Temperature monitoring of nonanaesthetised patients in a cardiac catheterisation laboratory

Aim and objectives To identify the prevalence that temperature reduced by more than 1°C from pre to post-procedure in a sample of non-anaesthetised patients undergoing procedures in a cardiac catheterisation laboratory. Background Advances in medical technology are minimising the invasiveness of diagnostic tests and treatments for disease, which is correspondingly increasing the number of medical procedures performed without sedation or anaesthesia. Procedural areas in which medical procedures are performed without anaesthesia are typically kept at a cool temperature for staff comfort. As such, there is a need to inform nursing practices in regard to the thermal management of non-anaesthetised patients undergoing procedures in surgical or procedural environments. Design Single-site observational study Methods Patients were included if they had undergone an elective procedure without sedation or anaesthesia in a cardiac catheterisation laboratory. Ambient room temperature was maintained between 18°C and 20°C. Passive warming with heated cotton blankets was applied. Nurses measured body temperature and thermal comfort before and after 342 procedures. Results Mean change in temperature was -0.08°C (Standard deviation 0.43). The reduction in temperature was more than 1°C after 11 procedures (3.2%). One patient whose temperature had reduced more than 1°C after their procedure reported thermal discomfort. A total of 12 patients were observed to be shivering post-procedure (3.6%). No demographic or clinical characteristics were associated with reduction in temperature of more than 1°C from pre to post-procedure. Conclusions Significant reduction in body temperature was rare in our sample of non-anaesthetised patients. Relevance to clinical practice Similar results would likely be found in other procedural contexts during procedures conducted in settings with comparable room temperatures where passive warming can also be applied with limited skin exposure.

Embedded PZT wafer sensors for structural health monitoring

Recent advances in structural integrity evaluation have led to the development. of PZT wafer sensors (PWAS) which can be embedded or surface mounted for both acoustic emission (AE) and ultrasonic (UT) modes, which forms an integrated approach for Structural Health Monitoring (SHM) of aerospace structures. For the fabrication of PWAS wafers, soft PZT formulation (SP-5H Grade containing dopants like BA, SM, CA, ZN, Y and HF) were used. The piezoelectric charge constant (d(33)) was measured by a d(33) meter. As a first step towards the final objective of developing Health monitoring methods with embedded PWAS, experiments were conducted on aluminum and composite plates of finite dimensions using PWAS sensors. The AE source was simulated by breaking 0.5mm pencil lead on the surface of a thin plate. Experiments were also conducted with surface mounted PZT films and conventional AE sensors in order to establish the sensitivity of PWAS. A comparison of results of theoretical and experimental work shows good agreement.

Energy efficient area monitoring using information coverage in wireless sensor networks

In this paper, we are concerned with energy efficient area monitoring using information coverage in wireless sensor networks, where collaboration among multiple sensors can enable accurate sensing of a point in a given area-to-monitor even if that point falls outside the physical coverage of all the sensors. We refer to any set of sensors that can collectively sense all points in the entire area-to-monitor as a full area information cover. We first propose a low-complexity heuristic algorithm to obtain full area information covers. Using these covers, we then obtain the optimum schedule for activating the sensing activity of various sensors that maximizes the sensing lifetime. The scheduling of sensor activity using the optimum schedules obtained using the proposed algorithm is shown to achieve significantly longer sensing lifetimes compared to those achieved using physical coverage. Relaxing the full area coverage requirement to a partial area coverage (e.g., 95% of area coverage as adequate instead of 100% area coverage) further enhances the lifetime.

Patient exposure monitoring and radiation qualities in two-dimensional digital x-ray imaging

The methods for estimating patient exposure in x-ray imaging are based on the measurement of radiation incident on the patient. In digital imaging, the useful dose range of the detector is large and excessive doses may remain undetected. Therefore, real-time monitoring of radiation exposure is important. According to international recommendations, the measurement uncertainty should be lower than 7% (confidence level 95%). The kerma-area product (KAP) is a measurement quantity used for monitoring patient exposure to radiation. A field KAP meter is typically attached to an x-ray device, and it is important to recognize the effect of this measurement geometry on the response of the meter. In a tandem calibration method, introduced in this study, a field KAP meter is used in its clinical position and calibration is performed with a reference KAP meter. This method provides a practical way to calibrate field KAP meters. However, the reference KAP meters require comprehensive calibration. In the calibration laboratory it is recommended to use standard radiation qualities. These qualities do not entirely correspond to the large range of clinical radiation qualities. In this work, the energy dependence of the response of different KAP meter types was examined. According to our findings, the recommended accuracy in KAP measurements is difficult to achieve with conventional KAP meters because of their strong energy dependence. The energy dependence of the response of a novel large KAP meter was found out to be much lower than with a conventional KAP meter. The accuracy of the tandem method can be improved by using this meter type as a reference meter. A KAP meter cannot be used to determine the radiation exposure of patients in mammography, in which part of the radiation beam is always aimed directly at the detector without attenuation produced by the tissue. This work assessed whether pixel values from this detector area could be used to monitor the radiation beam incident on the patient. The results were congruent with the tube output calculation, which is the method generally used for this purpose. The recommended accuracy can be achieved with the studied method. New optimization of radiation qualities and dose level is needed when other detector types are introduced. In this work, the optimal selections were examined with one direct digital detector type. For this device, the use of radiation qualities with higher energies was recommended and appropriate image quality was achieved by increasing the low dose level of the system.

Stimulating immune responses to fight cancer: Basic biology and mechanisms

Chronic inflammation is now recognized as a major cause of malignant disease. In concert with various mechanisms (including DNA instability), hypoxia and activation of inflammatory bioactive lipid pathways and pro-inflammatory cytokines open the doorway to malignant transformation and proliferation, angiogenesis, and metastasis in many cancers. A balance between stimulatory and inhibitory signals regulates the immune response to cancer. These include inhibitory checkpoints that modulate the extent and duration of the immune response and may be activated by tumor cells. This contributes to immune resistance, especially against tumor antigen-specific T-cells. Targeting these checkpoints is an evolving approach to cancer immunotherapy, designed to foster an immune response. The current focus of these trials is on the programmed cell death protein 1 (PD-1) receptor and its ligands (PD-L1, PD-L2) and cytotoxic T-lymphocyte-associated protein 4 (CTLA-4). Researchers have developed anti-PD-1 and anti-PDL-1 antibodies that interfere with the ligands and receptor and allow the tumor cell to be recognized and attacked by tumor-infiltrating T-cells. These are currently being studied in lung cancer. Likewise, CTLA-4 inhibitors, which have had success treating advanced melanoma, are being studied in lung cancer with encouraging results.

Methodological perspectives for register-based health system performance assessment : Developing a hip fracture monitoring system in Finland

The resources of health systems are limited. There is a need for information concerning the performance of the health system for the purposes of decision-making. This study is about utilization of administrative registers in the context of health system performance evaluation. In order to address this issue, a multidisciplinary methodological framework for register-based data analysis is defined. Because the fixed structure of register-based data indirectly determines constraints on the theoretical constructs, it is essential to elaborate the whole analytic process with respect to the data. The fundamental methodological concepts and theories are synthesized into a data sensitive approach which helps to understand and overcome the problems that are likely to be encountered during a register-based data analyzing process. A pragmatically useful health system performance monitoring should produce valid information about the volume of the problems, about the use of services and about the effectiveness of provided services. A conceptual model for hip fracture performance assessment is constructed and the validity of Finnish registers as a data source for the purposes of performance assessment of hip fracture treatment is confirmed. Solutions to several pragmatic problems related to the development of a register-based hip fracture incidence surveillance system are proposed. The monitoring of effectiveness of treatment is shown to be possible in terms of care episodes. Finally, an example on the justification of a more detailed performance indicator to be used in the profiling of providers is given. In conclusion, it is possible to produce useful and valid information on health system performance by using Finnish register-based data. However, that seems to be far more complicated than is typically assumed. The perspectives given in this study introduce a necessary basis for further work and help in the routine implementation of a hip fracture monitoring system in Finland.

Kirjallisuuskatsaus: Eturauhassyövän androgeeniresponsiiviset in vivo -mallit; Kokeellinen osa: Bioluminesenssiin perustuvan ortotooppisen eturauhassyöpämallin optimointi

Eturauhassyöpä on yksi yleisimmistä syövistä länsimaissa. Eturauhassyöpä on yleensä hitaasti kehittyvä tauti. Edetessään se voi kuitenkin muuntua aggressiivisemmaksi ja aiheuttaa metastaaseja, jotka ovat pääasiallisena syynä taudin kuolleisuuteen. Androgeenit ovat merkittäviä tekijöitä eturauhassyövän patogeneesissä ja eturauhassyöpäkudos on useimmiten riippuvainen androgeeneista. Tämän vuoksi hoidon tavoitteena on estää niiden eritys kirurgisella tai kemiallisella kastraatiolla ja/tai estää androgeenien vaikutus antiandrogeeneilla. Eturauhassyöpää sekä sen hoitoon tarkoitettuja uusia lääkehoitomahdollisuuksia tutkitaan kiivaasti. Eturauhassyövän tutkimiseen on kehitetty lukematon määrä erilaisia in vivo -malleja. Koska eturauhassyöpä on yleensä androgeeneille herkkä, kuvaavat androgeeniresponsiiviset eläinmallit ihmisen tautia parhaiten. Eturauhassyövän mallintamiseen in vivo voidaan käyttää eri eläinlajeja, mutta hiiri on ylivoimaisesti käytetyin mallieläin. Immuunipuutteisiin hiiriin voidaan aiheuttaa kasvaimia inokuloimalla ihmisen kasvainsoluja tai osia ihmisen kasvaimista. Ortotooppisesti eturauhaseen inokuloitavat kasvainmallit mallintavat eturauhassyövässä esiintyvää syöpäsolujen ja stroomasolujen välistä epänormaalia vuorovaikutusta. Muuntogeeniset hiirimallit ovat yhä yleisempiä eturauhassyövän tutkimuksessa. Muuntogeenisilla malleilla voidaan mallintaa taudin kehittymistä ja sen etenemistä kokonaisuudessaan parhaiten. Eturauhasessa olevaa kasvainta ja sen kasvua on vaikea seurata ilman prostataspesifisen antigeenin (PSA) pitoisuuden mittausta tai erityisiä kuvantamistekniikoita. Tällaisia menetelmiä, kuten optista kuvantamista, käytetään yhä enemmän hyödyksi erilaisissa eturauhassyövän in vivo -malleissa. Tutkielman kokeellisen osan tavoitteena oli optimoida bioluminesenssiin perustuva optinen kuvantamismenetelmä androgeeniresponsiivisessa LNCaP-luc2-solulinjassa ortotooppisessa eturauhassyöpämallissa. Bioluminesenssikuvantaminen perustuu kasvainsolujen ilmentämän lusiferaasin katalysoimaan reaktioon, jossa entsyymin substraatti, lusiferiini, hapettuu ja tuottaa näkyvää valoa. Lisäksi tavoitteena oli tutkia lääkehoitojen ja kastraation vasteita mallissa. Bioluminesenssiin perustuvalla kuvantamisella oli mahdollista seurata eturauhaskasvainten kasvua noninvasiivisesti, reaaliaikaisesti ja toistuvasti. Bioluminesenssikuvantamisen avulla kasvainten kvantitointi oli nopeampaa kuin ultraäänikuvantamisen avulla, ja kasvainten kasvua oli myös mahdollista seurata useammin kuin seerumin PSA-mittausten avulla. Bioluminesenssikuvantamisen todettiin korreloivan paremmin PSA-pitoisuuden kanssa kuin kasvaimen todelliseen kokoon lopetushetkellä. Seerumin PSA-pitoisuus korreloi kuitenkin bioluminesenssimittausta paremmin eturauhaskasvaimen kokoon tässä kokeessa. Kasvainten oletettua suurempaa kokoa voidaan pitää todennäköisimpänä syynä sille, ettei lääkehoitojen tai kastraation todettu vaikuttavan kasvainten kasvuun bioluminesenssikuvantamisella mitattuna. Bioluminesenssikuvantaminen ei sovellu suurille eikä nekroottisille kasvaimille, sillä kuvantamismenetelmä toimii vain elävillä soluilla. Bioluminesenssikuvantamisen hyödyntämisen kannalta oleellista tässä mallissa on myös lusiferiini-injektion onnistuminen. Jatkotutkimuksia tarvitaan edelleen mallin validoimiseksi mm. lääkehoitojen vasteiden osoittamiseksi.

An intelligent approach using support vector machines for monitoring and identification of faults on transmission systems

Power system disturbances are often caused by faults on transmission lines. When faults occur in a power system, the protective relays detect the fault and initiate tripping of appropriate circuit breakers, which isolate the affected part from the rest of the power system. Generally Extra High Voltage (EHV) transmission substations in power systems are connected with multiple transmission lines to neighboring substations. In some cases mal-operation of relays can happen under varying operating conditions, because of inappropriate coordination of relay settings. Due to these actions the power system margins for contingencies are decreasing. Hence, power system protective relaying reliability becomes increasingly important. In this paper an approach is presented using Support Vector Machine (SVM) as an intelligent tool for identifying the faulted line that is emanating from a substation and finding the distance from the substation. Results on 24-bus equivalent EHV system, part of Indian southern grid, are presented for illustration purpose. This approach is particularly important to avoid mal-operation of relays following a disturbance in the neighboring line connected to the same substation and assuring secure operation of the power systems.

Tool wear monitoring using ant behaviour

In this paper we show the applicability of Ant Colony Optimisation (ACO) techniques for pattern classification problem that arises in tool wear monitoring. In an earlier study, artificial neural networks and genetic programming have been successfully applied to tool wear monitoring problem. ACO is a recent addition to evolutionary computation technique that has gained attention for its ability to extract the underlying data relationships and express them in form of simple rules. Rules are extracted for data classification using training set of data points. These rules are then applied to set of data in the testing/validation set to obtain the classification accuracy. A major attraction in ACO based classification is the possibility of obtaining an expert system like rules that can be directly applied subsequently by the user in his/her application. The classification accuracy obtained in ACO based approach is as good as obtained in other biologically inspired techniques.

Filter design using radial basis function neural network and genetic algorithm for improved operational health monitoring

The problem of denoising damage indicator signals for improved operational health monitoring of systems is addressed by applying soft computing methods to design filters. Since measured data in operational settings is contaminated with noise and outliers, pattern recognition algorithms for fault detection and isolation can give false alarms. A direct approach to improving the fault detection and isolation is to remove noise and outliers from time series of measured data or damage indicators before performing fault detection and isolation. Many popular signal-processing approaches do not work well with damage indicator signals, which can contain sudden changes due to abrupt faults and non-Gaussian outliers. Signal-processing algorithms based on radial basis function (RBF) neural network and weighted recursive median (WRM) filters are explored for denoising simulated time series. The RBF neural network filter is developed using a K-means clustering algorithm and is much less computationally expensive to develop than feedforward neural networks trained using backpropagation. The nonlinear multimodal integer-programming problem of selecting optimal integer weights of the WRM filter is solved using genetic algorithm. Numerical results are obtained for helicopter rotor structural damage indicators based on simulated frequencies. Test signals consider low order polynomial growth of damage indicators with time to simulate gradual or incipient faults and step changes in the signal to simulate abrupt faults. Noise and outliers are added to the test signals. The WRM and RBF filters result in a noise reduction of 54 - 71 and 59 - 73% for the test signals considered in this study, respectively. Their performance is much better than the moving average FIR filter, which causes significant feature distortion and has poor outlier removal capabilities and shows the potential of soft computing methods for specific signal-processing applications.

Filter design using radial basis function neural network and genetic algorithm for improved operational health monitoring

The problem of denoising damage indicator signals for improved operational health monitoring of systems is addressed by applying soft computing methods to design filters. Since measured data in operational settings is contaminated with noise and outliers, pattern recognition algorithms for fault detection and isolation can give false alarms. A direct approach to improving the fault detection and isolation is to remove noise and outliers from time series of measured data or damage indicators before performing fault detection and isolation. Many popular signal-processing approaches do not work well with damage indicator signals, which can contain sudden changes due to abrupt faults and non-Gaussian outliers. Signal-processing algorithms based on radial basis function (RBF) neural network and weighted recursive median (WRM) filters are explored for denoising simulated time series. The RBF neural network filter is developed using a K-means clustering algorithm and is much less computationally expensive to develop than feedforward neural networks trained using backpropagation. The nonlinear multimodal integer-programming problem of selecting optimal integer weights of the WRM filter is solved using genetic algorithm. Numerical results are obtained for helicopter rotor structural damage indicators based on simulated frequencies. Test signals consider low order polynomial growth of damage indicators with time to simulate gradual or incipient faults and step changes in the signal to simulate abrupt faults. Noise and outliers are added to the test signals. The WRM and RBF filters result in a noise reduction of 54 - 71 and 59 - 73% for the test signals considered in this study, respectively. Their performance is much better than the moving average FIR filter, which causes significant feature distortion and has poor outlier removal capabilities and shows the potential of soft computing methods for specific signal-processing applications. (C) 2005 Elsevier B. V. All rights reserved.

Innate Immune Recognition of RNA-virus infection in human macrophages

Innate immunity and host defence are rapidly evoked by structurally invariant molecular motifs common to microbial world, called pathogen associated molecular patterns (PAMPs). In addition to PAMPs, endogenous molecules released in response to inflammation and tissue damage, danger associated molecular patterns (DAMPs), are required for eliciting the response. The most important PAMPs of viruses are viral nucleic acids, their genome or its replication intermediates, whereas the identity and characteristics of virus infection-induced DAMPs are poorly defined. PAMPs and DAMPs engage a limited set of germ-line encoded pattern recognition receptors (PRRs) in immune and non-immune cells. Membrane-bound Toll-like receptors (TLRs), cytoplasmic retinoic acid inducible gene-I (RIG-I)-like receptors (RLRs) and nucleotide-binding oligomerization domain-like receptor (NLRs) are important PRRs involved in the recognition of the molecular signatures of viral infection, such as double-stranded ribonucleic acids (dsRNAs). Engagement of PRRs results in local and systemic innate immune responses which, when activated against viruses, evoke secretion of antiviral and pro-inflammatory cytokines, and programmed cell death i.e., apoptosis of the virus-infected cell. Macrophages are the central effector cells of innate immunity. They produce significant amounts of antiviral cytokines, called interferons (IFNs), and pro-inflammatory cytokines, such as interleukin (IL)-1β and IL-18. IL-1β and IL-18 are synthesized as inactive precursors, pro-IL-1β and pro-IL-18, that are processed by caspase-1 in a cytoplasmic multiprotein complex, called the inflammasome. After processing, these cytokines are biologically active and will be secreted. The signals and secretory routes that activate inflammasomes and the secretion of IL-1β and IL-18 during virus infections are poorly characterized. The main goal of this thesis was to characterize influenza A virus-induced innate immune responses and host-virus interactions in human primary macrophages during an infection. Methodologically, various techniques of cellular and molecular biology, as well as proteomic tools combined with bioinformatics, were utilized. Overall, the thesis provides interesting insights into inflammatory and antiviral innate immune responses, and has characterized host-virus interactions during influenza A virus-infection in human primary macrophages.