Repeatable process for extracting health data from HL7 CDA documents.

Objective: This study aims to address the gap in the literature on converting real-world Clinical Document Architecture (CDA) data into the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM), focusing on the initial steps preceding the mapping phase. We highlight the importance of a repeatable Extract-Transform-Load (ETL) pipeline for health data extraction from HL7 CDA documents in Estonia for research purposes.

Methods: We developed a repeatable ETL pipeline to facilitate the extraction, cleaning, and restructuring of health data from CDA documents to OMOP CDM, ensuring a high-quality and structured data format.

Nanomaterial Texture-Based Machine Learning of Ciprofloxacin Adsorption on Nanoporous Carbon.

Drug substances in water bodies and groundwater have become a significant threat to the surrounding environment. This study focuses on the ability of the nanoporous carbon materials to remove ciprofloxacin from aqueous solutions under specific experimental conditions and on the development of the mathematical model that would allow describing the molecular interactions of the adsorption process and calculating the adsorption capacity of the material. Thus, based on the adsorption measurements of the 87 carbon materials, it was found that, depending on the porosity and pore size distribution, adsorption capacity values varied between 55 and 495 mg g.

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).

Using electronic health records to evaluate the adherence to cervical cancer prevention guidelines: A cross-sectional study.

Objective: The fight against cervical cancer requires effective screening together with optimal and on-time treatment along the care continuum. We examined the impact of cervical cancer testing and treatment guidelines on testing practices, and follow-up adherence to guidelines.

Methods: Data from Estonian electronic health records and healthcare provision claims for 50,702 women was used.

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.

Markov modeling for cost-effectiveness using federated health data network.

Objective: To introduce 2 R-packages that facilitate conducting health economics research on OMOP-based data networks, aiming to standardize and improve the reproducibility, transparency, and transferability of health economic models.

Materials And Methods: We developed the software tools and demonstrated their utility by replicating a UK-based heart failure data analysis across 5 different international databases from Estonia, Spain, Serbia, and the United States.

Results: We examined treatment trajectories of 47 163 patients.

Transforming Estonian health data to the Observational Medical Outcomes Partnership (OMOP) Common Data Model: lessons learned.

Objective: To describe the reusable transformation process of electronic health records (EHR), claims, and prescriptions data into Observational Medical Outcome Partnership (OMOP) Common Data Model (CDM), together with challenges faced and solutions implemented.

Materials And Methods: We used Estonian national health databases that store almost all residents' claims, prescriptions, and EHR records. To develop and demonstrate the transformation process of Estonian health data to OMOP CDM, we used a 10% random sample of the Estonian population ( = 150 824 patients) from 2012 to 2019 (MAITT dataset).

The use of prescription drugs and health care services during the 6-month post-COVID-19 period.

COVID-19 and other acute respiratory viruses can have a long-term impact on health. We aimed to assess the common features and differences in the post-acute phase of COVID-19 compared with other non-chronic respiratory infections (RESP) using population-based electronic health data. We applied the self-controlled case series method where prescription drugs and health care utilisation were used as indicators of health outcomes during the six-month-long post-acute period.

Modeling Patient Treatment Trajectories Using Markov Chains for Cost Analysis.

Electronically stored medical records offer a rich source of data for investigating treatment trajectories and identifying best practices in healthcare. These trajectories, which consist of medical interventions, give us a foundation to evaluate the economics of treatment patterns and model the treatment paths. The aim of this work is to introduce a technical solution for the aforementioned tasks.

Prevaccination Prevalence of Type-Specific Human Papillomavirus Infection by Grade of Cervical Cytology in Estonia.

Importance: Large-scale data on type-specific human papillomavirus (HPV) prevalence and disease burden worldwide are needed to guide cervical cancer prevention efforts. Promoting the research and application of health care big data has become a key factor in modern medical research.

Objective: To examine the prevaccination prevalence of high-risk HPV (hrHPV) and type distribution by cervical cytology grade in Estonia.

Intrinsic Aqueous Solubility: Mechanistically Transparent Data-Driven Modeling of Drug Substances.

Intrinsic aqueous solubility is a foundational property for understanding the chemical, technological, pharmaceutical, and environmental behavior of drug substances. Despite years of solubility research, molecular structure-based prediction of the intrinsic aqueous solubility of drug substances is still under active investigation. This paper describes the authors' systematic data-driven modelling in which two fit-for-purpose training data sets for intrinsic aqueous solubility were collected and curated, and three quantitative structure-property relationships were derived to make predictions for the most recent solubility challenge.

A role of flavonoids in cytochrome c-cardiolipin interactions.

The processes preceding the detachment of cytochrome c (cyt c) from the inner mitochondrial membrane in intrinsic apoptosis involve peroxidation of cardiolipin (CL) catalyzed by cyt c-CL complex. In the present work, we studied the effect of 17 dietary flavonoids on the peroxidase activity of cyt c bound to liposomes. Specifically, we explored the relationship between peroxidase activity and flavonoids' (1) potential to modulate cyt c unfolding, (2) effect on the oxidation state of heme iron, (3) membrane permeability, (4) membrane binding energy, and (5) structure.

SERENA: Particle Instrument Suite for Determining the Sun-Mercury Interaction from BepiColombo.

The ESA-JAXA BepiColombo mission to Mercury will provide simultaneous measurements from two spacecraft, offering an unprecedented opportunity to investigate magnetospheric and exospheric particle dynamics at Mercury as well as their interactions with solar wind, solar radiation, and interplanetary dust. The particle instrument suite SERENA (Search for Exospheric Refilling and Emitted Natural Abundances) is flying in space on-board the BepiColombo Mercury Planetary Orbiter (MPO) and is the only instrument for ion and neutral particle detection aboard the MPO. It comprises four independent sensors: ELENA for neutral particle flow detection, Strofio for neutral gas detection, PICAM for planetary ions observations, and MIPA, mostly for solar wind ion measurements.

Logistic Classification Models for pH-Permeability Profile: Predicting Permeability Classes for the Biopharmaceutical Classification System.

Permeability is used to describe and evaluate the absorption of drug substances in the human gastrointestinal tract (GIT). Permeability is largely dependent on fluctuating pH that causes the ionization of drug substances and also influences regional absorption in the GIT. Therefore, classification models that characterize permeability at wide ranges of pH were derived in the current study.

pH-permeability profiles for drug substances: Experimental detection, comparison with human intestinal absorption and modelling.

The influence of pH on human intestinal absorption is frequently not considered in early drug discovery studies in the modelling and subsequent prediction of intestinal absorption for drug candidates. To bridge this gap, in this study, experimental membrane permeability data were measured for current and former drug substances with a parallel artificial membrane permeability assay (PAMPA) at different pH values (3, 5, 7.4 and 9).

Validating Fitbit Zip for monitoring physical activity of children in school: a cross-sectional study.

Background: Modern activity trackers, including the Fitbit Zip, enable the measurement of both the step count as well as physical activity (PA) intensities. However, there is a need for field-based validation studies in a variety of populations before using trackers for research. Therefore, the purpose of the current study was to investigate the validity of Fitbit Zip step count, moderate to vigorous physical activity (MVPA) and sedentary minutes, in different school segments in 3rd grade students.

Whole-genome metabolic model of built by comparative reconstruction.

Background: is one of the main sources of biomass-hydrolyzing enzymes for the biotechnology industry. There is a need for improving its enzyme production efficiency. The use of metabolic modeling for the simulation and prediction of this organism's metabolism is potentially a valuable tool for improving its capabilities.

Quantitative structure-permeability relationships at various pH values for neutral and amphoteric drugs and drug-like compounds.

Human intestinal absorption is a key property for orally administered drugs and is dependent on pH. This study focuses on neutral and amphoteric compounds and their membrane permeabilities across the range of pH values found in the human intestine. The membrane permeability values for 15 neutral and 60 amphoteric compounds at pH 3, 5, 7.

Machine Learning of Protein Interactions in Fungal Secretory Pathways.

In this paper we apply machine learning methods for predicting protein interactions in fungal secretion pathways. We assume an inter-species transfer setting, where training data is obtained from a single species and the objective is to predict protein interactions in other, related species. In our methodology, we combine several state of the art machine learning approaches, namely, multiple kernel learning (MKL), pairwise kernels and kernelized structured output prediction in the supervised graph inference framework.

Ploidy influences the functional attributes of de novo lager yeast hybrids.

The genomes of hybrid organisms, such as lager yeast (Saccharomyces cerevisiae × Saccharomyces eubayanus), contain orthologous genes, the functionality and effect of which may differ depending on their origin and copy number. How the parental subgenomes in lager yeast contribute to important phenotypic traits such as fermentation performance, aroma production, and stress tolerance remains poorly understood. Here, three de novo lager yeast hybrids with different ploidy levels (allodiploid, allotriploid, and allotetraploid) were generated through hybridization techniques without genetic modification.

Characterization and mutagenesis of two novel iron-sulphur cluster pentonate dehydratases.

We describe here the identification and characterization of two novel enzymes belonging to the IlvD/EDD protein family, the D-xylonate dehydratase from Caulobacter crescentus, Cc XyDHT, (EC 4.2.1.

Xylose-induced dynamic effects on metabolism and gene expression in engineered Saccharomyces cerevisiae in anaerobic glucose-xylose cultures.

Xylose is present with glucose in lignocellulosic streams available for valorisation to biochemicals. Saccharomyces cerevisiae has excellent characteristics as a host for the bioconversion, except that it strongly prefers glucose to xylose, and the co-consumption remains a challenge. Further, since xylose is not a natural substrate of S.

Quantitative structure-permeability relationships at various pH values for acidic and basic drugs and drug-like compounds.

Absorption in gastrointestinal tract compartments varies and is largely influenced by pH. Therefore, considering pH in studies and analyses of membrane permeability provides an opportunity to gain a better understanding of the behaviour of compounds and to obtain good permeability estimates for prediction purposes. This study concentrates on relationships between the chemical structure and membrane permeability of acidic and basic drugs and drug-like compounds.

Identification of novel isoprene synthases through genome mining and expression in Escherichia coli.

Isoprene is a naturally produced hydrocarbon emitted into the atmosphere by green plants. It is also a constituent of synthetic rubber and a potential biofuel. Microbial production of isoprene can become a sustainable alternative to the prevailing chemical production of isoprene from petroleum.