Postoperatiivisen kipupotilaan näyttöön perustuva ei- farmakologisten kivunhoitomenetelmien ohjaus

Opinnäytetyö on osa Näyttöön perustuvan osaamisen vahvistaminen työelämässä-projektia, joka on Helsingin kaupungin terveyskeskuksen akuuttisairaalaosastojen ja Metropolia Ammattikorkeakoulun kehittämisprojekti vuosina 2006 - 2009. Projektin tavoitteena on vahvistaa näyttöön perustuvan osaamisen avulla käytännön hoitotyön laatua ja vaikuttavuutta sekä vahvistaa hoitohenkilökunnan tutkimus-, kehittämis- ja projektitaitoja. Tämän opinnäytetyön teoreettinen viitekehys perustui kuvailevalle katsaukselle kirjallisuuteen. Lisäksi työhön kuului toiminnallinen osio. Työn tarkoituksena oli kuvata postoperatiivisen kipupotilaan ohjaustilanteen suunnittelua ja ohjausrungon laatimista. Tavoitteena oli kehittää ohjausrunko, jonka pohjalta suunniteltiin ja toteutettiin ohjaustilanne Malmin sairaalan akuuttiosastolla. Työn toiminnallisessa osiossa kehitetyn ohjausrungon toimivuutta kokeiltiin käytännön ohjaustilanteessa. Opinnäytetyössä keskityttiin postoperatiivisen kipupotilaan näyttöön perustuvaan ohjaukseen ei- farmakologisista kivunhoitomenetelmistä. Työssä käsiteltiin kipua, kivun fysiologiaa ja psykologiaa sekä esiteltiin keskeisimmät ei-farmakologiset kivunhoitomenetelmät. Työssä hyödynnettiin tutkimuksellista tietoa ei-farmakologisista kivunhoitomenetelmistä ja niiden vaikutuksista postoperatiivisen potilaan hoidossa. Myös ohjaus oli työssä keskeisessä asemassa. Ohjausta käsiteltiin sekä yleisellä tasolla että postoperatiivisen potilaan näkökulmasta. Tämän työn teoreettisen viitekehyksen, ohjaustilanteen ja ohjausrungon tulosten sekä arvioinnin pohjalta voidaan yhteenvetona todeta ei-farmakologisten kivunhoitomenetelmien olevan tutkitusti ja todistetusti hyviä keinoja kivun lievittämiseksi, vaikkakin vielä valitettavan harvoin käytettyjä. Potilaille on tarjottava mahdollisuus halutessaan erilaisiin ei-farmakologisiin kivunhoitomenetelmiin ja annettava ohjausta ja neuvoja niihin liittyen. Hyvän ja antoisan ohjauksen onnistuminen edellyttää ohjaajalta tietämystä, motivaatiota, kokemusta ja valmiutta sekä vuorovaikutustaitoja ja asiakaslähtöisyyden ymmärtämistä. Myös eettiset periaatteet on huomioitava ohjausta annettaessa.This final project scholarly thesis is a part of Evidence based skill improvement in working life-project which is the development project of Helsinki city's health centre’s acute hospitals and Metropolia Polytechnic in the years 2006-2009. The aim of this project is to build up practical nursing quality and influence by the means of evidence based skills. Another goal is to strengthen the nursing personal research-, development- and project skills. The theoretical context of this final project was based on a descriptive review in literature. The project also included a functional part. The purpose of the project was to describe the planning of a guidance situation to a postoperative patient of pain and to compile a guidance frame. The aim was to develop a guidance frame which was the base of planning and carrying out a guidance situation in an acute ward in the Malmi hospital. The potential of the guidance frame developed in the functional part of this project was put to trial in a guidance situation in practical working life. This thesis focused on such terms as pain, physiology and psychology of pain. It also focuses on the most crucial methods of non-pharmacological pain management. This thesis utilized scientifically studied information referring to methods of non-pharmacological pain management and their effects on post-operative management of a patient. Patient guidance was also an essential part of this project. It was processed both on a general level as well as through the perspective of a post-operative patient. Based on the theoretical context, guidance situation and guidance frame of this thesis can scientifically point out that using non-pharmacological pain management methods is a very efficient way to alleviate pain, but unfortunately these methods are rarely used. There must be offered a possibility for different kinds of non-pharmacological pain management methods for the patient and offer them guidance and advice referring to those. Knowledge, motivation, experience, readiness, interaction skills and customer oriented approach is required from the instructor to provide a good and productive guidance. Ethical principles must also be taken into consideration when giving guidance

LysoPC and PAF Trigger Arachidonic Acid Release by Divergent Signaling Mechanisms in Monocytes

Oxidized low-density lipoproteins (LDLs) play an important role during the development of atherosclerosis characterized by intimal inflammation and macrophage accumulation. A key component of LDL is lysophosphatidylcholine (lysoPC). LysoPC is a strong proinflammatory mediator, and its mechanism is uncertain, but it has been suggested to be mediated via the platelet activating factor (PAF) receptor. Here, we report that PAF triggers a pertussis toxin- (PTX-) sensitive intracellular signaling pathway leading to sequential activation of sPLA2, PLD, cPLA2, and AA release in human-derived monocytes. In contrast, lysoPC initiates two signaling pathways, one sequentially activating PLD and cPLA2, and a second parallel PTX-sensitive pathway activating cPLA2 with concomitant activation of sPLA2, all leading to AA release. In conclusion, lysoPC and PAF stimulate AA release by divergent pathways suggesting involvement of independent receptors. Elucidation of monocyte lysoPC-specific signaling mechanisms will aid in the development of novel strategies for atherosclerosis prevention, diagnosis, and therapy

Henkilöstöryhmän määrittyminen työehtosopimusten soveltamisalojen mukaisesti

Tutkielmassa tarkastelen henkilöstöryhmän rajojen muodostumista työehtosopimustoiminnan perusteella. Henkilöstöryhmä on työoikeudessa paljon käytetty ja keskeinen käsite. Henkilöstöryhmien rajojen tarkka määrääminen on kuitenkin juridisen määrittelyn puuttuessa hankalaa. Henkilöstöryhmän käsite on muodostunut käytännön työehtosopimustoiminnan pohjalta. Yleisen käsityksen mukaisesti yrityksen henkilöstö voidaan jakaa työntekijöiden, toimihenkilöiden ja ylempien toimihenkilöiden henkilöstöryhmiin sekä yrityksen johtoon. Nämä henkilöstöryhmät ovat muodostuneet työmarkkinoiden järjestäytymisen ja työntekijäpuolen keskusjärjestöjen mukaisesti ja vakiinnuttaneet asemansa osana kollektiivista työoikeutta. Tutkielmassa tutkin, onko löydettävissä yleisiä kriteerejä, joiden mukaan voidaan määritellä henkilöstöryhmien väliset rajat. Työehtosopimusten soveltamisalamääräyksiä tulkitsemalla ja luokittelemalla pyrin selvittämään, millä perusteella jokin tehtävä luokitellaan tiettyyn henkilöstöryhmään kuuluvaksi. Tutkimusaineistonani ovat yleissitovaksi vahvistetut työehtosopimukset. Työehtosopimusten tarkastelun tuloksena selviää, ettei työntekijöiden ja toimihenkilöiden henkilöstöryhmien välistä rajaa voi yleisesti määritellä vaan ryhmien välinen rajanveto on puhtaasti sopimuksenvarainen asia. Toimihenkilöiden ja ylempien toimihenkilöiden ryhmät ovat eroteltavissa yleensä tehtävien itsenäisyyden sekä taloudellisen, hallinnollisen ja toiminnallisen vastuun mukaisesti. Vaikka kriteerit toimihenkilösopimusten ja ylempien toimihenkilöiden työehtosopimusten soveltamiselle ovat melko yhteneväiset kaikilla aloilla, on näiden kriteerien soveltaminen käytäntöön kuitenkin usein melko hankalaa yleisen mittarin puuttuessa. Rajanveto tehtävien itsenäisyyden ja vastuun mukaisesti jääkin usein ala- tai työpaikkakohtaiseksi, eikä ole yleisesti määritettävissä, kuinka suuri vastuu tai itsenäisyys on edellytyksenä tehtävän sijoittumiselle ylempien toimihenkilöiden henkilöstöryhmään. Ainoastaan yrityksen johto on selkeästi ja yhteneväisesti määritelty muiden henkilöstöryhmien yläpuolelle lähes kaikissa työehtosopimuksissa

Der Einfluss thorakaler Epiduralanästhesie auf die intestinale Mikrozirkulation und die Sterblichkeit der akuten nekrotisierenden Pankreatitis

Erstmals wurde ein Modell der kontinuierlichen TEA an der Ratte charakterisiert. In 2 Gruppen (15µl NaCl oder Bupivacain 0,5%) über 2h eine TEA durchgeführt. Durch 30-minütliche Messung der Hauttemperatur konnte eine signifikante thorakale Erwärmung (p<0,001 zu allen Zeiten) und kompensatorische Abkühlung am Schwanz nachgewiesen werden. Kardiorespiratorischen Parameter blieben unverändert. Mit diesem Modell wurde der TEA-Effekt auf die Ileummukosaperfusion und die Mortalität bei Pankreatitis untersucht. In 4 Gruppen wurde epidural entweder NaCl oder Bupivacain 0,5% infundiert. Der mukosale Blutfluss im Ileum sank bei Pankreatitis im Vergleich zur Kontrollgruppe. Eine TEA bei Pankreatitis verbesserte die Perfusion im Vergleich zur Pankreatitis ohne TEA (p<0,05). Ein Anstieg der Zwischenkapillarfläche kontinuierlich perfundierter Kapillaren konnte bei TEA und verzögerter TEA verhindert werden (je p<0,05). Die 7-Tage-Mortalität sank bei TEA um 66% (p<0,05)

A spoiler recovery method for rapid diffusion measurements

A method for rapid acquisition of multiple scans of NMR sequences is presented. The method initially applies two RF-pulses in combination with two magnetic field gradient pulses of opposite polarity, different strength and different duration. The basic idea is to spoil any magnetization in any direction before by letting the system recover to some degree of restoration of the thermal equilibrium magnetization. Thereafter any pulse sequence can be applied, and the next scan may be run immediately after the end of the pulse sequence. Thus one avoids the 5 times T1 delay between each scan. A set of PFG sequences are presented that apply the spoiler recovery method for significant reduction in acquisition time, and the method has been verified at 0.5 Tesla as well as at 11.7 Tesla

"Ei god blanding". Om kollektiv læring og lederstrategier som grunnlag for skolebasert kompetanseutvikling

Utgangspunktet for dette studiet er den nasjonale satsingen på ungdomstrinnet; skolebasert kompetanseutvikling. Formålet med strategien er å øke elevenes motivasjon slik at elevene lærer mer. Satsingen går ut på at kompetanseutviklingen skal foregå på hver enkelt skole, og dette utfordrer alle som jobber med elevenes læring og læringsutbytte. Oppgavens overordnede problemstilling er: ”Hvordan kan skolebasert kompetanseutvikling bidra til å fremme læring hos alle?” Det empiriske materialet er samlet inn gjennom kvalitative forskningsintervju med en fenomenologisk tilnærming i et hermeneutisk perspektiv. Vi har gjennomført både individuelle intervju og gruppeintervju. De individuelle intervjuene er utført på ulike styringsnivå i skolen, mens gruppeintervjuene er gjennomført med elever. Vi ønsket å få fram elevstemmen i vår studie, siden hele formålet med strategien er at skolebasert kompetanseutvikling skal nå helt ut i klasserommet. En sentral del av skolebasert kompetanseutvikling er kollektiv læring og lederstrategier. Skoleleders rolle i dette arbeidet har en avgjørende nøkkelfaktor for å utvikle profesjonelle læringsfellesskap. Tidligere forskning viser at den viktigste faktoren for elevenes læring er læreren. Gjennom studien drøfter vi funn i empirien sett i lys av aktuell forskning og i forhold til vår problemstilling

Termisk spalting av fettsyrehydroperoksyder i tran.

Fi-Kje-Te rapporte

DDoS angrep generering for å utmatte GPU/CPU ressurser til IoT baserte AI applikasjoner

I vår studie sikter vi på å teste dyktigheten til nettverksangrep deteksjonssystemer i Internet of Things enheter og deres egenskap til å motstå belastningsangrep på prosessorer gjennom et åpenkildet evolusjonær utviklingsprogram kalt NSGA-II. Dette er en nyoppdaget > som i dag generelt blir kalt DDoS AI angrep. Vi studerer muligheten for at det ikke krever komplisert utstyr for at slike angrep skal kunne bli utført, og om offentlig tilgjengelige programvarer og algoritmer kan ha like stor effekt som de komplekse algoritmene som har blitt utviklet for de få studiene innen dette emnet. Motivasjonen bak denne utforskningen er den økende veksten i dyplæringsteknologi samt mangel i cybersikkerhetstjenester. Ved et DDoS AI angrep gis et datainput til en maskinlæringsmodell (ML) med hensikt til å få modellen til å sløse bort så mye prosesseringskraft som mulig. Disse angrepene er malisiøse forsøk på å villede og forvirre maskinlæringsmodeller. En angriper kan f.eks. endre et bilde på en måte som gjør at et datasynssystem ikke greier å gjette hva det er meningen bildet skal representere, og dermed tvinger datasynsystemet til å sløse så mye prosesseringskraft som mulig for å prøve å gjette seg fram til riktig svar. Ut i fra våre resultater observerer vi at angrepsmetodens effektivitet varierer bemerkelig på hva type datasett og modell NSGA algoritmen blir utsatt for. Både vår testmodell og vår hovedmodell ville eventuelt nå en grense hvor den ikke sløste bort flere nevroner eller prosesseringskraft uansett hvor mye NSGA fortsatte å manipulere og finjustere inndataen. Derimot hadde NSGA en bemerkelig effekt på prosesseringskraften til modellene. På den enkle testmodellen kunne prosesseringskraften øke med 43\% gjennom datamanipulering, mens i vår komplekse datamodell fløt prosseseringsøkningen rundt 57\% etter datamanipulering. Ut i fra våre resultater kom vi fram til at angrepsmetoden har en bemerkelig effekt på modellenes ytelse, og at det bør implementeres metode for å begrense hvor mange noder som kan bli aktivert etter hvor bra systemapparatet modellen kjører på er. Motstridelsesøving bør også bli tatt inn til konsiderasjon, hvor modellene øver på manipulert data for å kunne oppdage dem og ikke sløse bort prosesseringskraft på dem. Videre utforskning kreves for å forstå fullskalaen av hvor stor innflytelse disse angrepsmetodene kan ha.In our study, we aim to test the ability of network attack detection systems in Internet of Things (IoT) devices to withstand load attacks on processors using an open-source evolutionary development program called NSGA-II, through a newly discovered hacking method generally referred to as DDoS AI attacks. We are studying the possibility that such attacks may not require complex equipment and that publicly available software and algorithms may have as much effect as the complex algorithms developed in the few studies on this subject. The motivation behind this exploration is the increasing growth in deep learning technology and the lack of cybersecurity services. In a DDoS AI attack, data inputs are given to a machine learning model with the intention of causing the model to waste as much processing power as possible. These attacks are malicious attempts to deceive and confuse machine learning models. For example, an attacker may modify an image in a way that makes an image recognition system unable to guess what the image is supposed to represent, thus forcing the image recognition system to waste as much processing power as possible in trying to guess the correct answer. Based on our results, we observe that the effectiveness of the attack method varies significantly depending on the type of dataset and model that the NSGA algorithm is exposed to. Both our test model and our main model would eventually reach a limit where they would not waste any more neurons or processing power, no matter how much NSGA continued to manipulate and fine-tune the input data. However, NSGA had a notable effect on the processing power of the models. In the simple test model, processing power could increase by 43% through data manipulation, while in our complex data model, the processing power increase was around 57% after data manipulation. Based on our results, we concluded that the attack method has a notable effect on the performance of the models, and that a method should be implemented to limit the number of nodes that can be activated depending on how well the system hardware is running. Adversarial training should also be considered, where models practice on manipulated data to detect them and not waste processing power on them. Further research is needed to understand the full scale of the influence of these attack methods

Human metapneumovirus driven IFN-β production antagonizes macrophage transcriptional induction of IL1-β in response to bacterial pathogens

Human metapneumovirus (HMPV) is a pneumovirus that may cause severe respiratory disease in humans. HMPV infection has been found to increase susceptibility to bacterial superinfections leading to increased morbidity and mortality. The molecular mechanisms underlying HMPV-mediated increase in bacterial susceptibility are poorly understood and largely understudied. Type I interferons (IFNs), while critical for antiviral defenses, may often have detrimental effects by skewing the host immune response and cytokine output of immune cells. It is currently unknown if HMPV skews the inflammatory response in human macrophages triggered by bacterial stimuli. Here we report that HMPV pre-infection impacts production of specific cytokines. HMPV strongly suppresses IL-1β transcription in response to LPS or heat-killed Pseudomonas aeruginosa and Streptococcus pneumonia, while enhancing mRNA levels of IL-6, TNF-α and IFN-β. We demonstrate that in human macrophages the HMPV-mediated suppression of IL-1β transcription requires TANK-binding kinase 1 (TBK1) and signaling via the IFN-β-IFNAR axis. Interestingly, our results show that HMPV pre-infection did not impair the LPS-stimulated activation of NF-κB and HIF-1α, transcription factors that stimulate IL-1β mRNA synthesis in human cells. Furthermore, we determined that sequential HMPV-LPS treatment resulted in accumulation of the repressive epigenetic mark H3K27me3 at the IL1B promoter. Thus, for the first time we present data revealing the molecular mechanisms by which HMPV shapes the cytokine output of human macrophages exposed to bacterial pathogens/LPS, which appears to be dependent on epigenetic reprogramming at the IL1B promoter leading to reduced synthesis of IL-1β. These results may improve current understanding of the role of type I IFNs in respiratory disease mediated not only by HMPV, but also by other respiratory viruses that are associated with superinfections.</p

DDoS angrep generering for å utmatte GPU/CPU ressurser til IoT baserte AI applikasjoner

I vår studie sikter vi på å teste dyktigheten til nettverksangrep deteksjonssystemer i Internet of Things enheter og deres egenskap til å motstå belastningsangrep på prosessorer gjennom et åpenkildet evolusjonær utviklingsprogram kalt NSGA-II. Dette er en nyoppdaget > som i dag generelt blir kalt DDoS AI angrep. Vi studerer muligheten for at det ikke krever komplisert utstyr for at slike angrep skal kunne bli utført, og om offentlig tilgjengelige programvarer og algoritmer kan ha like stor effekt som de komplekse algoritmene som har blitt utviklet for de få studiene innen dette emnet. Motivasjonen bak denne utforskningen er den økende veksten i dyplæringsteknologi samt mangel i cybersikkerhetstjenester. Ved et DDoS AI angrep gis et datainput til en maskinlæringsmodell (ML) med hensikt til å få modellen til å sløse bort så mye prosesseringskraft som mulig. Disse angrepene er malisiøse forsøk på å villede og forvirre maskinlæringsmodeller. En angriper kan f.eks. endre et bilde på en måte som gjør at et datasynssystem ikke greier å gjette hva det er meningen bildet skal representere, og dermed tvinger datasynsystemet til å sløse så mye prosesseringskraft som mulig for å prøve å gjette seg fram til riktig svar. Ut i fra våre resultater observerer vi at angrepsmetodens effektivitet varierer bemerkelig på hva type datasett og modell NSGA algoritmen blir utsatt for. Både vår testmodell og vår hovedmodell ville eventuelt nå en grense hvor den ikke sløste bort flere nevroner eller prosesseringskraft uansett hvor mye NSGA fortsatte å manipulere og finjustere inndataen. Derimot hadde NSGA en bemerkelig effekt på prosesseringskraften til modellene. På den enkle testmodellen kunne prosesseringskraften øke med 43\% gjennom datamanipulering, mens i vår komplekse datamodell fløt prosseseringsøkningen rundt 57\% etter datamanipulering. Ut i fra våre resultater kom vi fram til at angrepsmetoden har en bemerkelig effekt på modellenes ytelse, og at det bør implementeres metode for å begrense hvor mange noder som kan bli aktivert etter hvor bra systemapparatet modellen kjører på er. Motstridelsesøving bør også bli tatt inn til konsiderasjon, hvor modellene øver på manipulert data for å kunne oppdage dem og ikke sløse bort prosesseringskraft på dem. Videre utforskning kreves for å forstå fullskalaen av hvor stor innflytelse disse angrepsmetodene kan ha.In our study, we aim to test the ability of network attack detection systems in Internet of Things (IoT) devices to withstand load attacks on processors using an open-source evolutionary development program called NSGA-II, through a newly discovered hacking method generally referred to as DDoS AI attacks. We are studying the possibility that such attacks may not require complex equipment and that publicly available software and algorithms may have as much effect as the complex algorithms developed in the few studies on this subject. The motivation behind this exploration is the increasing growth in deep learning technology and the lack of cybersecurity services. In a DDoS AI attack, data inputs are given to a machine learning model with the intention of causing the model to waste as much processing power as possible. These attacks are malicious attempts to deceive and confuse machine learning models. For example, an attacker may modify an image in a way that makes an image recognition system unable to guess what the image is supposed to represent, thus forcing the image recognition system to waste as much processing power as possible in trying to guess the correct answer. Based on our results, we observe that the effectiveness of the attack method varies significantly depending on the type of dataset and model that the NSGA algorithm is exposed to. Both our test model and our main model would eventually reach a limit where they would not waste any more neurons or processing power, no matter how much NSGA continued to manipulate and fine-tune the input data. However, NSGA had a notable effect on the processing power of the models. In the simple test model, processing power could increase by 43% through data manipulation, while in our complex data model, the processing power increase was around 57% after data manipulation. Based on our results, we concluded that the attack method has a notable effect on the performance of the models, and that a method should be implemented to limit the number of nodes that can be activated depending on how well the system hardware is running. Adversarial training should also be considered, where models practice on manipulated data to detect them and not waste processing power on them. Further research is needed to understand the full scale of the influence of these attack methods