40 research outputs found

    Nanokristalse alumiiniumoksiidi α- ja ĂŒleminekufaaside uurimine VUV ja katoodluminestsentsi meetoditel

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    VĂ€itekirja elektrooniline versioon ei sisalda publikatsiooneAlumiiniumoksiidi leidub looduses korundina ehk suure kĂ”vadusega mineraalina. Korund kui vÀÀrtuslik vÀÀriskivi on tuntud ka punase rubiini ja sinise safiirina. Sellised vĂ€rvused tekivad lisades alumiiniumoksiidile kroomi, raua vĂ”i titaani ioone. Peale termodĂŒnaamiliselt stabiilse alumiiniumoksiidi, mida tĂ€histatakse α-Al2O3, kristalliseerub alumiiniumoksiid ka mitmete metastabiilsete struktuuridena. Need nn ĂŒleminekufaasid on sĂŒnteesitavad vaid nanokristallidena ja pĂŒsivad vaid teatud temperatuurideni – ĂŒle 1100 °C juures on vĂ”imalik vaid α-faas. Erinevate kristallstruktuuride rohkus sama keemilise koostise korral ja nanomÔÔtmelisus teevad alumiiniumoksiidi ĂŒleminekufaasid atraktiivseks uurimisobjektiks ja ka oluliseks rakenduslikuks materjaliks. Antud töö eesmĂ€rgiks oli alumiiniumoksiidi ĂŒleminekufaaside ja stabiilse α-faasi elektroonsete ja optiliste omaduste vĂ”rdlev uurimine ning vĂ”imalike kristalliidi suurusest tingitud efektide selgitamine. Peamiste uurimismeetoditena kasutati töös mitmeid luminestsents-spektroskoopilisi tehnikaid, mis on paljudel juhtudel ainete elektronstruktuuri uurimises asendamatud. Töös nĂ€idati kuidas muutused kristallstruktuuris mĂ”jutavad erinevate alumiiniumoksiidi faaside elektronstruktuuri ning omaergastustega seotud kiirguslikke protsesse. Leiti samuti, et hoolimata erinevustest kristallstruktuurides on punktdefektide luminestsentsomadused erinevates alumiiniumoksiidi faasides sarnased. Cr3+ lisanditsentri kiirguse uurimine nĂ€itas, et seda on vĂ”imalik kasutada erinevate alumiiniumoksiidi faaside tuvastamiseks. Demonstreeriti ka luminestsentsmeetodite vĂ”imekust tĂ€psustada rakenduslikult oluliste oksiid-dispersiooniga tugevdatud teraste koostist.Aluminium oxide or alumina is found in nature as a hard mineral called corundum. Corundum as a gemstone is known as a red ruby or blue sapphire. These colours are achieved by substituting chromium, iron or titanium ions into alumina. Besides the thermodynamically stable phase of alumina called α-Al2O3 there exist several metastable alumina phases with different crystal structures. These so-called transition phases can be synthesized only as nanocrystals and are stable up to certain temperatures – over 1100 °C only α-phase exists. Highly variable crystal structure of chemically the same composition and nanocrystallinity makes transition phases of alumina an attractive objects of investigation and relevant material for applications. The aim of this work was the investigation of electronic and optical properties of transition aluminas in comparison with that of α-phase and of possible effects caused by the crystallite size. Various luminescence spectroscopy techniques were used as the main investigation methods, which are in many cases indispensable for investigation of electronic structure. It was demonstrated how electronic structure in different transition aluminas is influenced by changes of crystal structure. It was found also that despite differences in crystal structures the luminescence properties of point defects of different transition aluminas are similar. The study of Cr3+ impurity centre emission showed the possibility to use it for identifying of different alumina phases. The capability of luminescence methods was demonstrated as well to clarify the composition of oxide dispersion strengthened steels, important material for many applications

    Eesti eelkooliealiste laste hĂ”lmatus immuniseerimiskava vaktsiinidega 2010. aasta sĂŒnnikohordi pĂ”hjal Eesti Haigekassa raviarvete alusel

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    Taust, eesmĂ€rk. Eestis puudub seni ĂŒlevaade, kui paljude lastega viiakse lĂ€bi kĂ”ik immuniseerimiskavas ette nĂ€htud plaanilised vaktsineerimised. Uurimuse eesmĂ€rk oli anda ĂŒlevaade, kui suurel osal eelkooliealistest lastest on Eesti Haigekassa raviarvete alusel kĂ”ik immuniseerimiskavas ette nĂ€htud vaktsineerimised tehtud. Metoodika. AnalĂŒĂŒsiti Eesti Haigekassale perearstide ja eriarstide esitatud raviarveid (n = 1 091 275) kĂ”igi 2010. aastal sĂŒndinud laste kohta (n = 16 464), kes Eestis perioodil 2010–2018 tervishoiuteenuseid tarbisid. Hinnati immuniseerimiskavas toodud vaktsineerimistega hĂ”lmatust 3- ja 8aastaste laste seas. Tulemused, jĂ€reldused. 3aastaseks saanud lastest lĂ€bis raviarvete alusel kĂ”ik sellele vanusele immuniseerimiskavas ette nĂ€htud vaktsineerimised 68,9%, osaliselt vaktsineeriti 24,4% ning vaktsineerimata oli 6,7%. 8aastastest olid kĂ”ik vaktsiinid manustatud 49,5%-le lastest, osaliselt 43,9%-le ja vaktsineerimata oli 6,5%. Seega on vaktsineerimisega hĂ”lmatus kogu immuniseerimiskava jĂ€rgi Eestis vĂ€iksem, kui ĂŒksikute vaktsiinikomponentidega hĂ”lmatuse pĂ”hjal vĂ”iks arvata. KĂ”ige sagedamini puudusid raviarved DTPa-IPV (DTPa – difteeria, teetanuse ja atsellulaarse lĂ€kaköha vaktsiin, IPV – inaktiveeritud poliomĂŒeliidi vaktsiin) teise revaktsineerimise kohta (vaktsineerimine 6–7 aasta vanuses)

    Calculating daily dose in the Observational Medical Outcomes Partnership Common Data Model

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    Purpose: We aimed to develop a standardized method to calculate daily dose (i.e., the amount of drug a patient was exposed to per day) of any drug on a global scale using only drug information of typical observational data in the Observational Medical Outcomes Partnership Common Data Model (OMOP CDM) and a single reference table from Observational Health Data Sciences And Informatics (OHDSI). Materials and Methods: The OMOP DRUG_STRENGTH reference table contains information on the strength or concentration of drugs, whereas the OMOP DRUG_EXPOSURE table contains information on patients' drug prescriptions or dispensations/claims. Based on DRUG_EXPOSURE data from the primary care databases Clinical Practice Research Datalink GOLD (United Kingdom) and Integrated Primary Care Information (IPCI, The Netherlands) and healthcare claims from PharMetricsÂź Plus for Academics (USA), we developed four formulas to calculate daily dose given different DRUG_STRENGTH reference table information. We tested the dose formulas by comparing the calculated median daily dose to the World Health Organization (WHO) Defined Daily Dose (DDD) for six different ingredients in those three databases and additional four international databases representing a variety of healthcare settings: MAITT (Estonia, healthcare claims and discharge summaries), IQVIA Disease Analyzer Germany (outpatient data), IQVIA Longitudinal Patient Database Belgium (outpatient data), and IMASIS Parc Salut (Spain, hospital data). Finally, in each database, we assessed the proportion of drug records for which daily dose calculations were possible using the suggested formulas. Results: Applying the dose formulas, we obtained median daily doses that generally matched the WHO DDD definitions. Our dose formulas were applicable to &gt;85% of drug records in all but one of the assessed databases. Conclusion: We have established and implemented a standardized daily dose calculation in OMOP CDM providing reliable and reproducible results.</p

    Calculating daily dose in the Observational Medical Outcomes Partnership Common Data Model

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    Purpose: We aimed to develop a standardized method to calculate daily dose (i.e., the amount of drug a patient was exposed to per day) of any drug on a global scale using only drug information of typical observational data in the Observational Medical Outcomes Partnership Common Data Model (OMOP CDM) and a single reference table from Observational Health Data Sciences And Informatics (OHDSI). Materials and Methods: The OMOP DRUG_STRENGTH reference table contains information on the strength or concentration of drugs, whereas the OMOP DRUG_EXPOSURE table contains information on patients' drug prescriptions or dispensations/claims. Based on DRUG_EXPOSURE data from the primary care databases Clinical Practice Research Datalink GOLD (United Kingdom) and Integrated Primary Care Information (IPCI, The Netherlands) and healthcare claims from PharMetricsÂź Plus for Academics (USA), we developed four formulas to calculate daily dose given different DRUG_STRENGTH reference table information. We tested the dose formulas by comparing the calculated median daily dose to the World Health Organization (WHO) Defined Daily Dose (DDD) for six different ingredients in those three databases and additional four international databases representing a variety of healthcare settings: MAITT (Estonia, healthcare claims and discharge summaries), IQVIA Disease Analyzer Germany (outpatient data), IQVIA Longitudinal Patient Database Belgium (outpatient data), and IMASIS Parc Salut (Spain, hospital data). Finally, in each database, we assessed the proportion of drug records for which daily dose calculations were possible using the suggested formulas. Results: Applying the dose formulas, we obtained median daily doses that generally matched the WHO DDD definitions. Our dose formulas were applicable to &gt;85% of drug records in all but one of the assessed databases. Conclusion: We have established and implemented a standardized daily dose calculation in OMOP CDM providing reliable and reproducible results.</p

    Clinical Characterization of Patients Diagnosed with Prostate Cancer and Undergoing Conservative Management:A PIONEER Analysis Based on Big Data

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    Background: Conservative management is an option for prostate cancer (PCa) patients either with the objective of delaying or even avoiding curative therapy, or to wait until palliative treatment is needed. PIONEER, funded by the European Commission Innovative Medicines Initiative, aims at improving PCa care across Europe through the application of big data analytics. Objective: To describe the clinical characteristics and long-term outcomes of PCa patients on conservative management by using an international large network of real-world data. Design, setting, and participants: From an initial cohort of &gt;100 000 000 adult individuals included in eight databases evaluated during a virtual study-a-thon hosted by PIONEER, we identified newly diagnosed PCa cases (n = 527 311). Among those, we selected patients who did not receive curative or palliative treatment within 6 mo from diagnosis (n = 123 146). Outcome measurements and statistical analysis: Patient and disease characteristics were reported. The number of patients who experienced the main study outcomes was quantified for each stratum and the overall cohort. Kaplan-Meier analyses were used to estimate the distribution of time to event data. Results and limitations: The most common comorbidities were hypertension (35–73%), obesity (9.2–54%), and type 2 diabetes (11–28%). The rate of PCa-related symptomatic progression ranged between 2.6% and 6.2%. Hospitalization (12–25%) and emergency department visits (10–14%) were common events during the 1st year of follow-up. The probability of being free from both palliative and curative treatments decreased during follow-up. Limitations include a lack of information on patients and disease characteristics and on treatment intent. Conclusions: Our results allow us to better understand the current landscape of patients with PCa managed with conservative treatment. PIONEER offers a unique opportunity to characterize the baseline features and outcomes of PCa patients managed conservatively using real-world data. Patient summary: Up to 25% of men with prostate cancer (PCa) managed conservatively experienced hospitalization and emergency department visits within the 1st year after diagnosis; 6% experienced PCa-related symptoms. The probability of receiving therapies for PCa decreased according to time elapsed after the diagnosis.</p

    Clinical Characterization of Patients Diagnosed with Prostate Cancer and Undergoing Conservative Management : a PIONEER Analysis Based on Big Data

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    Funding statement PIONEER is funded through the IMI2 Joint Undertaking and is listed under grant agreement No. 777492. This joint undertaking receives support from the European Union’s Horizon 2020 research and innovation programme and European Federation of Pharmaceutical Industries and Associations EFPIA. The European Health Data & Evidence Network has received funding from the Innovative Medicines Initiative 2 Joint Undertaking under grant agreement no. 806968. The Joint Undertaking is supported by the European Union’s Horizon 2020 research and innovation programme and EFPIA, a large association which represents the biopharmaceutical industry in Europe. The views communicated within are those of PIONEER. Neither the IMI nor the European Union, EFPIA, or any Associated Partners are responsible for any use that may be made of the information contained hereinPeer reviewe

    Avant-garde and experimental music

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    Photosynthetic responses of sun- and shade-grown barley leaves to high light: is the lower PSII connectivity in shade leaves associated with protection against excess of light?

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    Research Protocol for an Observational Health Data Analysis on the Adverse Events of Systemic Treatment in Patients with Metastatic Hormone-sensitive Prostate Cancer: Big Data Analytics Using the PIONEER Platform

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    Combination therapies in metastatic hormone-sensitive prostate cancer (mHSPC), which include the addition of an androgen receptor signaling inhibitor and/or docetaxel to androgen deprivation therapy, have been a game changer in the management of this disease stage. However, these therapies come with their fair share of toxicities and side effects. The goal of this observational study is to report drug-related adverse events (AEs), which are correlated with systemic combination therapies for mHSPC. Determining the optimal treatment option requires large cohorts to estimate the tolerability and AEs of these combination therapies in "real-life" patients with mHSPC, as provided in this study. We use a network of databases that includes population-based registries, electronic health records, and insurance claims, containing the overall target population and subgroups of patients defined by unique certain characteristics, demographics, and comorbidities, to compute the incidence of common AEs associated with systemic therapies in the setting of mHSPC. These data sources are standardised using the Observational Medical Outcomes Partnership Common Data Model. We perform the descriptive statistics as well as calculate the AE incidence rate separately for each treatment group, stratified by age groups and index year. The time until the first event is estimated using the Kaplan-Meier method within each age group. In the case of episodic events, the anticipated mean cumulative counts of events are calculated. Our study will allow clinicians to tailor optimal therapies for mHSPC patients, and they will serve as a basis for comparative method studies

    Frequently asked questions about chlorophyll fluorescence, the sequel

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    [EN] Using chlorophyll (Chl) a fluorescence many aspects of the photosynthetic apparatus can be studied, both in vitro and, noninvasively, in vivo. Complementary techniques can help to interpret changes in the Chl a fluorescence kinetics. Kalaji et al. (Photosynth Res 122: 121-158, 2014a) addressed several questions about instruments, methods and applications based on Chl a fluorescence. Here, additionalChl a fluorescence-related topics are discussed again in a question and answer format. Examples are the effect of connectivity on photochemical quenching, the correction of F-V/F-M values for PSI fluorescence, the energy partitioning concept, the interpretation of the complementary area, probing the donor side of PSII, the assignment of bands of 77 K fluorescence emission spectra to fluorescence emitters, the relationship between prompt and delayed fluorescence, potential problems when sampling tree canopies, the use of fluorescence parameters in QTL studies, the use of Chl a fluorescence in biosensor applications and the application of neural network approaches for the analysis of fluorescence measurements. The answers draw on knowledge fromdifferent Chl a fluorescence analysis domains, yielding in several cases new insights.Kalaji, H.; Schansker, G.; Brestic, M.; Bussotti, F.; Calatayud, A.; Ferroni, L.; Goltsev, V.... (2017). Frequently asked questions about chlorophyll fluorescence, the sequel. 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