Effect of variables on the thickness of an edible coating applied on frozen fish establishment of the concept of safe dipping time

Glazing is a technique used to retard fish deterioration during storage. This work focuses on the study of distinct variables (fish temperature, coating temperature, dipping time) that affect the thickness of edible coatings (water glazing and 1.5% chitosan) applied on frozen fish. Samples of frozen Atlantic salmon (Salmo salar) at -15, -20, and -25 °C were either glazed with water at 0.5, 1.5 or 2.5 °C or coated with 1.5% chitosan solution at 2.5, 5 or 8 °C, by dipping during 10 to 60 s. For both water and chitosan coatings, lowering the salmon and coating solution temperatures resulted in an increase of coating thickness. At the same conditions, higher thickness values were obtained when using chitosan (max. thickness of 1.41±0.05 mm) compared to water (max. thickness of 0.84±0.03 mm). Freezing temperature and crystallization heat were found to be lower for 1.5% chitosan solution than for water, thus favoring phase change. Salmon temperature profiles allowed determining, for different dipping conditions, whether the salmon temperature was within food safety standards to prevent the growth of pathogenic microorganisms. The concept of safe dipping time is proposed to define how long a frozen product can be dipped into a solution without the temperature raising to a point where it can constitute a hazard.

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

Step towards a patient timeline in intensive care units

In Intensive Medicine, the presentation of medical information is done in many ways, depending on the type of data collected and stored. The way in which the information is presented can make it difficult for intensivists to quickly understand the patient's condition. When there is the need to cross between several types of clinical data sources the situation is even worse. This research seeks to explore a new way of presenting information about patients, based on the timeframe in which events occur. By developing an interactive Patient Timeline, intensivists will have access to a new environment in real-time where they can consult the patient clinical history and the data collected until the moment. The medical history will be available from the moment in which patients is admitted in the ICU until discharge, allowing intensivist to examine data regarding vital signs, medication, exams, among others. This timeline also intends to, through the use of information and models produced by the INTCare system, combine several clinical data in order to help diagnose the future patients’ conditions. This platform will help intensivists to make more accurate decision. This paper presents the first approach of the solution designed

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.

Solving large 0–1 multidimensional knapsack problems by a new simplified binary artificial fish swarm algorithm

The artificial fish swarm algorithm has recently been emerged in continuous global optimization. It uses points of a population in space to identify the position of fish in the school. Many real-world optimization problems are described by 0-1 multidimensional knapsack problems that are NP-hard. In the last decades several exact as well as heuristic methods have been proposed for solving these problems. In this paper, a new simpli ed binary version of the artificial fish swarm algorithm is presented, where a point/ fish is represented by a binary string of 0/1 bits. Trial points are created by using crossover and mutation in the different fi sh behavior that are randomly selected by using two user de ned probability values. In order to make the points feasible the presented algorithm uses a random heuristic drop item procedure followed by an add item procedure aiming to increase the profit throughout the adding of more items in the knapsack. A cyclic reinitialization of 50% of the population, and a simple local search that allows the progress of a small percentage of points towards optimality and after that refines the best point in the population greatly improve the quality of the solutions. The presented method is tested on a set of benchmark instances and a comparison with other methods available in literature is shown. The comparison shows that the proposed method can be an alternative method for solving these problems.

Effect of short chain fructooligosaccharides (scFOS) on immunological status and gut microbiota of gilthead sea bream (Sparus aurata) reared at two temperatures

The effects of dietary short chain fructooligosaccharides (scFOS) incorporation on hematology, fish immune status, gut microbiota composition, digestive enzymes activities, and gut morphology, was evaluated in gilthead sea bream (Sparus aurata) juveniles reared at 18 °C and 25 °C. For that purpose, fish with 32 g were fed diets including 0, 0.1, 0.25 and 0.5% scFOS during 8 weeks. Overall, scFOS had only minor effects on gilthead sea bream immune status. Lymphocytes decreased in fish fed the 0.1% scFOS diet. Fish fed the 0.5% scFOS diet presented increased nitric oxide (NO) production, while total immunoglobulins (Ig) dropped in those fish, but only in the ones reared at 25 °C. Red blood cells, hemoglobin, bactericidal activity and NO were higher at 25 °C, whereas total white blood cells, circulating thrombocytes, monocytes and neutrophils were higher at 18 °C. In fish fed scFOS, lymphocytes were higher at 18 °C. Total Ig were also higher at 18 °C but only in fish fed 0.1% and 0.5% scFOS diets. No differences in gut bacterial profiles were detected by PCR-DGGE (polymerase chain reaction denaturing gradient gel electrophoresis) between dietary treatments. However, group's similarity was higher at 25 °C. Digestive enzymes activities were higher at 25 °C but were unaffected by prebiotics incorporation. Gut morphology was also unaffected by dietary prebiotic incorporation. Overall, gut microbiota composition, digestive enzymes activities and immunity parameters were affected by rearing temperature whereas dietary scFOS incorporation had only minor effects on these parameters. In conclusion, at the tested levels scFOS does not seem worthy of including it in gilthead sea bream juveniles diets.

Polymicrobial interactions in infections: the case Pseudomonas aeruginosa and Candida albicans in ventilator-associated pneumonia

Dissertação de mestrado integrado em Engenharia Biomédica (área de especialização em Engenharia Clinica)

Discrimination of bacteriophage infected cells using locked nucleic acid fluorescent in situ hybridization (LNA-FISH)

Bacteriophage-host interaction studies in biofilm structures are still challenging due to the technical limitations of traditional methods. The aim of this study was to provide a direct fluorescence in situ hybridization (FISH) method based on locked nucleic acid (LNA) probes, which targets the phage replication phase, allowing the study of population dynamics during infection. Bacteriophages specific for two biofilm-forming bacteria, Pseudomonas aeruginosa and Acinetobacter, were selected. Four LNA probes were designed and optimized for phage-specific detection and for bacterial counterstaining. To validate the method, LNA-FISH counts were compared with the traditional plaque forming unit (PFU) technique. To visualize the progression of phage infection within a biofilm, colony-biofilms were formed and infected with bacteriophages. A good correlation (r=0.707) was observed between LNA-FISH and PFU techniques. In biofilm structures, LNA-FISH provided a good discrimination of the infected cells and also allowed the assessment of the spatial distribution of infected and non-infected populations.

A new page on the road book of inorganic mercury in fish body - tissue distribution and elimination following waterborne exposure and post-exposure periods

prova tipográfica / uncorrected proof

Optimization of peptide nucleic acid fluorescence in situ hybridization (PNA-FISH) for the detection of bacteria: the effect of pH, dextran sulfate and probe concentration

Fluorescence in situ hybridization (FISH) is a molecular technique widely used for the detection and characterization of microbial populations. FISH is affected by a wide variety of abiotic and biotic variables and the way they interact with each other. This is translated into a wide variability of FISH procedures found in the literature. The aim of this work is to systematically study the effects of pH, dextran sulfate and probe concentration in the FISH protocol, using a general peptide nucleic acid (PNA) probe for the Eubacteria domain. For this, response surface methodology was used to optimize these 3 PNA-FISH parameters for Gram-negative (Escherichia coli and Pseudomonas fluorescens) and Gram-positive species (Listeria innocua, Staphylococcus epidermidis and Bacillus cereus). The obtained results show that a probe concentration higher than 300 nM is favorable for both groups. Interestingly, a clear distinction between the two groups regarding the optimal pH and dextran sulfate concentration was found: a high pH (approx. 10), combined with lower dextran sulfate concentration (approx. 2% [w/v]) for Gram-negative species and near-neutral pH (approx. 8), together with higher dextran sulfate concentrations (approx. 10% [w/v]) for Gram-positive species. This behavior seems to result from an interplay between pH and dextran sulfate and their ability to influence probe concentration and diffusion towards the rRNA target. This study shows that, for an optimum hybridization protocol, dextran sulfate and pH should be adjusted according to the target bacteria.

Aromatic amines sources, environmental impact and remediation

Aromatic amines are widely used industrial chemicals as their major sources in the environment include several chemical industry sectors such as oil refining, synthetic polymers, dyes, adhesives, rubbers, perfume, pharmaceuticals, pesticides and explosives. They result also from diesel exhaust, combustion of wood chips and rubber and tobacco smoke. Some types of aromatic amines are generated during cooking, special grilled meat and fish, as well. The intensive use and production of these compounds explains its occurrence in the environment such as in air, water and soil, thereby creating a potential for human exposure. Since aromatic amines are potential carcinogenic and toxic agents, they constitute an important class of environmental pollutants of enormous concern, which efficient removal is a crucial task for researchers, so several methods have been investigated and applied. In this chapter the types and general properties of aromatic amine compounds are reviewed. As aromatic amines are continuously entering the environment from various sources and have been designated as high priority pollutants, their presence in the environment must be monitored at concentration levels lower than 30 mg L1, compatible with the limits allowed by the regulations. Consequently, most relevant analytical methods to detect the aromatic amines composition in environmental matrices, and for monitoring their degradation, are essential and will be presented. Those include Spectroscopy, namely UV/visible and Fourier Transform Infrared Spectroscopy (FTIR); Chromatography, in particular Thin Layer (TLC), High Performance Liquid (HPLC) and Gas chromatography (GC); Capillary electrophoresis (CE); Mass spectrometry (MS) and combination of different methods including GC-MS, HPLC-MS and CE-MS. Choosing the best methods depend on their availability, costs, detection limit and sample concentration, which sometimes need to be concentrate or pretreated. However, combined methods may give more complete results based on the complementary information. The environmental impact, toxicity and carcinogenicity of many aromatic amines have been reported and are emphasized in this chapter too. Lately, the conventional aromatic amines degradation and the alternative biodegradation processes are highlighted. Parameters affecting biodegradation, role of different electron acceptors in aerobic and anaerobic biodegradation and kinetics are discussed. Conventional processes including extraction, adsorption onto activated carbon, chemical oxidation, advanced oxidation, electrochemical techniques and irradiation suffer from drawbacks including high costs, formation of hazardous by-products and low efficiency. Biological processes, taking advantage of the naturally processes occurring in environment, have been developed and tested, proved as an economic, energy efficient and environmentally feasible alternative. Aerobic biodegradation is one of the most promising techniques for aromatic amines remediation, but has the drawback of aromatic amines autooxidation once they are exposed to oxygen, instead of their degradation. Higher costs, especially due to power consumption for aeration, can also limit its application. Anaerobic degradation technology is the novel path for treatment of a wide variety of aromatic amines, including industrial wastewater, and will be discussed. However, some are difficult to degrade under anaerobic conditions and, thus, other electron acceptors such as nitrate, iron, sulphate, manganese and carbonate have, alternatively, been tested.

Pervasive patient timeline for intensive care units

This research work explores a new way of presenting and representing information about patients in critical care, which is the use of a timeline to display information. This is accomplished with the development of an interactive Pervasive Patient Timeline able to give to the intensivists an access in real-time to an environment containing patients clinical information from the moment in which the patients are admitted in the Intensive Care Unit (ICU) until their discharge This solution allows the intensivists to analyse data regarding vital signs, medication, exams, data mining predictions, among others. Due to the pervasive features, intensivists can have access to the timeline anywhere and anytime, allowing them to make decisions when they need to be made. This platform is patient-centred and is prepared to support the decision process allowing the intensivists to provide better care to patients due the inclusion of clinical forecasts.

Optimization techniques to detect early ventilation extubation in intensive care units

The decision support models in intensive care units are developed to support medical staff in their decision making process. However, the optimization of these models is particularly difficult to apply due to dynamic, complex and multidisciplinary nature. Thus, there is a constant research and development of new algorithms capable of extracting knowledge from large volumes of data, in order to obtain better predictive results than the current algorithms. To test the optimization techniques a case study with real data provided by INTCare project was explored. This data is concerning to extubation cases. In this dataset, several models like Evolutionary Fuzzy Rule Learning, Lazy Learning, Decision Trees and many others were analysed in order to detect early extubation. The hydrids Decision Trees Genetic Algorithm, Supervised Classifier System and KNNAdaptive obtained the most accurate rate 93.2%, 93.1%, 92.97% respectively, thus showing their feasibility to work in a real environment.

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%.