Bowel preparation for colonoscopy: comparison of mannitol and sodium phosphate. Results of a prospective randomized study

METHOD: Eighty patients were prospectively randomized for precolonoscopic cleansing either with 750 ml of 10% mannitol (Group M) or 180 ml of a sodium phosphate preparation (Group NaP). Laboratory examinations before and after preparation on all patients included hemoglobin, hematocrit, sodium, potassium, phosphorous, calcium and serum osmolarity. A questionnaire was used to assess undesirable side effects and patient tolerance to the solution. The quality of preparation was assessed by the endoscopist who was unaware of the solution employed. RESULTS: Statistically significant changes were verified in serum sodium, phosphorous, potassium and calcium between the two groups, but no clinical symptoms were observed. There were no significant differences in the frequency of side effects studied. Six of the eight patients in Group NaP who had taken mannitol for a previous colonoscopy claimed better acceptance of the sodium phosphate solution. The endoscopic-blinded trial reported excellent or good bowel preparation in 85% prepared with sodium phosphate versus 82.5% for mannitol (p=0.37). CONCLUSIONS: Quality of preparation and frequency of side effects was similar in the two solutions. The smaller volume of sodium phosphate necessary for preparation seems to be related to its favorable acceptance. Nevertheless, the retention of sodium and phosphate ions contraindicates the use of sodium phosphate in patients with renal failure, cirrhosis, ascites, and heart failure.

Conventional approach x laparoscopic abdominoperineal resection for rectal cancer treatment after neoadjuvant chemoradiation: results of a prospective randomized trial

OBJECTIVE: The aims of this study were to evaluate the safety and efficacy of laparoscopic abdominoperineal resection compared to conventional approach for surgical treatment of patients with distal rectal cancer presenting with incomplete response after chemoradiation. METHOD: Twenty eight patients with distal rectal adenocarcinoma were randomized to undergo surgical treatment by laparoscopic abdominoperineal resection or conventional approach and evaluated prospectively. Thirteen underwent laparoscopic abdominoperineal resection and 15 conventional approach. RESULTS: There was no significant difference (p<0,05) between the two studied groups regarding: gender, age, body mass index, patients with previous abdominal surgeries, intra and post operative complications, need for blood transfusion, hospital stay after surgery, length of resected segment and pathological staging. Mean operation time was 228 minutes for the laparoscopic abdominoperineal resection versus 284 minutes for the conventional approach (p=0.04). Mean anesthesia duration was shorter (p=0.03) for laparoscopic abdominoperineal resection when compared to conventional approach : 304 and 362 minutes, respectively. There was no need for conversion to open approach in this series. After a mean follow-up of 47.2 months and with the exclusion of two patients in the conventional abdominoperineal resection who presented with unsuspected synchronic metastasis during surgery, local recurrence was observed in two patients in the conventional group and in none in the laparoscopic group. CONCLUSIONS: We conclude that laparoscopic abdominoperineal resection is feasible, similar to conventional approach concerning surgery duration, intra operative morbidity, blood requirements and post operative morbidity. Larger number of cases and an extended follow-up are required to adequate evaluation of oncological results for patients undergoing laparoscopic abdominoperineal resection after chemoradiation for radical treatment of distal rectal cancer.

Design and implementation of an assurance map at Sonae retail business

The present Working Project aims at studying the topic of assurance mapping in a specific organizational context of a Portuguese retail company. For this purpose, an assurance map framework was designed to support the decision making process of stakeholders, through the delivery of comfort concerning risks, operations and control. In the end, the framework was successfully implemented for the process sourcing of goods in two business units of the company. Although, further implementation of the framework proved not to be feasible during the project���s timespan, it is expected to occur in the near future.

Urethral catheter removal 7 or 14 days after radical retropubic prostatectomy: clinical implications and complications in a randomized study

PURPOSE: To evaluate the hypothesis that a 7-day period of indwelling catheter after radical retropubic prostatectomy is effective and safe without the need of performing cystography. METHODS: In the period from January of 2000 to July of 2002, 73 patients underwent radical retropubic prostatectomy, and these patients were prospectively randomized in 2 groups: Group 1-37 patients who had the urethral catheter removed 7 days after the procedure, and Group 2-36 patients who had the catheter removed 14 days after the surgery. The 2 groups were similar, the surgeons and the technique were the same, and no cystography was performed to evaluate the presence of leaks. RESULTS: Two patients in Group 1 had bleeding and clot retention after having the catheter taken out in the seventh postoperative day and were managed by putting the catheter back in for 7 more days. Two patients in Group 2 developed bladder neck stricture and were treated by bladder neck incision with success. The continence rate was the same, with 2 cases of incontinence in each group. About 2 pads a day were used by the patients with incontinence. The average follow-up was 17.5 months (12-36 months). No urinary fistula, urinoma, or pelvic abscesses developed after catheter removal. Two patients were excluded from the analysis of this series: 1 died with a pulmonary embolus in the third postoperative day, and 1 developed a urinary suprapubic fistula before catheter withdrawal, which was maintained for 16 days. CONCLUSION: Withdrawal of the urethral catheter 7 days after radical retropubic prostatectomy, without performing cystography, has a low rate of short-term complications that are equivalent to withdrawal 14 days after the surgery.

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 junctions�� 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, Arti���cial 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 coe���cient of determination value (0.81). This model was then opened by using a sensitivity analysis procedure that revealed three in���uential input attributes: the hospital episode type, the physical service where the patient is hospitalized and the associated medical specialty. Such extracted knowledge con���rmed 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.

Biomass equations for forest regrowth in the eastern Amazon using randomized branch sampling

Forest regrowth occupies an extensive and increasing area in the Amazon basin, but accurate assessment of the impact of regrowth on carbon and nutrient cycles has been hampered by a paucity of available allometric equations. We develop pooled and species-specific equations for total aboveground biomass for a study site in the eastern Amazon that had been abandoned for 15 years. Field work was conducted using randomized branch sampling, a rapid technique that has seen little use in tropical forests. High consistency of sample paths in randomized branch sampling, as measured by the standard error of individual paths (14%), suggests the method may provide substantial efficiencies when compared to traditional procedures. The best fitting equations in this study used the traditional form Y=a��DBHb, where Y is biomass, DBH is diameter at breast height, and a and b are both species-specific parameters. Species-specific equations of the form Y=a(BA��H), where Y is biomass, BA is tree basal area, H is tree height, and a is a species-specific parameter, fit almost as well. Comparison with previously published equations indicated errors from -33% to +29% would have occurred using off-site relationships. We also present equations for stemwood, twigs, and foliage as biomass components.

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.

Cognitive stimulation for portuguese older adults with cognitive impairment: a randomized controlled trial of efficacy, comparative duration, feasibility and experiential relevance

Although some studies point to cognitive stimulation as a beneficial therapy for older adults with cognitive impairments, this area of research and practice is still lacking dissemination and is underrepresented in many countries. Moreover, the comparative effects of different intervention durations remain to be established and, besides cognitive effects, pragmatic parameters, such as cost-effectiveness and experiential relevance to participants, are seldom explored. In this work, we present a randomized con- trolled wait-list trial evaluating 2 different intervention durations (standard 1���4 17 vs brief 1���4 11 sessions) of a cognitive stimulation program developed for older adults with cognitive impairments with or without dementia. 20 participants were randomly assigned to the standard duration intervention program (17 sessions, 1.5 months) or to a wait-list group. At postintervention of the standard intervention group, the wait-list group crossed over to receive the brief intervention program (11 sessions, 1 month). Changes in neuropsychological, functionality, quality of life, and caregiver outcomes were evaluated. Experience during intervention and costs and feasibility were also evaluated. The current cognitive stimulation programs (ie, standard and brief) showed high values of experiential relevance for both intervention durations. High adherence, completion rates, and reasonable costs were found for both formats. Further studies are needed to definitively establish the potential efficacy, optimal duration, cost-effectiveness, and experiential relevance for participants of cognitive intervention approaches.

Nicolas Sanson and his Map: The Course of the Amazon River

One of the first scientific maps of the Amazon region, The Course of the Amazon River (Le Cours de La Rivi��re des Amazones), was constructed by Nicolas Sanson, a French cartographer of the seventeenth century, and served as the prototype for many others. The evaluation of this chart, until now, has been that it is a very defective map, a sketch based on a historical account, according to the opinion of La Condamine. Thus, the aim of the present work was to prove that the map of the Amazon River traced by Nicolas Sanson is a scientific work, a map that presents precise geographic coordinates considering its time, shows a well-determined prime meridian, and also employs a creative methodology to deduce longitudes from latitudes and distances that had been covered. To show such characteristics, an analysis of the accuracy of the map was made by comparing its latitudes and longitudes with those of a current map. We determined the prime meridian of this map and analyzed the methodology used for the calculation of longitudes. The conclusion is that it is actually a good map for the time, particularly considering the technology and the limited information that Sanson had at his disposal. This proves that the negative assertion of La Condamine is unfounded.

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

Development of computational and experimental methods for biomass composition and evaluation of its impact in genome-scale models prediction

The use of genome-scale metabolic models has been rapidly increasing in fields such as metabolic engineering. An important part of a metabolic model is the biomass equation since this reaction will ultimately determine the predictive capacity of the model in terms of essentiality and flux distributions. Thus, in order to obtain a reliable metabolic model the biomass precursors and their coefficients must be as precise as possible. Ideally, determination of the biomass composition would be performed experimentally, but when no experimental data are available this is established by approximation to closely related organisms. Computational methods however, can extract some information from the genome such as amino acid and nucleotide compositions. The main objectives of this study were to compare the biomass composition of several organisms and to evaluate how biomass precursor coefficients affected the predictability of several genome-scale metabolic models by comparing predictions with experimental data in literature. For that, the biomass macromolecular composition was experimentally determined and the amino acid composition was both experimentally and computationally estimated for several organisms. Sensitivity analysis studies were also performed with the Escherichia coli iAF1260 metabolic model concerning specific growth rates and flux distributions. The results obtained suggest that the macromolecular composition is conserved among related organisms. Contrasting, experimental data for amino acid composition seem to have no similarities for related organisms. It was also observed that the impact of macromolecular composition on specific growth rates and flux distributions is larger than the impact of amino acid composition, even when data from closely related organisms are used.