Phelan-McDermid Syndrome (PMS) is a rare genetic condition caused by a deletion of the terminal end of chromosome 22 in the 13.3 region, as well as, by point mutations within SHANK3 gene. The most characteristic clinical symptom is global developmental delay, absent or severely delayed speech, hypotonia and autism spectrum disorder. The syndrome is underdiagnosed and its real incidence remains unknown, but more than 2,000 cases have been reported worldwide. In the present investigation patients diagnosed with PMS for twelve years were recruited throughout the Spanish territory. The clinical patient information was obtained from the referral doctors using two standardized questionnaires completed with data from the medical reports and the interview with the parents. The molecular diagnosis of the disease was carried out using different formats of microarrays. Data were processed using Microsoft Excel and Statgraphics Centurion XVII.

Currently, there are 201 people diagnosed with PMS in Spain with a prevalence of 4x10-4/10,000. The community with the most diagnosed patients is Madrid and there are no significant differences in terms of sex. The mean age at diagnosis is around 6.67 years. Therefore, the prevalence of the disease in Spain is very low, and it can be stated that it is very likely that there are more people with this syndrome in the population.

Schistosomiasis is an acute and chronic parasitic disease, caused by blood flukes (trematode worms) of the genus Schistosoma. For 2019, the World Health Organization estimated that close to 240 million people required preventive treatment against this disease, mainly poor communities without access to safe drinking water and adequate sanitation. Similarly to others Neglected Tropical Diseases (NTDs), the treatments against this disease are limited, and new chemotherapies are necessary to the control and elimination of the Schistosomiasis. Natural products are an interesting alternative in the development of new treatments against NTDs. Interestingly, several studies have demonstrated the great potential of some sesquiterpene lactones (SLs) as potential therapeutic agents for some NTDs and the relationship between the biological activities with their chemical structure. In this study, using a machine learning model with more than 77% accuracy in both the cross-validation and test sets for Schistosoma mansoni, a ligand-based virtual screening was performed, using 1,300 SLs registered in SistematX database. The results show that close to 42% of the SLs tested reached active probability values above of 0.5, being identifies some common structural features in the best-ranked molecules. Finally, to explore the mechanism of action of these SLs against Schistosoma mansoni, molecular docking calculations were performed for the five best-ranked SLs using the crystal structure of Sm dihydroorotate dehydrogenase (DHODH), with 2-[(4-fluorophenyl)amino]-3-hydroxynaphthalene-1,4-dione as inhibitor. Through these calculations, some critical interactions between the tested SLs and the active site of DHODH were identified.

Introdução: Rosa aff rubiginosa, popularmente conhecida como Rosa mosqueta é amplamente utilizada na indústria cosmética e alimentícia por sua composição(1). O óleo fixo extraído por prensagem à frio de suas sementes é rico em ácidos graxos essenciais à nutrição humana, especialmente ácido linoleico, oleico e linolênico, e ácido trans-retinoico(2,3), além de flavonoides(3), vitamina C e carotenoides(2), substancias eficazes em tratamentos cutâneos(4,5). Por esse motivo, tem crescido o interesse por esse insumo para cosméticos com aplicação terapêutica. Contudo, por sua composição majoritariamente graxa, o óleo possui pouca estabilidade devido ao processo natural de peroxidação(6). Objetivos: Diante do exposto, o presente trabalho visou avaliar a atividade antioxidante do óleo incorporado em formulações cosmecêuticas. Método: Foram avaliadas três emulsões, sendo duas contendo 30% de óleo de Rosa aff rubiginosa, sem ou com BHT (2,6-Di-tert-butyl-p-cresol) como antioxidante sintético, e a terceira formulação elaborada sem a presença do óleo vegetal, porem com BHT, chamada creme branco para fins de comparação. Todas as emulsões, juntamente com o óleo puro foram armazenadas em frascos airless e mantidas em câmara climática por 15 dias à 40 ± 2^oC e 30 ± 5% de UR. As amostras recém-preparadas e expostas a tratamento climático por 15 dias foram submetidas a teste de atividade antioxidante pelo método DPPH proposto por Kim et al (2002)(7). A extração de compostos antioxidantes foi efetuada por dissolução de 1,5g da amostra em 1,5mL de solução de acetona:etanol (80:20 v/v). A mistura foi submetida a energia ultrassónica por 15min, seguido de centrifugação 5000rpm/15min. Adicionou-se 100µL do sobrenadante obtido a 2900µL do radical 0,1mM em etanol PA. Após agitação em vórtex, foram mantidos em repouso por 30min protegidos da luz. A densidade óptica inicial do radical foi avaliada em espectrofotômetro à absorbância de 517nm. A atividade antioxidante foi expressa em equivalente Trolox (TEAC). Resultados e discussão: A Figura 1 evidencia a ação antioxidante do óleo vegetal, puro ou incorporado em fórmulas cosmecêuticas, como também seu efeito sinérgico em presença de antioxidante sintético. Também foi possível observar a manutenção da ação, mesmo após tratamento em condições extremas. Entretanto, quando inserido na emulsão, os compostos ativos do óleo foram protegidos do tratamento climático empregado. Outra observação importante diz respeito a ação inferior inicial nas emulsões em comparação ao óleo vegetal, que pode ser justificada pela exposição térmica durante processo produtivo da forma farmacêutica.

The present communication illustrates the results of a computational study based on molecular topology, focused on the repositioning of drugs to treat the SARS-CoV-2 virus, better known as coronavirus, responsible for the COVID-19 disease. Using lopinavir, a well-known viral protease inhibitor as the reference drug, a mathematical pattern is found allowing the screening of the market drugs, searching for potential candidates to inhibit the said enzyme. This way new possible therapeutic alternatives to fight the coronavirus are found. Results indicate that antivirals such as brecanavir, as well as various groups of drugs, among which are antibiotics of the macrolide family (azithromycin, clarithromycin and erythromycin among others) could be useful in treating COVID-19 infection.

Knowing the chemical composition of a substance provides a great deal of information about it. Non-destructive analysis techniques can be used to try to know its chemical composition. Among them, near-infrared spectroscopy (NIRS) stands out. The devices used to obtain this information are known as spectrophotometers.

During the development of the Final Degree Project, an application for cell phones was implemented that connected to one of these devices to take measurements and store them in different databases. In addition, this application also allowed the execution of neural network models based on TensorFlow.

The training of models and the processing of the databases generated by the application is not feasible due to the characteristics of cell phones. This is why we have tried to solve this problem in this Master Thesis by developing a web server with a dedicated architecture based on microservices. This architecture tries to make the most of the characteristics of the different machines that contain it, dividing it into modules that cover only the needs of each one, avoiding the use of resources that are not necessary. The tasks carried out in the system are developed offline, avoiding the need for the user to stay connected while the different processes are executed. It has been necessary to divide the system into 2 large blocks taking into account a division based on functionalities:

The front-end is composed of a web page that enables the realization of all the functionalities in a simple way and is adapted to different devices, such as mobiles, tablets or computers.

The back-end is composed of 3 modules: user authentication and file management, model training and database processing. Both the model training module and the data processing module make use of the configuration files generated in the front-end part of the system. These modules are contained so that, if it is necessary to add computational capacity, it is sufficient to replicate the indicated service without consuming unnecessary resources.

Although diabetes is known to be a disease that is closely linked to genetics and epigenetics, the mechanisms underlying the onset and/or progression of the disease have sometimes not been fully addressed in order to help patients. In recent years and due to a large number of recent studies, it is known that changes in the balance of the microbiota can cause a battery of diseases. Nowadays, massive sequencing techniques allow us to obtain the metagenomic profile of an individual, whether from a part of the body, organ or tissue, thus being able to identify the composition of a given microbiota. The use of Machine Learning (ML) techniques, which do not have any biological assumptions, are capable of identifying expression patterns and relationships between characteristics. We present a model based on ML techniques and a metagenomic signature capable of stratifying patients with Type I Diabetes (TID), to serve as a support tool for clinical decision making.

The current SARS-CoV-2 pandemic has put the entire civilization, particularly medical systems around the world, to the test. Managers and decision-makers in the health-care system must maximize resource management. Because of their predictive capacity, Artificial Intelligence (AI) tools and procedures are extremely useful in decision-making in emergency situations including severe pandemics. The PRACTICUM DIRECT project is presented in this paper, which proposes the design and development of a tool to aid health system managers in making early management decisions of hospital resources using AI approaches.

The importance of aquaculture has been increasing in recent decades. Mussel farming is one of its main pillars at the international level. The main natural threat to this type of farming is harmful algal blooms (HABs). This is because these blooms produce toxins that, although harmless to the molluscs, accumulate in their flesh and pose a risk to humans after consumption. In Galicia, Spain's main producer of cultivated mussels, the opening and closing of production areas is controlled by a human-based monitoring programme. In addition to closures resulting from the presence of toxicity exceeding the legal threshold, in the absence of confirmatory sampling and the existence of risk factors, precautionary closures can be applied. These decisions are taken by experts without the support and formalisation of the expertise on which they are based. This study aims to create an application capable of predicting the appearance of harmful algal blooms. This will provide early support for decision-making in the management of mussel production areas.

This presentation is an introduction to the world of Artificial Neural Networks. Nowadays, Artificial Neural Networks are systems that have been very successful in many different problems, so their study is very useful for researchers in many different environments. Specifically, in the biomedical field, knowledge of Artificial Neural Networks is currently of great importance given the large number of applications they have, especially in the intelligent processing of images and signals. In this presentation we will review from the simplest models such as ADALINE to more complex models such as the multilayer perceptron, leaving the way open to start learning Deep Learning techniques. A historical introduction is given and the main algorithms of operation are reviewed, but special emphasis is placed on practical applications, especially in the biomedical field.

Dementia is a brain disorder that severely affects a person's ability to carry out daily activities. Alzheimer's disease is the most common form in which dementia dementia manifests itself in people over the age of 60. Since there are no drugs that can stop this disease, it is important to detect it early, trying avoid (or at least delay) the accelerated cognitive decline through constant physical and mental activity. On the other hand, the monitoring of the elderly by the public health system is a major challenge. It involves a great economic cost and consultation time. This work shows a mobile application and a web application, to help specialists to monitor elderly people in a simple and dynamic way. Its use should make it possible to detect patients with symptoms of cognitive impairment in their early stages. In addition, it will try to delay the arise of such symptoms, by encouraging physical and mental activity on a daily basis.