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Subject and purpose of work: The purpose of the study was to identify the areas of activity of the local authorities in the context of the implementation of public tasks that could be financed with funds from the issue of the so-called green bonds.

Background/Objectives: Male infertility is a significant global health issue, comprising approx. 50% of all infertility cases. Semen cryopreservation, a critical component of assisted reproductive technologies (ARTs), is a method commonly used in a wide range of situations, including gonadotoxic treatments such as radiation or chemotherapy, hazardous occupational exposures, and various medical conditions. Although historically viewed as potentially damaging to sperm, recent findings suggest that cryopreservation, when performed with appropriate techniques, may in fact enhance semen quality by improving the proportion of healthy spermatozoa, particularly in terms of their morphological parameters. The aim of this study was to evaluate the impact of cryopreservation on sperm morphology and viability, utilizing advanced morphological assessments pre- and post-freezing. Methods: Semen samples were collected from 97 patients undergoing infertility treatment at the KRIOBANK clinic (Białystok, Poland). The semen was liquefied and prepared in the form of slides. Sperm morphology was then assessed using an OLYMPUS BX40 microscope at 60× magnification. Results: The findings of the study revealed significant improvements in sperm morphology, with increased percentages of normal sperm and reductions in deformation indices post-thaw. Conclusions: The findings indicate that optimized cryopreservation protocols may support the selection of higher-quality sperm, offering valuable benefits for ART applications. These results challenge certain past assumptions regarding the impact of cryopreservation and underscore the need for refined freezing techniques to maintain and potentially enhance semen quality for reproductive use. [ABSTRACT FROM AUTHOR]

Electrotherapy is a dynamically developing method of treatment of sinus node dysfunction and atrioventricular conduction disturbances. It is an extremely important method used in the treatment of heart failure. The aim of this paper was to use classification trees for the differentiation between patients implanted with one of the three electrotherapy devices, i.e. SC-VVI/AAI, DC-DDD, ICD/CRT. The analysed data concerned 2071 patients who underwent implantation or device replacement procedures in the years 2010–2018, hospitalized in a coronary care unit. CART-type classification trees with 5-fold cross-validation were used for the analysis. The decision concerning the choice of a particular electrotherapy device is always made based on the latest guidelines and the patient’s clinical condition. The used classification trees may enable verification of the state of implementation of guidelines in real-life therapeutic decisions.

Preventive vaccination is one of the greatest successes of modern medicine. The SARS-CoV-2 epidemic, during which vaccination is the main method of prevention against death and severe disease, gave rise to a resurgence of anti-vaccine (anti-vax) movements. The aim of this study was to analyse the attitudes of students towards vaccination and the COVID-19 pandemic. The statistical analysis was performed with the use of the following data-mining methods: correspondence analysis and basket analysis. The obtained results show that students of medicine are characterized with the highest level of knowledge. Students of other medical faculties, on the other hand, have a significantly less uniform views, as do students of non-medical faculties.

Renewable electricity sources are now widely used worldwide. Currently, the most common sources are those that use energy contained in biomass, water, sun, and wind. When connected to a medium-voltage grid, individual wind power plants must meet specific conditions to maintain electricity quality. This article presents field study results on the impact of switching operations (turning the power plant on and off) at a 2 MW Vestas V90 wind turbine on the voltage parameters at the connection point of a farm located 450 m from the source. The analysis showed that the wind turbine under study significantly affects customers' voltage near the source, causing it to increase by approximately 2.5%. Sudden cessation of generation during the afternoon peak causes a 3% voltage fluctuation, potentially affecting equipment sensitive to rapid voltage changes. [ABSTRACT FROM AUTHOR]

Techniques of text data analysis have been known for many years and commonly used in many areas of life. Text mining enables, among others, the acquisition of information from the text, its filtering, and studying of similarities and relationships. The aim of this paper is to design a method that would make it possible to assess the health quality of dietary supplements, on the basis of text mining techniques. A fictional plant-based product was used in the study, which was compared with other products containing at least one of the tested ingredients registered in the years 2007–2019 in the register of dietary supplements kept by the Chief Sanitary Inspectorate (GIS), which were given either the “consistent” or “to be clarified” status. The obtained results concern the frequency of occurrence of the individual ingredients (St John’s wort/Hypericum, melissa, rose root/Rhodiola) in other products, considering their status in the register. The data thus obtained was subjected to classical statistical analysis in order to find correlations between the presence of a given ingredient and the product status. In view of the obtained results, the text mining analysis may be considered as a helpful tool in the process of internal risk assessment performed by manufacturers of dietary supplements.

Constructing agricultural microbial gasification plants near livestock farms is essential for technical, economic, and environmental reasons. Utilising substrates from these farms allows for producing electricity, heat, and environmentally friendly manure. However, biogas plants often face technical challenges. This study evaluates the power quality of an agricultural biogas plant on a dairy farm. It was found that the plant was connected via a cable with an insufficient conductor cross-section, leading to significant voltage overshoots exceeding 14.6%, which prevented the activation of the second generator. Both generators could operate after replacing the feed-in cable, but considerable fluctuations in the feed-in voltage persisted. Further measurements indicated the need for changes in the digester design. Specifically, replacing the current two mixers with more lower-powered mixers operating alternately was proposed. Sharing these solutions more broadly can help prevent similar issues in future microbial gas plant constructions and optimise electricity production. [ABSTRACT FROM AUTHOR]

Syphilis is a bacterial sexually transmitted disease (STD), whose main route of infection is through sexual contact. In order to diagnose syphilis, Treponema pallidum must be detected in the material sampled from a lesion and a blood test must be performed in order to detect serological response to syphilis. Since 1946, a statutory obligation to report all cases of syphilis has been in force in Poland, which is why data concerning the incidence is available. The aim of this paper is to analyse trends in syphilis incidence in the years 1950–2017 using Joinpoint Regression and to present the impact of prophylaxis and education of society on syphilis prevention. The Joinpoint Regression method indicated the splitting time points of the trend corresponding to real changes in incidence, which corroborates the purpose of using the method in question in epidemiological studies.

Log-linear analysis is a practical tool for examining relationships, successfully applied in many fields of science. This paper discusses the topic of estimation of the chance of getting pregnant in couples that underwent ART insemination. The authors focus on finding significant interactions between variables, on the basis of which statistical models are built. With the use of results of log-linear analysis, a model predicting the chances of achieving a clinical pregnancy that contained interactions was successfully built. Moreover, it was more complete than the model obtained with the use of logistic regression alone.

Health behaviours of preschool children have a considerable impact on the shaping of habits later on in their lives. Parents’ and guardians’ role is to develop positive health patterns and represent exemplary models to be followed by children. The aim of the paper is to present the use of correspondence analysis for the assessment of the relationship between eating habits of parents and children, as well as for the determination of the most common situations in which preschool children consume fast-food products and to find the relationship between the frequency of fast-food consumption and BMI values in preschool children. The tests were carried out with the use of an own survey carried out in kindergartens in Białystok among parents dropping off and picking up children. 149 correctly filled questionnaires were obtained. The statistical analysis employs the chi-squared test and correspondence analysis. Among the tested children, a statistically significant relationship between body weight and sex was obtained. In the group of children and parents consuming fast-foods, a statistically significant relationship between the frequency of children’s and parents’ consumption of the products in question was noticed. A statistically significant relationship between the age of introduction of fast-food products into the child’s diet and their BMI was found. A situation that was statistically significant as far as contribution to frequent consumption of fast-food products by children, i.e. at least once a week (as compared to sporadic), were children and parents shopping together. The relationship between the frequency of fast-food consumption by parents and children was presented in the form of correspondence maps, as well as the relationship between the child’s BMI and the age when the first fast-food product is served, and the relationship between the child’s BMI and the frequency of their consumption of fast-foods. Unfortunately, despite the high awareness among parents of the harmful effects of fast-food products and the widespread health education programmes, a number of the children in kindergartens were overweight or even obese. For this reason, the quality of the educational programmes in kindergartens, as well as in various media outlets, needs to be improved, with emphasis put on their effectiveness, in order to minimise the problem of the occurrence of overweight and obesity in children. It is also important for parents rearing children to pay special attention not only to their children’s menus, but also to foods consumed in the presence of children.

Infertility is a huge problem nowadays, not only from the medical but also from the social point of view. A key step to improve treatment outcomes is the possibility of effective prediction of treatment result. In a situation when a phenomenon with more than 2 states needs to be explained, e.g. pregnancy, miscarriage, non-pregnancy, the use of multinomial logistic regression is a good solution. The aim of this paper is to select those features that have a significant impact on achieving clinical pregnancy as well as those that determine the occurrence of spontaneous miscarriage (non-pregnancy was set as the reference category). Two multi-factor models were obtained, used in predicting infertility treatment outcomes. One of the models enabled to conclude that the number of follicles and the percentage of retrieved mature oocytes have a significant impact when prediction of treatment outcome is made on the basis of information about oocytes. The other model, built on the basis of information about embryos, showed the significance of the number of fertilized oocytes, the percentage of at least 7-cell embryos on day 3, the percentage of blasts on day 5, and the day of transfer.

Background: The period of pregnancy is optimal for vaccination to prevent some diseases in women and newborns. Healthcare workers have a crucial role in informing women and recommending vaccinations. The aim of the study was to assess the knowledge, both theoretical and practical, and attitudes of Polish midwifes and nurses towards vaccination in pregnancy. Material and Methods: The study was conducted in 2019 among 263 medical practitioners (152 nurses and 111 midwives) who answered the authors' questionnaire. Results: A positive approach to maternal vaccination was more often expressed by the surveyed nurses than the midwives (p = 0.002). The mean score for assessing all participants' knowledge based on 12 questions was 5.92. The level of knowledge of participants with a positive attitude towards vaccination in pregnancy was statistically different from the groups with a neutral or negative attitude in the whole study group (p < 0.001). A positive correlation between knowledge and attitude was found in all participants (r = 0.360, p < 0.001). Logistical regression models also indicate the presence of a statistically significant relationship between the level of knowledge and the group, profession, attitude and own assessment of knowledge. Conclusion: Positive attitudes to vaccination were reported mainly among those surveyed on a scientific congress on vaccinations in Poland. Therefore, participation in different forms of education may have a supportive impact on the views of midwives and nurses, and consequently on their willingness to recommend vaccination to pregnant women. [ABSTRACT FROM AUTHOR]

Infertility is currently a common problem with causes that are often unexplained, which complicates treatment. In many cases, the use of ART methods provides the only possibility of getting pregnant. Analysis of this type of data is very complex. More and more often, data mining methods or artificial intelligence techniques are appropriate for solving such problems. In this study, classification trees were used for analysis. This resulted in obtaining a group of patients characterized most likely to get pregnant while using in vitro fertilization.

There are high hopes for using the artificial neural networks (ANN) technique to predict results of infertility treatment using the in vitro fertilization (IVF) method. Some reports show superiority of the ANN approach over conventional methods. However, fully satisfactory results have not yet been achieved. Hence, there is a need to continue searching for new data describing the treatment process, as well as for new methods of extracting information from these data. There are also some reports that the use of principal component analysis (PCA) before the process of training the neural network can further improve the efficiency of generated models. The aim of the study herein presented was to verify the thesis that the use of PCA increases the effectiveness of the prediction by ANN for the analysis of results of IVF treatment. Results for the PCA-ANN approach proved to be slightly better than the ANN approach, however the obtained differences were not statistically significant.

One of the most effective methods of infertility treatment is in vitro fertilization (IVF). Effectiveness of the treatment, as well as classification of the data obtained from it, is still an ongoing issue. Classifiers obtained so far are powerful, but even the best ones do not exhibit equal quality concerning possible treatment outcome predictions. Usually, lack of pregnancy is predicted far too often. This creates a constant need for further exploration of this issue. Careful use of different classification methods can, however, help to achieve that goal.

Many factors play an important role in prediction of infertility treatment outcome (for example, female age and quality of oocytes or embryos are the most important prognostic factors concerning positive IVF outcome). The purpose of this study was to identify a set of variables that could fulfill criteria for prediction of pregnancy in IVF patients through the application of data mining – using the discriminant analysis method. The principle of this method is to establish a set of rules that allows one to place multi-dimensional objects into one of two analyzed groups (pregnant or not pregnant). Six hundred and ten IVF cycles were included in the analysis and the following variables were taken into consideration: female age, number and quality of retrieved oocytes, number and quality of embryos, number of transferred embryos, and outcome of treatment. Discriminant analysis allowed for the creation of a model with a 51.22% correctness of prediction to achieve pregnancy during IVF treatment and with 74.07% correctly predicted failure of pregnancy. Therefore, the created model is more suitable for the prediction of a negative outcome (lack of pregnancy) during IVF treatment and offers an option for adjustments to be made during infertility treatment.

The IVF ET method is a scientifically recognized infertility treat- ment method. The problem, however, is this method’s unsatisfactory efficiency. This calls for a more thorough analysis of the information available in the treat- ment process, in order to detect the factors that have an effect on the results, as well as to effectively predict result of treatment. Classical statistical methods have proven to be inadequate in this issue. Only the use of modern methods of data mining gives hope for a more effective analysis of the collected data. This work provides an overview of the new methods used for the analysis of data on infertility treatment, and formulates a proposal for further directions for research into increasing the efficiency of the predicted result of the treatment process.

Principal Component Analysis is one of the data mining methods that can be used to analyze multidimensional datasets. The main objective of this method is a reduction of the number of studied variables with the mainte- nance of as much information as possible, uncovering the structure of the data, its visualization as well as classification of the objects within the space defined by the newly created components. PCA is very often used as a preliminary step in data preparation through the creation of independent components for further analysis. We used the PCA method as a first step in analyzing data from IVF (in vitro fertilization). The next step and main purpose of the analysis was to create models that predict pregnancy. Therefore, 805 different types of IVF cy- cles were analyzed and pregnancy was correctly classified in 61-80% of cases for different analyzed groups in obtained models.

Infertility is recognized as a major problem of modern society. Assisted Reproductive Technology (ART) is the one of many available treatment options to cure infertility. However, the efficiency of the ART treatment is still inadequate. Therefore, the procedure’s quality is constantly improving and there is a need to determine statistical predictors as well as contributing factors to the successful treatment. There is a concern over the application of adequate statistical analysis to clinical data: should classic statistical methods be used or would it be more appropriate to apply advanced data mining technologies? By comparing two statistical models, Multivariable Logistic Regression analysis and Artificial Neural Network it has been demonstrated that Multivariable Logistic Regression analysis is more suitable for theoretical interest but the Artificial Neural Network method is more useful in clinical prediction.

Intrauterine insemination (IUI) is one of many treatments provided to infertility patients. Many factors such as, but not limited to, quality of semen, the age of a woman, and reproductive hormone levels contribute to infertility. Therefore, the aim of our study is to establish a statistical probability concerning the prediction of which groups of patients have a very good or poor prognosis for pregnancy after IUI insemination. For that purpose, we compare the results of two analyses: Cluster Analysis and Kohonen Neural Networks. The k-means algorithm from the clustering methods was the best to use for selecting patients with a good prognosis but the Kohonen Neural Networks was better for selecting groups of patients with the lowest chances for pregnancy.