MicroRNA-375 plays a dual role in prostate carcinogenesis

Background: Prostate cancer (PCa), a highly incident and heterogeneous malignancy, mostly affects men from developed countries. Increased knowledge of the biological mechanisms underlying PCa onset and progression are critical for improved clinical management. MicroRNAs (miRNAs) deregulation is common in human cancers, and understanding how it impacts in PCa is of major importance. MiRNAs are mostly downregulated in cancer, although some are overexpressed, playing a critical role in tumor initiation and progression. We aimed to identify miRNAs overexpressed in PCa and subsequently determine its impact in tumorigenesis. Results: MicroRNA expression profiling in primary PCa and morphological normal prostate (MNPT) tissues identified 17 miRNAs significantly overexpressed in PCa. Expression of three miRNAs, not previously associated with PCa, was subsequently assessed in large independent sets of primary tumors, in which miR-182 and miR-375 were validated, but not miR-32. Significantly higher expression levels of miR-375 were depicted in patients with higher Gleason score and more advanced pathological stage, aswellaswithregionallymph nodesmetastases. Forced expression of miR-375 in PC-3 cells, which display the lowest miR-375 levels among PCa cell lines, increased apoptosis and reduced invasion ability and cell viability. Intriguingly, in 22Rv1 cells, which displayed the highest miR-375 expression, knockdown experiments also attenuated the malignant phenotype. Gene ontology analysis implicated miR-375 in several key pathways deregulated in PCa, including cell cycle and cell differentiation. Moreover, CCND2 was identified as putative miR-375 target in PCa, confirmed by luciferase assay. Conclusions: A dual role for miR-375 in prostate cancer progression is suggested, highlighting the importance of cellular context on microRNA targeting.

Stage at presentation of breast cancer in Luanda, Angola - a retrospective study

Background: It is expected that, by 2020, 15 million new cases of cancer will occur every year in the world, one million of them in Africa. Knowledge of cancer trends in African countries is far from adequate, and improvements in cancer prevention efforts are urgently needed. The aim of this study was to characterize breast cancer clinically and pathologically at presentation in Luanda, Angola; we additionally provide quality information that will be useful for breast cancer care planning in the country. Methods: Data on breast cancer cases were retrieved from the Angolan Institute of Cancer Control, from 2006 to 2014. For women diagnosed in 2009 (5-years of follow-up), demographic, clinical and pathological information, at presentation, was collected, namely age at diagnosis, parity, methods used for pathological diagnoses, tumor pathological characteristics, stage of disease and treatment. Descriptive statistics were performed. Results: The median age of women diagnosed with breast cancer in 2009 was 47 years old (range 25–89). The most frequent clinical presentation was breast swelling with axillary lymph nodes metastasis (44.9 %), followed by a mass larger than 5 cm (14.2 %) and lump (12.9 %). Invasive ductal carcinoma was the main histologic type (81.8 %). Only 10.1 % of cancer cases had a well differentiated histological grade. Cancers were diagnosed mostly at advanced stages (66.7 % in stage III and 11.1 % in stage IV). Discussion: In this study, breast cancer was diagnosed at a very advanced stage. Although it reports data from a single cancer center in Luanda, Angola it reinforces the need for early diagnosis and increasing awareness. According to the main challenges related to breast cancer diagnosis and treatment herein presented, we propose a realistic framework that would allow for the implementation of a breast cancer care program, built under a strong network based on cooperation, teaching, audit, good practices and the organization of health services. Conclusion: Angola needs urgently a program for early diagnosis of breast cancer.

Bone turnover markers for early detection of fracture healing disturbances: A review of the scientific literature

Imaging techniques are the standard method for assessment of fracture healing processes. However, these methods are perhaps not entirely reliable for early detection of complications, the most frequent of these being delayed union and non-union. A prompt diagnosis of such disorders could prevent prolonged patient distress and disability. Efforts should be directed towards the development of new technologies for improving accuracy in diagnosing complications following bone fractures. The variation in the levels of bone turnover markers (BTMs) have been assessed with regard to there ability to predict impaired fracture healing at an early stage, nevertheless the conclusions of some studies are not consensual. In this article the authors have revised the potential of BTMs as early predictors of prognosis in adult patients presenting traumatic bone fractures but who did not suffer from osteopenia or postmenopausal osteoporosis. The available information from the different studies performed in this field was systematized in order to highlight the most promising BTMs for the assessment of fracture healing outcome.

Using data mining for prediction of hospital length of stay: an application of the CRISP-DM Methodology

Hospitals are nowadays collecting vast amounts of data related with patient records. All this data hold valuable knowledge that can be used to improve hospital decision making. Data mining techniques aim precisely at the extraction of useful knowledge from raw data. This work describes an implementation of a medical data mining project approach based on the CRISP-DM methodology. Recent real-world data, from 2000 to 2013, were collected from a Portuguese hospital and related with inpatient hospitalization. The goal was to predict generic hospital Length Of Stay based on indicators that are commonly available at the hospitalization process (e.g., gender, age, episode type, medical specialty). At the data preparation stage, the data were cleaned and variables were selected and transformed, leading to 14 inputs. Next, at the modeling stage, a regression approach was adopted, where six learning methods were compared: Average Prediction, Multiple Regression, Decision Tree, Artificial Neural Network ensemble, Support Vector Machine and Random Forest. The best learning model was obtained by the Random Forest method, which presents a high quality coefficient of determination value (0.81). This model was then opened by using a sensitivity analysis procedure that revealed three influential input attributes: the hospital episode type, the physical service where the patient is hospitalized and the associated medical specialty. Such extracted knowledge confirmed that the obtained predictive model is credible and with potential value for supporting decisions of hospital managers.

Intelligent decision support to predict patient barotrauma risk in intensive care units

The occurrence of Barotrauma is identified as a major concern for health professionals, since it can be fatal for patients. In order to support the decision process and to predict the risk of occurring barotrauma Data Mining models were induced. Based on this principle, the present study addresses the Data Mining process aiming to provide hourly probability of a patient has Barotrauma. The process of discovering implicit knowledge in data collected from Intensive Care Units patientswas achieved through the standard process Cross Industry Standard Process for Data Mining. With the goal of making predictions according to the classification approach they several DM techniques were selected: Decision Trees, Naive Bayes and Support Vector Machine. The study was focused on identifying the validity and viability to predict a composite variable. To predict the Barotrauma two classes were created: “risk” and “no risk”. Such target come from combining two variables: Plateau Pressure and PCO2. The best models presented a sensitivity between 96.19% and 100%. In terms of accuracy the values varied between 87.5% and 100%. This study and the achieved results demonstrated the feasibility of predicting the risk of a patient having Barotrauma by presenting the probability associated.

A new empirical system for rock slope stability analysis in exploitation stage

An empirical system was developed to obtain a quality index for rock slopes in road infrastructures, named Slope Quality Index (SQI), and it was applied to a set of real slopes.The SQI is supported in nine factors affecting slope stability that contemplate the evaluation of different parameters. Consequently, each factor is classified by the degree of importance and influence by assigned weights. These weights were established through a statistical analysis of replies to a survey that was distributed to several experienced professionals in the field. The proposed SQI varies between1 and 5, corresponding to slopes in very good and very bad condition state, respectively. Besides the advantage linked to a quantitative and qualitative evaluation of slopes, theSQI also allows identifying the most critical factors on the slope stability, which is a fundamental issue for an efficient management of the slope network in the road infrastructure, namely in the planning of conservation and maintenance operations.

Methodology for real-time adaptation of tunnels support using the observational method

The observational method in tunnel engineering allows the evaluation in real time of the actual conditions of the ground and to take measures if its behavior deviates considerably from predictions. However, it lacks a consistent and structured methodology to use the monitoring data to adapt the support system in real time. The definition of limit criteria above which adaptation is required are not defined and complex inverse analysis procedures (Rechea et al. 2008, Levasseur et al. 2010, Zentar et al. 2001, Lecampion et al. 2002, Finno and Calvello 2005, Goh 1999, Cui and Pan 2012, Deng et al. 2010, Mathew and Lehane 2013, Sharifzadeh et al. 2012, 2013) may be needed to consistently analyze the problem. In this paper a methodology for the real time adaptation of the support systems during tunneling is presented. In a first step limit criteria for displacements and stresses are proposed. The methodology uses graphics that are constructed during the project stage based on parametric calculations to assist in the process and when these graphics are not available, since it is not possible to predict every possible scenario, inverse analysis calculations are carried out. The methodology is applied to the “Bois de Peu” tunnel which is composed by two tubes with over 500 m long. High uncertainty levels existed concerning the heterogeneity of the soil and consequently in the geomechanical design parameters. The methodology was applied in four sections and the results focus on two of them. It is shown that the methodology has potential to be applied in real cases contributing for a consistent approach of a real time adaptation of the support system and highlight the importance of the existence of good quality and specific monitoring data to improve the inverse analysis procedure.

Development of a new 3D OpenFOAM® solver to model the cooling stage in profile extrusion

[Excerpt] The advantages resulting from the use of numerical modelling tools to support the design of processing equipment are almost consensual. The design of calibration systems in profile extrusion is not an exception . H owever , the complex geome tries and heat exchange phenomena involved in this process require the use of numerical solvers able to model the heat exchange in more than one domain ( calibrator and polymer), the compatibilization of the heat transfer at the profile - calibrator interface and with the ability to deal with complex geometries. The combination of all these features is usually hard to find in commercial software. Moreover , the dimension of the meshes required to ob tain accurate results, result in computational times prohibitive for industrial application. (...)

Narrative innovations predict symptom improvement: Studying innovative moments in narrative therapy of depression

Objective:Innovative moments (IMs) are moments in the therapeutic dialog that constitute exceptions toward the client's problems. These narrative markers of meaning transformation are associated with change in different models of therapy and diverse diagnoses. Our goal is to test if IMs precede symptoms change, or, on the contrary, are a mere consequence of symptomatic 15 change. Method: For this purpose, IMs and symptomatology (Outcome Questionnaire-10.2) were assessed at every session in a sample of 10 cases of narrative therapy for depression. Hierarchical linear modeling was conducted to explore whether (i) IMs in a given session predict patients' symptoms in the following session and/or (ii) symptoms in a given session predict IMs in the next session. Results: Results suggested that IMs are better predictors of symptoms than the reverse. Conclusions: These results are discussed considering the contribution of meanings and narrative processes' changes to symptomatic improvement.

Ki-67 expression in CRC lymph node metastasis does not predict survival

Colorectal cancer is one of the most common malignancies and a leading cause of cancer death worldwide. Molecular markers may improve clinicopathologic staging and provide a basis to guide novel therapeutic strategies which target specific tumourassociated molecules according to individual tumour biology; however, so far, no ideal molecular marker has been found to predict disease progression. We tested Ki-67 proliferation marker in primary and lymph node metastasis of CRC. We observed a statistical significant difference between the positive rates of neoplastic cells positively stained byKi-67 in both sites, with remarkable increased number of Ki-67 positive cells in primary tumor cells compared to cancer cells that invaded lymph nodes. We can speculate that the metastatic CRC in lymph node can be more resistant to the drugs that target cellular division.

Diagnóstico e ensaio de carga de asnas tradicionais de madeira

Dissertação de mestrado Integrado em Engenharia Civil

Assessment of prognosis in patients with stage II colon cancer

Assessment of prognosis of patients with stage II colon cancer.

The role of IFNγ in higher brain function: in health and under chronic stress

Tese de Doutoramento em Ciências da Saúde

Post-divorce representations of marital negotiation during marriage predict parenting alliance in newly divorced parents

Literature demonstrates that marital and co-parenting subsystems are intercorrelated and have autonomous functions in the family system. This study explored representations of marital negotiation strategies for conflict resolution during marriage and parenting alliance relationship after divorce, using data from Portuguese newly divorced parents. In multiple regression analysis, representations of marital negotiation strategies for conflict resolution during marriage used by ex-spouses predict positive parenting alliance relationship after divorce. These results suggest that representations of pre-divorce marital relationship influence positively current interparental relationship regarding child rearing after marital dissolution. Implications for clinical interventions are also discussed.

Pervasive decision support to predict football corners and goals by means of data mining

Football is considered nowadays one of the most popular sports. In the betting world, it has acquired an outstanding position, which moves millions of euros during the period of a single football match. The lack of profitability of football betting users has been stressed as a problem. This lack gave origin to this research proposal, which it is going to analyse the possibility of existing a way to support the users to increase their profits on their bets. Data mining models were induced with the purpose of supporting the gamblers to increase their profits in the medium/long term. Being conscience that the models can fail, the results achieved by four of the seven targets in the models are encouraging and suggest that the system can help to increase the profits. All defined targets have two possible classes to predict, for example, if there are more or less than 7.5 corners in a single game. The data mining models of the targets, more or less than 7.5 corners, 8.5 corners, 1.5 goals and 3.5 goals achieved the pre-defined thresholds. The models were implemented in a prototype, which it is a pervasive decision support system. This system was developed with the purpose to be an interface for any user, both for an expert user as to a user who has no knowledge in football games.