening assays is limited.

The current method for HCV diagnosis involves two stages. The first is to screen for specific HCV antibodies, but this does not reveal whether patients were infected in the past (and experienced spontaneous HCV clearance) or are still chronically infected. So the second stage requires a PCR1 assay to detect HCV RNA in the blood to confirm or rule out chronic infection.

There are rapid serological assays for HCV antibodies, but PCR screening requires dedicated infrastructure and qualified staff. In countries with limited resources, this type of assay is only available in centralized laboratories, which means that less than 1% of infected individuals in these regions actually know that they are infected. PCR screening may also involve several visits, and the time required between each result increases the risk of losing patients before the final diagnosis. To improve patient care from diagnosis to treatment, a screening assay for HCV RNA that can be “decentralized” and used in rural or low-income areas is urgently needed.

The team of scientists led by Darragh Duffy (Immunobiology of Dendritic Cells Unit, Institut Pasteur / Inserm) developed an assay in collaboration with the company genedrive that detects HCV RNA as reliably as existing assays but is faster and can be utilized at the point of care. PCR can be performed with the miniaturized device that enables the necessary succession of 40 reaction cycles to be carried out more quickly than in a conventional platform. The analysis can be performed in approximately an hour. This type of device is ultimately less costly than the current assays, which require significant laboratory infrastructure and maintenance.

The scientists began by clinically validating the assay on cohorts from the Institut Pasteur in France and the National Health Service in Nottingham, UK, then with data from Johannesburg-based Lancet Laboratories using samples from South Africa, Kenya, Ghana, Nigeria and Uganda.

The study demonstrated that the assay had a specificity of 100% – in other words there were no false positives – and a sensitivity of 98.6%, thereby meeting WHO requirements for this type of assay.

The kit has obtained CE certification for distribution in Europe and will be available for sale in the Middle East, Africa, South-East Asia and India once local regulatory clearance is obtained.

This study was funded by the organizations listed above and by the EU FP7 project POC-HCV.

Genedrive HCV assay. ©Institut Pasteur

 

 

1 PCR: Polymerase chain reaction, an enzyme reaction used to select then amplify an RNA fragment in large quantities. PCR consists of a series of repeated cycles (20 to 40 on average), each involving three temperature steps.

Artificial Intelligence to serve health research: Inserm and Owkin join forces

Posted on by Lea Roy

Accelerate artificial intelligence research to benefit health: such is the shared objective underpinning the agreement signed by Inserm and the start-up Owkin, specialized in machine learning applied to biological and medical research. The tools developed by Owkin, combined with the mass of health data either produced or used by Inserm, will lead to the development of disruptive innovations unprecedented in the field of medical and clinical research.

At a time when Emmanuel Macron intends to position France as an artificial intelligence giant, Cédric Villani has just presented his report highlighting four priority fields, including Health: Inserm and OWKIN are fully committed to this ambition. The research agreement that binds together the two partners today will allow Inserm researchers to benefit from SOCRATES artificial intelligence software developed by Owkin.

The Owkin SOCRATES platform is aimed at academic or hospital researchers, as well as pharmaceutical industry researchers, to help them discover and develop new drugs. It uses machine learning technologies to analyze medical imaging libraries, genomic molecular data and clinical data sets in order to discover complex biomarker models associated with diseases or variable responses to treatments.

“Joining forces with Inserm will allow us to pool our efforts towards a shared objective. The partnership is a sign of our determination to drive research forward with a view to gaining a better understanding of diseases and making new discoveries. Our goal is to use artificial intelligence to analyze existing data and uncover new research avenues, broadening access to AI technologies for researchers, in the hope that this will result in new treatment strategies,”explains Gilles Wainrib, co-founder and Scientific Director at Owkin.

According to Yves Levy, Chairman and CEO of Inserm: “This partnership with Owkin is emblematic of how academic research and the very best French talent should join forces to generate knowledge of the highest quality. There is no doubt that AI will lead to significant benefits for research, medical practice and the national healthcare system as a whole, underpinned by a rigorous scientific approach and solid ethics. Our role as a public research institute is to do all we can to make sure this happens quickly and smoothly.”

Yves Lévy, the Chairman and CEO of Inserm, and Gilles Wainrib, the co-founder of the start-up Owkin, at the signing of the framework agreement between Inserm and Owkin (press conference of April 4, 2018)

 

More broadly, Inserm deploys a national strategy aimed at firmly establishing the leadership of French biomedical research in the field of artificial intelligence, via:

• The mobilization of the best teams currently involved in AI development (almost 300 research teams), data production and analysis or cohort follow-up.

• A key contribution to the use of data from the Health Data Hub announced by the French President on March 29 following the submission of the Villani report. This infrastructure will draw on the Système National des Données de Santé (SNDS – French National Health Data System), extended to clinical and biological research data.

• The setting-up of a new infrastructure for the collection and analysis of medical genomics data in the context of the French Plan for Genomic Medicine

• The reinforcement of its policy of public and private partnerships with national research organizations and industry in the fields of artificial intelligence: mathematics, algorithms, modeling, software

 

Inserm’s strengths in the field of AI:

– its scientific excellence, scientific integrity and innovation capacities, within a rigorous scientific environment

– its understanding of the biological and medical issues to be addressed

– its knowledge of the data associated with these issues

– its mastery of regulatory and ethical aspects

– its key role in the production and use of major data in the fields of biology and health

 

To find out more about artificial intelligence in the field of health:

Big data in health

The technical, human and ethical challenges to be addressed: a report that can be consulted on the Inserm website

https://www.inserm.fr/information-en-sante/dossiers-information/big-data-en-sante

The evolution of AI since the 1990s:

https://www.youtube.com/watch?v=4UINCQ36eeY

Recherche à suivre: a fun series about research from the 1990s. Humankind is a champion among mammals, with some 10 billion neurons, but does intelligence simply come down to the number of neurons? This animated film compares a computer with a brain to help us understand how the human brain works. It retraces the history of the design of computers to lead us into the vast world of neurosciences.

Flunarizine: a New Drug Candidate in the Treatment of Spinal Muscular Atrophy

Posted on by Lea Roy

©Adobestock

A team of researchers from Inserm (“Toxicology, pharmacology and cell signaling” JRU 1124) and the universities of Paris Descartes and Paris Diderot have recently discovered that flunarizine – a drug already used to treat migraine and epilepsy – enables the repair of a molecular defect related to spinal muscular atrophy, a severe and incurable disease. This discovery is the culmination of research efforts ongoing since 1995, when the Inserm team – comprising Suzie Lefebvre, leader of the current research projects – identified the gene responsible for infantile spinal muscular atrophy. The results of the initial animal tests, published in Scientific Reports, demonstrate a marked improvement in health. These extremely promising findings must now be confirmed in humans.

 

 Spinal muscular atrophy is a rare genetic disease, affecting between 1 and 9 out of every 100,000 people. It is caused by degeneration of the motor neurons in the spinal cord, resulting in progressive muscle loss. In the majority of cases, symptoms appear either following birth – with the infant unable to hold up his or her head, or a little later in early childhood – with the inability to walk. More rarely, symptoms can begin in adolescence, in which case the muscular disorders are substantial but compatible with a more-or-less normal life.

The disease is caused by a mutation of the SMN1 gene, leading to a deficiency in the SMN protein. The SMN2 gene, which is virtually identical, then takes over. However, the SMN protein that it produces is for the most part truncated and not highly functional.

 

An SMN protein targeting problem

In healthy individuals, the SMN protein is drawn into cell nucleus structures known as Cajal bodies. There, small non-coding RNA is formed, which is implicated in a maturation step of the messenger RNA (known as splicing), a precursor of the proteins. In spinal muscular atrophy, the truncated SMN proteins are unable to reach the Cajal bodies. The Cajal bodies then function poorly and the production of the small non-coding RNA is altered. As such, many messenger RNA present maturation problems and result in abnormal or deficient proteins – a phenomenon occurring in all tissues.

In an attempt to restore this mechanism, the researchers tested therapeutic molecules in vitro, on cells taken from patients with a severe form of the disease. The objective was to find one or more cells able to retransport the SMN proteins to the Cajal bodies so that they regain their function.

 

Flunarizine effective on cells from a variety of patients

Just one molecule has demonstrated an effect on a large number of cells from various patients: flunarizine, which is already used in the treatment of migraine and epilepsy. In a second step, it was used to treat mice with spinal muscular atrophy, at a rate of one spinal cord injection per day from birth. Their life expectancy increased by 40% on average, from 11 to 16 days and even up to 36 days in one case. Analysis of the motor neurons and muscles show that they are preserved for longer in the treated animals. “The molecule presents a major neuroprotective effect even if we currently don’t know why that is,” declares Lefebvre, research leader and a member of the team having discovered the gene responsible for infantile spinal muscular atrophy in 1995. In addition, her team observed that flunarizine makes it possible to restore the functioning of the small non-coding RNA produced in the Cajal bodies for the maturation of the messenger RNA.

 

Findings to be confirmed in humans

Flunarizine remains to be tested in humans, a stage which will face the challenge of enrolling patients in the context of a rare disease. In addition, most of these patients are already enrolled in a clinical trial to evaluate a new-generation drug that was granted marketing authorization in 2016, meaning that they cannot be mobilized to participate in a second trial. Ultimately, the two therapeutic approaches – each of which targeting a different mechanism – could very well complement each other to promote patient survival and quality of life.

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