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

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.

Inkjet printing technology for flexible pressure sensors

Tese de Doutoramento Ciência e Engenharia de Polímeros e Compósitos.

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.

Extensive admixture and selective pressure across the Sahel Belt

Genome-wide studies of African populations have the potential to reveal powerful insights into the evolution of our species, as these diverse populations have been exposed to intense selective pressures imposed by infectious diseases, diet, and environmental factors. Within Africa, the Sahel Belt extensively overlaps the geographical center of several endemic infections such as malaria, trypanosomiasis, meningitis, and hemorrhagic fevers. We screened 2.5 million single nucleotide polymorphisms in 161 individuals from 13 Sahelian populations, which together with published data cover Western, Central, and Eastern Sahel, and include both nomadic and sedentary groups. We confirmed the role of this Belt as a main corridor for human migrations across the continent. Strong admixture was observed in both Central and Eastern Sahelian populations, with North Africans and Near Eastern/Arabians, respectively, but it was inexistent in Western Sahelian populations. Genome-wide local ancestry inference in admixed Sahelian populations revealed several candidate regions that were significantly enriched for non-autochthonous haplotypes, and many showed to be under positive selection. The DARC gene region in Arabs and Nubians was enriched for African ancestry, whereas the RAB3GAP1/LCT/MCM6 region in Oromo, the TAS2R gene family in Fulani, and the ALMS1/NAT8 in Turkana and Samburu were enriched for non-African ancestry. Signals of positive selection varied in terms of geographic amplitude. Some genomic regions were selected across the Belt, the most striking example being the malaria-related DARC gene. Others were Western-specific (oxytocin, calcium, and heart pathways), Eastern-specific (lipid pathways), or even population-restricted (TAS2R genes in Fulani, which may reflect sexual selection).

Optimization of filling systems for low pressure by Flow 3D

Dissertação de mestrado integrado em Engenharia Mecânica

Patterned CNT-PDMS nanocomposites for flexible pressure sensors

This paper reports the fabrication process and characterization of a flexible pressure sensor based on polydimethylsiloxane (PDMS) and multi-walled carbon nanotubes (CNT-PDMS). The proposed approach relies on patterned CNT-PDMS nanocomposite strain gauges fabricated with SU-8 microstructures (with the micropatterns) in a low‑cost and simple fabrication process. This nanocomposite polymer is mounted over a PDMS membrane, which, in turn, lies on top of a PDMS diaphragm like structure. This configuration enables the PDMS membrane to bend when pressure is applied, thereby affecting the nanocomposite strain gauges, effectively changing their electrical resistance. Carbon nanotubes have several advantages such as excellent mechanical properties, high electrical conductivity and thermal stability. Furthermore, the measurement range of the proposed sensor can be adapted according to the application by varying the CNTs content and geometry of microstructure. In addition, the sensor’s biocompatibility, low cost and simple fabrication makes it very appealing for biomechanical strain sensing. The sensor’s sensitivity was about 0.073%ΔR/mmHg.

Flexible gastrointestinal motility pressure sensors based on aluminum thin-film strain-gauge arrays

This paper reports on an innovative approach to measuring intraluminal pressure in the upper gastrointestinal (GI) tract, especially monitoring GI motility and peristaltic movements. The proposed approach relies on thin-film aluminum strain gauges deposited on top of a Kapton membrane, which in turn lies on top of an SU-8 diaphragm-like structure. This structure enables the Kapton membrane to bend when pressure is applied, thereby affecting the strain gauges and effectively changing their electrical resistance. The sensor, with an area of 3.4 mm2, is fabricated using photolithography and standard microfabrication techniques (wet etching). It features a linear response (R2 = 0.9987) and an overall sensitivity of 2.6 mV mmHg−1. Additionally, its topology allows a high integration capability. The strain gauges’ responses to pressure were studied and the fabrication process optimized to achieve high sensitivity, linearity, and reproducibility. The sequential acquisition of the different signals is carried out by a microcontroller, with a 10-bit ADC and a sample rate of 250 Hz. The pressure signals are then presented in a user-friendly interface, developed using the Integrated Development Environment software, QtCreator IDE, for better visualization by physicians.

An epidemiological study determining blood pressure in a Portuguese cohort: the Guimaraes/Vizela study

Surveying the evolution of blood pressure (BP) levels and hypertension (HTN) prevalence is important. A stringent strategy was utilized in a population cohort study. The BP was measured at two visits at least 3 months apart, and the results were analyzed using the following two methods: the Surveillance method (three BP measurements were performed in one visit, and the results were compared with those published previously for the identical method) and the Clinical method (three measurements per visit for two visits, and the concordant results in both visits were used to determine the BP classification). A total of 2542 subjects completed the evaluation. Using the Clinical method, an average systolic/diastolic BP value of 129.8/76.8?mm?Hg was obtained, and the prevalence of HTN was 31.6%. Of the hypertensive patients, 74.3% were aware of his/her condition; 69.1% were treated and 40.8% of those treated had adequate BP control. A total of 24.7% of subjects changed his/her BP classification between visits, and 13.7% misreported HTN. Using the Surveillance method, we determined that the average global SBP has been maintained, with HTN prevalence increasing in this region, drifting from reported trends nationally and worldwide. There has been improvement in the proportion of treated and controlled subjects; however, the Surveillance method overestimated the HTN prevalence and underestimated the proportion of treated and controlled subjects. The BP levels were higher than observed worldwide in high-cardiovascular (CV) risk countries as well as higher than the minimum risk exposure level for developing CV disease.

Influence of different fibers on the change of pore pressure of self-consolidating concrete exposed to fire

The focus of this paper is given to investigate the effect of different fibers on the pore pressure of fiber reinforced self-consolidating concrete under fire. The investigation on the pore pressure-time and temperature relationships at different depths of fiber reinforced self-consolidating concrete beams was carried out. The results indicated that micro PP fiber is more effective in mitigating the pore pressure than macro PP fiber and steel fiber. The composed use of steel fiber, micro PP fiber and macro PP fiber showed clear positive hybrid effect on the pore pressure reduction near the beam bottom subjected to fire. Compared to the effect of macro PP fiber with high dosages, the effect of micro PP fiber with low fiber contents on the pore pressure reduction is much stronger. The significant factor for reduction of pore pressure depends mainly on the number of PP fibers and not only on the fiber content. An empirical formula was proposed to predict the relative maximum pore pressure of fiber reinforced self-consolidating concrete exposed to fire by considering the moisture content, compressive strength and various fibers. The suggested model corresponds well with the experimental results of other research and tends to prove that the micro PP fiber can be the vital component for reduction in pore pressure, temperature as well spalling of concrete.

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.

Real-Time decision support using data mining to predict blood pressure critical events in intensive medicine patients

Patient blood pressure is an important vital signal to the physicians take a decision and to better understand the patient condition. In Intensive Care Units is possible monitoring the blood pressure due the fact of the patient being in continuous monitoring through bedside monitors and the use of sensors. The intensivist only have access to vital signs values when they look to the monitor or consult the values hourly collected. Most important is the sequence of the values collected, i.e., a set of highest or lowest values can signify a critical event and bring future complications to a patient as is Hypotension or Hypertension. This complications can leverage a set of dangerous diseases and side-effects. The main goal of this work is to predict the probability of a patient has a blood pressure critical event in the next hours by combining a set of patient data collected in real-time and using Data Mining classification techniques. As output the models indicate the probability (%) of a patient has a Blood Pressure Critical Event in the next hour. The achieved results showed to be very promising, presenting sensitivity around of 95%.