Innovation

IN.Pulse is the Business Innovation Scientific Unit (UCIE, in spanish) of the Institute of Neuroscience (IN) of Alicante. IN.Pulse was established in 2018 and it is funded by the Valencian Innovation Agency (AVI). IN.Pulse’s main aim is connecting the research carried out at the IN with the industry sector, by transferring research results towards innovative products and services that can make people’s lives easier.

IN’s discoveries in the field of neuroscience often lead to applications that can improve the clinical progress of patients with neurological or psychiatric pathologies, to the discovery of biomarkers with potential use in the detection of neurodegenerative diseases, or to uses in healthy ageing and mental health. Recent milestones of IN.Pulse include the creation of 1 spin-off of the IN or the development of more than 30 patents, of which 15% are already operational.

IN.Pulse also closely collaborates with the industry, through agreements with companies and technological institutes. These collaborations represent a significant economic return to the unit and, importantly, contribute to enrich the culture of innovation in the IN’s scientific community.

For any information regarding our consultancy services for product licensing, co-development or collaboration with industry do not hesitate to contact us at in.pulse@umh.es.

UCIE

Technology Transfer

The IN has developed more than 30 commercial patents for a wide range of applications, including bioactive molecular compounds, brain activity capture devices, nerve regeneration procedures, dry eye pathology treatment, neuronal inhibitors with cosmetic and biomedical applications, experimental animal models aimed to the study of autism, anxiety or depression, and methods of diagnosis for to Alzheimer’s disease, Parkinson’s disease and cancer.

IN.Pulse facilitates the application of the knowledge and results produced by IN’ scientists to market and contributes to guide IN research towards productive activities, since this will generate wealth for private companies and the IN research groups – by attracting new funding possibilities -, and will have a positive impact in our society.

For any information regarding our consultancy services for product licensing, co-development or collaboration with industry do not hesitate to contact us at in.pulse@umh.es.

 

InPulse
UCIE

Industrial partnership

IN.Pulse collaborates with the industry and technology institutes to turn the results generated at the IN into tangible benefits. These collaborations improve both research and innovation competences of the IN, as they enhance the experience and competitiveness of our researchers.

IN.Pulse also provides to the companies with access to the knowledge developed in the IN by its highly specialized technological platforms, scientific models, procedures, ideas and experience that are available for R+D+i collaborations. Hence, IN.Pulse acts as a link between science and industry, providing access to the services and necessary resources to participate in any kind of collaborative project with our scientific leaders.

The IN offers collaboration to private companies and organizations in:

  • Hiring services of innovation and developing research projects.
  • Arranging collaborations with IN research groups for specific experiments and activities, with an agreed scope and budget.
  • Designing and developing pre-competitive innovation projects.

Our main collaborating institutions and companies are:

  • Institute of Biomechanics of Valencia
  • Vega Baja Hospital (Orihuela)
  • Rivera Salud

 

 

InPulse
UCIE

Innovation Projects

We participate in national and international innovation programs, with public and private funding and often with the participation of industry partners.

Projects promoted from IN.pulse:

 

InPulse
  • Exitus: Cancer can be considered one of the greatest challenges in current medicine. While treatment for certain cancers is improving, it is obvious that when it fails, patients face a process of gradual decline from their health toward death. When the cure does not success, caring becomes essential. And although sometimes there are certain very clear signs and symptoms that confirm the patient’s condition a few hours before death, in many cases the situation is not so clear, and less in previous weeks or days. The long-standing clinical practice of doctors and nurses provide a good understanding of what patients need. However, the “clinical eye” is often intuitive (difficult to convey, contrast and train) and sometimes conflicting, with more than one possible solution and no instruments to make a clear decision. In the time of “Big Data”, and in the face of available technological innovations, the potential is enormous to coordinate these seemingly disparate disciplines in a productive way. The current state of the technique allows the possibility of finding a mechanism to measure with precision, high performance and in a totally non-invasive way the terminal patient (signs and physiological changes) and their environment to understand the worsening process, and to be able to improve substantially clinical practice: diagnosis, prognosis, treatment, care in general and Palliative Care in particular. The objective is to develop a tool (using automatic learning methods) to improve the quality of care for stage IV cancer patients. Specifically, it is intended to determine as reliably and in advance as possible that the patient has entered the agonizing phase in order to reduce their discomfort and suffering.

    Principal researcher: Dr. Santiago Canals

  • Tear: The objective of the project is the development of a system for stimulating the eye using CO2 that encourages the production of tears by the patient without affecting the integrity of the eye, based on the Belmonte esthesiometer, to improve the precision of the dry eye diagnosis.

    Principal researcher: Dr. Carlos Belmonte

  • Glioblastoma Multiforme: The novelty of the project consists of developing biodegradable molecular vectors. Glioblastoma multiforme (GB) is considered the most frequent and aggressive form of brain cancer, representing 15.4% of all primary brain tumors with a survival rate of 14-15 months. The facts that glioma stem cells resist conventional treatments increase the urgent need to tackle new treatment therapies for GB. Based on its own research data, the IN has found:
    • That GB cells need to physically interact with other cells around the vessels, known as cerebral pericytes (PC), to survive and infiltrate normal brain tissue. This interaction requires the normal functioning of the CD44 and Cdc42 molecules. Its blockade implies the disappearance of GB in experimental animal models (Caspani et al., 2015)
    • That as a consequence of this interaction between GB-PC, PC deactivates the immune response against tumor cells through activation of chaperone-mediated autophagy (AMC). This step is also necessary for the stability of the interaction between cells, the elimination of the anticancer properties of PCs and the inhibition of the immune response, since by blocking its key molecule (Lamp2a) the tumor disappears in experimental animal models (Valdor et al, 2017 and 2019).

      Principal researcher: Dr. Salvador Martínez

  • Diagnosis of Alzheimer’s by sAPP Glycosylation test: Alzheimer’s disease (AD) is the most common form of senile dementia and is characterized by the presence of brain proteinaceous deposits, extracellular amyloid plaques and intracellular neurofibrillary tangles. The neurofibrillary tangles of abnormally hyperphosphorylated cytoskeletal protein tau (P-tau) are found in other pathologies, collectively called tauopathies; but AD is the only tauopathy that presents with fibrillar amyloid deposits, plaques, consisting mainly of ß-amyloid or Aß. Aß is thus the protein most specifically related to AD. Aß is a 40-42 amino acid peptide product of the proteolytic processing of the transmembrane protein known as the precursor of amyloid protein (APP), a glycoprotein of about 700 amino acids. Especially the Aß42 form (42 amino acids) is commonly accepted as the determinant of neurotoxicity in AD. It is important to note that APP is normally processed by different pathways that coexist under normal physiological conditions. The called amyloidogenic processing would yield, in addition to Aß, a long extracellular fragment called sAPPß. The non-amyloidogenic -which does not yield Aß- also generates a long soluble fragment called sAPPa. The determination of tau and P-tau in cerebrospinal fluid (CSF) has been shown to have a diagnostic potential for AD, but it lacks specificity against other tauopathies and neurological disorders. For this reason, many studies have been carried out on the determination of Aß42 as a more specific diagnostic marker. Regarding Aß42 as a biomarker, there is the paradox that what exists in the CSF of AD is a decrease in its values. This is assumed to be the result of two opposing effects: i) there is an increase in the production of Aß in the brain, ii) the excess brain Aß produced in turn promotes increased fibrillation, plaque formation and is sequestered in said plaques amyloid. In this way, the amount of Aß that reaches the CSF turns out to be less than that found in subjects without plaques, that is, without AD. For this reason, it is difficult for Aß in the CSF to be considered an early or progression marker, and new biomarkers are necessary. It is proposed to develop a novel analysis protocol halfway between the ELISA (enzyme-linked immunosorbent assay) and the ELLA (enzyme-linked lectin assay) for both sAPPa y sAPPß. This approximation revolves around the PHA lectin and proposes strategies that can be combined. All this with the aim of determining early changes in APP glycosylation in AD CSF that constitute the basis of an early biomarker for AD

    Principal researcher: Dr. Javier Sáez

  • Viscoelastic cushioning for sports shoes based on Sodium Hyaluronic: Currently, in the manufacture of footwear, a great variety of shock-absorbing devices is used, especially in sports shoes. These are elastic solids, often called gels, made from different materials. The outsole is generally made of carbon rubber or a similar material. The most common materials for the midsole are ethylene vinyl acetate (EVA), polyurethane (PU), or a combination of both. In addition, the shock-absorbing devices of sports shoes, include air chambers (airbags) inside the sole of the shoe, located at the appropriate points, so that the foot compresses the chamber and the air contained in it applies an elastic cushioning effect. The natural damping system of the human body is not built with solid silicone, rubber or gels, but with an elastoviscous polysaccharide, HA, dissolved in water. Therefore, we imitate the natural shock absorption system by creating a combination of native HA and modified HA gel to incorporate into the sole of the shoes. HA was successfully developed over millions of years of evolution to incorporate into a single molecule the viscoelastic properties required to optimally absorb the pressure and shear variations that affect the joints when standing, walking or running. The objective of this project is, to incorporate into the sole of sports shoes, a product that imitates the characteristics of natural synovial fluid in human joints and whose rheological properties provide similar or slightly higher protection than that of native HA. The elastoviscous gel to be used is HA of high concentration. This project is based on the observation that HA, designed to absorb the impacts of different characteristics in the joints of the lower extremities produced by the vertical force when standing, or during walking, running and jumping, dissipating said force in the horizontal plane (elastic protection). With this, the intensity of the force transmitted to the joints of the kinematic chain of the leg decreases, reducing physical wear and degeneration of the cartilage over time. This system provides the joints with an additional system of natural protection, useful for the athletic activity of young people and also of older people, in whom there is greater joint vulnerability to mechanical trauma during exercise and also pain reduction. The object of the project is, since the behavior of HA has already been evaluated in the laboratory, to optimize the appropriate proportion of its use within the sole of a shoe, maintaining its beneficial properties for the joints. Principal researchers: Dra. Elvira de la Peña, Dr. Carlos Belmonte

  • Eye comfort: Despite the huge supply of artificial tears, currently on the market, most only offer symptomatic relief from eye discomfort and pain in the very short term or have numerous sequel effects. Therefore, it is a great opportunity to develop a treatment based on the use of active molecules with few or no sequel effects.
    The objective of this project is to validate in the laboratory the efficacy of the ONG-003 molecule for the treatment of ocular discomfort, eliminating the secondary effects of current treatments for the symptomatic relief of ocular discomfort and pain. It is based on the positive results obtained in a proof of concept of the effect of acute treatment (with a single drop) in albino guinea pigs, whose results show that topical ocular treatment with ONG-003 increases baseline lacrimation without causing irritation or apparent eye discomfort. The guinea pig model, both for “ex vivo” electrophysiological recordings and “in vivo” eye discomfort studies, has already been used by the IN research group to test other substances as potential pharmacological treatments or their toxicity, and has been validated as a good model of preliminary study to the translation to the study of the activity in humans. In this sense, it is worth highlighting, the preclinical studies carried out by our laboratory, using this guinea pig model, during the development of the SYL 1001 and AVX-012 molecules, protected by both patent families and which have completed phase III and IIa clinical trials respectively.

    Principal researchers: Dra. Juana Gallar, Dra. María del Carmen Acosta.

  •  Predictive CPE model in Parkinson’s patients: Parkinson’s disease (PD) is a neurodegenerative disease that affects more than 10 million people. It is characterized by the progressive degeneration of the dopaminergic neurons of the black substance. This loss induces changes in the functioning of neural circuits that trigger severe motor symptoms such as tremor, stiffness, and gait lock or freezing, among others. Along with pharmacological treatment, based on the administration of the drug L-DOPA, deep brain stimulation (DBS) has been developed in recent decades as an effective treatment to reduce motor symptoms of the disease. However, and despite the benefits of this treatment in global terms, it is an invasive technique that requires intracranial surgery, which carries associated risks that can lead to significant physical and psychological problems. Therefore, it is essential to identify a priori, before the intervention, in which patients the DBS will be successful or in which it will fail, as well as the characteristics and particularities of the treatment that are best adapted to each patient. DBS is a technique that consists of bilateral implantation of electrodes in the STN of the brain. It is an invasive surgical therapy, which in some cases can evolve into physical complications (bleeding, infections or others) and also cognitive and psychiatric complications (depression, confusion or others). There are established guidelines for the selection of CPE candidates. Among the pre-CPE clinical symptoms, the presence of CDM is accepted when it occurs only in the off-medication condition and shows a complete response to dopaminergic medication. However, it remains to be understood why the expected benefit is not obtained in some patients, particularly when it concerns to CDM. Therefore, to determine clinical, anatomical, stimulation or pharmacological factors that are related to a worsening of CDM after DBS. On the other hand, predictive models based on Artificial Intelligence have shown a very promising potential in medical applications and new applications based on this technique are emerging every time for the clinical and biomedical field, for example, the diagnosis of Parkinson’s (Satapathy et al. 2014; Challa et al. 2016; Kotsavasiloglou et al. 2017). In this project, advanced data analysis and artificial intelligence techniques will be used to achieve the objectives. Parkinson’s disease is caused by the progressive disappearance of dopaminergic neurons. The lack of dopamine produces changes in the basal ganglia, mainly in the activity of the striatum, leading to the motor and cognitive alterations that characterize the disease. There is no cure for Parkinson’s disease. In many cases, the recommended treatment is deep brain stimulation (DBS). Although it is usually effective in mitigating symptoms, it is not always so, for reasons still unknown. The objective of this project is the development of a predictive model of the efficacy of DBS, based on demographic, clinical and stimulation data. This tool will help in the design of the treatment and in the decision of whether to perform a DBS surgery or not, as it carries an obvious risk for the patient. In addition, the modeling of the data will provide a guide to the electrical stimulation parameters (intensity, duration, polarity …) and even anatomical parameters, on the best location of the electrodes within the SNT, in the event that it is decided to perform the ECP surgery.    Principal researcher: Dr. Ramón Reig

  • Loneliness in geriatrics: Support networks and interactions are an important pillar in people’s mental health. The current pandemic, has further evidenced how loneliness and social isolation can have a negative impact on emotional well-being, factors that can exacerbate other physical and cognitive health problems. The geriatric population has been one of the population sectors most affected by the covid19 pandemic, both in terms of mortality and in relation to the negative effects of social isolation. However, the lack of objective markers that make it possible to identify those people who are in a situation of vulnerability, hinders the effectiveness of care work and hinders the application of personalized treatments at times when health and care personnel have had to adapt their routines to strict protocols to maximize security. A new approach is proposed for the early identification of moments of subjective loneliness and emotional stress that can alert healthcare personnel or family members, to design and implement healthcare activities. For this, multidimensional telemetric measurements of different physiological records will be used, accompanied by measures of social distance and subjective reports of emotional well-being. We propose to take advantage of the analysis of this Big Data, and through artificial intelligence analysis, to identify the marker networks indicative of episodes of subjective loneliness perceived by the geriatric population. With the use of portable telemetry devices, in conjunction with periodic and simple assessments of emotional well-being, the predictive potential of certain markers to identify states of physical emotional vulnerability can be validated. In a first stage, this project will focus on the geriatric population located in nursing home, since they provide a more controlled context. However, in future stages, our intention is to extend these objective identification tools to other areas, such as people who live alone in their homes.
    Principal researcher: Dra. Cristina Márquez
  •  Atmosphere: The objective of the Atmosphere project is to study behavior in groups of children to establish patterns that allow making better, evaluating and measuring academic performance, quality of teaching and learning environment on a scientific basis. Principal researcher: Dr. Álex Gómez-Marín

  • Multimodal Imaging: The objective of this project is the development of analysis techniques based on big data approximations and machine learning to add a new dimension to brain imaging biomarkers. It is about combining magnetic resonance imaging (MRI) with other sources of omic data to make better the diagnosis and prognosis of neurological and psychiatric diseases. The goal is to complete a combined brain imaging and machine learning analysis protocol. Principal researcher: Dr. Santiago Canals.
  • Deep Learning: The pathologies included in the group of psychoses, are complex diseases that appear mainly during adolescence with a prevalence of around 2% in the general population. It is a chronic disease of multifactorial etiology that has been related to predisposing factors (genetic, obstetric and perinatal complications, among others) and precipitating factors (use of substances and vital stressors) and that affects brain functions that are decisive for the functioning in social, family and personal life such as emotions, perception, thought and behavior. For the correct diagnosis of this group of diseases, it is necessary to monitor patients in the mental units of hospitals for at least 24 months, where different interviews, protocols and physiological tests are carried out in order to be able to distinguish between schizophrenia (a long-lasting form of psychosis) and psychotic break (an abrupt manifestation of what could be considered the positive symptomatology of schizophrenia for a short period of time). The referral hospital mental health care units treat about 2,600 patients a year, of which about 700 will eventually be diagnosed as schizophrenic. This fact shows that it is a complex and costly disease, in financial and personal terms, both for health entities and for patients and their families. According to data from 2018, the cost associated with schizophrenia in Spain was about 8,000 million Euros, including direct and indirect costs; that is, approximately 10% of the annual health budget. The application of Artificial Intelligence (AI) has great potential in this context and can be very relevant in the precise diagnosis of each patient. The use of machine learning could help to identify combinations of variables, already collected in these mental units, where conventional statistics cannot reach, and that will make possible the anticipated prediction of the diagnosis within the first weeks from the first psychotic episode, considering factors family, environmental, response to treatment, etc. The final objective is to transfer and validate this model in First Psychotic Episodes Care Units or Mental Units of hospitals to improve services to citizens and reduce costs.Principal researcher: Dr. Luis Miguel Martínez
UCIE