EDUCATIONAL ASPECTS TO INCREASE THE CONTRIBUTION ROLE OF THE NURSE IN THE MANAGEMENT OF PAIN IN PATIENTS WITH ONCOLOGICAL DISEASES

Introduction: The widespread prevalence of pain indicates the need for comprehensive pain education for all nurses. They have an essential role in the assessment and management of acute and chronic pain. Health care professionals serve as the patient's care coordinators to facilitate the selfmanagement plan.Purpose: The aim of this report is to explore the attitudes, knowledge and further training needs of nurses regarding pain management in cancer patients.Materials and methods: The study was carried out in November 2022. A total of 62 nurses from the Shumen Medical Center were surveyed. When carrying out the scientific research, sociological methods were used through our own developed toolkit - survey card and statistical methods for displaying graphs.Results and discussion: The results reflect the opinion of the respondents that a significant part expressed their agreement for professional upgrading (77%). A large part of the nurses are of the opinion that they want to increase their educational and qualification level (61%) and for better realization (19%). First of all, regarding what should be studied in additional training, more than a quarter of nurses indicated pain assessment, its measurement and recording in the nursing record (27%). According to a significant part of the respondents, they are familiar with the Consensus for quality oncological care (58%).Conclusion: The inclusion of nurses in additional training will complement their knowledge and enable them to provide quality health care. It is a process of continuous lifelong learning, responsive to the needs of the individual and the relevant community

A statistical test vs. a validation experiment in Gene Expression Study

Report published in the Proceedings of the National Conference on "Education and Research in the Information Society", Plovdiv, May, 2014Comparative CT Method compares the Ct value of one target gene to another using the formula called 2-ΔΔCT. To make this method valid, the efficiency of the target amplification (the gene of interest) and the efficiency of the reference amplification (the endogenous control) must be equal. In this article we propose to test statistical hypotheses instead to perform validation biological experiments when we want to show that the efficiencies of the target and endogenous control amplifications are approximately equal.Association for the Development of the Information Society, Institute of Mathematics and Informatics Bulgarian Academy of Sciences, Plovdiv University "Paisii Hilendarski

Lipid Composition of Paulownia Seeds Grown in Bulgaria

DergiPark: 246128trakyafbdThe chemical composition of seeds from paulownia (Paulownia tomentosa) was investigated. The main components in the triacylglycerol fraction of the oil were linoleic (64.1%), oleic (21.2%) and palmitic acids (7.3%). ?-Tocopherol (approx. 100.0%) predominated in the tocopherol fraction, and in the sterol fraction – ß-sitosterol (79.2%), campesterol (10.3%) and stigmasterol (7.7%). In the seeds were established 10.6% protein, 9.5% cellulose and 38.2% hydrolysable carbohydrates

[Cost-Utility Analysis of Dupilumab for the Treatment of Severe Atopic Dermatitis in Children and Adolescents in Italy]

BACKGROUND AND OBJECTIVE: Atopic dermatitis (AD) is a chronic, multifactorial, inflammatory condition characterized by a significant impact on patients' quality of life. Dupilumab is reimbursed by the Italian Medicines Agency (AIFA) for the treatment of adolescent and adult patients with severe AD (according to AIFA registry criteria). Recently, dupilumab has been reimbursed in the treatment of children with severe AD. The objective of this analysis was to estimate the incremental cost-utility ratio (ICUR) of dupilumab compared to current supportive care (SC), for the treatment of severe AD in children (6-11 years) and adolescents (12-17 years) in Italy.MATERIALS AND METHODS: Cost-effectiveness analysis was conducted using a 1-year decision tree followed by a Markov model over a lifetime period. The base case analysis was performed on the overall population of the LIBERTY AD ADOL (NCT03054428) and LIBERTY AD PEDS (NCT03345914) studies, adopting the National Health Service (NHS) perspective. The following costs were considered: acquisition of treatment, management of disease, adverse events and complications. The robustness of the model was tested through sensitivity analysis. In addition, a scenario analysis adopting the social perspective was performed.RESULTS: In the base case, over a lifetime, dupilumab was more effective than SC in both children and adolescents (+2.44 and +1.62 quality-adjusted life years—QALYs, respectively). The introduction of dupilumab generated an increase in treatment costs (+€ 64,800 and +€ 52,853 € for children and adolescents, respectively), partially offset by a decrease in the costs of disease management and complications. Incremental cost-utility ratios (ICURs) were € 21,189 per QALY gained, for children, and € 26,569 per QALY gained, for adolescents. In both cases, the ICUR was lower than the willingness to pay threshold considered in Italy (€ 50,000 per QALY gained). Both the deterministic and probabilistic sensitivity analysis confirmed the robustness of the base case results. Finally, the scenario analysis, adopting the social perspective, showed coherent results compared to the base case.DISCUSSION: Dupilumab is a cost-effective option for the treatment of children and adolescents with severe AD eligible for systemic treatment in Italy compared to SC, from both the NHS and social perspective, confirming the results obtained in the adult population

Egg parasitoids of Thaumetopoea pityocampa in the region of Gyumyurdzhinski Snezhnik in Eastern Rhodopes, Bulgaria

The region of Gyumyurdzhinski Snezhnik in the Eastern Rhodopes is the closest in Bulgaria to the Aegean Sea. However, the climate is characterized by specific parameters that are determined by its relief. It is poorly protected from the invasion of cold air masses from the north. From the south, the Gyumyurdzhinski Snezhnik hill restrains the Mediterranean influence. The orography of the area favors the retention of cold air masses and a further drop in temperatures. The experimental material for the study includes 5 generations of Thaumetopoea pityocampa (2016, 2017, 2018, 2019, and 2022), collected in 31 locations of four State Forestry Enterprises: Kirkovo, Ardino, Momchilgrad, and Zlatograd. The sample for analysis included 693 egg batches with 148420 eggs in them. Seven primary egg parasitoids were established in this region: Ooencyrtus pityocampae, Baryscapus servadeii, Pediobius bruchicida, Anastatus bifasciatus, Eupelmus vesicularis, E. vladimiri, Trichogramma sp. and one hyperparasitoid (B. transversalis). Dominant parasitoids were B. servadeii and O. pityocampae, and E. vladimiri and P. bruchicida – occasional parasitoids. The hyperparasitoid B. transversalis participated in the complex with a relatively low share. The survival of the egg parasitoids in the laboratory conditions, in which the samples were kept, was low. The total mortality of the parasitoids in larval and adult stages was 47.8%. After collecting the samples, in laboratory conditions, a total of 442 individuals of the hyperparasitoid B. transversalis emerged, of which 56.3% were females and 43.7% were males. The average number of pine processionary moth eggs in a batch was 214.2. 70.8% of all the eggs in the samples hatched successfully. The egg parasitoids are a very serious natural factor, regulating the density of the pine processionary moth, but their impact varied from 2.1% to 30.3%. The natural characteristics of the area, the air temperature during the stages of eggs and young larvae, are favorable for the development of the pine processionary moth. Unhatched larvae without the influence of entomophages were 7.2%

Attributable deaths and disability-adjusted life-years caused by infections with antibiotic-resistant bacteria in the EU and the European Economic Area in 2015: a population-level modelling analysis

Background: Infections due to antibiotic-resistant bacteria are threatening modern health care. However, estimating their incidence, complications, and attributable mortality is challenging. We aimed to estimate the burden of infections caused by antibiotic-resistant bacteria of public health concern in countries of the EU and European Economic Area (EEA) in 2015, measured in number of cases, attributable deaths, and disability-adjusted life-years (DALYs). Methods: We estimated the incidence of infections with 16 antibiotic resistance–bacterium combinations from European Antimicrobial Resistance Surveillance Network (EARS-Net) 2015 data that was country-corrected for population coverage. We multiplied the number of bloodstream infections (BSIs) by a conversion factor derived from the European Centre for Disease Prevention and Control point prevalence survey of health-care-associated infections in European acute care hospitals in 2011–12 to estimate the number of non-BSIs. We developed disease outcome models for five types of infection on the basis of systematic reviews of the literature. Findings: From EARS-Net data collected between Jan 1, 2015, and Dec 31, 2015, we estimated 671 689 (95% uncertainty interval [UI] 583 148–763 966) infections with antibiotic-resistant bacteria, of which 63·5% (426 277 of 671 689) were associated with health care. These infections accounted for an estimated 33 110 (28 480–38 430) attributable deaths and 874 541 (768 837–989 068) DALYs. The burden for the EU and EEA was highest in infants (aged <1 year) and people aged 65 years or older, had increased since 2007, and was highest in Italy and Greece. Interpretation: Our results present the health burden of five types of infection with antibiotic-resistant bacteria expressed, for the first time, in DALYs. The estimated burden of infections with antibiotic-resistant bacteria in the EU and EEA is substantial compared with that of other infectious diseases, and has increased since 2007. Our burden estimates provide useful information for public health decision-makers prioritising interventions for infectious diseases

Dosage Effects of Cohesin Regulatory Factor PDS5 on Mammalian Development: Implications for Cohesinopathies

Cornelia de Lange syndrome (CdLS), a disorder caused by mutations in cohesion proteins, is characterized by multisystem developmental abnormalities. PDS5, a cohesion protein, is important for proper chromosome segregation in lower organisms and has two homologues in vertebrates (PDS5A and PDS5B). Pds5B mutant mice have developmental abnormalities resembling CdLS; however the role of Pds5A in mammals and the association of PDS5 proteins with CdLS are unknown. To delineate genetic interactions between Pds5A and Pds5B and explore mechanisms underlying phenotypic variability, we generated Pds5A-deficient mice. Curiously, these mice exhibit multiple abnormalities that were previously observed in Pds5B-deficient mice, including cleft palate, skeletal patterning defects, growth retardation, congenital heart defects and delayed migration of enteric neuron precursors. They also frequently display renal agenesis, an abnormality not observed in Pds5B−/− mice. While Pds5A−/− and Pds5B−/− mice die at birth, embryos harboring 3 mutant Pds5 alleles die between E11.5 and E12.5 most likely of heart failure, indicating that total Pds5 gene dosage is critical for normal development. In addition, characterization of these compound homozygous-heterozygous mice revealed a severe abnormality in lens formation that does not occur in either Pds5A−/− or Pds5B−/− mice. We further identified a functional missense mutation (R1292Q) in the PDS5B DNA-binding domain in a familial case of CdLS, in which affected individuals also develop megacolon. This study shows that PDS5A and PDS5B functions other than those involving chromosomal dynamics are important for normal development, highlights the sensitivity of key developmental processes on PDS5 signaling, and provides mechanistic insights into how PDS5 mutations may lead to CdLS

Data-driven evaluation of real estate liquidity : predicting days on market to optimize the sales strategy of a startup

Project report presented as partial requirement for obtaining the Master’s degree in Information Management, with a specialization in Information Systems and Technologies ManagementThis is a research project for applying data mining techniques on Real Estate data in cooperation with Homeheed, a startup in the area of real estate, providing a platform solution as a single source of truth in Sofia, Bulgaria. This project suggests the development of a predictive model by using LASSO regression with the premise to determine days on market. As a consequence, the discoveries are expected to contribute to the Startup by providing insights about more attractive listings, and so will support faster return on investment. Additionally, the paper provides an experimental part where misleading and fake listings are targeted in order to support fraud and real availability of a listing detection. The project’s main objectives and assumptions are that advanced statistics and information management can build such a synergy with data and business models that allows enhancement of both market entry strategy and quality of service

Predicting days on market to optimize real estate sales strategy

Irregularities and frauds are frequent in the real estate market in Bulgaria due to the substantial lack of rigorous legislation. For instance, agencies frequently publish unreal or unavailable apartment listings for a cheap price, as a method to attract the attention of unaware potential new customers. For this reason, systems able to identify unreal listings and improve the transparency of listings authenticity and availability are much on demand. Recent research has highlighted that the number of days a published listing remains online can have a strong correlation with the probability of a listing being unreal. For this reason, building an accurate predictive model for the number of days a published listing will be online can be very helpful to accomplish the task of identifying fake listings. In this paper, we investigate the use of four different machine learning algorithms for this task: Lasso, Ridge, Elastic Net, and Artificial Neural Networks. The results, obtained on a vast dataset made available by the Bulgarian company Homeheed, show the appropriateness of Lasso regression