Role of biofilms in the oral health of animals

In humans the importance of biofilms in disease processes is now widely recognised together with the difficulties in treating such infections once established. One of the earliest and certainly most studied biofilm in humans is that of dental plaque which is responsible for two of the most prevalent human infections, namely dental caries and periodontal disease. However, comparable studies of dental plaque in animals are relatively limited, despite the fact that similar infections also occur, and in the case of farm animals there is an associated economic impact. In addition, biofilms in the mouths of animals can also be detrimental to human health when transferred by animal bites. As a result, an understanding of both the microbial composition of animal plaque biofilms together with their role in animal diseases is important. Through the use of modern molecular studies, an insight into the oral microflora of animals is now being obtained and, to date, reveals that despite differences in terms of microbial species and relative proportions occurring between humans and animals, similarities do indeed exist. This information can be exploited in our efforts to both manage and treat infections in animals arising from the presence of an oral biofilm. This Chapter describes our current understanding of the microbial composition of animal plaque, its role in disease and how oral hygiene measures can be implemented to reduce subsequent infection.

A soft computing approach to kidney diseases evaluation

Kidney renal failure means that one’s kidney have unexpectedly stopped functioning, i.e., once chronic disease is exposed, the presence or degree of kidney dysfunction and its progression must be assessed, and the underlying syndrome has to be diagnosed. Although the patient’s history and physical examination may denote good practice, some key information has to be obtained from valuation of the glomerular filtration rate, and the analysis of serum biomarkers. Indeed, chronic kidney sickness depicts anomalous kidney function and/or its makeup, i.e., there is evidence that treatment may avoid or delay its progression, either by reducing and prevent the development of some associated complications, namely hypertension, obesity, diabetes mellitus, and cardiovascular complications. Acute kidney injury appears abruptly, with a rapid deterioration of the renal function, but is often reversible if it is recognized early and treated promptly. In both situations, i.e., acute kidney injury and chronic kidney disease, an early intervention can significantly improve the prognosis.The assessment of these pathologies is therefore mandatory, although it is hard to do it with traditional methodologies and existing tools for problem solving. Hence, in this work, we will focus on the development of a hybrid decision support system, in terms of its knowledge representation and reasoning procedures based on Logic Programming, that will allow one to consider incomplete, unknown, and even contradictory information, complemented with an approach to computing centered on Artificial Neural Networks, in order to weigh the Degree-of-Confidence that one has on such a happening. The present study involved 558 patients with an age average of 51.7 years and the chronic kidney disease was observed in 175 cases. The dataset comprise twenty four variables, grouped into five main categories. The proposed model showed a good performance in the diagnosis of chronic kidney disease, since the sensitivity and the specificity exhibited values range between 93.1 and 94.9 and 91.9–94.2 %, respectively.

An assessment of chronic kidney diseases

Kidney renal failure means that one’s kidney have unexpectedlystoppedfunctioning,i.e.,oncechronicdiseaseis exposed, the presence or degree of kidney dysfunction and its progression must be assessed, and the underlying syndrome has to be diagnosed. Although the patient’s history and physical examination may denote good practice, some key information has to be obtained from valuation of the glomerular filtration rate, and the analysis of serum biomarkers. Indeed, chronic kidney sickness depicts anomalous kidney function and/or its makeup, i.e., there is evidence that treatment may avoid or delay its progression, either by reducing and prevent the development of some associated complications, namely hypertension, obesity, diabetes mellitus, and cardiovascular complications. Acute kidney injury appears abruptly, with a rapiddeteriorationoftherenalfunction,butisoftenreversible if it is recognized early and treated promptly. In both situations, i.e., acute kidney injury and chronic kidney disease, an early intervention can significantly improve the prognosis. The assessment of these pathologies is therefore mandatory, although it is hard to do it with traditional methodologies and existing tools for problem solving. Hence, in this work, we will focus on the development of a hybrid decision support system, in terms of its knowledge representation and reasoning procedures based on Logic Programming, that will allow onetoconsiderincomplete,unknown,and evencontradictory information, complemented with an approach to computing centered on Artificial Neural Networks, in order to weigh the Degree-of-Confidence that one has on such a happening. The present study involved 558 patients with an age average of 51.7 years and the chronic kidney disease was observed in 175 cases. The dataset comprise twenty four variables, grouped into five main categories. The proposed model showed a good performance in the diagnosis of chronic kidney disease, since the sensitivity and the specificity exhibited values range between 93.1 and 94.9 and 91.9–94.2 %, respectively.

Artificial neural networks in diagnosis of liver diseases

Liver diseases have severe patients’ consequences, being one of the main causes of premature death. These facts reveal the centrality of one`s daily habits, and how important it is the early diagnosis of these kind of illnesses, not only to the patients themselves, but also to the society in general. Therefore, this work will focus on the development of a diagnosis support system to these kind of maladies, 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.

Autoimmune diseases and pregnancy: analysis of a series of cases

BACKGROUND: An autoimmune disease is characterized by tissue damage, caused by self-reactivity of different effector mechanisms of the immune system, namely antibodies and T cells. All autoimmune diseases, to some extent, have implications for fertility and obstetrics. Currently, due to available treatments and specialised care for pregnant women with autoimmune disease, the prognosis for both mother and child has improved significantly. However these pregnancies are always high risk. The purpose of this study is to analyse the fertility/pregnancy process of women with systemic and organ-specific autoimmune diseases and assess pathological and treatment implications. METHODS: The authors performed an analysis of the clinical records and relevant obstetric history of five patients representing five distinct autoimmune pathological scenarios, selected from Autoimmune Disease Consultation at the Hospital of Braga, and reviewed the literature. RESULTS: The five clinical cases are the following: Case 1-28 years old with systemic lupus erythematosus, and clinical remission of the disease, under medication with hydroxychloroquine, prednisolone and acetylsalicylic acid, with incomplete miscarriage at 7 weeks of gestation without signs of thrombosis. Case 2-44 years old with history of two late miscarriages, a single preterm delivery (33 weeks) and multiple thrombotic events over the years, was diagnosed with antiphospholipid syndrome after acute myocardial infarction. Case 3-31 years old with polymyositis, treated with azathioprine for 3 years with complete remission of the disease, took the informed decision to get pregnant after medical consultation and full weaning from azathioprine, and gave birth to a healthy term new-born. Case 4-38 years old pregnant woman developed Behcet's syndrome during the final 15 weeks of gestation and with disease exacerbation after delivery. Case 5-36 years old with autoimmune thyroiditis diagnosed during her first pregnancy, with difficult control over the thyroid function over the years and first trimester miscarriage, suffered a second miscarriage despite clinical stability and antibody regression. CONCLUSIONS: As described in literature, the authors found a strong association between autoimmune disease and obstetric complications, especially with systemic lupus erythematosus, antiphospholipid syndrome and autoimmune thyroiditis.

Nanotechnological approaches using curcumin and Withania somnifera: neuroprotection and antimicrobial activities

Tese de Doutoramento em Biologia de Plantas

Development of folate-targeted liposomes for rheumatoid arthritis therapy

Tese de Doutoramento em Biologia Molecular e Ambiental (área de especialização em Biologia Celular e Saúde).

Stress-triggered synaptic malfunction: a gate along the path from depressionto dementia

Tese de Doutoramento em Ciências da Saúde.

Marine origin polysaccharides in drug delivery systems

Oceans are a vast source of natural substances. In them, we find various compounds with wide biotechnological and biomedical applicabilities. The exploitation of the sea as a renewable source of biocompounds can have a positive impact on the development of new systems and devices for biomedical applications. Marine polysaccharides are among the most abundant materials in the seas, which contributes to a decrease of the extraction costs, besides their solubility behavior in aqueous solvents and extraction media, and their interaction with other biocompounds. Polysaccharides such as alginate, carrageenan and fucoidan can be extracted from algae, whereas chitosan and hyaluronan can be obtained from animal sources. Most marine polysaccharides have important biological properties such as biocompatibility, biodegradability, and anti-inflammatory activity, as well as adhesive and antimicrobial actions. Moreover, they can be modified in order to allow processing them into various shapes and sizes and may exhibit response dependence to external stimuli, such as pH and temperature. Due to these properties, these biomaterials have been studied as raw material for the construction of carrier devices for drugs, including particles, capsules and hydrogels. The devices are designed to achieve a controlled release of therapeutic agents in an attempt to fight against serious diseases, and to be used in advanced therapies, such as gene delivery or regenerative medicine.

The choroid plexus is modulated by various peripheral stimuli: implications to diseases of the central nervous system

The blood brain barrier (BBB) and the blood cerebrospinal fluid barrier (BCSFB) form the barriers of the brain. These barriers are essential not only for the protection of the brain, but also in regulating the exchange of cells and molecules in and out of the brain. The choroid plexus (CP) epithelial cells and the arachnoid membrane form the BCSFB. The CP is structurally divided into two independent compartments: one formed by a unique and continuous line of epithelial cells that rest upon a basal lamina; and, a second consisting of a central core formed by connective and highly vascularized tissue populated by diverse cell types (fibroblasts, macrophages and dendritic cells). Here, we review how the CP transcriptome and secretome vary depending on the nature and duration of the stimuli to which the CP is exposed. Specifically, when the peripheral stimulation is acute the CP response is rapid, strong and transient, whereas if the stimulation is sustained in time the CP response persists but it is weaker. Furthermore, not all of the epithelium responds at the same time to peripheral stimulation, suggesting the existence of a synchrony system between individual CP epithelial cells.