M. Rosário Martins

Modelling molecular and inorganic data of Amanita ponderosa mushrooms using artificial neural networks

Wild edible mushrooms Amanita ponderosa Malençon and Heim are very appreciated in gastronomy, with high export potential. This species grows in some microclimates, namely in the southwest of the Iberian Peninsula. The results obtained demonstrate that A. ponderosa mushrooms showed different inorganic composition according to their habitat and the molecular data, obtained by M13-PCR, allowed to distinguish the mushrooms at species level and to differentiate the A. ponderosa strains according to their location. Taking into account, on the one hand, that the characterisation of different strains is essential in further commercialisation and certification process and, on the other hand, the molecular studies are quite time consuming and an expensive process, the development of formal models to predict the molecular profile based on inorganic composition comes to be something essential. In the present work, Artificial Neural Networks (ANNs) were used to solve this problem. The ANN selected to predict molecular profile based on inorganic composition has a 6-7-14 topology. A good match between the observed and predicted values was observed. The present findings are wide potential application and both health and economical benefits arise from this study.

MSP-PCR and RAPD molecular biomarkers to characterizeAmanita ponderosa mushrooms

Amanita ponderosa is a specie of wild edible mushrooms growing spontaneously in some Mediterranean microclimates, namely in Alentejo and Andaluzia, in the Iberian Peninsula. The nutritional values of these fungi make them highly exportable. Due to the wide diversity of mushrooms in nature, it is essential to differentiate and to identify the various edible species. RAPD markers have been used as a valuable tool to distinguish the different genotypes, although this method has not yet been used toAmanita ponderosa. Two methods were used to establish different genetic fingerprinting patterns of edible mushrooms. Samples ofAmanita ponderosa were collected in six different regions of the southwest of the Iberian Peninsula and compared by RAPD-PCR and MSP-PCR. Additionally, to compare molecular profiles with others genera of edible mushrooms, three species of Basidiomycetes (Pleurotus ostreatus, Lactarius deliciosus andCoriolus versicolor) and an Ascomycete were used. Results showed that some molecular markers discriminate among an Ascomycete from Basidiomycetes (Amanita ponderosa, Pleurotus ostreatus, Lactarius deliciosus andCoriolus versicolor) and discriminate among the different genera within basidiomycetes, as it is expected. Moreover, OPF-6, OPG-2, OPG3 and M13 primes allowed to unravel a level of genetic polymorphism withinAmanita ponderosa mushrooms collected from different geographic origin.

Molecular approach to characterize ectomycorrhizae fungi from Mediterranean pine stands in Portugal.

Stone pine (Pinus pinea L.), like other conifers, forms ectomycorrhizas (ECM), which have beneficial impact on plant growth in natural environments and forest ecosystems. An in vitro co-culture of stone pine microshoots with pure mycelia of isolated ECM sporocarps was used to overcome the root growth cessation not only in vitro but also to improve root development during acclimation phase. Pisolithus arhizus (Scop.) Rauschert and Lactarius deliciosus (L. ex Fr.) S.F. Gray fungi, were collected, pure cultured and used in in vitro co-culture with stone pine microshoots. Samples of P. arhizus and L. deliciosus for the in vitro co-cultures were collected from the pine stands southwest Portugal. The in situ characterization was based on their morphotypes. To confirm the identity of the collected material, ITS amplification was applied using the pure cultures derived from the sporocarps. Additionally, a molecular profile using PCR based genomic fingerprinting comparison was executed with other genera of Basidiomycetes and Ascomycetes. Our results showed the effectiveness of the techniques used to amplify DNA polymorphic sequences, which enhances the characterization of the genetic profile of ECM fungi and also provides an option to verify the fungus identity at any stage of plant mycorrhization.

Chemical Composition, Antibacterial, Antibiofilm and Synergistic Properties of Essential Oils from Eucalyptus globulus Labill. and Seven Mediterranean Aromatic Plants

Essential oils (EOs) from Eucalyptus globulus Labill. ssp. globulus and from Mediterranean autochthonous aromatic plants – Thymus mastichina L., Mentha pulegium L., Rosmarinus officinalis L., Calamintha nepeta (L.) Savi ssp. nepeta, Cistus ladanifer L., Foeniculum vulgare L., Dittrichia viscosa (L.) Greuter ssp. viscosa – were extracted by hydrodistillation and characterized by GC‐FID and NMR spectroscopy. EOs were evaluated for antimicrobial properties against several bacterial strains, using diverse methods, namely, the agar disc‐diffusion method, the microdilution method, the crystal violet assay and the Live/Dead staining for assessment of biofilm formation. Potential synergy was assessed by a checkerboard method. EOs of R. officinalis and C. ladanifer showed a predominance in monoterpene hydrocarbons (> 60%); EOs of C. nepeta, M. pulegium, T. mastichina, E. globulus and F. vulgare were rich in oxygenated monoterpenes (62 – 96%) whereas EO of D. viscosa was mainly composed of oxygenated sesquiterpenes (54%). All EOs showed antimicrobial activity; M. pulegium and E. globulus generally had the strongest antimicrobial activity. EO of C. nepeta was the most promising in hampering the biofilm formation. The combinations D. viscosa/C. nepeta and E. globulus/T. mastichina were synergistic against Staphylococcus aureus. These results support the notion that EOs from the aromatic plants herein reported should be further explored as potential pharmaceuticals and/or food preservatives.

A case-based reasoning view of thrombophilia risk

Thrombophilia stands for a genetic or an acquired tendency to hypercoagulable states that increase the risk of venous and arterial thromboses. Indeed, venous thromboembolism is often a chronic illness, mainly in deep venous thrombosis and pulmonary embolism, requiring lifelong prevention strategies. Therefore, it is crucial to identify the cause of the disease, the most appropriate treatment, the length of treatment or prevent a thrombotic recurrence. Thus, this work will focus on the development of a diagnosis decision support system in terms of a formal agenda built on a logic programming approach to knowledge representation and reasoning, complemented with a case-based approach to computing. The proposed model has been quite accurate in the assessment of thrombophilia predisposition risk, since the overall accuracy is higher than 90% and sensitivity ranging in the interval [86.5%, 88.1%]. The main strength of the proposed solution is the ability to deal explicitly with incomplete, unknown, or even self-contradictory information.

Thrombophilia Screening

Thrombotic disorders have severe consequences for the patients and for the society in general, being one of the main causes of death. These facts reveal that it is extremely important to be preventive; being aware of how probable is to have that kind of syndrome. Indeed, this work will focus on the development of a decision support system that will cater for an individual risk evaluation with respect to the surge of thrombotic complaints. The Knowledge Representation and Reasoning procedures used will be based on an extension to the Logic Programming language, allowing the handling of incomplete and/or default data. The computational framework in place will be centered on Artificial Neural Networks.

Molecular evaluation of some Amanita ponderosa and the fungal strains living in association with these mushrooms in the southwestern Iberian Peninsula

Amanita ponderosa are wild edible mushrooms that grow only in some microclimates, particularly those in the southwestern part of the Iberian Peninsula. Due to the vast diversity of mushrooms in nature, as well as nutrient variability, which is highly dependent on soil type and environmental conditions, it is essential to be able to characterize fungal microbiota that lives in association with mushrooms and to differentiate A. ponderosa strains of different regions for certification purposes. In this study, we characterized the genetic profile of A. ponderosa mushrooms and the fungal strains that live in association with them in their natural habitat and compared the fingerprinting profiles obtained by M13-PCR amplification of the genomic DNA. We found that the predominant fungal isolates living in association with A. ponderosa were Aspergillus spp., Penicillium spp. and Mucor spp. M13-PCR molecular analysis showed that different fungal isolates had different genetic profiles. This approach allowed us to differentiate the different fungi strains isolated from fruiting bodies of A. ponderosa both rapidly and in a reproducible manner and to group them according to genus. Our fingerprinting analyses also distinguished different A. ponderosa mushrooms collected from different regions. Consequently, we conclude that this method is a very discriminatory approach for differentiating both A. ponderosa from different sites and the fungal microbiota that lives in association with these mushrooms.

Antioxidant activity and cholinesterase inhibition studies of four flavouring herbs from Alentejo

Abstract Essential oils (EOs) and aqueous extracts of aerial parts of four aromatic species, Calamintha nepeta, Foeniculum vulgare, Mentha spicata and Thymus mastichina, from southwest of Portugal were characterised chemically and analysed in order to evaluate their antioxidant potential and cholinesterase inhibitory activities. The main components of EOs were oxygenated monoterpenes, and aqueous extracts were rich in phenol and flavonoid compounds. EOs and aqueous extracts presented a high antioxidant potential, with ability to protect the lipid substrate, free radical scavenging and iron reducing power. Furthermore, EOs and extracts showed AChE and BChE inhibitory activities higher than rivastigmine, the standard drug. Results suggested the potential use of EOs and aqueous extracts of these flavouring herbs as nutraceutical or pharmaceutical preparations to minimise the oxidative stress and the progression of degenerative diseases.

Antiphospholipid Syndrome Risk Evaluation

The antiphospholipid syndrome is an acquired autoimmune disorder produced by high titers of antiphospholipid antibodies that cause both arterial and veins thrombosis as well as pregnancy-related complications and morbidity, as clinical manifestations. This autoimmune hypercoagulable state, often associated with coronary artery disease and recurrent Acute Myocardium Infraction, has severe consequences for the patients, being one of the main causes of thrombotic disorders and death. Therefore, it is extremely important to be preventive; being aware of how probable is to have that kind of syndrome. Despite the updated of the APS classification published as Sydney criteria, diagnosis of this syndrome remains challenging. Further research on clinically relevant antibodies and standardization of their quantification are required to improve clinical risk assessment in APS. This work will focus on the development of a diagnosis support system to antiphospholipid syndrome, built under a formal framework based on Logic Programming, in terms of its knowledge representation and reasoning procedures, complemented with an approach to computing grounded on Artificial Neural Networks. The proposed model allowed to improve the diagnosis, classifying properly the patients that really presented this pathology (sensitivity about 92 %) as well as classifying the absence of APS (specificity ranging from 89 to 94 %).

Artificial Neural Networks in Acute Coronary Syndrome Screening

In Acute Coronary Syndrome (ACS), early use of correct therapy plays a key role in altering the thrombotic process resulting from plaque rupture, thereby minimizing patient sequels. Indeed, current quality improvement efforts in acute cardiovascular care are focused on closing treatment gaps, so more patients receive evidence-based therapies. Beyond ensuring that effective therapies are administered, attention should also be directed at ensuring that these therapies are given both correctly and safely. Indeed, this work will focus on the development of a diagnosis support system, in terms of its knowledge representation and reasoning procedures, under a formal framework based on Logic Programming, complemented with an approach to computing centered on Artificial Neural Networks, to evaluate ACS predisposing and the respective Degree-of-Confidence that one has on such a happening.