Nanokristalse alumiiniumoksiidi α- ja üleminekufaaside uurimine VUV ja katoodluminestsentsi meetoditel

Väitekirja elektrooniline versioon ei sisalda publikatsiooneAlumiiniumoksiidi leidub looduses korundina ehk suure kõvadusega mineraalina. Korund kui väärtuslik vääriskivi on tuntud ka punase rubiini ja sinise safiirina. Sellised värvused tekivad lisades alumiiniumoksiidile kroomi, raua või titaani ioone. Peale termodünaamiliselt stabiilse alumiiniumoksiidi, mida tähistatakse α-Al2O3, kristalliseerub alumiiniumoksiid ka mitmete metastabiilsete struktuuridena. Need nn üleminekufaasid on sünteesitavad vaid nanokristallidena ja püsivad vaid teatud temperatuurideni – üle 1100 °C juures on võimalik vaid α-faas. Erinevate kristallstruktuuride rohkus sama keemilise koostise korral ja nanomõõtmelisus teevad alumiiniumoksiidi üleminekufaasid atraktiivseks uurimisobjektiks ja ka oluliseks rakenduslikuks materjaliks. Antud töö eesmärgiks oli alumiiniumoksiidi üleminekufaaside ja stabiilse α-faasi elektroonsete ja optiliste omaduste võrdlev uurimine ning võimalike kristalliidi suurusest tingitud efektide selgitamine. Peamiste uurimismeetoditena kasutati töös mitmeid luminestsents-spektroskoopilisi tehnikaid, mis on paljudel juhtudel ainete elektronstruktuuri uurimises asendamatud. Töös näidati kuidas muutused kristallstruktuuris mõjutavad 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 näitas, et seda on võimalik kasutada erinevate alumiiniumoksiidi faaside tuvastamiseks. Demonstreeriti ka luminestsentsmeetodite võimekust täpsustada 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 hõlmatus immuniseerimiskava vaktsiinidega 2010. aasta sünnikohordi põhjal Eesti Haigekassa raviarvete alusel

Taust, eesmärk. Eestis puudub seni ülevaade, kui paljude lastega viiakse läbi kõik immuniseerimiskavas ette nähtud plaanilised vaktsineerimised. Uurimuse eesmärk oli anda ülevaade, kui suurel osal eelkooliealistest lastest on Eesti Haigekassa raviarvete alusel kõik immuniseerimiskavas ette nähtud vaktsineerimised tehtud. Metoodika. Analüüsiti Eesti Haigekassale perearstide ja eriarstide esitatud raviarveid (n = 1 091 275) kõigi 2010. aastal sündinud laste kohta (n = 16 464), kes Eestis perioodil 2010–2018 tervishoiuteenuseid tarbisid. Hinnati immuniseerimiskavas toodud vaktsineerimistega hõlmatust 3- ja 8aastaste laste seas. Tulemused, järeldused. 3aastaseks saanud lastest läbis raviarvete alusel kõik sellele vanusele immuniseerimiskavas ette nähtud vaktsineerimised 68,9%, osaliselt vaktsineeriti 24,4% ning vaktsineerimata oli 6,7%. 8aastastest olid kõik vaktsiinid manustatud 49,5%-le lastest, osaliselt 43,9%-le ja vaktsineerimata oli 6,5%. Seega on vaktsineerimisega hõlmatus kogu immuniseerimiskava järgi Eestis väiksem, kui üksikute vaktsiinikomponentidega hõlmatuse põhjal võiks arvata. Kõige sagedamini puudusid raviarved DTPa-IPV (DTPa – difteeria, teetanuse ja atsellulaarse läkaköha vaktsiin, IPV – inaktiveeritud poliomüeliidi vaktsiin) teise revaktsineerimise kohta (vaktsineerimine 6–7 aasta vanuses)

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

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 PharMetrics® 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

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 PharMetrics® 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

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

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

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

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

[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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