Predicting plateau pressure in intensive medicine for ventilated patients

Barotrauma is identified as one of the leading diseases in Ventilated Patients. This type of problem is most common in the Intensive Care Units. In order to prevent this problem the use of Data Mining (DM) can be useful for predicting their occurrence. The main goal is to predict the occurence of Barotrauma in order to support the health professionals taking necessary precautions. In a first step intensivists identified the Plateau Pressure values as a possible cause of Barotrauma. Through this study DM models (classification) where induced for predicting the Plateau Pressure class (>=30 cm

Mycotoxin contamination of Maize and Guinea corn from markets in Plateau State, Nigeria

Maize (Zea mays) and guinea corn (Sorghum bicolor) are major food items in Plateau state, Nigeria. A multistage sampling technique was used to select the markets and store/warehouses used for this study; sample collection employed a simple random sampling method from different sampling points within designated areas. A total of 18 representative samples were collected and analyzed for the following mycotoxins: aflatoxins (Aflatoxin B1 - AFB1, Aflatoxin B2 - AFB2, Aflatoxin G1 - AFG1 and Aflatoxin G2 - AFG2), fumonisins (Fumonisin B1 - FB1 and Fumonisin B2 - FB2 ) and cyclopiazonic acid (CPA). Out of 12 samples analyzed for Aflatoxins, AFB1 was detected in 5, AFB2 in 1, AFG1 in 1 and AFG2 in 6 samples respectively. The highest concentration of AFB1 and AFG2 were found in maize samples from Pankshin market. Only maize samples from Mangu market were contaminated with AFB2 and also harboured the lowest concentration of AFG2. AFG1 contamination occurred in only guinea corn from Shendam market. and FB1 was detected in all 18 samples analyzed. The mycotoxin CPA was not detected in any of the samples. Aflatoxins levels in analyzed samples were regarded as safe based on Nigerian and European Union maximum permissible levels of 4g/kg. With the exception of two samples, FB1 levels in analyzed maize samples were within European Union maximum permissible levels of 1,000 to 3000g/kg. The health and food safety implications of these results for the human and animal population are further discussed.

Clustering barotrauma patients in ICU–A data mining based approach using ventilator variables

Lecture Notes in Computer Science, 9273

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.

Measurement of charged-particle spectra in Pb+Pb collisions at √sNN = 2.76TeV with the ATLAS detector at the LHC

Charged-particle spectra obtained in 0.15nb−1 of Pb+Pb interactions at sNN−−−√=2.76TeV and 4.2pb−1 of pp interactions at s√=2.76TeV with the ATLAS detector at the LHC are presented in a wide transverse momentum (0.5plateau at a centrality-dependent value, within the uncertainties. The value is 0.55±0.01(stat.)±0.04(syst.) in the most central collisions. The RAA distribution is consistent with flat |η| dependence over the whole transverse momentum range in all centrality classes.

Metabolic profiling and classification of propolis samples from Southern Brazil: an NMR-based platform coupled with machine learning

The chemical composition of propolis is affected by environmental factors and harvest season, making it difficult to standardize its extracts for medicinal usage. By detecting a typical chemical profile associated with propolis from a specific production region or season, certain types of propolis may be used to obtain a specific pharmacological activity. In this study, propolis from three agroecological regions (plain, plateau, and highlands) from southern Brazil, collected over the four seasons of 2010, were investigated through a novel NMR-based metabolomics data analysis workflow. Chemometrics and machine learning algorithms (PLS-DA and RF), including methods to estimate variable importance in classification, were used in this study. The machine learning and feature selection methods permitted construction of models for propolis sample classification with high accuracy (>75%, reaching 90% in the best case), better discriminating samples regarding their collection seasons comparatively to the harvest regions. PLS-DA and RF allowed the identification of biomarkers for sample discrimination, expanding the set of discriminating features and adding relevant information for the identification of the class-determining metabolites. The NMR-based metabolomics analytical platform, coupled to bioinformatic tools, allowed characterization and classification of Brazilian propolis samples regarding the metabolite signature of important compounds, i.e., chemical fingerprint, harvest seasons, and production regions.

Biodegradable drug-eluting stents: Targeting urothelial tumors of upper urinary tract

INTRODUCTION & OBJECTIVES: Urothelial tumors of upper urinary tract are ranked among the most common types of cancers worldwide. The current standard therapy to prevent recurrence is intravesical Bacillus Calmetteâ Guerin (BCG) immunotherapy, but it presents several disadvantages such as BCG failure and intolerance. Another way is to use chemotherapy, which is generally better tolerated that BCG. In this case, drugs such as epirubicin, doxorubicin, paclitaxel and gemcitabine are used. Nevertheless, intravesical chemotherapy only prevents recurrence in the short-term. These failings can be partially attributed to the short residence time and low bioavailability of the drug within the upper urinary tract and the cancer cells, resulting in a need for frequent drug instillation. To avoid these problems, biodegradable ureteral stents impregnated by supercritical fluid CO2 (SCF) with each of the four anti-cancer drugs were produced. MATERIAL & METHODS: Four formulations with different concentrations of gelatin and alginate and crosslink agent were tested and bismuth was added to confer radiopaque properties to the stent. The preliminary in vivo validation studies in female domestic pigs was conducted at the University of Minho, Braga, after formal approval by the institutionâ s review board and in accordance with its internal ethical protocol for animal experiments. Paclitaxel, epirubicin, doxorubicin and gemcitabine were impregnated in the stents and the release kinetics was measured in artificial urine solution (AUS) for 9 days by UV spectroscopy in a microplate reader. The anti-tumoral effect of the developed stents in transitional cell carcinoma (TCC) and HUVEC primary cells, used as control, was evaluated. RESULTS: The in vivo validation of this second-generation of ureteral stents performed was herein demonstrated. Biodegradable ureteral stents were placed in the ureters of a female pigs, following the normal surgical procedure. The animals remained asymptomatic, with normal urine flow. The in vitro release study in AUS of the stent impregnated showed a higher release in the first 72h for the four anti-cancer drugs impregnated after this time the plateau was achieved and the stent degraded after 9 days. The direct and indirect contact of the anti-cancer biodegradable stents with the TCC and HUVEC cell lines confirm the anti-tumor effect of the stents impregnated with the four anti-cancer drugs, reducing around 75% of the viability of the TCC cell line after 72h and no killing effect in the HUVEC cells. CONCLUSIONS: The use of biodegradable ureteral stent in urology clinical practice not only reduce the stent-related symptoms but also open new treatment therapyâ s, like in urothelial tumors of upper urinary tract. Furthermore, we have demonstrated the clinical validation in vivo pig model. This study has thus shown the killing efficacy of the anti-cancer drug eluting biodegradable stents in vitro for the TCC cell line, with no toxicity observed in the control, non-cancerous cells.The direct and indirect contact of the anti-cancer biodegradable stents with the TCC and HUVEC cell lines confirm the anti-tumor effect of the stents impregnated with the four anti-cancer drugs, reducing around 75% of the viability of the TCC cell line after 72h and no killing effect in the HUVEC cells. This study has thus shown the killing efficacy of the anti-cancer drug eluting biodegradable stents in vitro for the TCC cell line, with no toxicity observed in the control, non-cancerous cells.

Critical events in mechanically ventilated patients

Mechanical Ventilation is an artificial way to help a Patient to breathe. This procedure is used to support patients with respiratory diseases however in many cases it can provoke lung damages, Acute Respiratory Diseases or organ failure. With the goal to early detect possible patient breath problems a set of limit values was defined to some variables monitored by the ventilator (Average Ventilation Pressure, Compliance Dynamic, Flow, Peak, Plateau and Support Pressure, Positive end-expiratory pressure, Respiratory Rate) in order to create critical events. A critical event is verified when a patient has a value higher or lower than the normal range defined for a certain period of time. The values were defined after elaborate a literature review and meeting with physicians specialized in the area. This work uses data streaming and intelligent agents to process the values collected in real-time and classify them as critical or not. Real data provided by an Intensive Care Unit were used to design and test the solution. In this study it was possible to understand the importance of introduce critical events for Mechanically Ventilated Patients. In some cases a value is considered critical (can trigger an alarm) however it is a single event (instantaneous) and it has not a clinical significance for the patient. The introduction of critical events which crosses a range of values and a pre-defined duration contributes to improve the decision-making process by decreasing the number of false positives and having a better comprehension of the patient condition.