Parasitic thyroid nodule in a patient with Hashimoto's chronic thyroiditis

A case of parasitic thyroid nodule is presented. The patient was a non symptomatic 53-year-old white woman, on irregular course of L-thyroxine to treat hypothyroidism due to Hashimoto's thyroiditis. Without a history of thyroid trauma or surgery, she presented a 1.6 x 0.7 x 0.5cm right pre-laryngeal lymph node-like mass which, on ultrasonography, appeared distinct from the gland. TSH, thyroid peroxidase antibody and thyroglobulin antibody serum levels were elevated and T4-free level was normal. Thyroid and total body 99mTc isonitrile scintiscan showed a topic thyroid without radionuclide uptake in the nodule. Fine-needle aspiration of the nodule showed epithelial cells with nuclear atypia and oncocytic changes plus intense lymphoid infiltration and germinative center formation, simulating lymph node metastasis of papillary thyroid carcinoma. Conventional biopsy revealed a parasitic thyroid nodule with Hashimoto's chronic thyroiditis. Parasitic thyroid nodule must always be remembered so that unnecessary surgical assessment and undesirable sequels may be avoided.

Chemotherapy versus best supportive care in stage IV non-small cell lung cancer, non metastatic to the brain

Stage IV non-small cell lung cancer is a fatal disease, with a median survival of 14 months. Systemic chemotherapy is the most common approach. However the impact in overall survival and quality of life still a controversy. OBJECTIVES: To determine differences in overall survival and quality of life among patients with stage IV non-small cell lung cancer non-metastatic to the brain treated with best supportive care versus systemic chemotherapy. PATIENTS: From February 1990 through December 1995, 78 eligible patients were admitted with the diagnosis of stage IV non-small cell lung cancer . Patients were divided in 2 groups: Group A (n=31 -- treated with best supportive care ), and Group B (n=47 -- treated with systemic chemotherapy). RESULTS: The median survival time was 23 weeks (range 5 -- 153 weeks) in Group A and 55 weeks (range 7.4 -- 213 weeks) in Group B (p=0.0018). In both groups, the incidence of admission for IV antibiotics and need of blood transfusions were similar. Patients receiving systemic chemotherapy were also stratified into those receiving mytomycin, vinblastin, and cisplatinum, n=25 and those receiving other combination regimens (platinum derivatives associated with other drugs, n=22). Patients receiving mytomycin, vinblastin, and cisplatinum, n=25 had a higher incidence of febrile neutropenia and had their cycles delayed for longer periods of time than the other group. These patients also had a shorter median survival time (51 versus 66 weeks, p=0.005). CONCLUSION: In patients with stage IV non-small cell lung cancer, non-metastatic to the brain, chemotherapy significantly increases survival compared with best supportive care.

Computed tomography-guided cutting needle biopsy of pulmonary lesions

PURPOSE: To report the experience of a radiology department in the use of computed tomography - guided cutting needle biopsy of pulmonary nodules, by evaluating diagnostic yield and incidence of complications. METHODS: This is a retrospective analysis of 52 consecutive patients who underwent lung lesion biopsy guided by computed tomography, performed between May 1997 and May 2000. Thirty-five patients were male and 17 were female, with ages ranging from 5 to 85 years (median, 62 years). The size of the lesions ranged from 1.8 to 15 cm (median, 5.4 cm). RESULTS: In a total of 52 biopsies of lung lesions, 51 biopsies (98.1%) supplied appropriate material for histopathological diagnosis, with 9 diagnosed (17.3%) as benign and 42 (80.8%) as malignant lesions. Specific diagnosis was obtained in 44 (84.6%) biopsies: 4 benign (9.1%) and 40 (90.9%) malignant lesions. The sensitivity, specificity, and accuracy of the cutting needle biopsies for determining presence of malignancy were 96.8%, 100%, and 97.2%, respectively. Complications occurred in 9 cases (17.3%), including 6 cases (11.5%) of small pneumothorax, 1 (1.9%) of hemoptysis, 1 (1.9%) of pulmonary hematoma, and 1 (1.9%) of thoracic wall hematoma. All had spontaneous resolution. There were no complications requiring subsequent intervention. CONCLUSION: The high sensitivity and specificity of the method and the low rate of complications have established cutting needle biopsy as an efficient and safe tool for the diagnosis of lung lesions. In our hospital, cutting needle biopsy is considered a reliable procedure for the evaluation of indeterminate pulmonary nodules.

Thumb metastasis from small cell lung cancer treated with radiation

A rare case of thumb metastasis from small cell lung cancer is presented. The patient underwent local radiotherapy with complete palliation of symptoms. She died 4 months later with disseminated disease. Considerations about incidence, treatment, and physiopathology of this kind of dissemination are made. Conservative treatment of finger metastasis with radiation may be considered due to the poor outcome of these patients.

Effect of clarithromycin on the cell profile of bronchoalveolar lavage fluid in mice with neutrophil-predominant lung disease

OBJECTIVE: Macrolide antibiotics have anti-inflammatory properties in lung diseases. The aim of this study was to investigate the effect of clarithromycin in pulmonary cellular inflammatory response in mice. METHOD: Eight adult Swiss mice were studied. All animals received an intranasal challenge (80 µL) with dead Pseudomonas aeruginosa (1.0 x 10(12) CFU/mL). Bronchoalveolar lavage was performed 2 days later, with total cell count and differential cell analysis. The study group (n = 4) received clarithromycin treatment (50 mg/kg/day, intraperitoneal) for 5 days. Treatment was initiated 2 days before intranasal challenge. RESULTS: There was no significant difference in total cell count between the groups (mean: 2.0 x 10(6) and 1.3 x 10(6), respectively). In both groups, there was a predominance of neutrophils. However, the study group had a higher percentage of lymphocytes in the bronchoalveolar lavage than the control group (median of 19% vs 2.5%, P = .029). CONCLUSION: Clarithromycin alters the cytological pattern of bronchoalveolar lavage of Swiss mice with neutrophil pulmonary inflammation, significantly increasing the percentage of lymphocytes.

Nonsmall cell lung cancer: evaluation of 737 consecutive patients in a single institution

OBJECTIVE: To analyze surgical and pathological parameters and outcome and prognostic factors of patients with nonsmall cell lung cancer (NSCLC) who were admitted to a single institution, as well as to correlate these findings to the current staging system. METHOD: Seven hundred and thirty seven patients were diagnosed with NSCLC and admitted to Hospital do Cancer A. C. Camargo from 1990 to 2000. All patients were included in a continuous prospective database, and their data was analyzed. Following staging, a multidisciplinary team decision on adequate management was established. Variables included in this analysis were age, gender, histology, Karnofsky index, weight loss, clinical stage, surgical stage, chemotherapy, radiotherapy, and survival rates. RESULTS: 75.5% of patients were males. The distribution of histologic type was squamous cell carcinoma 51.8%, adenocarcinoma 43.1%, and undifferentiated large cell carcinoma 5.1%. Most patients (73%) presented significant weight loss and a Karnofsky index of 80%. Clinical staging was IA 3.8%, IB 9.2%, IIA 1.4%, IIB 8.1%, IIIA 20.9%, IIIB 22.4%, IV 30.9%. Complete tumor resection was performed in 24.6% of all patients. Surgical stage distribution was IA 25.3%, IB 1.4%, IIB 17.1%, IIIA 16.1%, IIIB 20.3%, IV 11.5%. Chemotherapy and radiotherapy were considered therapeutic options in 43% and 72%, respectively. The overall 5-year survival rate of nonsmall cell lung cancer patients in our study was 28%. Median survival was 18.9 months. CONCLUSIONS: Patients with NSCLC who were admitted to our institution presented with histopathologic and clinical characteristics that were similar to previously published series in cancer hospitals. The best prognosis was associated with complete tumor resection with lymph node dissection, which is only achievable in earlier clinical stages.

Dynamic range prediction for an electric vehicle

Electric Vehicles (EVs) have limited energy storage capacity and the maximum autonomy range is strongly dependent of the driver's behaviour. Due to the fact of that batteries cannot be recharged quickly during a journey, it is essential that a precise range prediction is available to the driver of the EV. With this information, it is possible to check if the desirable destination is achievable without a stop to charge the batteries, or even, if to reach the destination it is necessary to perform an optimized driving (e.g., cutting the air-conditioning, among others EV parameters). The outcome of this research work is the development of an Electric Vehicle Assistant (EVA). This is an application for mobile devices that will help users to take efficient decisions about route planning, charging management and energy efficiency. Therefore, it will contribute to foster EVs adoption as a new paradigm in the transportation sector.

Integration of geometric consistency contributory factors in three-leg junctions collision prediction models of Portuguese two-lane national highways

This paper aims at developing a collision prediction model for three-leg junctions located in national roads (NR) in Northern Portugal. The focus is to identify factors that contribute for collision type crashes in those locations, mainly factors related to road geometric consistency, since literature is scarce on those, and to research the impact of three modeling methods: generalized estimating equations, random-effects negative binomial models and random-parameters negative binomial models, on the factors of those models. The database used included data published between 2008 and 2010 of 177 three-leg junctions. It was split in three groups of contributing factors which were tested sequentially for each of the adopted models: at first only traffic, then, traffic and the geometric characteristics of the junctions within their area of influence; and, lastly, factors which show the difference between the geometric characteristics of the segments boarding the junctionsâ area of influence and the segment included in that area were added. The choice of the best modeling technique was supported by the result of a cross validation made to ascertain the best model for the three sets of researched contributing factors. The models fitted with random-parameters negative binomial models had the best performance in the process. In the best models obtained for every modeling technique, the characteristics of the road environment, including proxy measures for the geometric consistency, along with traffic volume, contribute significantly to the number of collisions. Both the variables concerning junctions and the various national highway segments in their area of influence, as well as variations from those characteristics concerning roadway segments which border the already mentioned area of influence have proven their relevance and, therefore, there is a rightful need to incorporate the effect of geometric consistency in the three-leg junctions safety studies.

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.

Using customer lifetime value to improve the prediction of bank deposit subscription in telemarketing campaigns

Customer lifetime value (LTV) enables using client characteristics, such as recency, frequency and monetary (RFM) value, to describe the value of a client through time in terms of profitability. We present the concept of LTV applied to telemarketing for improving the return-on-investment, using a recent (from 2008 to 2013) and real case study of bank campaigns to sell long- term deposits. The goal was to benefit from past contacts history to extract additional knowledge. A total of twelve LTV input variables were tested, un- der a forward selection method and using a realistic rolling windows scheme, highlighting the validity of five new LTV features. The results achieved by our LTV data-driven approach using neural networks allowed an improvement up to 4 pp in the Lift cumulative curve for targeting the deposit subscribers when compared with a baseline model (with no history data). Explanatory knowledge was also extracted from the proposed model, revealing two highly relevant LTV features, the last result of the previous campaign to sell the same product and the frequency of past client successes. The obtained results are particularly valuable for contact center companies, which can improve pre- dictive performance without even having to ask for more information to the companies they serve.

Impact performance prediction of injection-molded talc-filled polypropylene through thermomechanical environment assessment

Due to the fact that different injection molding conditions tailor the mechanical response of the thermoplastic material, such effect must be considered earlier in the product development process. The existing approaches implemented in different commercial software solutions are very limited in their capabilities to estimate the influence of processing conditions on the mechanical properties. Thus, the accuracy of predictive simulations could be improved. In this study, we demonstrate how to establish straightforward processing-impact property relationships of talc-filled injection-molded polypropylene disc-shaped parts by assessing the thermomechanical environment (TME). To investigate the relationship between impact properties and the key operative variables (flow rate, melt and mold temperature, and holding pressure), the design of experiments approach was applied to systematically vary the TME of molded samples. The TME is characterized on computer flow simulation outputsanddefined bytwo thermomechanical indices (TMI): the cooling index (CI; associated to the core features) and the thermo-stress index (TSI; related to the skin features). The TMI methodology coupled to an integrated simulation program has been developed as a tool to predict the impact response. The dynamic impact properties (peak force, peak energy, and puncture energy) were evaluated using instrumented falling weight impact tests and were all found to be similarly affected by the imposed TME. The most important molding parameters affecting the impact properties were found to be the processing temperatures (melt andmold). CI revealed greater importance for the impact response than TSI. The developed integrative tool provided truthful predictions for the envisaged impact properties.

Prediction of drug targets in human pathogens

The identification of new and druggable targets in bacteria is a critical endeavour in pharmaceutical research of novel antibiotics to fight infectious agents. The rapid emergence of resistant bacteria makes today's antibiotics more and more ineffective, consequently increasing the need for new pharmacological targets and novel classes of antibacterial drugs. A new model that combines the singular value decomposition technique with biological filters comprised of a set of protein properties associated with bacterial drug targets and similarity to protein-coding essential genes of E. coli has been developed to predict potential drug targets in the Enterobacteriaceae family [1]. This model identified 99 potential target proteins amongst the studied bacterial family, exhibiting eight different functions that suggest that the disruption of the activities of these proteins is critical for cells. Out of these candidates, one was selected for target confirmation. To find target modulators, receptor-based pharmacophore hypotheses were built and used in the screening of a virtual library of compounds. Postscreening filters were based on physicochemical and topological similarity to known Gram-negative antibiotics and applied to the retrieved compounds. Screening hits passing all filters were docked into the proteins catalytic groove and 15 of the most promising compounds were purchased from their chemical vendors to be experimentally tested in vitro. To the best of our knowledge, this is the first attempt to rationalize the search of compounds to probe the relevance of this candidate as a new pharmacological target.

PTX3-based genetic testing for risk of aspergillosis after lung transplant

PTX3-based genetic testing for risk of aspergillosis after lung transplant

The Data Mining applied for the prediction of highway roughness due to overloaded trucks

Currently, the quality of the Indonesian national road network is inadequate due to several constraints, including overcapacity and overloaded trucks. The high deterioration rate of the road infrastructure in developing countries along with major budgetary restrictions and high growth in traffic have led to an emerging need for improving the performance of the highway maintenance system. However, the high number of intervening factors and their complex effects require advanced tools to successfully solve this problem. The high learning capabilities of Data Mining (DM) are a powerful solution to this problem. In the past, these tools have been successfully applied to solve complex and multi-dimensional problems in various scientific fields. Therefore, it is expected that DM can be used to analyze the large amount of data regarding the pavement and traffic, identify the relationship between variables, and provide information regarding the prediction of the data. In this paper, we present a new approach to predict the International Roughness Index (IRI) of pavement based on DM techniques. DM was used to analyze the initial IRI data, including age, Equivalent Single Axle Load (ESAL), crack, potholes, rutting, and long cracks. This model was developed and verified using data from an Integrated Indonesia Road Management System (IIRMS) that was measured with the National Association of Australian State Road Authorities (NAASRA) roughness meter. The results of the proposed approach are compared with the IIRMS analytical model adapted to the IRI, and the advantages of the new approach are highlighted. We show that the novel data-driven model is able to learn (with high accuracy) the complex relationships between the IRI and the contributing factors of overloaded trucks

Looking for mechanisms regulating lung growth in CDH: rat and human studies

Tese de Doutoramento em Ciências da Saúde